Category: What We’re Reading

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 31 July 2022:

1. Matthew Ball – A Manual to The Metaverse – Patrick O’Shaughnessy and Matthew Ball

[00:09:15] Patrick: Can you just define what you mean by the metaverse and what you think a good working definition is that allows us to test things to say, is this thing that everyone’s excited about, or is it not? It seems obvious from our many conversations that the trend has been towards more digital engagement and participation. And that somehow, people think of the metaverse as the natural endpoint of this, where there’s more sensory immersion in some virtual world, there’s more navigability, there’s less walled gardens. How do you define the metaverse in its simplest definition so that we can all work off the same idea?

[00:09:50] Matthew: A live 3D version of the internet as we know today is the best and simplest way to think about this. Why? Because it not only explains how it’s a little bit different visually experientially. It keys into some of what you just mentioned, which is how it might be more intuitive. Of course, we didn’t evolve for thousands of years to tap glass, to interact with 2D interfaces, static information. We explore, we immerse in 3D. It’s a much better interface for many tasks. Far from all, but many tasks. But most importantly, we take for granted the interoperability of the internet and how important that is to everything we do and create. We don’t think about this question of the New York Times can’t link to Washington Post. We don’t even think about the idea that you can’t link directly to the specific piece of content on the Washington Post. We don’t concern ourselves with, “Darn. I took a photo on my iPhone that I stored to iCloud, and the file format therefore doesn’t work on Facebook.” You can take a photo, upload it to Facebook, right click save as, put it onto Snapchat, screenshot it on Snapchat, upload it to TikTok. So we have this vast network that manages hierarchy, communications, reference, the transference of data across different autonomous systems coherently, safely, consistently. And then file formats and conventions that run university. We have a lot of 3D stuff today. There’s a tremendous amount of time being spent in 3D platforms. What we don’t have is a scalable network that actually interconnects. And as we’ve learned from the global economy, world trade, as we’ve learned with the internet at large, the utility that comes from that is extraordinary.

[00:11:26] Patrick: Can you say a few words about the state of the engines behind three dimensional creativity or three dimensional output? You already mentioned Unity, and you already mentioned Unreal being the two dominant engines that people might be familiar with. But maybe paint a more vivid picture of the history here. And if 3D is literally in the definition of metaverse, it stands to reason that the engine that produces and allows people to produce beautiful 3D outputs is really, really important or central. Can you give us the details on the state of the world today as it relates to 3D engines?

[00:12:00] Matthew: Sure. I love this question. And let me actually start answering it by talking about the state of the world decades ago. I like to position the metaverse as a fourth era of computing and networking. The first was the mainframe era began in the 1950s for the most part, ran until the late 1970s. Let’s keep in mind, mainframe still exists. It’s actually a bigger business than ever. But it was largely superseded in the late 1970s, early 1980s by the advent of the personal computer, Apple, Microsoft, and TCPIP the internet. By the mid 2000s, we hit the mobile and cloud era. And now we’re starting to talk about the next era in the metaverse. What’s fascinating about the metaverse in contrast to the preceding three waves is where it seems to be starting. Seems to be starting as you’ve identified in gaming, consumer leisure. In a small segment in consumer leisure. People like to talk about gaming being larger than film. It’s a bit of a misconception. You’re talking about the theatrical box office. That’s about 40 billion, but of course the video industry’s over 600 billion, gaming’s still around 200. When you take a look at those prior waves, mainframes started with mega enterprises. The internet began with government. Most of the early adopters were again, large corporations. Mobile began with enterprise and government as well. Each of these waves either never came to consumer leisure as was the case in mainframes or came there last. YouTube 2005, Netflix 2007, streaming wars 2019. Consumer leisure tends to be last.

So why is it that the metaverse seems to be starting in the other direction? The answer relates to constraints. Constraints always define a technology from inception to its termination. The constraints for computing and networking in those prior waves was often processing power, broadband speeds, bandwidth, etc. And the result was you couldn’t do much live. You couldn’t share a photo with your grandmother. You couldn’t stream video. But certainly, you could send a Blackberry message in the early mobile era. You could send an unstructured data file or CSV if you were a banker or accountant. So we needed substantial improvements in bandwidth and processing power for these new technologies to have consumer applicability. But the constraints that affected simulation, real-time rendered simulation, game engines was fidelity. It didn’t have the bandwidth or the processing power to do a rich simulation. And what that meant is the government had very little use for it. You couldn’t actually do a military simulation with fake fire. But it was fine for pong. It was fine for space invaders, Legend of Zelda. So we’ve spent decades with the primary area of development of game engines, of real-time rendering and GPU chips coming from consumer leisure. What has happened in the past few years is we have reached a point where the maturing and sophistication of these game engines coupled with the wide deployment of powerful processors means that that applicability has expanded. Automotive companies as I mentioned are using Unreal so that you can help drive your car. You can use LIDAR to scan the environment around you in your Range Rover, understand how to navigate, and then actually pre-drive your vehicle. You can live simulate a building.

And what has happened is the companies that happen to have that expertise come from gaming. Nvidia’s Jensen Huang. Of course, Nvidia’s now the seventh largest company globally. Known to many investors for several years, but largely under the radar compared to most other top 10 companies was founded in the early 1990s, not long after Snow Crash was published. And Jensen has said they never wanted to be a video game company, but focusing on gaming was the best strategic choice they’ve ever made because it had three unique attributes. It was large, it was fast changing, and it was technologically intensive. Many industries like pharmaceuticals have two of those attributes, but they don’t change that quickly. So the mixture of the intensity of these problems, the rapid improvements per Moore’s law has meant that almost all these expertise sit here. Lastly, when you take a look at what this means for the metaverse today, Microsoft’s Activision Blizzard acquisition, the largest big tech acquisition in history, 75 billion in enterprise value. Satya’s press release, the final line of the very first paragraph says it provides the building blocks for the metaverse, the foundations for the metaverse. And a lot of that comes down to game engines.

[00:16:25] Patrick: If you think about the role of IP in the bootstrapping of the first metaverses, what comes to mind? I remember from our first conversation going really deep on Disney, and Marvel, and the incredible gravity and momentum that great IP universes allow for. And that very often, technology supports IP and not the other way around. That ultimately IP is the thing people show up for. They want to do something, they want to watch something, they want to be immersed in something. Activision Blizzard maybe is a good example here, but what is the role of existing or new IP as it relates to speeding up this change?

[00:17:01] Matthew: As with anything that we want to do, any place that we want to go, there’s a reason why Disneyland is more fun than six flags is. If there’s a place that we want to go, especially in a consumer leisure environment, it stands to reason that we want to go to the places filled with the stories and characters that we love. This has classically been Horizon World’s fundamental problem. It’s technically more robust than it is popular. It has better distribution than many other platforms. It doesn’t have content, partly because it doesn’t have as many developers, but in particular, the other platforms have been populated by produced in UGC IP for years now. If we’re to go to a fantastical place, want that to be the place populated with the things we love. This isn’t new. Of course, medieval gardens are adored with gargoyles and giant statues of lions because it provides immersion. We don’t just want to walk down hedgerows. We want to feel like we’re in the place we imagine. This is why many come to the inevitable conclusion that another medium full entertainment or another technological wave which has IP in it is naturally going to advantage those that have the most resident IP today. Disney does not have a gaming business. I think that remains a problem. Mostly because they don’t have the capabilities for it. Whether or not they publish their own titles is a different question, but we’re going to want to live in Disney IP…

…[00:26:53] Patrick: As I think about my own usage of Oculus, and I’m a person that wants all this stuff to happen. The idea of the Ready Player One haptic suit, and omnidirectional treadmill, and everything that goes into it, this full immersive experience just sounds fun. I was a video game junkie as a kid. Spent countless hours in some of these virtual worlds. I’m inclined to want to do it and be an early adopter of the technologies. I think I had one of the first Oculus. And when I put it on, what stands out as the Star Wars game, I’m forgetting the name of it. But really being blown away looking around like this is wild. And obviously, it’s only going to get more and more perfectly rendered. I had the problem of getting a headache or a stomach ache when I moved artificially, which I want to hear about what you think about that. But at the same time as blown away as I was, I really haven’t spent much time with Oculus on. And I’m curious if my experience as someone prone to want this to happen who tried it but really didn’t last, is that indicative of the broader experience so far with Oculus? What do you think the reasons for something like that might be?

[00:27:50] Matthew: I want to hit a few different points here, because I think we’re really talking about the suitability of alternatives that challenges with the current technology. And then the likely progression of said technology. Neal Stephenson, who of course coined the term metaverse. So he didn’t originate the idea that spans nearly a century, has talked about the fact that yes, his conception of the metaverse was primarily an AR and VR experience. And he highlights the fact that that was a reasonable, if not the best hypothesis at the time, especially in the science fiction community. But he’s highlighted that technology is path dependent. What we found out in the decade sense is that actually, hundreds of millions can adequately navigate 3D space with WASD on their keyboard. Forward, left, right, and back. Billions can navigate 3D space and choose to do so on a monthly basis using a touch screen. He says that he no longer considers that essential. It may be the best, most popular preferred way eventually. But it’s not a requirement. And if you see Tim Sweeney at Epic shows very little interest, at least today in those new devices. The second part is talking about the experiences that you’ve mentioned. We have a good sense of what is likely to be required for min spec, for mainstream adoption of VR hardware. And I go into this a lot in my book. We tend to think for example that we need a refresh rate of 120 hertz on a VR headset. That’s 120 frames per second. We probably need an 8K display. And I’m going to put aside other concerns for now like battery life, the weight of the device, the heat generated by the device, the number of additional sensors and tracking cameras we need, which constrain all available resources, but just 120 hertz and 8K display. Right now, the top of the line devices typically do 90 hertz and 4K.

So we need a roughly 3X increase in the number of rendered pixels per second, just to hit min spec where people aren’t suffering from nausea. You then layer in these devices have essentially late PS3, early PS4 graphics. Their computational load is much lower. Call of Duty: Warzone is limited to PC and gaming consoles only, but in exchange, the graphics are great. You can have 150 users. Fortnite plays on most devices, but as a result, you can only have 100 users. Free Fire from Garena is designed to work on all low end Androids. And as a result, it only has 50 players. Population one on the Oculus, their battle royale has only 18 players. So now we have a device that has half the resolution we want, two thirds of the frame rate that we want. It has PS3, PS4 graphics. And you can only have 18 other users. And then again, it probably has one eighth the sensors that we want, one third the battery life. It’s probably 25% too heavy. There’s a lot that we need to solve for these just to become min spec for nausea and usage. And then on top of that of course, we need these devices to be more than min spec. They need to feel better. The rejection of sensors, which is unique to VR. You can multitask on your PlayStation, you know whether or not your house is on fire. Your kids are upset. Your dog is getting into trouble. Probably raises that above min spec. We’re getting there. We’ve roughly doubled resolution density. We’ve doubled processing power since 2017. So those who say we’ve been here before, people don’t like VR don’t appreciate the headway that we’ve made in these devices. But it’s a lot like GPAs. It’s easier to go from a 3 to a 3.4 than it is to go from a 3.6 to a 3.8. And we saw this recently as medic kicked out their releases for AR devices again, for the third time this decade. This is a hard tech problem…

…[00:37:07] Patrick: This idea of everything talking to everything brings up one of the most interesting topics in all of this, which is the word interoperability. This word has gone from no one ever saying it to everyone saying it in five years. Both because of the metaverse and this next era of computing, but also because of blockchains, interoperability means a lot, is critical. No one really knows what the hell they’re talking about it seems like when they bring it up. But it brings to mind things like standards and protocols. Again, the boring base layer stuff that makes the modern world possible. Whether it’s visa, or TCP/IP, or SMTP it, some of these things you and I have talked about before. Can you talk about why interoperability gets its own chapter in your book, why this is such a key critical concept? And who might actually sponsor or start these things? Because many times in history, they’re not for profit. It’s a protocol. So walk us through this concept, because it seems like it’s at the bottom of everything.

[00:38:00] Matthew: The fundamental premise of interoperability is a little bit foreign to the average person because we take for granted how interoperable online existence is today. You have a common identity at least in some way, shape, or form that you can take into multiple different avenues. Your content, the content that you create is inherently interoperable because the file formats are relatively standard and embraced everywhere. Ping runs everywhere. JPEG runs everywhere. Every unit of content we create today essentially runs everywhere else. Your text, your audio, your video, not just an image can be uploaded to every different environment. And in fact, the worldwide web itself works because of elements of TCP/IP, but also other consortiums and working groups of feathers that maintain a cohesive hierarchy or IP address, the domain registrar system. This is important not just for the continuity of the web, the cohesion of your personal experience, the persistence of things done online. But they’re also important for competition. If you don’t like your web hosting company, you can just move your information to another. You can change domain all the time. So we should think of interoperability as important to content creation, as important to user rights, as important to the actual thriving economy of the internet. And you’re right to talk about the ways in which those are not managed by a central for profit body. But ultimately, we can reduce it to a simple idea. It’s expanding the network effects of everything that you do online, but that doesn’t exist in the virtual world. Roblox individual worlds can identify one another, but they have no ability to understand, least of all, even identify another virtual world. Be it in Fortnite, an educational forum, a training sample.

Communicating from one live services suite to another doesn’t really exist either. There’s no consistent way to store information. And least of all, the ability to take a 3D object whether it’s created for industrial purposes for a simulation. Northrop Grumman wants to test an engine and then see how it performs in a specific environment. They don’t really have a cohesive way to take that object from one simulation to another. So this question about interoperability is really about expanding the utility, practical applicability of everything in the metaverse. And I’ll drop this down to a simpler example. The world economy of course runs on standards, defacto and otherwise. And it’s essential to reducing the friction to all transactions to increasing the utility of all investments and purchases. What are those standards? USD is one. English is another. The metric system is a third. The intermodal shipping container. You’ll note of course that there are often many other standards. We also use the Euro. We also use German. There are multiple different types of shipping containers, not all intermodal. Metric and imperial sit side-by-side, often in the same country, often within the same business. So we shouldn’t think of interoperability from a panacea perspective. And this is one of the flaws I’m often asked, can we ever have interoperability? Yes, we’ll never have it perfectly, never have it exhaustively. The world doesn’t work that way. The internet doesn’t work that way. We still have private networks, offline networks. We still have paid and proprietary protocols. You often need an installer to access experience A or B, and there are often paywalls. But making it so that every 3D object, every experience, more purchases can move, can endure, have utility beyond that first creation is going to be key to actually building up this economy.

[00:41:45] Patrick: Help me understand how we can bridge that gap of something I build, own, earn, achieve in some place that I want to bring to some place else. If I think about games, and maybe that’s the wrong way to think about it. The object I win and spend hours looking for in Diablo is really not relevant in Fortnite, is really not relevant in Sims. It seems like even though there are all these great worlds, the idea of bringing stuff between them is hard to imagine because the worlds themselves are so different. So how do you think this happens? How does Roblox get connected to Fortnite? How far down do we have to go to build the bridge?

[00:42:23] Matthew: One of the challenges with interoperability discussions is that we often focus on the easiest to understand, but arguably the least useful example. And that is taking your Peely banana skin from Fortnite and bringing it into Call of Duty. There are so many problems with that. The engines of course are different. And I just want to highlight how different these engines often are. Unity and Unreal actually have different coordinate systems for X, Y, Z. They store information Y and Z differently. Now that’s easy for a computer to manage. You just have a translator. It works the same way that English to German does. You say let’s swap Y and Z. But if they have fundamental disagreements on coordinates, you can imagine how sophisticated some of the disagreements are. That naturally leads game designers in particular to say, “Why does that matter?” Putting aside the economic considerations, do you want to be appealing in Call of Duty? Is it cohesive with the aesthetic? Does it fit in a doorframe? Frankly, the Peely file format might be stored in a way that makes it three stories tall in Call of Duty. When you look at my metaverse definition, I talk about the continuity of data from 3D objects to entitlements, payments, communications, history. The object itself, the avatar is probably the least important element of that. We’re a little bit more concerned with when you do an educational exercise in school, 3D simulation in school is probably one of the most important innovations that we can see. We’ve learned that distanced education is terrible. We have long expected the advent of the internet and digital devices to see productivity improvements in education. Haven’t happened. We know that multiple choice is terrible, that playing a YouTube video is terrible, that Zoom school is terrible. But the ability to make real The Magic School Bus is intuitive.

I grew up making volcanoes out of paper mache, baking soda, and vinegar. Now, you can start to do that in realistic simulations where you are personally agitating the magma being ejected into the atmosphere, and seeing the time lapse implications on the environment. Building a Rube Goldberg machine to learn physics, rather than just watching a video of a NASA commander drop a feather and hammer on the moon. You can take that Rube Goldberg machine to the moon, to Mars and Venus. But of course, we believe that some form of interoperability or continuity of what you’ve done, and what that information is, and who you are is essential. We’re not going to have the same school pack for chemistry as for English. So it’s not as important that I take my banana skin from A to B, it’s more important that I can consistently manage my profile. But all of this requires formats, more importantly conventions, a whole bunch of other buzzwords. Frameworks of frameworks, systems of systems. But how does it emerge? It emerges in a well known way, the same way that USD and English did. It’s often network effects reiterated the incentives of changing systems. But again, never perfectly. Last week, we had the establishment by the Khronos foundation, the metaverse standards form 28 companies. Many notable omissions, Apple, Google, many other content providers. But Epic, and Meta, and so forth, Qualcomm all saying, “Let’s start to standardize our roadmap. We have to understand what we can build towards for the collective utility.” That’s the easiest step. No one has to make a sacrifice to their tech roadmap. No one has to pick a standard they didn’t like. But that formation period has already begun. And even Roblox has started talking extensively about their need for their developer economy to start to figure this out.

2. CRISPR Technology for DNA Editing Might Raise Cancer Risk, Israeli Scientists Warn – Gid’on Lev

But Tel Aviv University researchers are highlighting the risks of CRISPR, which stands for clustered regularly interspaced short palindromic repeats. The scientists examined how the technology affects the immune system’s white blood cells – T cells – and found that some of the patient’s cells had chromosomal truncations – a loss of DNA fragments, a characteristic of cancer.

The first trial approved for using CRISPR to treat people was done by researchers under Prof. Carl June at the University of Pennsylvania. The scientists removed T cells from a healthy donor and changed the so-called cell receptor on them to better identify cancer cells.

The researchers also used CRISPR to destroy the original cell receptor so that the engineered T cells wouldn’t recognize and mistakenly attack cells of the recipient and another molecule that cancer cells use to exhaust T cells. The engineered cells were injected into cancer patients whose cancers did not respond to any other treatment.

The results were published in 2020 in the journal Science: The engineered cells survived in the patient’s body for a long period and homed in on the cancer cells, even though they didn’t destroy the growth entirely.

The general consensus in the field was that after CRISPR’s excising of undesired parts of DNA, the cell carries out repair. In the new study, the researchers conducted a test to determine if indeed this repair mechanism works perfectly or that maybe repair doesn’t always occur, and when it does, it’s not always complete.

To examine the technology that presents this risk, the scientists reconstructed the trial conducted at the University of Pennsylvania. They used CRISPR to cut the genome of T cells in exactly the same places where June and his colleagues did: at chromosomes 2, 7 and 14. (Each human cells has 23 pairs of chromosomes.)

They then analyzed thousands of cells and found that up to 10 percent of the chromosomes that were cut did not repair themselves…

…With CRISPR, T cells can be made to better recognize cancer cells and prevent the recognition of normal cells. Furthermore, CRISPR can be used to remove the molecules that act as brakes on T cells, allowing the cells to exert their full killing potential.

Following the use of CRISPR, a mechanism in the cell repairs the cut DNA, but sometimes the cell fails to be repaired and might even lose large parts of the chromosome. This is serious because of the association with diseases including cancer.

In reenacting the research at the University of Pennsylvania, the Tel Aviv University scientists – aided by the students Alessio Nahmad and Ella Goldschmidt, and research assistant Eli Reuveni – sought to investigate CRISPR’s safety in general, not just in treating cancer.

“CRISPR only cuts and removes the DNA sequence at desired points. The natural mechanism of DNA repair in a cell is what’s fusing the cuts together and keeping the chromosome intact,” Ben-David says.“But sometimes the cell fails to execute the repair, and after this failure large parts of the chromosome – or even the entire chromosome – are lost. That creates a very serious situation because of aneuploidy – a change in the number of chromosomes.”

Ben-David says aneuploidy occurs in 90 percent of solid tumors; it’s the most frequent genetic change in cancer, more so than DNA mutations.

“In healthy cells, it never happens. There are always 46 chromosomes,” he says. “If in the process of genome editing via CRISPR, aneuploidy cells are generated and injected into the patient, this could be a serious problem. Until now, this problem hadn’t been examined in depth.”

3. Tails, You Win – Morgan Housel

Long tails drive everything. They dominate business, investing, sports, politics, products, careers, everything. Rule of thumb: Anything that is huge, profitable, famous, or influential is the result of a tail event. Another rule of thumb: Most of our attention goes to things that are huge, profitable, famous, or influential. And when most of what you pay attention to is the result of a tail, you underestimate how rare and powerful they really are.

Venture capital is a tail-driven business. You’ve likely heard that. Make 100 investments, and almost all of your return will come from five of them; most of your return from one or two.

Correlation Ventures crunched the numbers. Out of 21,000 venture financings from 2004 to 2014, 65% lost money. Two and a half percent of investments made 10x-20x. One percent made more than 20x return. Half a percent – about 100 companies – earned 50x or more. That’s where the majority of the industry’s returns come from. It skews even more as you drill down. There’s been $482 billion of VC funding in the last ten years. The combined value of the ten largest venture-backed companies is $213 billion. So ten venture-backed companies are valued at half the industry’s deployed capital.

There is a feeling, I’ve noticed, that this low-hit, high-stakes path is unique to VC in the investment world.

I want to show you that it’s not. Long tails drive everything…

…J.P. Morgan Asset Management published the distribution of returns for the Russell 3000 from 1980 to 2014. Forty percent of all Russell 3000 stock components lost at least 70% of their value and never recovered. Effectively all of the index’s overall returns came from 7% of components. That’s the kind of thing you’d associate venture capital. But it’s what happened inside your grandmother’s index fund.

You can drill this down even more.

Amazon drove 6.1% of the S&P 500’s returns last year. And Amazon’s growth is almost entirely due to Prime and AWS, which itself are tail events inside a company that has experimented with hundreds of products, from the Fire Phone to travel agencies.

Apple was responsible for almost 7% of the index’s returns. And it is driven overwhelmingly by the iPhone, which in the world of tech products is as tail-y as tails get.

Who’s working at these companies? Google’s hiring acceptance rate is 0.2%. Facebook’s is 0.13%. Apple’s is about 2%. So the people working on these tail projects that drive tail returns have tail careers…

…A takeaway from that is that no matter what you’re doing, you should be comfortable with a lot of stuff not working. It’s normal. This is true for companies, which need to learn how to fail well. It’s true for investors, who need to understand both the normal tail mechanics of diversification and the importance of time horizon, since long-term returns accrue in bunches. And it’s important to realize that jobs and even entire careers might take a few attempts before you find a winning groove That’s how these things work.

4. Speculation in 1980s Taiwan – Michael Fritzell

The financial bubble that gripped Taiwan in the 1980s is one of the greatest that the world has ever seen. Stock prices went up by more than 12x in less than four years.

At the peak of the bubble, over 5 million – one-third of all Taiwanese over the age of 15 – were actively playing the stock market.

This is the story about the boom and the bust of Taiwan’s little-discussed but spectacular stock market bubble, as retold in the book The Great Taiwan Bubble by Steven Champion…

…Before 1983, there wasn’t really any effective, legal way for an international investor to buy Taiwanese stocks. In the mid-1980s, however – the stock market was finally opened to foreign investors. Money started pouring in.

Capital inflows caused interest rates to plummet. Meanwhile, households felt loss aversion now that their bank deposits yielded almost nothing. So they sought higher in other financial instruments. Some of that money ended up in the stock market.

In 1985 – at an early stage of the bubble – the Taiwan Capitalization Weighted Stock Index (Taiex) was trading around the 700-mark. It had come off a bit from a previous high in 1984, but not many people were paying attention to the market yet. That would soon change.

An illegal lottery called “Dajia Le” (大家樂) was launched in 1985. It spread like wildfire through the nation, especially in Central and Southern Taiwan. For as little as 300 or 500 Taiwan Dollars, you picked a number from 00 to 99 and could get a jackpot of 15-19x – far more than any of the state-sponsored lotteries. It became a great success throughout Taiwan. Suddenly, just about everybody was into illegal gambling.

The reason why Dajia Le flourished was because of the economic slump in the summer of 1985. The unemployment rate increased to a decade-high of 4.1% and 320,000 unemployed Taiwanese were walking the streets in search of jobs. Many of them became disillusioned and turned to gambling.

After a few years, the government cracked down on these illegal lotteries. The state-sponsored Patriotic Lottery ended abruptly in 1987 and illegal lotteries such as Dajia Le were also shut down shortly thereafter due to government pressure.

Author Steven Champion described how hundreds of thousands of these gamblers were then in search of a new fix. He imagined that many of them must have turned into stock market speculation as an outlet for their lust for gambling.

The author worked as a fund manager in Taiwan in the 1980s. In 1986, he observed the market and saw it rise through the key 1,000 level. In his letters to investors, he opined that the market had become extended and suspected it was due for a correction. He just couldn’t imagine that it would rise any further.

Optimism was in the air though. In 1987, Taiwan officially became a democracy and many believed that the future was bright. Martial law was lifted, new political parties were formed and media censorship was eased.

There was scepticism throughout the whole stock market boom, but the scepticism gradually dissipated as bullish voice turned louder. Financial professionals were most nervous when the market bolted through the 1,500 and 2,000 levels but got calmer and calmer as prices went into the stratosphere.

The central bank issued new brokerage licenses and eased listing requirements. There was a huge increase in the number of licensed brokerages, from 27 in June 1988 to 297 in March 1990. Easily available but generally illegal, margin credit was provided through many of these brokers.

The new brokerage firms pushed stocks to retail investors. The larger cities Taipei and Kaohsiung were blanketed with new brokerage offices. The major firms then set up new shops in the secondary cities of Taichung, Tainan, Chiayi, Hsinchu and Changwha. When those reached saturation, brokerage firms finally opened up offices in even the most obscure country villages like Shalu, Fuhsing, Chubei, Huwei, Wuchi, and Huaton. Looking for new market niches, brokers established offices specifically targeted at housewives, doctors, farmers and students to lure more customers in…

…Suddenly, just about every single Taiwanese became involved in the market. An astonished foreign media jokingly started calling Taiwan “the Republic of Casino” instead of its official name “Republic of China”.

Students at National Taiwan University began to cut morning classes. Primary school teachers quizzed their students to see what stocks their parents were buying. High school girls desperate to accumulate savings to throw into the market turned to part-time prostitution.

60% of financial reporters owned stocks, and 84% of this group admitted their involvement in insider trading. When interviewed, 30% of those reporters admitted to considering abandoning their profession so that they could play the market full time.

In 1988, Taiex broke through the 7,000 mark – up over 10x since 1985. In brokerage offices, retail investors celebrated with champagne and happy faces…

…The market wobbled briefly in 1988 but quickly regained momentum. During that year, new president Lee Teng-hui appointed Shirley Kuo as Minister of Finance. To control the stock market bubble, Kuo announced a tax on gains derived from securities transactions. Taiex plummeted for 19 straight days, dropping from above 7,000 to below 5,000. Investors took to the streets and laid siege to the Ministry of Finance and Kuo’s residence. Fearful of losing next year’s elections, the government backed down and cancelled the tax. Stock prices exploded with renewed fervour.

It only took a few months to recover the previous high. As the market surged through the 6,000 level, then 7,000 and 8,000 stockbrokers held wild celebrations on their trading floors. Brokers offered free champagne, exploding firecrackers, balloons, buffet lunches and musical performances to their most loyal customers…

…The compounded return over the previous five years had hit international records. The Taiex had gone from 1,000 points in 1986 to 12,000 in 1990. Stock prices multiplied by more than twelve times in less than four years.

By the fall of 1989, the average price-earnings ratio on the Taiex was 100x – roughly double the already-high P/E multiple of 51x in Japan at the time…

…In early 1990, the market started wobbling. It looked like the market was taking a rest before scaling new, unchartered heights – just like it had done so many times in the past. But instead, the market entered into a vicious bear market that stunned most retail investors.

Many retail investors thought that the government provided almost guaranteed protection on the downside. The Kuomintang party had used the booming stock market as a slogan for their recent election campaign slogan, “Big Profits and Great Prosperity”. Many interpreted this as an implied guarantee against market losses. Yet despite continued optimism, the market drifted lower.

Trading volume reached new highs just after the crash, with traders doubling down on every single dip.

And then slowly, denial turned to anger, to depression and a gradual acceptance of the new reality.

Taiwan’s stock market bubble was finally over.

5. TIP466: The Bear Has Arrived w/ Jeremy Grantham – Trey Lockerbie and Jeremy Grantham

Jeremy Grantham (00:10:27):

I wrote a piece, Reinvesting When Terrified that by sheer luck came out the day the market hit its low and it said, “Get a policy, get a plan, present it to your committee or yourself and start to throw your money back into the market. You feel paralyzed, everyone always does and now’s the time to wake up, the market is cheap.”

Jeremy Grantham (00:10:46):

Of course, that happened in 1974 and ’82, which were classic lows and the market got down to seven PE and what I call terminal paralysis, sets in where you’re so frightened you can hardly move. You can hardly get to work, forget to buy stocks and that’s of course, as Warren Buffet would’ve said, that’s exactly the time you have to do it and it’s only 5% of the time, they are much quicker than the crazy bull markets.

Trey Lockerbie (00:11:11):

Now, I know you’re a huge skeptic of the Fed. Have the Feds rate increases and tightening efforts on the market or the market’s response surprised you in any way?

Jeremy Grantham (00:11:22):

No, I expect the Fed to be behind the curve, to be deep into optimism and it doesn’t really have a clue about market bubbles and the damage they do when they break. They’ve been eager now since early Greenspan to encourage bull markets because they help the economy, they really do and they always forget that the bear markets to go along with them hurt the economy at just the wrong time.

Jeremy Grantham (00:11:46):

If I’d been asked a bet, would the Fed get inflation wrong when inflation came along? At any time I would’ve said, of course they’ll miss it, they’ll be late, their responses will be pretty ill-judged. The Fed’s record is terrible. What is impressive is how much room they have been cut by the market. I mean the market is incredibly forgiving to the Fed. The Fed happened for 25 years to benefit from that amazing era as 500 million Chinese erased into the big cities and were plugged from marginal farming into highly profitable industrial system.

Jeremy Grantham (00:12:22):

Then they joined the World Trade Association and made everybody’s stuffed dogs and everybody’s iPhones for that matter. During that phase 500 million extra Chinese, 200 million Eastern Europeans plugging away from communism into capitalism. That was a golden era, Goldilocks if ever there was one and the Fed got to take credit for that.

Jeremy Grantham (00:12:46):

Prime Minister of England once, Mr. Wilson got reelected because England unexpectedly won the World cup in soccer and he got credit for it. I mean the president gets in the end credit for everything, good weather, he takes the shock for inflation. These things are all way bigger than the president of the United States, but the president and the Fed gets to enjoy the environment, so this Fed had a wonderful environment, they did nothing right, but they were seen to be presiding over low inflation and decent growth. The growth rate actually has slowed way down since Greenspan.

Jeremy Grantham (00:13:22):

It was averaging three and a half before Greenspan and averaging two and a half afterwards and today more like one and a half. It’s done nothing in terms of increasing the growth rate, but superficially it felt like a golden age because asset prices went up. Asset prices went up because inflation came down and rates were allowed to come down and in the end, rates were forced down and low rates make leverage cheap, make private equity deals wonderfully easy and profitable and they push up the price of real estate and they push up the price of stocks and that’s the way it was and the Fed gets the credit for that and it’s due none.

Jeremy Grantham (00:14:02):

D merit accrues from the fact that it kept on pushing down interest rates far too long and dangerously increasing inequality which is, I like to say the greatest poison in the system these days. The degree of inequality we have in the US now it does damage the strength of the economy and that is probably part of the reason why the growth rate has slowed and continues to slow.

Trey Lockerbie (00:14:27):

Now that inflation has arrived, there’s a lot of concern that we’re entering into a 1970s or eighties scenario stagflation. As a historian, could you give our audience an idea of what was happening during that period and how it resembles today?

Jeremy Grantham (00:14:42):

Well, every period is unique. The seventies had problems with the oil crises. You can call it one giant crisis or you can call it two or three, but in any case a triple, quadruple, quintuple the price of oil. In a hurry, we’d come off 50 years of fairly stable, low prices and they shot up and stayed up for a long time and inflicted enormous pain on the system. They lowered the growth rate. Why wouldn’t it? If you have to pay three, four times for your energy and it also, of course pushes up the price, so there’s nothing like an oil price increase to increase stagflation and it did. This time, if you adjust for the passage of time, the price of oil is not as high, but it’s still multiplied recently by three times and so that is imposing pain on consumption and is imposing inflationary pressure.

Jeremy Grantham (00:15:36):

Because of the invasion of Ukraine, we have had some extra spikes in the price of food, fertilizer and natural gas particularly in Europe. Interestingly, they are now almost all of them lower in price than the day before the invasion and this is a lovely example of how the stock market works. The start market is saying, “Whoops, there’s so much damage from commodity prices et cetera, et cetera, that we’re going to have a recession.” The recession isn’t bad news because the recession is going to get the Fed back in our camp of lowering interest rates again and helping stock prices and we’re looking out into the future and therefore that’s the good news, so the fear of a recession becomes wishful thinking about future interest rates and so the market gets a repressed for a while. It’s quite remarkable, but it’s fairly typical.

Jeremy Grantham (00:16:30):

That’s what we’re having now and that’s why we might have a bit of a rally for a few weeks, I think. Yes, what we should cover is how dangerous it is to get involved in a bubble that has more than one asset class, equities, growth stocks mainly. This time we’ve also moved into housing. Housing was chugging along okay, but last year it had the biggest advance, 20% in 2021 [inaudible 00:16:56] it had ever had in history and it went up to a higher multiple of family income, house priced divided by family income. Higher multiple than the peak of the housing bubble of 2006, it just means there’s a lot of value there that can be lost and it is dependent on interest rates. As you know when you’re paying a mortgage that the bottom of the mortgage was two and a half and it went up to 5.7, 5.8.

Jeremy Grantham (00:17:19):

This is a brutal increase in mortgage and means a lot of people will not move houses who otherwise would’ve done, which means a lot of people will not take a new job because they’re not prepared to double their mortgage payments. Everyone expanded to pay as much mortgage as they could afford, which meant that they put merciless pressure upwards on housing prices as the mortgage rates came down, so that’s a problem and then you have problems with a bubbly commodities market inflicting pain on consumption. As if that wasn’t enough, we have the lowest interest rates in 6,000 years as Jim Graham would say or Edward Chancellor’s written a brilliant new book, The Price Of Time. Of course, with the lowest rates in 6,000 years, you have the highest bond prices and that’s obviously been taken to the cleaners this year, too.

Jeremy Grantham (00:18:04):

You have bonds, housing, stocks and commodities. The only people who’ve tried that was Japan in ’89, they’re still not back to the price of the equity market. They’re still not back to the price of the land and the housing market from 89, that’s 33 years and counting. We did some of that in the housing bubble where the stock market came down in sympathy and that was brutal. They give you much greater pressure on recessionary forces and we are playing with fire this time, which was not anywhere near as obvious a year ago before that huge move upwards in housing.

Trey Lockerbie (00:18:40):

The interesting thing about the housing part to me is that with high inflation and to your point about expecting it to have inflation for the years to come, is that it seems like you’d want to own hard assets, so the demand should be there to keep propping up for the foreseeable future.

Jeremy Grantham (00:18:55):

Yes, in the long run, of course housing and stocks are very good protectors of steady inflation. The bad news is that psychologically inflation is associated with a negative, with a drop in PE from a psychological point and pressure on proper margins in the short-term and then it’s adjusts, but it’s very painful adjusting. Of course, it’s associated with a much higher mortgage, but once it’s adjusted, then of course you’re in much better shape.

Jeremy Grantham (00:19:23):

The world is much better off with moderately high interest rates. You get money on your savings, people don’t speculate as much, they don’t leverage as much, the risk in the system declines and you can afford to buy a house at lower prices and you can afford to buy stocks and build a portfolio.

Jeremy Grantham (00:19:40):

At the moment at the peak in December, if you’re young, you can’t get into the game. You can’t buy your first house, you can’t buy an equity portfolio. The yields are half of what they used to be…

…Jeremy Grantham (00:20:38):

I think in the longer term, forget the next few quarters who knows what happens really, but in the longer term, we are really running the risk that this is back to the seventies. We have problems with the availability of plentiful cheap resources and we have problems with plentiful cheap labor. The birth rate has crunched in every developed country except Israel and China.

Jeremy Grantham (00:21:04):

That’s a very, very important segment of the global economy to say the least. Every one of them has a population growth rate lower than replacement level so in the end, after accumulating lots of older people as a higher percentage, we start to actually have the population drop. Secondly, we’re 10 and 20 years in depending on the country into having smaller baby cohorts, so we know with absolute certainty, since they’re alive already that the 20 year olds arriving in the market will be fewer and fewer for the next to 20 years.

Jeremy Grantham (00:21:40):

We have not experienced this before. This has happened incredibly fast. China has gone from plenty of babies to a baby crunch almost overnight and a fertility rate that needs to be 2.1 is probably running about 1.4. Even in the US, the UK we’re running about 1.7. We’ve never seen levels like this, so we’re going to have a hard time getting enough labor. We’re going to accumulate old people who are very resource intensive. They need a lot of medical care, they need a lot of people care and we’re not going to have all that many people there.

Jeremy Grantham (00:22:14):

The supply of people to look after us old fogies is dropping steadily from now on for the rest of your life about, for sure. At the same time, I believe the correct interpretation of the commodity data is that it wasn’t only the China shock, the rapid growth rate for 30 years in China, but it was also showing signs that the best and cheapest, most plentiful resources had simply been mined or pumped and that we are running down into the second tier.

Jeremy Grantham (00:22:45):

If you look at the copper ore for example, King Copper is really important to the industrial system. Over 80, 90 years, the amount of copper in a ton of oil has dropped to a third of what it was, so you’re using an awful lot more energy and the energy also, which used to run for a hundred years at $20 a barrel in today’s currency, now runs at a hundred, so you’re spending five times the cost of energy to mine one third the quality of copper oil.

Jeremy Grantham (00:23:14):

You better believe technology can’t keep up with that. It did for a long time, it did very, very well but starting about 2002, the real price of the typical commodity has gone up a lot, it’s basically tripled.

Jeremy Grantham (00:23:27):

In a hundred years it went from… Starting at a hundred, it went down to 30, a brilliant help for getting rich and then from 2002 until today, it’s gone from 30 to 90, so over 122 years commodities are just about flat adjusted for inflation. Only 20 years ago, they were down at 30 cents on the dollar. This is a huge shift, hasn’t been nearly enough fuss made about it, but it’s the direction that is interesting to me. The direction is steadily up.

Jeremy Grantham (00:23:56):

Now, there’s a lot of volatility and commodities everybody knows. You produce an extra ton and the price collapses and your short a ton and the price triples, but if you look at the trend, the trend has been pretty reversed since 2002. My guess is it will continue to rise and that will pose real stagflationary pressure for a couple of decades and that’s why I fear this is re-entering the seventies and eighties…

…Jeremy Grantham (00:39:04):

I don’t want to get into wishful thinking, but basically as a society, we show all of the signs the failing societies in history have shown and the top of the list is Hubris, “Oh, you’ve been saying bad things for a hundred years and it didn’t work out.” You think the Romans didn’t say that? 400 years and so on and some of the civilizations down in central America where around for a thousand years and they built water storage, they built aqueducts and they had wonderful armies, but eventually they fall foul of a lot of failings and we check them all off.

Jeremy Grantham (00:39:40):

We look like a failing civilization book, but I’m hoping we have a little escape clause We have a couple of things going for us that have never worked before. One of them is population, that has never been a gleam in the eye of [Mouthes and the boys 00:39:54] even as we got wealthier that we would choose to have fewer children.

Jeremy Grantham (00:40:01):

This is remarkable and then adding on top of our choice is the fact that the world is getting so toxic, that even when you decide to have children it’s now getting to be much harder. One way or the other we are likely to have over the next couple of hundred years, a declining population and we have some chance that, that will be a great help. It isn’t a sufficient condition, but it is a necessary condition. The planet, under any circumstances could not support for the 10 billion that one reads about all the time for a hundred years, it can’t be done.

Jeremy Grantham (00:40:35):

We would need two and a half to three planets to cope with that. We can perhaps deal with a couple of billion and we might get there quite graceful. The other one of course, is technology and the rebuttal to the technology argument is that every wave of technology takes more energy back and it takes more complexity, which is a killer because complexity itself is a failing characteristic.

Jeremy Grantham (00:40:57):

It takes too much effort, too much manpower, too much energy itself and if that wasn’t enough, it increases your [inaudible 00:41:06] your overconfidence, every wave of scientific progress and so it can be quite deadly, but this time we have some open ended technologies.

Jeremy Grantham (00:41:15):

I call them, Get Out Of Jail Free cards and because they’re almost infinite fusion, geothermal and brilliantly cheap, effective storage any one of those three and we may get out of jail because that’s enough green cheap energy to in the long run take care of poverty if we chose to, for sure and take care of climate change. The thing about climate change is when we finish, we started 150 years ago with 280 paths per million carbon dioxide in the atmosphere. It’s only a little bit, but it’s a very powerful commodity. If we had no parts, we would be frozen at minus 20 to 25 degrees centigrade, a frozen ball with just bacteria around if we were lucky.

Jeremy Grantham (00:42:00):

It’s a very, very potent greenhouse gas. We started with 280 parts, a million we’re up to 420. That’s a bigger jump than the difference between the ice age, two miles of ice on Manhattan and the pleasant enough world that we have now. That’s a bigger jump, the ice age gap was just a 120 points and we have just gone up by 160 and we’re going to go up to about 525 and we need to go back to 300, so we’re going to have to get rid of 225 parts million of carbon dioxide as well as the methane.

Jeremy Grantham (00:42:31):

If you want to think about the carbon dioxide, that is 2 trillion tons or more, that is the absolute minimum. 2 trillion tons absolutely has to be taken out of the atmosphere over the next couple of hundred years and the Grantham Foundation, that’s all we do with our private investments, our venture capital. We have a team of half a dozen and all we do is focus on carbon dioxide extraction, biologically and every other method that we can get at, but that needs a huge amount of energy. However you do it, you’re going to need a lot of energy.

Jeremy Grantham (00:43:07):

One of our Get Out Of Jail Free cards would be very handy indeed. What are the probabilities? I think there’s probably 50/50 that fusion in the next few decades will come out with a viable engineering system, engineering and physics. The thing that I have doubt about is the cost. They’re going to be fairly costly plans, but 50/50 will have the technology and maybe it will be cheap and maybe it will not be cheap enough. Geothermal looks incredibly promising because the fracking industry has gone through the most amazing set of experiments, tens of thousands of wells, pushing, prodding, experimenting, shocking the rock using extra special mixtures of liquids to pump down and lateral drilling.

Jeremy Grantham (00:43:57):

It’s really been a revolution of engineering talent and if you could take all of that, which we can and apply it to geothermal and then start the same process with geothermal it would be almost surprising if we couldn’t, at least in some parts of the world have a really economically viable source of energy. The heat from the center of the planet here is more or less infinite, so that would do it.

Jeremy Grantham (00:44:22):

The third one would be a brilliant breakthrough in storage. We’ve come down to 10 cents on the dollar in the last 15 years. If we could come down, once again over the next 20 or 30 years to 10 cents on the dollar or even 20 would probably do it. We wouldn’t need a fusion or geothermal any one of those three will give us a chance of success. The problem is how much of the planet spirals out of control because of food problems, energy problems, creating fail states of the kind that we begin to see in Africa.

Jeremy Grantham (00:44:55):

If the temperature alone continues to rise, the whole Indian subcontinent, that becomes very questionable as to whether you could do regular farming. It has a wonderful share of the world’s arable land. If you see one of these maps, which is green for arable, you’ll see that India is one of the few places where practically the entire sub subcontinent is green. The problem is once you get over 35 degrees centigrade, which is about 95 Fahrenheit and you get humidity with it and you can’t stay out more than a few hours and they recently had 45 degrees centigrade for three weeks, as you probably read, the hottest they have ever had.

6. The Nightmare Scenario For Central Banks – Darlo Perkins

Officials feel utterly embarrassed about their “transitory” call in 2021, and you should never ignore the human element in policymaking. But the new bias goes deeper than that. It is also important to remember that the reason we have independent central banks is to ensure that the 1970s cannot happen again. So, we are talking about a risk that undermines the central bankers’ entire raison d’être, an existential threat. In fact, “price stability” is a prerequisite for everything else they do. It is the foundation of monetary policy. When investors ask about the pain central banks are prepared to tolerate, they are thinking about the wrong trade-off. The authorities are prepared to suffer a recession now because they fear a much worse recession in the future if price stability is lost. The trade-off, as officials see it, is intertemporal.

A recent report from the BIS outlines the nightmare scenario for central banks… The BIS analysis is largely statistical. It argues that there are two basic inflation regimes – “low” and “high”, each of which has its own self-reinforcing properties, although economies occasionally transition from one to the other. In the low-inflation regime, “relative” or sector-specific price changes are the dominant driver of the CPI. These tend to have a transitory effect, as they die out quickly. This is not a regime in which wage- or price-setters need to pay a great deal of attention to the overall inflation rate. Aggregate price pressures are subdued, and everyone takes this for granted. In the “high-inflation regime”, on the other hand, broader CPI developments start to have a much more discernible impact, with inflation itself becoming the focal point for private-sector decisions. This shift in emphasis leads, in turn, to behavioural changes that will cause inflation to become entrenched. In the high-inflation regime, even relative price shifts – such as spikes in energy prices – have persistent effects. And you know transitioning from a low inflation regime to a high inflation regime is under way based on the behaviour of prices within the CPI. Once they become more correlated, as they have over the past 12 months, there is a good chance – according to the BIS – that the economy is transitioning. 

7. Ben Clymer – Rolex: Timeless Excellence – Patrick O’Shaughnessy and Ben Clymer 

[00:14:33] Patrick: Maybe before we go into the history, which is so interesting and really important, we could just do a level set for the audience on Rolex the business and just some basics like how many watches do they produce a year, the revenue that they produce a year, maybe say a little bit about their very unique business structure, which is certainly shocking to me and I think will shock some people too that aren’t familiar with it. Just level set us on the size and type of the Rolex as a business.

[00:14:55] Ben: And I want to be completely clear, and I’m sure Rolex will listen to this, so I want to be clear for them and for the audiences that they don’t communicate anything so this is all speculative. The information that I’ll provide and that I’m sure you read is completely speculative. We have a good idea of what they might produce and what the revenue might be. The assumption is is that Rolex is making just north of around a million watches per year with an average wholesale price of around $7,000. So you can do the math there to kind of give you an idea of size and revenue. And a million watches per year is a lot, but it’s not the biggest by quantity. Apple, of course, would be bigger than that. Arguably, if you included Apple, they would be an even bigger watch brand than Rolex, but different thing obviously. But if you were to combine, for example, the entire Swatch group, which ranges from Swatch to Breguet, including Omega, it would be a larger business than Rolex, in theory. But again, Swatch is a publicly traded company, you can see exactly what their revenue is, whereas Rolex is, as I think you’ve alluded to, Rolex is quite the opposite. Rolex is in fact run by something called the Hans Wilsdorf Foundation, which was founded in 1945 when the founder, Hans Wilsdorf, set it up to basically be effectively, a nonprofit run by a group of families that are still highly involved with the business today that have, I would say, effectively zero public interaction. I’m pretty close with Rolex and I’m pretty close with the watch industry you could say and I have met, I think, one board member, one time. That was not by design. I think I met him at a bar and I was like, “Oh, you’re so and so,” and he said, “Yes.” These people are in fact, the most powerful people in watches and nobody even knows their name. Nobody even knows what they look like. I happen to because this is my job, but most people have no clue who’s really pulling the strings at Rolex. There’s a wonderful CEO named Jean-Frederic Dufour, who used to be the president of Zenith, which is an LVMH brand and he is absolutely the base and brain behind much of Rolex, but there is a board there and like any board, they have a different kind of influence over the brand.

Rolex is effectively a nonprofit, some say one of the largest nonprofits in the world, which I would believe. There are dozens and dozens of rumors that you may have heard, such as they’ve got the largest private art collection next to the Vatican, or they own more real estate. They make more money in real estate than they do in watches. Any of those things could be true. What I can say with the utmost certainty is that they will never reveal any of that to be true, even if it is. They’re not the type of brand, type of company that will ever stand on the rooftop and shout about anything. I mentioned this in the story that I wrote in 2015, whereas most brands, they’re trying to create stories where there aren’t any, a lot of brands will say a watch is in-house and you manufacture it in-house when it’s not. Rolex doesn’t do any of that. In fact, what’s so remarkable and I found this out on my own, doing my own research for that story in 2015, they will make several updates to products at some significant cost to themselves and not change the retail price and not even tell anybody about it and the only reason that I found out was when doing research for that story, I spoke to an independent watchmaker who is in New York City and is one of the best watchmakers in the country, if not the world, and he said, he works on Rolex, as well as other brands. These are the changes that they made to their movements without anybody knowing. By the way, these other brands that are communicating about LIGA, which is a manufacturing technique that allows you to have frictionless gears, a brand sent out a press release about that. Then, he came to find out that Rolex had been doing that for five years. It was in half their watches already. That’s what’s so wonderful about Rolex is they just are so remarkably Swiss. They are so conservative and thoughtful in the way that they communicate. When I wrote that story in 2015, I was one of the first journalists who ever be invited inside Rolex’s manufacturing headquarters in Bienne, Switzerland, which is what movements are made. They just are not out there to talk about themselves really ever. It’s incredibly charming. The luxury world is so much about Instagram and influencers and people touting how prestigious any brand might be and Rolex is just quietly the most prestigious…

…[00:19:39] Patrick: Maybe you can give us the, I don’t really care how long it is, as long as you want to make it because it’s so damn interesting, the history of Rolex, the brand and the company, its founding and its key timeline milestones.

[00:19:49] Ben: Rolex is younger than most other brands. It’s younger than Omega. It’s younger than Vacheron by 150 years. It’s younger than Patek. In the Swiss watch world, I wouldn’t call it a baby by any means, but not one of these grandfathers. I mean, Vacheron was founded in 1755. That’s older than the United States of America. Rolex was founded by a guy named Hans Wilsdorf, who’s Austrian, but he was a total anglophile. He really just was obsessed with the United Kingdom in the early part of the 1900s. He goes into the UK and starts a company called Wilsdorf & Davis in 1905. Back then, you have to remember that, forget digital watch making. Wrist watches were not a thing. The wrist watch was really a product of World War I, which was guys and trenches, trench watches, were strapping pocket watches to the wrist so that they didn’t have to pull out of their pocket. It was a little bit more complex, but that’s it at a high level. Wilsdorf, in 1905, decides to focus on wrist watches, which is crazy. I mean, it was a little bit, frankly like Elon Musk focusing on EVs 10, 15 years ago. People just weren’t ready for it. He committed to doing the wrist watch in the early part of the 1900s, 1905, 1908 and in 1908, he creates a company called Rolex. Again, there’s lots of hearsay on why it’s Rolex. I think at the very least it’s safe to say that he chose that word because it’s the same pronunciation in all languages. Some people say it’s the sound it makes. There’s no confirmation on that, but effectively what he does is he’s just the distributor. He’s not making anything from 1905 to about 1908.

In ’08, he buys a movement, which is what powers watch from a company called Aegler, A-E-G-L-E-R, which we’ll get back to later. They’re still around today, buys a movement, puts it in a case made by himself and sends it off to, it’s called an observatory, but effectively what it is, it’s a testing facility, effectively a nonprofit that says, these watches or time telling devices, clock, Marine chronometer or whatever, are accurate within, we’ll say X and Y, effectively saying, these are the most precise time telling devices on earth, typically done for Marine chronometers and if you know anything about this history of longitude, like that is effectively how longitude was discovered. This is just paramount to basically all exploration of the time period. Up until that point, no wrist watches had ever even been submitted to this thing called the QA, which is a British testing facility at that point. In 1908, he does that with an Aegler powered watch. It’s a 44 day test and it is given the QA certificate. Again, nobody had ever done it. Some years later, about 10 years later, he submitted 136 movements back to the QA. I think 24 of them were cased in 34 millimeter gold cases and then another 112 were in what we call boy size, which is really very small. I mean, at this point, it would look like a nickel, but these are effectively the formula one cars of watch movements. There were other watch movements at the time that to you and me and most people, would look exactly the same, but these were high performance calibers and they did it with a special escapement. Escapement is basically how time telling was regulated. These were effectively the formula one cars of watchmaking and they were done in a way that was very, even back then, very Rolex. There was really no indication that these were anything special on a dial, besides it would say QA on them. They’re around. You can buy them today for really less than you might think.

Once he had been given the QA certificates for the first wrist watch ever, he decided really to focus on three tenants of watchmaking, which really were not at all prevalent in that day at all, because it was really about utility. The watch can tell you the time pretty well or it was about luxury. At that point, we’re talking the Cartiers, Patek Philippes, complications, really. History tenants of manufacturing, which remain true to this day, would be precision, accuracy, waterproofness, which didn’t really exist at the time, and then self winding. What I mean by that is ability to not have to wind the watch manually. Precision was done. We’ve covered that. These watches were based on QA. They came up with a new escapement to make them more precise. Waterproofness, I think, goes back to the question you asked about five minutes ago, which is, how did you get something that more people know about that basically can afford or confine? He created something called the oyster case and now almost all Rolexes with the exception of the Cellini line, use an oyster case. That basically just means a waterproof case. Nobody was doing it at the time. Omega had something that was pretty close and actually predates the oyster case, but never really took off in the same way. Instead of using seals, it was almost like a locking system. It didn’t really take off, but effectively there was this woman named Mercedes Gleitz, who was a typist, of all things, basically a secretary in the UK and she had swum the English channel successfully, the first woman to swim the English channel successfully.

Hans Wilsdorf, the founder said, “Hey, wouldn’t it be cool if this woman, A, she’s a woman, B, she’s doing this amazing feat that no one had ever done before. Wouldn’t it be cool if she wore the watch around her neck?” She didn’t wear it on her wrist to be clear. She put her around her neck and she attempted to swim the English channel. She actually didn’t successfully do it. She failed, but nobody really cared because she had already done it before. He took out an ad celebrating the fact that this watch was around this woman’s neck for 10 hours in the English channel and the time keeping was flawless. That solidified Rolex as A, a household name because the oyster case had been validated in the English channel with this early brand ambassador, I guess you would call her. That was a huge deal and I think one of the earliest examples of real marketing by any luxury brand or any brand really at all, and then the final tenant would be self winding, which is, I would equate it to the automatic transmission. When the automatic transmission came around, all of a sudden, driving a car became a hell of a lot easier. It just became wider accepted, et cetera. Prior to, I guess it was around 19, I’m going to say, 30 something that Rolex patented the first self winding movement. To be clear, there was somebody called John Harwood that actually had a different self winding movement first. I think that was in the twenties and his idea was to make a hammer. Some of them would bounce back and forth like this to continue to power the watch. Rolex said, let’s go a different array. Let’s create a rotor, so a weight that would oscillate around inner circle to power the watch.

That worked and Rolex had, actually I remember the date. It was 1933 because it had a 20 year patent on it and Patek Philippe, which was another stall watch of traditional watchmaking, saw this and said, “Oh shit, we need to do that too,” but they couldn’t actually release anything until 20 years later because of the patent. The Patek 2526, which is their first self winding watch that came out in 1953. That’s a different thing all together, but effectively, Hans Wilsdorf said, “I want the watches to be precise,” check with the QA. “I want them to be waterproof,” check with the oyster case, “And I want them to be self winding,” check with the perpetual. If you see oyster perpetual on any Rolex, which you’d see in all Rolexes now, oysters are waterproof. Perpetual is the self winding. From there, Rolex went out to make watches and they were doing things very much in a similar style to everyone else at that time until the early fifties. In the post-war era, post World War II era, they created their first sports watch. By sports watch, the technical term is professional watch by Rolex nomenclature. It’s the Submariner, which is the diver’s watch, which is 1953. It’s the GMT, which is the pilot’s watch, which is 1955. Explorer I, which is an Explorer’s watch or an all day everyday watch, which is 53 as well. Then, you had the Daytona in 1963. Later, you had the Sea-Dweller in 67, which is a beef up version of the Sub. Then, the Explorer II in, I guess, 1970 or so. Those are the watches that I think most people now think of when they think of watch. You see rotating bezel in most cases. You see a black dial in most cases. You see an oyster bracelet, which is a very wonderfully produced bracelet with an oyster lock bracelet. That is really when things change.

To be clear, Rolex was not alone. There were other brands doing it. Some would say earlier, some would say around the same time, but the Blancpain Fifty Fathoms is credited to 1953. The Omega Seamaster 300 and 120 are around the same time as well. They were not alone and you have to remember that even Rolex in the watch industry in that period, it was really a smattering of different suppliers. If you look at, for example, and I wrote about this several times over the past few years, if you look at, say the Rolex Daytona from 1963 and the Heuer Carrera from 1963, it uses the exact same movement and I’m saying the exact same movement, and that’s a Valjoux 72. Same case maker, same dial maker, same hand maker. What is the actual difference? The assembly was done by Rolex and the assembly was done by Heuer, but the product itself was really very similar. If you look at early Seamasters and early Submariners, a lot of similarity there. The difference was of course the oyster case, but that was how watch making was done. If you look at say, example of Patek Philippe 2499, that’s based on a Valjoux movement. You don’t think of that. You think of Patek as Patek. This is the holy grail, but up until really, I mean the 2000s, they were using what you call an Ebauche movement, which is just really movement blank and then, it would be finished by Patek or finished by AP or Vacheron or whoever. Rolexes were really, I would say, finished to a higher quality than most, but I mean, any good Blancpain or any good Omega would do much of what Rolex was doing. It was really not until much, much later in the seventies first when we had the Quartz crisis, which is effectively the creation of Quartz, which is analog time, but with a battery, which is dramatically more precise, I mean, dramatically more precise than mechanical watch making.

What is interesting to think about, and I give full credit to my old colleague, Joe Thompson, who’s the legend in the watch writing world, is the Japanese came in with Quartz, Seiko created it, effectively, came in and there was a war between Swiss mechanical watch making and Quartz analog timekeeping. To be clear, the Swiss lost. The Swiss lost by a country mile. All of a sudden, those guys that were buying a Rolex or Omega or a Heuer, because they were the most precise thing in the world just said, “You know what” why would I do that? I can buy a Quartz watch that is 10 times more accurate,” 10 times, and by the way, you don’t need to have good service. You just swap out the new battery or whatever all the time and that decimated the Swiss watch industry to a point where very, very few brands were producing things at a profit of any kind. Jack Heuer, whose family owned TAG Heuer before it was TAG. It was just Heuer at the time. In his biography, I mean, he talks almost going into bankruptcy. If you talk to Gerry Stern, whose father Philippe Stern and his grandfather have owned Patek for generations. In the late seventies, they had to borrow with their bank.

[00:29:53] Patrick: It’s existential.

[00:29:54] Ben: Yeah, this was real. It wasn’t just the smaller brands. Patek had issues. Heuer had issues. Rolex was really smart in that, through that period of real turmoil when, I would say, their chief competitor Omega decided to make some Quartz watches, decided to make some funky looking things, Rolex stayed the course, and yes, they did make Quartz watches. The Beta 21, which was a Swiss conglomerates answer to Quartz, is the most expensive Quartz watch ever made. I mean like thousands and thousands of dollars, they said, “We’re going to focus on what we do best,” and the focus went away from precision and accuracy and time telling to luxury. That is when you start seeing the gold Rolex on, I hate to say it, but the used car salesman and the gold Rolex became the thing, seventies and eighties, opulence, you understand what the eighties were, of course. It just changed what Rolex was, but by the way, it worked and it allowed them to continue to be relevant when everybody else being Omega, Patek to a degree, Vacheron, these brands really struggled. That is why so many of them ended up in conglomerates. In the eighties and nineties, when watches were, mechanical watches, were really not doing so hot, a lot of folks came in and bundled them all up. Richemont owns a bunch of the great ones, including Vacheron. Swatch owns everything from Swatch to Blancpain, Breguet, Omega. It was a time of great challenge for sure. Rolex, they struggled as well, never to the degree that the others did, but it was not great for them. Then, the nineties started to come around and Rolex had a CEO by the name of Patrick Heiniger. His father was actually also CEO too, to give you an idea of how things work at Rolex. And he said, “I want to take Rolex in-house,” and he was really the first to do, frankly well before Patek or well before anybody else. Rolex was using 27 different suppliers to make, say, Submariner.

After he was done with it, they’re using four and now, those four are completely owned by Rolex. There are four different production facilities, two in Geneva, one in, what I would call, Proper Geneva one and Plan-les-Ouates, which is a little bit outside and then there’s one in Chêne-Bourg, which does dials and there’s one in Bienne, which makes the movements. What’s so amazing is, Rolex, to me, the secret sauce is equal parts case, equal parts movement. The case is, you can reverse engineer if you’re a competitor and say, “Okay, what’s an oyster case? It’s got this. It’s got that. It’s polished there. Seals are done by X, Y, and Z.” Movements are a different thing entirely. And what’s amazing is Rolex Geneva, which is basically dials, cases, bracelets, all that stuff, and Rolex BN, which is up in the mountains in Vallée de Joux, had a handshake deal for 70 years. And I mean, an actual handshake deal that the calibers made by what was then called Aegler, who made the first movement for Hans Wilsdorf, that company was making movements solely for Rolex Geneva based on nothing but a handshake. And I mean that literally. There was nothing in writing up until 2004, which is just insane to think about. I mean, Rolex was certainly a multi-billion dollar a year business before then. And up until 2004, there was no contract in place to say that Aegler couldn’t make movements for Omega or TAG Heuer or whoever. And so in 2004, Rolex said, “You know what? Enough’s enough. Let’s get married here,” and they purchased the company. And so now Rolex BN is basically what Aegler was up in the mountains until 2004. And so Rolex now has four different production facilities. As I wrote that story, I was among the first to be invited inside the movement manufacturing, which is really the source of the IP. That is where the sausage is made, so to speak. Just remarkable. If you haven’t read the story, it’s on Hodinkee called Inside The Manufacturer: Visiting All Four Rolex Locations. It was remarkable. As I started out in that story, this was 10 years ago or seven years ago, I was a lover of watches.

I was a lover of Rolex and I had four Rolexes then than had anything else at that point. None of them were as old as I was. They were all considerably older than me. And it’s funny, I reread the story to prepare for this interview. And now since then, I’ve bought more modern Rolexes than I have vintage. And the world is just a different place. But once you see everything that Rolex does to a watch, and what I mean by that is the fact that they have their own foundry, even the steel, not just the gold and precious metals, even the steel on Rolex is proprietary and it’s made by Rolex. It’s 904L. It’s wild. They make their own gold. It’s called Everose if it Rolex gold. It’s just remarkable. And what I think is even more telling of what Rolex is about is that one of their facilities they actually have, and I mean this literally, more than two Nobel prize-winning scientists on staff working on watches. Think about what that must mean from a material science perspective. The innovation done by Rolex is just above and beyond anything I’ve seen. I’ve been to every watch maker in the world. I’ve been to several car manufacturers doing this. I’ve been all over. I’ve been inside Hermès. There’s just nothing like this. They create machines to test their machines that make watches. They have their own oyster test, which of course provides artificial pressure on a watch to know that it’s waterproof. They have a machine that can open and close a Rolex clasp a thousand times a minute, which is kind of amazing, because you actually have to open. It’s wild. So, you open this. It’s actually kind difficult to do. Imagine doing that a thousand times a minute. They invented a machine to do that. They have a machine in Chêne-Bourg, which is their dial and gem-setting location, to sort through all the stones that they’re given. First of all, Rolex only works with IF, which is internally flawless stones, which is obviously the most expensive, highest end. To ensure that the stones that they’re given, whether it’s diamonds or rubies or anything are real, they created a machine to sort them at scale and ensure that all of them are real. And I said, “Well, are bad stones or fake stones are real problem for you?”

They said, “No, not really. But we just want to ensure that every watch we sell is what we want it to be.” And I was like, “How often do you get a fake diamond or a fake anything?” And the answer was, “One out of 10 million.” To be clear, this machine was created either by them or they paid somebody to make it for them. This is their machine. It’s not like it exists outside Rolex. And this gives you an idea of what they’re about and how they do things. And it is so wonderful and so different than traditional luxury, which frankly, I may say, even as a purveyor of luxury items, is full of shit half the time. I’m into sneakers, but not in the way that I’m into other things. Why would a pair of so special edition sneakers sell for $5,000? Sneakers are made in China by machines. They’re hand- stitched here and there. It’s just designed. It’s artificial scarcity, et cetera. When you see what goes into a, really, any high-end mechanical watch, but in particular Rolex, you really start to understand. The Submariner, we’ll say, is 8,000 bucks. That might be a deal after you see what goes into this thing. And I mentioned in the story, several competing brand presidents had told me before I went on this trip that, “Oh, not a human hand touches a Rolex before it’s made. It’s all done by machine.” Which is effectively the most insulting thing a Swiss person can say, meaning that it’s void of character. It’s void of humanity. It’s like luxury should be about people. It should be about craftsmanship. And they’re saying, “Rolex doesn’t have any of that.” And I was like, “Oh, okay. That’s kind of a bummer.” That may have informed why I didn’t own any modern Rolex at the time. You walk into Rolex HQ in Geneva and you see hundreds of people finishing watches. And they’re not finishing in the same way Patek or Lange would, by hand with little pieces of wood. They’re finishing maybe six or seven Rolexes at a time on a polishing wheel, but they’re still polishing really the way that it should be done.

They’re assembling dials by hand. They’re assembling the bracelet by hand. There’s an incredible amount of hand work that goes into the most basic of Rolex, being like a Submariner or a Datejust. Beyond that, what’s so fun about them is they know exactly how different they are than everyone else. They also know that everyone wants to be like them. So, at at least two of their four facilities you might drive by and say, “Oh, there’s Rolex. It’s five stories high. It’s, I don’t know, a few hundred thousand square feet.” When you go inside, you realize that it’s actually 10 or 11 stories high, but five or six of those stories are below ground. And I just remember thinking, “Why would they do that? What’s the point of that?” And it was in fact to suggest to anybody that drives by that Rolex is smaller than they actually are. And I think if we had any idea of how big the foundation was, it would blow all of us away. I think it’s enormous. I think they’re probably producing more than a million a year, but that’s the generally accepted number. It’s who they are. And if they’re going to do something, they want to do things at the highest level. I’m a golfer and have been lucky enough to meet some of their players. And Adam Scott became a good friend and I asked, “Why don’t you sponsor X, Y, and Z?” or, “Why don’t you go grassroots?” Whatever. And they said, “This is Rolex like my golf. If we’re going to do golf, we only want to be involved with the majors. So, the US Open, the BJ Championship, the Masters, and of course the British Open. And that’s it.” If they’re going to do tennis, it’s going to be Wimbledon and the US Open. They don’t even want to mess with some of the other majors in tennis. It’s just remarkable how committed they are to working with the very best. It’s wild. It really is.

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We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

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Here are the articles for the week ending 24 July 2022:

1. CRISPR, 10 Years On: Learning to Rewrite the Code of Life – Carl Zimmer

In just a decade, CRISPR has become one of the most celebrated inventions in modern biology. It is swiftly changing how medical researchers study diseases: Cancer biologists are using the method to discover hidden vulnerabilities of tumor cells. Doctors are using CRISPR to edit genes that cause hereditary diseases.

“The era of human gene editing isn’t coming,” said David Liu, a biologist at Harvard University. “It’s here.”

But CRISPR’s influence extends far beyond medicine. Evolutionary biologists are using the technology to study Neanderthal brains and to investigate how our ape ancestors lost their tails. Plant biologists have edited seeds to produce crops with new vitamins or with the ability to withstand diseases. Some of them may reach supermarket shelves in the next few years…

…Will the coming wave of CRISPR-altered crops feed the world and help poor farmers or only enrich agribusiness giants that invest in the technology? Will CRISPR-based medicine improve health for vulnerable people across the world, or come with a million-dollar price tag?

The most profound ethical question about CRISPR is how future generations might use the technology to alter human embryos. This notion was simply a thought experiment until 2018, when He Jiankui, a biophysicist in China, edited a gene in human embryos to confer resistance to H.I.V. Three of the modified embryos were implanted in women in the Chinese city of Shenzhen.

In 2019, a court sentenced Dr. He to prison for “illegal medical practices.” MIT Technology Review reported in April that he had recently been released. Little is known about the health of the three children, who are now toddlers.

Scientists don’t know of anyone else who has followed Dr. He’s example — yet. But as CRISPR continues to improve, editing human embryos may eventually become a safe and effective treatment for a variety of diseases.

Will it then become acceptable, or even routine, to repair disease-causing genes in an embryo in the lab? What if parents wanted to insert traits that they found more desirable — like those related to height, eye color or intelligence?

Françoise Baylis, a bioethicist at Dalhousie University in Nova Scotia, worries that the public is still not ready to grapple with such questions…

…In the 1980s, microbiologists discovered puzzling stretches of DNA in bacteria, later called Clustered Regularly Interspaced Short Palindromic Repeats. Further research revealed that bacteria used these CRISPR sequences as weapons against invading viruses.

The bacteria turned these sequences into genetic material, called RNA, that could stick precisely to a short stretch of an invading virus’s genes. These RNA molecules carry proteins with them that act like molecular scissors, slicing the viral genes and halting the infection.

As Dr. Doudna and Dr. Charpentier investigated CRISPR, they realized that the system might allow them to cut a sequence of DNA of their own choosing. All they needed to do was make a matching piece of RNA.

To test this revolutionary idea, they created a batch of identical pieces of DNA. They then crafted another batch of RNA molecules, programming all of them to home in on the same spot on the DNA. Finally, they mixed the DNA, the RNA and molecular scissors together in test tubes. They discovered that many of the DNA molecules had been cut at precisely the right spot.

For months Dr. Doudna oversaw a series of round-the-clock experiments to see if CRISPR might work not only in a test tube, but also in living cells. She pushed her team hard, suspecting that many other scientists were also on the chase. That hunch soon proved correct.

In January 2013, five teams of scientists published studies in which they successfully used CRISPR in living animal or human cells. Dr. Doudna did not win that race; the first two published papers came from two labs in Cambridge, Mass. — one at the Broad Institute of M.I.T. and Harvard, and the other at Harvard.

2. A Revolution Sweeping Railroads Upends How America Moves Its Stuff – Paul Ziobro

Freight railroads generally have operated the same way for more than a century: They wait for cargo and leave when customers are ready. Now railroads want to run more like commercial airlines, where departure times are set. Factories, farms, mines or mills need to be ready or miss their trips.

Called “precision-scheduled railroading,” or PSR, this new concept is cascading through the industry. Under pressure from Wall Street to improve performance, Norfolk Southern and other large U.S. freight carriers, including Union Pacific Corp. and Kansas City Southern, are trying to revamp their networks to use fewer trains and hold them to tighter schedules. The moves have sparked a stock rally that has added tens of billions of dollars to railroad values in the past six months as investors anticipate lower costs and higher profits.

The new approach was pioneered by the late railroad executive Hunter Harrison, who engineered turnarounds at two major Canadian railroads and Jacksonville, Fla.-based CSX Corp. by radically revamping their logistics.

His template won over Wall Street by boosting profits and stock prices, but it generated chaos on the tracks. The 2017 revamp at CSX caused crippling congestion east of the Mississippi River, jeopardizing operations at plants that made Pringles potato snacks, threatening deliveries of McDonald’s french fries and idling Cargill Inc. soybean-processing plants because of lack of railcars…

…“The board does not want to see any carrier implement so-called PSR the way CSX did,” said Ann Begeman, chairman of the Surface Transportation Board, the federal agency that oversees freight railroads. “It had unacceptable impacts on so many of its shippers and, frankly, other carriers.”

CSX spokesman Bryan Tucker said the company could have done better communicating the changes to customers, but he defended the actions by pointing to its financial results. He said CSX trains are running faster and with less downtime, and the railroad is hauling more cargo with fewer locomotives, railcars and employees.

Norfolk Southern estimates that its own plan will similarly allow its system to operate faster and more efficiently, while cutting about 3,000 employees from its current workforce of about 26,000 and shedding 500 locomotives from its fleet of about 4,100.

Ideally, the end result would be a more fluid railroad network that operates much like a moving conveyor belt, with fewer jams. It would allow shippers and customers to ship finished goods on a just-in-time basis, reducing carrying costs across the board.

Norfolk Southern is starting its overhaul with a process it calls “clean sheeting,” which involves dismantling and reassembling schedules and processes—one yard at a time…

…BNSF Railway Co., which operates alongside Union Pacific in the Western U.S., has resisted the industrywide push to cut capital spending and drastically change service plans. Executive Chairman Matthew Rose, who is scheduled to retire this month, said railroads that cut back on service risk pushback from regulators. Mr. Rose said BNSF, owned by Warren Buffett’s Berkshire Hathaway Inc., is focused on carrying more loads. “More volume leads to more investment,” he said.

Norfolk Southern, Kansas City Southern and Union Pacific all had service issues last year that they said exposed the perils of maintaining the status quo. When, in some cases, they responded by adding cars to handle the extra volume, congestion in some corridors got worse.

As Union Pacific tried to clear gridlock, it experimented with some strategies modeled after Mr. Harrison’s, which it then decided to adopt more broadly.

“We came to the realization that experimenting with pieces of precision-scheduled railroading was less effective on our network than going the whole way,” said Chief Executive Lance Fritz. The catalyst, he said, “was nothing more complex than our growing frustration and our customers’ growing frustration with the service product at that time.”

Norfolk’s Mr. Farrell, a 53-year-old former All American wrestler at Oklahoma State University, previously worked at both Canadian railroads where Mr. Harrison’s plan went into effect—Canadian National Railway Co. and Canadian Pacific Railway Ltd. At Norfolk Southern, he spent more than a year crisscrossing the network as a consultant to identify problem spots, a process he jokingly called the longest-ever episode of “Undercover Boss.”

After he formally joined the company in November, he ramped up clean-sheeting sessions. As of mid-February, the railroad says, trains were running 13% faster and dwelling 20% less in yards compared with last year.

3. Little Ways The World Works – Morgan Housel

If you find something that is true in more than one field, you’ve probably uncovered something particularly important. The more fields it shows up in, the more likely it is to be a fundamental and recurring driver of how the world works…

…Part of the second law of thermodynamics is that you get the most efficiency out of a system when the hottest heat source meets the coldest sink – that’s when an engine will waste the least amount of heat, converting as much energy into power as it can.

And isn’t it the same in business and careers?

A genius entering a crowded and competitive field may find a little success, but put her in a “cold” industry full of idiots and she’ll create a monopoly, destroying competitors. Jeff Bezos famously said “your margin is my opportunity,” which is the same concept. The biggest opportunities happen when a hot talent meets a cold industry. Thermodynamics has proven this since the beginning of the universe – no one should doubt how true and powerful it is…

…Muller’s ratchet (evolution): Dangerous mutations tend to pile up when there’s no genetic recombination, ultimately leading to extinction. It’s is why so few species reproduce asexually. In the absence of variety, bad ideas tend to stick around, which is also exactly what happens in closed societies and large corporations…

…Cope’s Rule (evolutionary biology): Species evolve to get bigger bodies over time, because there are competitive advantages to being big. But big has its own drawbacks, and can often be the cause of extinction. So the same force that pushes you to become big can also cause you to go extinct. It describes the lifecycle not only of species, but most companies and industries.

Emergence (complexity): When two plus two equals ten. A little cool air from the north is no big deal. A little warm breeze from the south is pleasant. But when they mix together over Missouri you get a tornado. The same thing happens in careers, when someone with a few mediocre skills mixed together at the right time becomes multiple times more successful than someone who’s an expert in one thing…

…Tocqueville Paradox (sociology): People’s expectations rise faster than living standards, so a society that becomes exponentially wealthier can see a decline in net happiness and satisfaction. There is virtually nothing people can’t get accustomed to, which also helps explain why there is so much desire for innovation and improvement.

Cromwell’s rule (statistics): Never say something cannot occur, or will definitely occur, unless it is logically true (1+1=1). If something has a one-in-a-billion chance of being true, and you interact with billions of things during your lifetime, you are nearly assured to experience some astounding surprises, and should always leave open the possibility of the unthinkable coming true.

Liebig’s law of the minimum (agriculture): A plant’s growth is limited by the single scarcest nutrient, not total nutrients – if you have everything except nitrogen, a plant goes nowhere. Liebig wrote, “The availability of the most abundant nutrient in the soil is only as good as the availability of the least abundant nutrient in the soil.” Most complex systems are the same, which makes them more fragile than we assume. One bad bank, one stuck container ship, or one broken supply line can ruin an entire system’s trajectory.

4. Munger on Airlines, Cereal Makers, and Bottlers – The Investments Blog

From this 1994 USC speech by Charlie Munger:

“Here’s a model that we’ve had trouble with. Maybe you’ll be able to figure it out better. Many markets get down to two or three big competitors—or five or six. And in some of those markets, nobody makes any money to speak of. But in others, everybody does very well.

Over the years, we’ve tried to figure out why the competition in some markets gets sort of rational from the investor’s point of view so that the shareholders do well, and in other markets, there’s destructive competition that destroys shareholder wealth.

If it’s a pure commodity like airline seats, you can understand why no one makes any money. As we sit here, just think of what airlines have given to the world—safe travel, greater experience, time with your loved ones, you name it. Yet, the net amount of money that’s been made by the shareholders of airlines since Kitty Hawk, is now a negative figure—a substantial negative figure. Competition was so intense that, once it was unleashed by deregulation, it ravaged shareholder wealth in the airline business.

Yet, in other fields—like cereals, for example—almost all the big boys make out. If you’re some kind of a medium grade cereal maker, you might make 15% on your capital. And if you’re really good, you might make 40%. But why are cereals so profitable—despite the fact that it looks to me like they’re competing like crazy with promotions, coupons and everything else? I don’t fully understand it.

Obviously, there’s a brand identity factor in cereals that doesn’t exist in airlines. That must be the main factor that accounts for it.

And maybe the cereal makers by and large have learned to be less crazy about fighting for market share—because if you get even one person who’s hell-bent on gaining market share…. For example, if I were Kellogg and I decided that I had to have 60% of the market, I think I could take most of the profit out of cereals. I’d ruin Kellogg in the process. But I think I could do it.

In some businesses, the participants behave like a demented Kellogg. In other businesses, they don’t. Unfortunately, I do not have a perfect model for predicting how that’s going to happen.

For example, if you look around at bottler markets, you’ll find many markets where bottlers of Pepsi and Coke both make a lot of money and many others where they destroy most of the profitability of the two franchises. That must get down to the peculiarities of individual adjustment to market capitalism. I think you’d have to know the people involved to fully understand what was happening.”

5. The Dark Side of Solar Power – Atalay Atasu, Serasu Duran, and Luk N. Van Wassenhove

Solar’s pandemic-proof performance is due in large part to the Solar Investment Tax Credit, which defrays 26% of solar-related expenses for all residential and commercial customers (just down from 30% during 2006–2019). After 2023, the tax credit will step down to a permanent 10% for commercial installers and will disappear entirely for home buyers. Therefore, sales of solar will probably burn even hotter in the coming months, as buyers race to cash in while they still can.

Tax subsidies are not the only reason for the solar explosion. The conversion efficiency of panels has improved by as much as 0.5% each year for the last 10 years, even as production costs (and thus prices) have sharply declined, thanks to several waves of manufacturing innovation mostly driven by industry-dominant Chinese panel producers. For the end consumer, this amounts to far lower up-front costs per kilowatt of energy generated.

This is all great news, not just for the industry but also for anyone who acknowledges the need to transition from fossil fuels to renewable energy for the sake of our planet’s future. But there’s a massive caveat that very few are talking about.

Economic incentives are rapidly aligning to encourage customers to trade their existing panels for newer, cheaper, more efficient models. In an industry where circularity solutions such as recycling remain woefully inadequate, the sheer volume of discarded panels will soon pose a risk of existentially damaging proportions.

To be sure, this is not the story one gets from official industry and government sources. The International Renewable Energy Agency (IRENA)’s official projections assert that “large amounts of annual waste are anticipated by the early 2030s” and could total 78 million tonnes by the year 2050. That’s a staggering amount, undoubtedly. But with so many years to prepare, it describes a billion-dollar opportunity for recapture of valuable materials rather than a dire threat. The threat is hidden by the fact that IRENA’s predictions are premised upon customers keeping their panels in place for the entirety of their 30-year life cycle. They do not account for the possibility of widespread early replacement.

Our research does. Using real U.S. data, we modeled the incentives affecting consumers’ decisions whether to replace under various scenarios. We surmised that three variables were particularly salient in determining replacement decisions: installation price, compensation rate (i.e., the going rate for solar energy sold to the grid), and module efficiency. If the cost of trading up is low enough, and the efficiency and compensation rate are high enough, we posit that rational consumers will make the switch, regardless of whether their existing panels have lived out a full 30 years…

…If early replacements occur as predicted by our statistical model, they can produce 50 times more waste in just four years than IRENA anticipates. That figure translates to around 315,000 metric tonnes of waste, based on an estimate of 90 tonnes per MW weight-to-power ratio.

Alarming as they are, these stats may not do full justice to the crisis, as our analysis is restricted to residential installations. With commercial and industrial panels added to the picture, the scale of replacements could be much, much larger.

The industry’s current circular capacity is woefully unprepared for the deluge of waste that is likely to come. The financial incentive to invest in recycling has never been very strong in solar. While panels contain small amounts of valuable materials such as silver, they are mostly made of glass, an extremely low-value material. The long life span of solar panels also serves to disincentivize innovation in this area.

As a result, solar’s production boom has left its recycling infrastructure in the dust. To give you some indication, First Solar is the sole U.S. panel manufacturer we know of with an up-and-running recycling initiative, which only applies to the company’s own products at a global capacity of two million panels per year. With the current capacity, it costs an estimated $20–$30 to recycle one panel. Sending that same panel to a landfill would cost a mere $1–$2.

The direct cost of recycling is only part of the end-of-life burden, however. Panels are delicate, bulky pieces of equipment usually installed on rooftops in the residential context. Specialized labor is required to detach and remove them, lest they shatter to smithereens before they make it onto the truck. In addition, some governments may classify solar panels as hazardous waste, due to the small amounts of heavy metals (cadmium, lead, etc.) they contain. This classification carries with it a string of expensive restrictions — hazardous waste can only be transported at designated times and via select routes, etc.

The totality of these unforeseen costs could crush industry competitiveness. If we plot future installations according to a logistic growth curve capped at 700 GW by 2050 (NREL’s estimated ceiling for the U.S. residential market) alongside the early-replacement curve, we see the volume of waste surpassing that of new installations by the year 2031. By 2035, discarded panels would outweigh new units sold by 2.56 times. In turn, this would catapult the LCOE (levelized cost of energy, a measure of the overall cost of an energy-producing asset over its lifetime) to four times the current projection. The economics of solar — so bright-seeming from the vantage point of 2021 — would darken quickly as the industry sinks under the weight of its own trash.

6. Why America Will Lose Semiconductors – Dylan Patel

The US has always been the world leader in semiconductors: design, manufacturing, and the tools to produce them. Semiconductors are the base of all technological innovation in computing and information technology. Without them, companies such as Amazon, Google, Microsoft, Meta, Apple, and Tesla would not exist. The US has slowly been losing its dominance over the semiconductor industry over the last couple of decades. In recent years, the rate of loss has been accelerating. If it is lost, then the foundational building block of modern technology is lost, and the US will cede its overarching technology advantage. In this article we will discuss the major causes of this problem and offer solutions which should be bipartisan in nature.

Before we get into the problem, let’s talk about the current state of the US’s semiconductor dominance. Most of the largest semiconductor equipment, design, and software companies are based in the US or have critical engineering in the US. In the equipment space, Lam Research, Applied Materials, and KLA are based out of the US. ASML, the widely known leader in lithography, does much of their critical engineering for the EUV Source and EUV Collector out of San Diego. These technology assets and teams come from the acquisition of San Diego based Cymer. ASML pays royalties to the EUV-LLC whose membership includes multiple US national labs. Without these tools, it is impossible to manufacture chips.

The critical software needed to be used to design chips is called EDA and it all comes from the US. Cadence, Synopsys, and Mentor Graphics (now owned by Siemens) are located in the US. Without this software, it is impossible to design modern chips.

American companies like Texas Instruments and Intel hold leading market shares in their respective fields while manufacturing their own chips. The 4 largest companies that design chips for external sale and use contract manufacturers are also American. They are Qualcomm, Broadcom, Nvidia, and AMD.

But that dominance is shifting away to countries that pose as geopolitical risks. US share of chip manufacturing is at an all-time low. The US will lose the semiconductor industry unless immediate action is taken. This is a national security crisis.

The US has been the hallmark of innovation through entrepreneurship, education, and making large investments. All three of these tenets are eroding, partially due to the private market’s attitude and partially because the government’s policies incentivize certain behaviors. The shift is occurring in favor of countries that have favorable government policies, regulatory support, focus on STEM higher education, and a general cultural recognition of the importance of semiconductor manufacturing…

…The US private market of venture capital and angel investing is completely off its rockers investing in software platform based “tech” companies. While this type of investing is fine, these same venture capital and angel investors have completely ignored the semiconductor and hardware space. We here at SemiAnalysis have seen it firsthand as we have helped a few firms in the semiconductor industry raise money. It’s extremely difficult to convince venture capitalists to invest in startups, even if they have promising technology and exceptional track records.

The private market has a strong prejudice against hardware startups. Semiconductors in general have higher startup costs, and the market potential is limited in comparison to a platform-based tech company. US based venture and angel investors that require them tend to think in terms of tens or hundreds of billions of dollars addressable markets. They want software platforms that can have a few dozen employees with the potential to scale to billions in revenue. There can only be so many Instagram’s, Uber’s, Shopify’s and Airbnb’s though. Hardware entrepreneurship is needed even if it doesn’t meet the wild dreams that US based venture and angel investors have. A friend of SemiAnalysis, Jay Goldberg has written about this phenomenon on his newsletter in posts titled Hard or Soft, and Hard or Soft with Math…

… Even if the startups and production facilities were in the US, there is now a severe shortage of skilled workers in the field. By 2025, this shortage is projected to be as high as 300,000 workers. Educated and skilled personnel is a cornerstone of innovation, and without them, the job cannot be done.

Most Americans who pursue a higher education do so in a non-STEM field. While not a negative in and of itself, this is a huge concern when viewed in light of the expected growing shortage of skilled workers in the semiconductor industry. Over 5 million people were granted degrees/certificates at postsecondary institutions in the US, yet not even 1/5th were in STEM according to the chart below from Statista.

2/3 of STEM PHD students in the US are foreigners. They were able to get student visas for their education, yet many of them have a very difficult time immigrating after their education despite hoping to do so. China has nearly 5 million people graduating with STEM degrees annually, population size differences make the gap between China and the US impossible to fill with domestic population alone.

The US must make it easier for educated people around the world to immigrate. It was much easier at other points in US history, which was part of the recipe for the US outpacing the rest of the world in innovation. The concept of brain drain is very real, and the best and most qualified in the world must be allowed to move to the US. 

7. Twitter thread on Three Arrows Capital’s bankruptcy – Jack Niewold

Three Arrows Capital was one of the biggest crypto hedge funds, at one point managing over $10 billion in capital— Until the founders dropped off the map. A 1000-page legal document came out today, bringing clarity to the case. I went through it. This is what I found:

To get you up to speed: After making a series of large directional trades (GBTC, LUNA, stETH) and borrowing from 20+ large institutions, Three Arrows Capital (3ac) went bust. Then the founders ran, and the loan defaults have lead to mass contagion in crypto.

As founders Su Zhu and Kyle Davies are nowhere to be seen, legal proceedings move forwards. Today, a court document was leaked, one which asks the Singapore Government (where 3AC is based) to recognize liquidation proceedings and cooperate with liquidators…

…1. CREDITORS
• 3AC owes over $3b
• The biggest creditor is Genesis, with $2.3b loaned
• Default on debts contributed to insolvency of Celsius and Voyager Digital…

…3. Reasonably Sized Yachts/Houses/Crimes
Between Sep 20 and June 22, Zhu bought two Singapore ‘Good Class Bungalows’ and a yacht that has yet to be delivered. It’s likely that borrowed money was used to fund it; the yacht was shown to lenders as proof of 3AC’s creditworthiness.

It looks like there was some really suspicious movement of ETH and stablecoins just before 3AC was widely known as insolvent. At one point, they made a down payment for the yacht while ignoring an outstanding loan payment.

Other potential crimes:
• Lying about extent of losses to lenders
• Lying about leverage and directional market exposure
• Movement of funds
• Not disclosing their liquidation to shareholders/creditors

4. The Business Structure.
Some reporting has recently been done around TPS/Tai Ping Shan LTD, which is a legal entity related to 3AC and owned by Su Zhu and Kyle Davies’ partner, Kelly Chen. It was recently transferred $31m in stablecoins by a 3AC account.

As for Su Zhu and Kyle Davies (well, his wife), they’re actually creditors in the suit against 3AC, claiming that 3AC owes them money.  That’s not part of these documents, but it’s wild enough to include…

…6. What’s left?
Equity and token agreements in 3ACs illiquid investments, some of which have surely been sold off. JPEGs, including ‘Crypto Dickbutt #1462’…

…7. How did this happen? 
Well, it looks like these lenders just didn’t do their homework. Take Blockchain.com as an example:
• 3AC was asked to to ‘keep them informed’ if their leverage went above 1.5x
• Davies signed the below letter confirming over $2.3b in TAM

And when can you pay back the loan, by the way…?

“Yo
uh
hmm”

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in ASML and Shopify. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 17 July 2022:

1. The first CRISPR gene-editing drug is coming—possibly as soon as next year – Sy Mukherjee

Until recently, CRISPR—the gene-editing technology that won scientists Jennifer Doudna and Emmanuelle Charpentier the 2020 Nobel Prize in chemistry—sounded more like science fiction than medicine; lab-created molecular scissors are used to snip out problematic DNA sections in a patient’s cells to cure them of disease. But soon we could see regulators approve the very first treatment using this gene-editing technology in an effort to combat rare inherited blood disorders that affect millions across the globe.

In a $900 million collaboration, rare disease specialist Vertex and CRISPR Therapeutics developed the therapy, dubbed exa-cel (short for exagamglogene autotemcel). It has already amassed promising evidence that it can help patients with beta thalassemia and sickle cell disease (SCD), both of which are genetic blood diseases that are relatively rare in the U.S. but somewhat more common inherited conditions globally…

…The latest exa-cel clinical data, unveiled during the 2022 European Hematology Association Congress in Switzerland, found that all 75 patients with either beta thalassemia or SCD given the gene-editing therapy showed zero or a greatly reduced need for blood transfusions (in the case of beta thalassemia) or incidences of life-threatening blockages (in the case of SCD). All but 2 of the 44 patients with thalassemia hadn’t needed a single blood transfusion in the 1 to 37 months of follow-up after the treatment’s administration, and the remaining 2 had 75% and 89% reduction in how much blood they needed transfused.

Similarly impressive, all 31 patients with a severe and life-threatening form of SCD experienced no vaso-occlusive crises (the life-threatening incidents in which healthy blood is blocked from moving freely) in anywhere from 2 to 32 months of posttreatment follow-up. Those same patients usually experienced, on average, nearly four of these crises per year for the two years before they received exa-cel…

…CRISPR isn’t the only type of gene therapy that’s made waves in just the past few weeks. Earlier in June, a group of advisers to the Food and Drug Administration (FDA) gave unanimous recommendations for a pair of non-CRISPR-based gene therapies from Bluebird Bio. The treatments target genes associated with beta thalassemia and a rare disorder afflicting children called cerebral adrenoleukodystrophy (CALD). The latter is a disease that eats away at white brain matter in children as young as 4, has few treatments, and usually leads to death within 5 to 10 years.

Bluebird’s eli-cel therapy has faced clinical setbacks because of its association with higher risk of a type of cancer, but the independent advisers decided its benefits still outweighed the risks for some patients with few other options. The FDA doesn’t have to follow the recommendations of its advisory panels, but typically does.

2. Stock Market Experiment Suggests Inevitability Of Booms and Busts – Jerry E. Bishop

Vernon L. Smith knows why the stock market crashed. He ought to. He’s seen dozens of “bubbles” — booms followed by sudden crashes — in the past three years.

Almost every time, Mr. Smith says, the bubble occurred because inexperienced traders dominated the market. In fact, traders had to go through at least two booms and crashes before they collectively learned to avoid these bubbles.

Mr. Smith is one of a new breed of economists who test economic theories by setting up laboratory experiments. For the past few years he and his associate at the University of Arizona in Tucson, Gerry L. Suchanek, and Arlington W. Williams at Indiana University in Bloomington have been running experimental stock markets in their labs…

…In these experimental markets a dozen or so volunteers, usually economics students, are given a set number of “shares” of stock, along with some working capital. All the volunteers are connected by terminals to a computer, which is set up to duplicate trading on the stock-market floor. A trading “day” lasts about four minutes during which the traders may have entered two or three dozen bids and offers resulting in anywhere from five to a dozen trades. A typical experiment during an afternoon or evening runs 15 days.

The booms and crashes occurred in a recent series of 60 experiments aimed at testing an aspect of one of the most basic of all stock-market theories — rational expectations. This theory says a stock’s price is determined by investors’ expectations of what dividend the share will pay. If investors are rational in their expectations, they all place the same value on the stock and it will trade at a price reflecting its true dividend value. The price will change only when new information comes along that changes the dividend expectations.

Mr. Smith and his colleagues assumed, however, that even if investors had the same information, their dividend expectations would differ and they would value the stock differently. Price speculation would then be possible. But, they hypothesized, investors would soon realize that speculative profits are uncertain and unsustainable and they would begin changing their dividend expectations until, at some point, they all came to a common and rational expectation. The stock would then trade at its dividend value.

To find out how long this learning process would take, they set up a laboratory market in which all traders began with the same information about dividend prospects. Traders were told a payout would be declared after each trading day. The amount would be determined randomly from four possibilities — zero, eight, 28 or 60 cents. The average daily payout would be 24 cents. Thus, a share’s dividend value on the first trading day in a 15-day experiment was $3.60 (24 cents times 15 days). As the days passed and dividends were paid, the dividend value would drop.

One typical experiment involved nine students. On the first four-minute “day,” trading opened when a student’s offer to sell a share for $1.50 was quickly accepted. A moment later a bid to buy a share for $1.30 was snapped up. Such bargain prices triggered a flurry of rising bids, and a boom quickly developed. By the middle of the fourth trading day the price topped $5.50 even though the stock’s dividend value had dropped to $3.

But at such high prices offers to sell began to outnumber bids to buy. A crash began and by day 11 prices were below the stock’s $1 dividend value. Only on the last two trading days prices settle at or near the dividend value.

Some of the more astute traders were able to post gains of as much as $50 in dividends and trading profits while others ended up with as little as $5, Mr. Smith says. If the stock had consistently traded at or near its dividend value, all nine students could have had a profit of $16.

Such market bubbles occurred repeatedly. “We find that inexperienced traders never trade consistently near fundamental value, and most commonly generate a boom followed by a crash in stock prices,” Mr. Smith says. Moreover, traders who have experienced one crash “continue to bubble and crash, but at reduced volume,” he says. But, he adds, “Groups brought back for a third trading session tend to trade near fundamental dividend value.”

To counter any criticism that the boom and crash reflected students’ naivete, the researchers used Tucson businessmen who had “real world” experience. They generated the biggest bubble of all and, like the students, had to go through two booms and crashes before settling down to trade at a mutually profitable dividend value.

3. Lifestyles – Morgan Housel

Anyone alone at sea for nine months will start to lose their mind, and there’s evidence both Crowhurst and Moitessier were in poor mental states when their decisions were made. Crowhurst’s last diary entries were incoherent ramblings about submitting your soul to the universe; Moitessier wrote about his long conversations with birds and dolphins.

But their outcomes seemed to center on the fact that Crowhurst was addicted to what other people thought of his accomplishments, while Moitessier was disgusted by them. One lived for external benchmarks, the other only cared about internal measures of happiness.

They are the most extreme examples you can imagine. But their stories are important because ordinary people so often struggle to find balance between external and internal measures of success.

I have no idea how to find the perfect balance between internal and external benchmarks. But I know there’s a strong social pull toward external measures – chasing a path someone else set, whether you enjoy it or not. Social media makes it ten times more powerful. But I also know there’s a strong natural desire for internal measures – being independent, following your quirky habits, and doing what you want, when you want, with whom you want. That’s what people actually want.

4. The Biggest Problem With Remote Work – Derek Thompson

But if the work-from-anywhere movement has been successful for veteran employees in defined roles with trusted colleagues, for certain people and for certain objectives,  remote or hybrid work remains a problem to be solved.

First, remote work is worse for new workers. Many inexperienced employees joining a virtual company realize that they haven’t joined much of a company at all. They’ve logged into a virtual room that calls itself a company but is basically a group chat. It’s hard to promote a wholesome company culture in normal times, and harder still to do so one misunderstood group Slack message and problematic fire emoji at a time. “Small talk, passing conversations, even just observing your manager’s pathways through the office may seem trivial, but in the aggregate they’re far more valuable than any form of company handbook,” write Anne Helen Petersen and Charlie Warzel, the authors of the book Out of Office. Many of the perks of flexible work—like owning your own schedule and getting away from office gossip—can “work against younger employees” in companies that don’t have intentional structured mentorship programs, they argued.

Second, remote is worse at building new teams to take on new tasks. In 2020, Microsoft tapped researchers from UC Berkeley to study how the pandemic changed its work culture. Researchers combed through 60,000 employees’ anonymized messages and chats. They found that the number of messages sent within teams grew significantly, as workers tried to keep up with their colleagues. But information sharing between groups plummeted. Remote work made people more likely to hunker down with their preexisting teams and less likely to have serendipitous conversations that could lead to knowledge sharing. Though employees could accomplish the “hard work” of emailing and making PowerPoints from anywhere, the Microsoft-Berkeley study suggested that the most important job of the office is “soft work”—the sort of banter that allows for long-term trust and innovation…

…Third, and relatedly, remote work is worse at generating disruptive new ideas. A paper published in Nature by Melanie Brucks, at Columbia Business School, and Jonathan Levav, at the Stanford Graduate School of Business, analyzed whether virtual teams could brainstorm as creatively as in-person teams. In one study, they recruited about 1,500 engineers to work in pairs and randomly assigned them to brainstorm either face-to-face or over videoconference. After the pairs generated product ideas for an hour, they selected and submitted one to a panel of judges. Engineers who worked virtually generated fewer total ideas and external raters graded their ideas significantly less creative than those of the in-person teams…

…The work-from-anywhere revolution has something of a kick-starter problem: It’s harder for new workers, new groups, and new ideas to get revved up.

So how do we fix this? One school of thought says face-to-face interactions are too precious to be replaced. I disagree. I’m an optimist who believes the corporate world can solve these problems, because I know that other industries already have.

Modern scientific research is a team sport, with groups spanning many universities and countries. Groups working without face-to-face interaction have historically been less innovative, according to a new paper on remote work in science. For decades, teams split among several countries were five times less likely to produce “breakthrough” science that replaced the corpus of research that came before it. But in the past decade, the innovation gap between on-site and remote teams suddenly reversed. Today, the teams divided by the greatest distance are producing the most significant and innovative work.

I asked one of the co-authors of the paper, the Oxford University economist Carl Benedikt Frey, to explain this flip. He said the explosion of remote-work tools such as Zoom and Slack was essential. But the most important factor is that remote scientists have figured out how to be better hybrid workers. After decades of trial and error, they’ve learned to combine their local networks, which are developed through years of in-person encounters, and their virtual networks, to build a kind of global collective brain.

If scientists can make remote work work, companies can do it too. But they might just have to create an entirely new position—a middle manager for the post-pandemic era.

In the middle of the 19th century, the railroads and the telegraph allowed goods and information to move faster than ever. In 1800, traveling from Manhattan to Chicago took, on average, four weeks. In 1857, it took two days. Firms headquartered in major cities could suddenly track prices from Los Angeles to Miami and ship goods across the country at then-record-high speeds.

To conduct this full orchestra of operations, mid-1800s companies had to invent an entirely new system of organizing work. They needed a new layer of decision makers who could steer local production and distribution businesses. A new species of employee was born: the “middle manager.”

“As late as 1840, there were no middle managers in the United States,” Alfred Chandler observed in The Visible Hand, his classic history of the rise of America’s managerial revolution. In the early 1800s, all managers were owners, and all owners were managers; it was unheard of for somebody to direct employees without being a partner in the company. But once ownership and management were unbundled, new kinds of American companies were made possible, such as the department store, the mail-order house, and the national oil and steel behemoths…

…The synchronizer—or, for large companies, a team of synchronizers—would be responsible for solving the new-worker, new-group, and new-idea problems. Synchronizers would help new workers by ensuring that their managers, mentors, and colleagues are with them at the office during an early onboarding period. They would plan in-person time for new teammates to get to know one another as actual people and not just abstracted online personalities. They would coordinate the formation of new groups to tackle new project ideas, the same way that modern teams in science pull together the right researchers from around the world to co-author new papers. They would plan frequent retreats and reunions across the company, even for workers who never have to be together, with the understanding that the best new ideas—whether in science, consulting, or media—often come from the surprising hybridization of disparate expertise.

5. The Trillion-Dollar Vision of Dee Hock – M. Mitchell Waldrop

This is one of Dee Hock’s favorite tricks to play on an audience. “How many of you recognize this?” he asks, holding out his own Visa card.

Every hand in the room goes up.

“Now,” Hock says, “how many of you can tell me who owns it, where it’s headquartered, how it’s governed, or where to buy shares?”

Confused silence. No one has the slightest idea, because no one has ever thought about it.

And that, says Hock, is exactly how it ought to be. “The better an organization is, the less obvious it is,” he says. “In Visa, we tried to create an invisible organization and keep it that way. It’s the results, not the structure or management that should be apparent.” Today the Visa organization that Hock founded is not only performing brilliantly, it is also almost mythic, one of only two examples that experts regularly cite to illustrate how the dynamic principles of chaos theory can be applied to business.

It all started back in the late 1960s, when the credit card industry was on the brink of disaster. The forerunner of the Visa system — the very first credit card — was BankAmericard, which had originated a decade earlier as a statewide service of the San Francisco-based Bank of America. The card got off to a rocky start, then became reasonably profitable — until 1966, when five other California banks jointly issued a competing product they called MasterCharge.

Bank of America promptly responded, franchising BankAmericard nationwide. (In those days, banks were forbidden to have their own out-of-state branches.) Other large banks quickly responded with their own proprietary cards and franchise systems. A credit card orgy ensued: banks mass-mailed preapproved cards to any list they could find. Children were getting cards. Pets were getting cards. Convicted felons were getting cards. Fraud was rampant, and the banks were hemorrhaging red ink.

By 1968, the industry had become so self-destructive that Bank of America called its licensees to a meeting in Columbus, Ohio to find a solution. The meeting promptly dissolved into angry finger-pointing.

Enter Dee Hock, then a 38-year-old vice president at a licensee bank in Seattle. When the meeting was at its most acrimonious, he got up and suggested that the group find a method to study the issues more systematically. The thankful participants immediately formed a committee, named Hock chairman, and went home.

It was the chance Hock had been waiting for. Even then, he was a man who thought Big Thoughts. Born in 1929, the youngest child of a utility lineman in the mountain town of North Ogden, Utah, he was a loner, an iconoclast, a self-educated mountain boy with a deeply ingrained respect for the individual and a hard-won sense of self-worth. And he stubbornly refused to accept orthodox ideas: before he’d started with the Seattle bank he’d already walked away from fast-track jobs at three separate financial companies, each time raging that the hierarchical, rule-following, control-everything organizations were stifling creativity and initiative at the grass roots — and in the process, making the company too rigid to respond to new challenges and opportunities.

He’d been a passionate reader since before he could remember, even though his formal schooling ended after two years at a community college. He read history, economics, politics, science, philosophy, poetry — anything and everything, without paying the slightest attention to disciplinary boundaries.

What he read convinced him that the command-and-control model of organization that had grown up to support the industrial revolution had gotten out of hand. It simply didn’t work. Command-and-control organizations, Hock says, “were not only archaic and increasingly irrelevant. They were becoming a public menace, antithetical to the human spirit and destructive of the biosphere. I was convinced we were on the brink of an epidemic of institutional failure.”

He also had a deep conviction that if he ever got to create an organization, things would be different. He would try to conceive it based on biological concepts and metaphors.

Now he had that chance. In June 1970, after nearly two years of brainstorming, planning, arguing, and consensus building, control of the BankAmericard system passed to a new, independent entity called National BankAmericard, Inc. (later renamed Visa International). And its CEO was one Dee W. Hock.

The new organization was indeed different — a nonstock, for-profit membership corporation with ownership in the form of nontransferable rights of participation. Hock designed the organization according to his philosophy: highly decentralized and highly collaborative. Authority, initiative, decision making, wealth — everything possible is pushed out to the periphery of the organization, to the members. This design resulted from the need to reconcile a fundamental tension. On the one hand, the member financial institutions are fierce competitors: they — not Visa — issue the cards, which means they are constantly going after each other’s customers. On the other hand, the members also have to cooperate with each other: for the system to work, participating merchants must be able to take any Visa card issued by any bank, anywhere.

That means that the banks abide by certain standards on issues such as card layout. Even more important, they participate in a common clearinghouse operation, the system that reconciles all the accounts and makes sure merchants get paid for each purchase, the transactions are cleared between banks, and customers get billed.

To reconcile that tension, Hock and his colleagues employed a combination of Lao Tse, Adam Smith, and Thomas Jefferson. For example, instead of trying to enforce cooperation by restricting what the members can do, the Visa bylaws encourage them to compete and innovate as much as possible. “Members are free to create, price, market, and service their own products under the Visa name,” he says. “At the same time, in a narrow band of activity essential to the success of the whole, they engage in the most intense cooperation.” This harmonious blend of cooperation and competition is what allowed the system to expand worldwide in the face of different currencies, languages, legal codes, customs, cultures, and political philosophies.

No one way of doing business, dictated from headquarters, could possibly have worked. “It was beyond the power of reason to design an organization to deal with such complexity,” says Hock, “and beyond the reach of the imagination to perceive all the conditions it would encounter.” Instead, he says, “the organization had to be based on biological concepts to evolve, in effect, to invent and organize itself.”

Visa has been called “a corporation whose product is coordination.” Hock calls it “an enabling organization.” He also sees it as living proof that a large organization can be effective without being centralized and coercive. “Visa has elements of Jeffersonian democracy, it has elements of the free market, of government franchising — almost every kind of organization you can think about,” he says. “But it’s none of them. Like the body, the brain, and the biosphere, it’s largely self-organizing.”

It also works. Visa grew phenomenally during the 1970s, from a few hundred members to tens of thousands. And it did so more or less smoothly, without dissolving into fiefdoms and turf wars. By the early 1980s, in fact, the Visa system had surpassed MasterCard as the largest in the world. It had begun to fulfill Hock’s vision of a universal currency, transcending national boundaries. And Dee Hock was seen as the system’s essential man.

“Utter nonsense,” Hock says. “It’s the organizational concepts and ideas that were essential. I merely came to symbolize them. Such organizations should be management-proof.”

In May 1984, at 55, Hock put his beliefs to the test. He resigned from Visa and three months later, with his successor in place, dropped completely from sight. Six years later, in an acceptance speech as a laureate of the Business Hall of Fame, Hock put it this way: “Through the years, I have greatly feared and sought to keep at bay the four beasts that inevitably devour their keeper — Ego, Envy, Avarice, and Ambition. In 1984, I severed all connections with business for a life of isolation and anonymity, convinced I was making a great bargain by trading money for time, position for liberty, and ego for contentment — that the beasts were securely caged.”

Visa never missed a beat.

6. America’s freight railroads are incredibly chaotic right now – Rachel Premack

A railroad engineer or conductor typically earns a six-figure salary, retires with a pension and enjoys union benefits. They don’t need a college degree; the monthslong training is provided on the job. It’s the kind of career that ought to be popular — but Doering said trainees and longtimers alike are getting burned out. It used to be a job with eight- or nine-hour shifts and plenty of time at home. Now, Doering says railroading demands too much time away from one’s family and workdays that last up to 19 hours, combining 12-hour shifts with hours of waiting around for transportation or relief crews. 

Union Pacific is struggling to find railroad crews after years of slashing headcounts. The $22 billion railroader had 30,100 employees during the first three months of 2022, according to its latest earnings report. Five years prior, the company had nearly 12,000 more workers. (A representative from Union Pacific declined to provide a comment for this article, as the company is reporting its second-quarter earnings later this month. The rep did share a company blog on the importance of supply chain fluidity and cooperation.)

This employment issue isn’t unique to Union Pacific. America’s railways are in an unusually chaotic state as Class I lines struggle to find employees. That’s led to congestion that analysts say is even worse than 2021, which saw some of the biggest rail traffic in history. Now, a strike of 115,000 rail workers could happen as soon as next week. 

“We’re spending more time at home-away terminals than we are at home,” Doering said. Doering is also the Nevada legislative director for SMART Transportation Division, a labor union of train, airline and other transportation workers. “So the attitudes out here, I think, are warranted. Morale is at an all-time low.” …

…So, while you may not have been keeping up to date with rail congestion, industrial bigwigs and lawmakers alike are furious. The coal industry is slamming rail for the “meltdown” in service capacity and grain shippers said they had to spend $100 million more in shipping costs to get their product moved amid poor rail service. The Port of Los Angeles is taking to the press to demand rail move those gosh darn containers away, saying that railroaders could cause a “nationwide logjam” with the unmoved containers sitting around. Members of the federal government’s Surface Transportation Board recently demanded answers from railroad executives in a May two-day hearing, but tensions seemed to have only worsened since then.

Even more exhausted are the rail workers themselves. Rail unions have been negotiating with their employers since January 2020, with a “dead end” in negotiations reported two years later. Now, President Joe Biden is being charged with appointing a “Presidential Emergency Board” to nail down a new contract. If he doesn’t do so by Monday, railroad crews could legally have their first nationwide strike since 1992. Such a strike, according to the U.S. Chamber of Commerce, would be “disastrous.”…

…Let me tell you the hottest rail trend of the 2010s: precision-scheduled railroading. As The Wall Street Journal’s Paul Ziobro explained in a 2019 story, PSR means that railroads have set times for when they pick up cargo from their customers, not unlike a commercial airline. Before, railroads would wait for the cargo. 

There are endless implications that come from this system, some of which my colleague Mike Baudendistel delved into in this 2020 article. PSR allowed railroads to reduce capital budgets, slash headcount and merge internal operations with glee. But its biggest boon to the railroaders was how much it boosted their cred on Wall Street, creating billions in shareholder value.

“The railroad stocks have greatly outperformed the broader market in the past 15 years, which took place despite the major deterioration of coal volume, the railroads’ historical business,” Baudendistel wrote.

There are serious service issues with PSR, though. When the tactic was first implemented at CSX Transportation, dwell time at some terminals increased by as much as 26 hours, according to another 2019 WSJ piece by Ziobro. Trips that would take a few days stretched out to more than two weeks — a struggle for customers that relied on just-in-time supply chains…

…Rail giants, as you could guess, struggled during the early months of COVID. In April 2020, for example, rail carloads saw their biggest year-over-year drop since 1989 and intermodal loadings saw a decline not experienced since 2009.

Railroads were shedding employees from April until July 2020, when my colleague Joanna Marsh reported that crew headcount had finally begun to increase again. Still, there were 25% fewer crews than in 2019 and 28% fewer than 2018, according to data from the Surface Transportation Board.

The financial status of these firms was in question, which motivated them to furlough workers. “At least one Class I railroad held meetings to decide whether they had enough cash through the summer, if they had enough cash to pay the bills and could they stay in business,” Hatch said. “When they began to lay people off, much to the consternation today of the regulators and whatnot, you need some understanding that they did not know how long this would last.”

Railroaders struggled to re-hire those crews they furloughed. Many of them found work in construction or manufacturing, industries that allow workers to spend evenings and weekends at home, Tranausky said.

Unlike its siblings in trucking or ocean shipping, the railroad industry didn’t have a bonkers 2021 — but it survived. 2021 saw healthier volumes from the year before. They were still below 2019’s levels…

…Some issues are completely out of the railroads’ control. Most kinds of employers across the country are still struggling to find workers. Even finding shuttle drivers to take railroad crews to their terminal has been a struggle, from Doering’s observations. Recently, Union Pacific has put him in a taxi to go from Las Vegas to inland California. “We’re watching the little ticker up there in the cab go up to $400 or $500 for a trip,” Doering said.

Even in the best of times, it’s hard to find someone to sign up to be a railroad crew member. They have a similar lifestyle to, say, airline pilots, who must be away from their families for days at a time, living in hotels and manning massive, potentially dangerous pieces of equipment. Railroad crews are on call even during their home time.

They require months of training and after that need years or decades on the job to become truly masters of the rail. “There’s a learning curve,” Tranausky said. “[New crews are] not as efficient, not as productive as those higher-seniority crews.”

While Tranausky and Hatch said labor is the main driver of today’s congestion, one factor is totally outside the control of railroads. Unlike 2021, many warehouses are packed with inventory. Some insiders told FreightWaves that shippers are essentially using railcars as storage rather than moving the cargo into their own warehouse. That’s causing a shortage of chassis and increasing congestion — particularly in rail yards like Chicago.

7. TIP462: What Is Money? w/ Lyn Alden – Stig Brodersen and Lyn Alden

Stig Brodersen (01:11):

To kick this episode off, perhaps you can tell this story of the ancient Greek democracy in sixth century b.c. that used partial jewelry as a solution to avoid a catastrophic class conflict and how that relates to where we are in the debt cycle today.

Lyn Alden (01:28):

You have something that builds up decade to decade, even generation to generation, two or three generations, and it doesn’t self-correct enough, right? So, there’s basically the structural issues in society where things build up and get worse and worse. Basically, things have a tendency to concentrate, especially the way we structure things. And so, you have a given society where let’s say farmers, they harvest crops. They have a big harvest every year, but of course, they have to pay for things throughout the year. So, they might, for example, use debt with the promise to pay them back once their harvest comes in. That might work for 10 years in a row, but on the 11th year, they have a crop failure and suddenly, they find themselves in massive debt that they can’t pay.

Lyn Alden (02:13):

And back then, you could become a debt slave. There are all sorts of ways to deal with that in society. Problem is that over time, you have things build up where wealth consolidates and then it also feeds on itself. So, once you’re wealthy, you’re able to influence politics more, right? You have the ear of the king. Or if it’s a republic, you might have more voting power. Back then especially, only rich people could really vote anyway in societies that were republics. So, you can further make the rules in your favor and you get this tendency to consolidate one way or another. Societies had to deal with that in different ways and we have records going back to ancient Sumeria, Babylon, and Greece.

Lyn Alden (02:56):

And the one I used in this piece was Plutarch wrote about the ancient King Solon in Greece and this was an excerpt from Lessons of History by Will and Ariel Durant. I’ll just read it because it’s actually a really good paragraph. And in the Athens of 594 b.c., the poor finding their status worsened with each year, the government in the hands of their masters and the corrupt courts deriving every issue against them, began to talk a violent revolt. The rich, angry at the challenge to their property, prepared to defend themselves by force. Good sense prevailed, moderate element secured the election of Solon, a businessman of aristocratic lineage to the supreme archonship. He devalued the currency thereby easing the burden of all debtors, although he himself was a creditor.

Lyn Alden (03:43):

He reduced all personal debts and ended imprisonment for debt. He canceled arrears for taxes and mortgage interest. He established a graduated income tax that made the rich pay at a rate 12 times that required of the poor. He reorganized the courts on a more popular basis. He arranged that the sons of those who had died in war for Athens should be brought up and educated at the government’s expense. The rich protested that these measures were outright confiscation and then the radicals on the other side complained that he had not redivided the land. But within a generation, almost all had agreed that his reforms had saved Athens from revolution.

Lyn Alden (04:21):

And so, basically, when we talk about this multi-decade, multi-generational compoundings, usually, what you have is these sharp events at some point where either people revolt, right? Everyone’s a debt slave now. So, they say, “Wait a second. We outnumber these guys 100 to 1. Let’s just go burn their house down.” So, there’s that. Or they through politics basically say, “Okay, this is not sustainable. The courts are corrupt. We have so entrenched cronyism and the policy is not good. Let’s sharply reverse some of this without going too far.” And so, this was an example where they managed to moderate it. Most examples are not that successful.

Lyn Alden (05:00):

And so, this shows over time that when you have massive debts and wealth concentration built up in a society, there’s usually some release valve that in various ways, it’s painful for various groups. And then depending on how it goes, it could be extraordinarily painful for everyone if you have a collapse or a revolution of some sort…

…Lyn Alden (11:47):

The best money isn’t always the absolute hardest. There’s other attributes like the ease of transaction, the divisibility, the speed with which you could move it around. And that’s why I would argue for example that for the past 150 years, paper currencies have really outpaced gold, because even though gold’s harder, in practical terms, it has trouble keeping up. So, for thousands of years, commerce and money moved at about the same speed, which is the speed of humans, right? So, we go around on horses and chips and on foot and we transact with each other with gold, silver, copper pieces, or even our ledgers. Even if we started keeping ledgers, those physical ledgers could still only move at the speed of humans.

Lyn Alden (12:29):

We had to really bring them somewhere if you want to give them to another city. But with the introduction of telecommunications equipment in the 1800s, first with the telegraph and then the telephone, we lay these undersea cables under the Atlantic, starting in the 1800s. And so, by the time you got to late 1800s, institutions around the world could talk to each other almost instantly. And so, you could update ledgers and perform certain types of transactions on multi-continent basis far faster than physical goods, including physical money, could settle. And so, gold was no longer able to keep up with the speed of human commerce, and that really further, I think, led to the need for abstraction.

Lyn Alden (13:12):

So, historically, gold was abstracted, because it had limits on its visibility. Whereas now, we also had the even more important thing where we had limits to its speed relative to the speed we wanted transact. So, we had to abstract it more and that eventually opened up the divide between gold and paper currencies. And then eventually, they could be separated. Whereas, in some other world, if there was an element like gold that we could just mentally teleport to each other, it would’ve been much harder to ever introduce paper currencies, because it would’ve been seen right away as an inferior product, but because gold had those limitations and there was an advantage to using paper claims for gold, it was able to lead to that separation.

Lyn Alden (13:53):

So, in many ways, even though dollars are less hard than gold, they have other advantages over the past 150 years or so that has allowed them to at least keep up with gold and that more people use dollars than use gold, even though gold is a better store of value. So, gold as its hardness is better retained its store of value property, but it lost the medium of exchange and unit of account aspects to what is basically better technology. When you look at pure commodity monies, the stock to flow ratio is pretty paramount. There’s actually a really good example in the early American colonies. There was almost an accelerated version of why most monies fail.

Lyn Alden (14:39):

And basically, in pre-American before the revolution, you had these Southern colonies in the 1600s and they started using tobacco as money. They grew tobacco. It was a high value crop. It was reasonably liquid and fungible. And so, they started using tobacco as money. They even made it legal tender in some colonies like Virginia, where you could pay your taxes, you could pay all debts in tobacco. And so, it became money. Problem is that when you put on a monetary premium to something, you basically give everyone an incentive to make more of it. And so, tobacco’s not very resistant to debasement, so anyone could go and plant more tobacco. And so, they started to inflate the value of tobacco away.

Lyn Alden (15:22):

Basically, the prices of things as dominant to tobacco began to increase. And then so the government imposed restrictions. They said certain class of people couldn’t grow tobacco or there’s only so much tobacco that can be grown a year. They’re trying to artificially increase the stock to flow ratios. Then you had another problem where unlike gold, tobacco is not very fungible, right? So, there’s higher quality tobacco, there’s lower quality tobacco. And so, there’s an incentive to pay your debts in this marginal tobacco, the worst tobacco, and then to say, sell the good tobacco overseas or smoke that or whatever you want to do. Use that for better purposes. Give other people the worst ones.

Lyn Alden (16:00):

You basically have Gresham’s law in play, where the weak money dries out the good money. So, everyone’s trading around the bad tobacco. Then they had to say, “Okay, well, we need external auditors to come in and check the quality of tobacco.” So, they put tobacco in warehouses, grade it, and then trade paper claims for that tobacco. So, they basically had to try to increase the fungibility. And then eventually, they abandoned the whole situation, because it was untenable. And so, that’s an accelerated version of why any commodity that’s not resistant to debasement, meaning it can’t maintain a high stock to flow ratio given our level of technology, ultimately fails as money.

Lyn Alden (16:41):

When you look at the broad spectrum, all the different commodities of history, that’s one reason why gold keeps reemerging, because no matter how good our technology is, we’re not really good at making more gold. So, in the 1970s, for example, when the price of gold went up more than tenfold, if you look at the gold production, it barely changes at all, because we just literally don’t have the capability to make a ton of new gold in a short period of time.

Stig Brodersen (17:08):

I’m holding this amazing blog post. It’s another one of Lyn’s great blog post. The title is, “What is Money, Anyway?” I’ll make sure to link to that in show notes and you tell different stories about commodity money. I love all of them. One of them is about African beads, which is amazing itself and really the illustration of what you’re saying there about new technology coming in. I don’t know if I could ask you to share that story with the audience.

Lyn Alden (17:31):

Yeah. So, the African bead story, that’s probably the most tragic one, one of the most tragic examples in that piece. So, basically, for a long period of time, you had different groups in West Africa using beads as money. So, again, that goes back to the idea that money is technology. So, what is rare, liquid, fungible, desirable in an area could be different in other area. So, in that region, they didn’t have glass-making technology. And so, glass beads were very rare and desirable. And then also you had a pastoral society. So, you might have your herd of animals, shepherd. You’re moving around. So, you want to be able to bring your money with you. So, you could literally wear your beads. You could wear your wealth. And so, that was a useful type of money.

Lyn Alden (18:17):

They also used things like fine fabrics and things like that and certain herbs. These were money-like instruments, but beads were a key one for them. And the tragedy was that Europeans who at the time had glass-making technology when they were traveling around, they would identify and say, “These people like to use beads as money and so we can use that to our advantage. It’s cheap for us to make fancy glass beads and then we can start trading it for things of actual value. We can buy their animals. We can buy their resources.” And then sadly, there was a slave trade. So, you could buy slaves with these beads that you could make for almost free. So, they became known as slave beads in some circles. But then of course, the Africans had resistances against this.

Lyn Alden (19:06):

So, if the Europeans flooded everything with these say clear glass beads, they would start to say, “Okay, these clear glass ones are… No, they’re not good money. We want the purple kind.” But then of course, over time, the Europeans would adapt and say, “Okay, well, they want the purple kind now.” So, there was this cat and mouse game where beads were not fully fungible. There were different types and so there were different taste preferences. And then there were different reactions to the perceived scarcity of different types of beads. But eventually, that money became untenable for obvious reasons that there was a technology asymmetry between the cultures and then over time, that technology spread everywhere.

Lyn Alden (19:46):

And so, glass beads are in the long arc of time not good money. And it also that shows that if you don’t have hard money and if your money is not hard and if you’re using something as money that another culture or that some other group within your society can produce more of, then you’re at a disadvantage. So, that’s one of the tragic examples of why money is so critical, especially when different groups interact with each other…

…Stig Brodersen (33:52):

Yeah. And on that note, I would like to talk about the private to public de-leveraging. Usually, that is a process that’s inflationary and then we look at a country like Japan. They mainly stood for decades without almost any price inflation and with very low monetary inflation, but still many macro analysts look at this and they assume that “Well, if this is what’s happening to Japan, it’s going to happen to the US, to the rest of the world perhaps even.” You think that they’re wrong, why is that?

Lyn Alden (34:24):

Two main reasons. One, they own a lot more foreign assets than the collective foreign sector owns of Japanese assets. So, they actually have over trillion dollars, trillions of dollars of claims basically, that they can draw in to pay obligations as needed, right? So, they have this large investment base relative to the size of their GDP. So, that gives them one advantage that many countries now don’t have, especially United States. Number two is that that whole massive private de-leveraging and public leveraging happened primarily during the 2010s decade, which was a very disinflationary decade in terms of commodity market. So, there’s roughly this 10- to 15- to 20-year commodity cycle that happens worldwide where prices are low.

Lyn Alden (35:15):

So, nobody invests in commodities. They don’t build new production. Eventually, that causes a shortage. So, lots of money rushes in for a decade and builds all sorts of new commodity production. And eventually, we oversupply the world with commodities and then that breaks the price. And we start this cycle anew and that takes quite a while. That takes maybe 10 years of building and then 10 years working it out and 10 years of building and 10 years of working it out. Most countries are not big enough to really affect that cycle. United States and China are, but if you’re a country that’s a small percentage of GDP, you don’t really affect that on a global scale.

Lyn Alden (35:51):

And so, Japan happened to do this de-leveraging during a time of substantial commodity disinflation or actually outright deflation. Commodities were literally going down in price while they were doing this. And so, they basically had all the commodity needs available to them at low prices and you weren’t getting this scarcity and undersupply of commodities. That is a much harder thing to do if you’re not at the right part of the commodity cycle or if you’re big enough to affect the commodity cycle. And so, I would argue that Japan represents an almost perfect example of doing this with the right conditions and at the right time that we shouldn’t then just look at that and say, “Well, when all the other countries go through a similar process, it’s going to be just like Japan.”

Lyn Alden (36:41):

Actually, third factor I would say is that unlike most countries in the developed world, Japan has very little political polarization or rising populism intentions in the country. And part of that’s you have a rather homogeneous society and they’ve governed pretty well domestically. They don’t spend a lot of money on military, things like that. And so, a lot of the money just goes back to the people. You have a rather harmonious society. That doesn’t mean it’s perfect. There are obviously disagreements in society, but when you look at just quantitative ways of measuring political polarization, the United States and most European countries are in a much tougher spot there, whereas Japan has a rather harmonious society rather low levels of wealth concentration.

Lyn Alden (37:30):

And so, that gives them a deeper tool chest, I would say, to handle those storms. But of course, their main disadvantages right now are that they are a commodity importer, which now is becoming relevant. And then they also do have now a ton of public debt. And so, they have less ability to raise rates or otherwise protect the value of their currency because they can’t really service that debt otherwise. And so, I think, people over extrapolate the Japanese example by not realizing a lot of the nuances around the timing and the details for how they’re able to do that without it being inflationary.

Stig Brodersen (38:07):

So, let’s talk more about that. It’s very hard to talk about Japan macroeconomic terms without looking that huge debt burden they have. And we started out this interview by talking about forgiveness of debt restructuring. And so, keeping that in mind, the listener might be sitting out there thinking, “Well, we do know that a lot of money is being printed and that’s on the Bank of Japan’s balance sheet. Can’t they just forgive the government debt that they hold and thereby bringing down the debt burden?” I know that’s a question that you have asked yourself. What’s the answer to that?

Lyn Alden (38:44):

The answer is mostly no, but it is a good question, right? So, people think, “Okay, so if the central bank buys a lot of the government bonds and the central bank is more or less the government, why can’t they just forgive the debt that they more or less owe to themselves?” So, if the Bank of Japan ends up owning 75% of Japanese government debt, why can’t they just wipe that off their ledger and then they’ve lowered Japanese government debt by 75% and they start fresh? The problem is that the whole crux of this fiat currency system runs on the premise that a central bank and the government have some degree of independence from each other, right?

Lyn Alden (39:27):

Because if you have a dictator with absolute power over the money, that money’s going to get debased a lot quicker, just the way human nature works. Even if say there’s some philosopher king running a country, as soon as he dies, the next one’s going to be worse and he’s going to miss it, right? So, if you have centralized power, you’re more quickly going to debase the money. Whereas if you have all these checks and balances to make it hard to create more money, so you have to have Congress approve it, then you have to have the central bank finance it, you have to have the president not veto it, so you have to have all these different groups agree to create a lot more money, that really slows down the money creation.

Lyn Alden (40:08):

And that’s why countries with strong institutions and independent institutions generally have a much longer track record of maintaining a reasonably successful fiat currency compared to smaller countries with less histories of institutions and then the institutions get co-opted by some more authoritarian type of ruler. And so, going back to the premise, central banks are at least in theory supposed to be independent. Obviously, in times of war and things like that, that independence get seriously threatened, but it’s still not the president or a head of a country can just go and tell the central bank head to do exactly what he wants. If they do, they’re more like a banana republic. They’re more like that authoritarian model.

Lyn Alden (40:52):

And so, even though they might appoint the central bank chief, that central bank chief now has a term that can potentially persist through multiple administrations and that has checks and balances for how they can be removed, how a new one can be added, right? And so, there’s some degree of separation there. So, a president can’t just do something like cut interest rates three months before the election, make everyone happy, and then go back to having higher rates, right? So, they don’t have that fine control over interest rates, because it’s in someone else’s hands. It’s supposed to be independent.

Lyn Alden (41:24):

And part of maintaining central bank independence is that they can’t be insolvent, that they have to have assets that are equal or higher than their liabilities, because otherwise, they’re reliant on financing from the government and that they’re entirely reliant on that government. And therefore, they no longer have any credible independence. And so, the way central bank balance sheets work is that the currency is their liability. So, physical currency is their liability and bank reserves of commercial banks that are assets for them are liabilities of the central bank. So, those are their primary liabilities. And on the other side of that ledger, their assets are things like primarily their government debt that they own. That’s their key asset.

Lyn Alden (42:10):

And then depending on the different central banks, some of them have mortgage-backed securities. Some of them even have equities, but the core of their assets is that government debt. And so, if they were to erase that government debt and say, “Look, you don’t owe us anymore. Let’s just start fresh,” well, now, that central bank has a multitrillion dollar hole on the asset side of its ledger but still has all those liabilities. And so, they are now technically insolvent organization. They have no independence. They’re entirely reliant on government financing. And so, that whole model of some degree of credible decentralization goes away, credible independence.

Lyn Alden (42:51):

And so, while you might not have any overnight effect from just say, wiping away central bank owned government debt, it’s not like you wake up tomorrow and the currency hyperinflated. But going forward, that central bank is now 100% captured by the government more or less. And so, the long term ability to do that degrades and that’s why most of them have laws in place to prevent that from happening, that the central bank can’t just wipe it away. Now, there are other tricks that they can do, right? So, you could, for example, make the government could issue a special bond that is 100-year bond that doesn’t pay interest.

Lyn Alden (43:30):

Now, you have this bond that’s different from the other government debt that doesn’t pay interest. And so, basically, there are things that they can do like that and there are other tricks they can do to keep the ledger, the asset side, and the liability side technically solvent, but merely deleting the asset side is generally untenable at least the way that we’ve structured the system now for a century or more in many countries.

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We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

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Here are the articles for the week ending 10 July 2022:

1. Kenneth Stanley – Greatness Without Goals – Patrick O’Shaughnessy and Kenneth Stanley

[00:08:44] Patrick: In the book and in the presentation you gave last week, there’s a key central example that, like you said, you stumbled upon via some of your own research. I would like to walk through that story. I want to just plant the key idea before we do that with another quote from the book, which is that, “Almost no prerequisite to any major invention was invented with that invention in mind.” You used that term stepping stones, the things that we combine. You gave the example of vacuum tubes and computers. People working on vacuum tubes weren’t thinking about computers, and there’s a million examples like this. So I just want to plant that idea out there. The stepping stones thing not resembling the final invention is the reason why it can’t be so deterministic, and here’s our objective, set up the steps between now and there. Maybe you can start to introduce that concept via the Picbreeder example that I think was the way that you originally alighted upon this idea in your research.

[00:09:31] Ken: It’s neat because, in a way, this is a story of serendipity, which is about serendipity. I mean, basically, this pic breeder just serendipitously led to this insight. Picbreeder was an experiment that I was running with my lab. I was a professor at the time at the University of Central Florida, where we allowed people on the internet to go and breed pictures. I know this is a major digression from what we were just discussing. We were discussing all these important things and we’re talking about breeding pictures. So how do these things connect? Breeding pictures, it is a little esoteric from general societal concerns perspective, but it’s basically about searching through a space in a way. This was an opportunity for us when we were doing artificial intelligence research to crowdsource. Crowdsourcing is really interesting. Let’s say you to take people on the internet because you’ve got access to potentially thousands, millions of people and have them try to do something collectively. Wouldn’t have been possible in the past if you didn’t have access to the internet. What we wanted to do was to crowdsource people, to search through the space of images or pictures and what that meant. So we used breeding. So basically, what it meant was that you could take an image, say a blob or something, and in fact, the site would start you off with random blobs if you started from scratch and you could say, “Look at some blobs and you could pick the one you like the best,” just like you might if you were breeding horses or dogs, “Pick the one you like the best.” You might have different reasons or criteria, but whatever your criteria is it’s fine, and then it would have children.

So it’s a little strange. It sounds strange. The picture has children, but this is inside of a computer. So if you think about it, why not? The picture can have children. The children or the offspring of the picture are like any other children. They look like it. They’re not exact duplicates just like if you have children, they look a little bit like you. They’re not exact duplicates of you or your spouse either. That’s the case here. So then what’s cool is that then you can see that if your picture that you chose has children, then you can look at the children and then you can pick from those children which one you like the best. You can see that this is in effect breeding. So then out of those, you pick your favorite there. It has children, and then you get to choose from those, and then so on and so forth. You’re basically iterating generations of breeding, where it goes depends on what you choose up to you. To tie this back quickly, what does this have to do with anything? If you think about those images, they’re basically a metaphor for discovery in general. If you think about like what you said about vacuum tubes and computers, computers are a discovery, vacuum tubes are a stepping stone on the road to that discovery. So somebody chose to use those vacuum tubes to try to build a computer. When it comes to image breeding, if I see an image that looks like something interesting, and then I choose to breed it further and then I get something else, maybe a picture of a skull, which actually was discovered, then I basically used that stepping stone to get to a discovery. So somehow, there’s a metaphor, an analogous metaphor here.

What’s cool about this site, what made it, I think, compelling to me was that because it’s crowdsourced, what we allowed people to do was to come in and look at what other people had bred. So there’s this big database and it’s being displayed in a natural way, a way that makes it easy for people to see what’s been discovered to surface things that are interesting. So those you can think of as stepping stones. You might see a butterfly or a face or something like that. Then someone who sees that is allowed to instead of starting from scratch, instead of starting from blobs like you would if you were starting from scratch, they can start from your discovery. If you found a butterfly and somebody wants to breed new butterflies, then no problem. They don’t have to start from scratch and get to a butterfly. They can start from your butterfly and then breed from there. It’s called branching. So that means that people are building off of the discoveries of their predecessors or you could think of as standing on the shoulders of their predecessors, which is, again, it’s a really nice analogy, I think, to how human innovation proceeds in general, where someone invents something, discovers something, comes up with an idea, and then someone else that they might not even know later in the future goes back in history and sees that thing and realizes this could be used for that, and it transfers that idea over and it becomes a stepping stone to something else. This has been going on for as long as civilization, basically is civilization. That’s what basically causes civilization to happen. So pic breeders are a microcosm of that, but here’s where the thing that leads to the insight that’s profound and to me was shocking was that after running this site for a couple years, so this is a long time, and letting people just breed and discover things and they discovered all kinds of things, butterflies and cars and planets.

[00:14:01] Patrick: We’ll put a link in and a collection to some of these. It’s really staggering, the things that you see that started with black blobs.

[00:14:07] Ken: Yeah. Yeah. So you’ll get a chance to see it. They found all this stuff after a couple years of watching this. Then what we found was that underneath the hood, we were able to look at how. If you think about just for a second, just think about why Picbreeder is fascinating. At first, it might seem like a toy or something. What is it actually for? People are playing around and breeding images, which have no purpose other than just that they’re images, but actually, what is, I think, profound about having something like that is that it is basically a history of discovery in all of its minute detail. Every little thing that everybody decided to do throughout the history is recorded. We don’t have artifacts like that. We don’t know every step of every invention that’s ever been made. A lot of it just happened inside of someone’s head. So this is not recorded, but Picbreeder is one of the few things, maybe the only thing where every single step of everything is recorded completely. So that meant that after a couple years, we could go back and find out what actually explains how everything was discovered, and I turned out to be, I think, shocking. The shocking revelation was that in almost every single case, more than 99% of cases, if you looked at something interesting, like a car, for example, or a butterfly or a bird or whatever it might be, if you go back in its history and you look at what were the steps that led to that thing, the steps look nothing like it at some point back. Right before you get to it, it might look like it, but if you go back far enough, you will find a stepping stone that looks absolutely nothing like it in 99.9% of cases.

Why is that a revelation? Well, the problem is that if you think about it, what that means is that the only way to discover any of these things was to not be trying to discover them. Now, usually if you say things like that, that sounds like some new age statements, discover things by not trying to discover, and that’s mystical or something. Now, think about this. I’m not talking in the new age perspective. This is an empirical observation. This is actually what happened. The people who discovered these things who are responsible for the stepping stones that led to the discoveries were not actually trying to discover those things because if they had been, then they wouldn’t have chosen the things when they had their selections. They had these blobs they could look at. They could choose one of them. They wouldn’t have chosen the ones they chose if they were trying to get the final product. For example, you have a case where there was an alien face that led to a car. Who would choose an alien face if they want a car? That would not be a good idea, but what happened was the wheels of the car, which was depicted from the side, actually derived from the eyes of the alien face. Again and again and again, you see this phenomenon that in hindsight, you can see what happened, but looking forward, you would never imagine that these connections could be made. This shows, in fact, it’s true in Picbreeder that you can only find things in the long run by not looking for them. You need to take your eyes off the ball in order to be able to accept the stepping stones that ultimately make finding the ball possible, which I think is totally contrary to our culture, to our way of making discovery, the way we think things should be done, which is always objectively driven. So the connection that I need to make, I think, beyond that is to justify why I would extend from that discovery to real life.

[00:17:21] Patrick: If you think about the power of these images, most of them were achieved across what I’ll call a modest amount of generations. We’ll talk about AI and machine learning a little bit later on, which is so interesting because almost all of it has an objective function. It’s almost all objective-based. So that’ll be an interesting part of our conversation, but when you put up the number of generations of breeding to get from a blob to a clear bird, let’s say, it was only 80, 90, 40, 100. It wasn’t that many iterations. Then you showed us a skull, a picture of a skull, and really drove the point home by describing, “Okay. Now, let’s imagine this specific skull or one very close to it is our objective.” Could we get close to it across way, way, way more generations and actually targeting it? Maybe you can describe that experience because I found that to be a powerful nail in the coffin.

[00:18:10] Ken: So basically, we took this and we said, “Let’s try to drive the point home and also just see if we can validate this hypothesis that you can only find things by not looking for them by actually looking for them explicitly.” Just to make it fun, I think this twist makes it fun, let’s look for things that we already saw were discovered. That makes this crazy because it’s like we know that these can be discovered in this space. Like you said, I think it is an important point that these things were not discovered with a lot of compute, so to speak. If I recall, I think it’s 72 generations, might be 74, 72, 74 steps or iterations. That is just ridiculously low. When you think about it in terms of compute, of course, these are humans making these selection steps, but in machine learning, modern machine learning, it’s pretty reasonable to have millions of iterations to get to something meaningful. Here, we’re talking about dozens. In some way, that says these are easy. These are not hard discoveries. In some sense, they’re still impressive because of the fact if I just randomly choose blobs in blob space, in the space of the Picbreeder, you’ll never find anything. 99.99999% of the space is just garbage blobs. So these are still needles in haystack, but what’s weird is that the needles in the haystack are discoverable within a few dozen steps. One conclusion you might draw naively would be that, “Oh, they can’t be that hard to find.” The skull is let’s say 74 steps trivial, basically, from a compute perspective. So let’s set up an experiment and see. So what we can do is we can say, “Let’s get an image matching algorithm,” which are available, which basically tells me if I show this algorithm a blob, I input this blob and I ask it to compare it to the skull, it’ll tell me how far away we are, how close is this image to a skull.

That comparison will help me because when I show a bunch of blobs, I can just have it automatically pick the one that’s closest to the skull. It’s really simple. Then every iteration can be done now by the computer instead of by a human. So we can automate it. Good old fashioned machine learning here. We just automate Picbreeder. No more humans in the loop, and we’ll just automate it to go to the skull. I think to me, this sounds like a worthy adversary. I would be worried this might actually work. It shouldn’t work though our hypothesis is correct because our hypothesis here is that you can only find things by not looking for them. Now, this is explicitly looking for the skull. This is a metaphor for how we do things in our culture. So we say, “This is our goal. This is our OKR. This is what we’re going to achieve this quarter, and now we’re going to work towards it. You’re going to give me a metric. In this case, it’s skull matching. Let’s match the skull picture,” and then you’re going to cut off branches that don’t seem to be maximizing that metric and go by the branches that do seem to be maximizing the metric and just move towards the skull. We’re going to do that now explicitly. We gave it 30,000 steps. This takes about 74 steps, let’s say, for the first discovery by a human. Now, we’re giving an automated algorithm, 30,000 steps, just for fun, just in case, I don’t know, it needs extra time. We’ll give it way extra time, orders of magnitude. What happens? Failure every single time. We ran this dozens of times. It’s every single time failure.

It’s also fun to look at the failures because you can see it’s trying. You see it shadows. It’s like somebody stumbling, almost getting there but not quite. Well, it’s not even close, but it’s like getting the silhouette shadow of what it wants, but it can’t get even close. It’s just fascinating. That’s much more compute. It should be able to eventually overcome it, but the thing is that it highlights the reason that this is happening in if you look at it. Why are all discoveries happening this way in Picbreeder? It’s actually because the world is deceptive, which means that the things that lead to skulls don’t look like skulls. This is the fundamental insight, which is not being recognized across society. It’s that the things that lead to the things you want don’t look like the things that you want. There’s actually a name for this in philosophy. It’s called the like causes like fallacy. I think it’s from Mills. We all seem to assume. It seems to be almost like built in to us biologically that the things that lead to what we want are going to resemble what we want. I don’t know why we all believe this, but it’s not how the world works. If you think about it, that makes total sense. If the world actually worked that way, if the like causes like fallacy was actually true, actually things do resemble where you want to go, we would solve all that problems…

…[00:24:01] Patrick: There’s one piece of this that I love that hasn’t been mentioned yet, which is the role of the individual and their decisions relative to I’ll call this heterogeneous decision making versus homogeneous ruling by committee or something or making choice by committee. Talk about the importance of the individual and their choice in this web of invention and disruption.

[00:24:20] Ken: Yeah. This is a funny thing. It’s true. This is another very popular mythology, I think, in our culture is let’s get together and collaborate, bring all the smart people into the room. It’s not just like, “Let’s get interdisciplinary collaboration. Let’s get the computer scientists sitting there with the economist.” All these things are very exciting to us. I just want to say I’m not saying we shouldn’t have collaboration. That again would be this crazy cranky thing to say. What I do want to get to what you’re asking about is that collaboration itself also is subject to a number of caveats because of the insight about the paradox, the objective paradox, and that means there’s a right way and a wrong way to think about collaboration. It’s quite dangerous. We tend to do it the wrong way. The issue that comes up here is that if you look at Picbreeder, I think something that’s very intriguing about what happens in it is that once somebody sees a stepping stone on the site, so if you recall, like I said, all the discoveries that other people had made are made available for you. So what it means is you are seeing a history of stepping stones when you go to this site. You don’t have to start from scratch. If somebody found a butterfly, you can start from their butterfly.

When you come in and see that butterfly, that is a point of collaboration. It’s implicit collaboration, but it is collaboration because somebody else did work, they found the butterfly, and now you’re building off of that work. So collaboration is happening. However, the moment you choose to continue or what we call branch from the butterfly to breed it further, you are on your own. This is a very unique thing. At first, it sounds like, “Oh, well, what’s the big deal? You’re on your own, okay,” but think about it. We almost never allow people to do that in collaborative situations in our culture. We always bring people together and move towards consensus almost immediately, but in Picbreeder, it’s not like that. Instead, you choose the thing you think is interesting and it was your choice and nobody else was involved in that choice. Now, think about this compared to, for example, I was a professor for a long time. So I think a lot about asking for grants, science grants. That’s like picking an image. It’s like what project do I want to pursue. You come in and you see a butterfly and you want to pursue the butterfly. It’s like you’re sending a grant proposal to the NSF. You think something interesting will happen if you choose this butterfly, but the thing about the NSF is now it’s going to go to a committee. I am not allowed to just go off on my own and work on that butterfly now. There’s going to be a committee that thinks about the decision that I’m making, and I have to justify usually objectively in the sense that I’m going to have to say where it’s going to lead.

What are you going to get by doing this butterfly? That is not how Picbreeder is. You are on your own completely, and not only are you on your own by choosing the butterfly, you’re on your own every single step of the way until you publish the thing you discovered. So there’s no interference, whatsoever, and you’re just on your own. Think about the difference between that and the way we run things where it’s basically you come into a room with all these people, you bring up these ideas, you have this discussion, you try to come to consensus. All the crazy things you would’ve done are basically cut off at the start by this surge towards consensus, which is going to lead to what I would call convergent consensus because we’re trying to move toward convergence very quickly. What you’d understand from Picbreeder is the proliferation of the stepping stones that gives the power to the process. The reason that I can get to cars, there was a discovery of a car which came from an alien face, was because of the discovery of the alien face. No one would ever think that you needed an alien face to get to a car, but the alien face is there not because somebody was thinking about cars, but because there is a general culture inside of Picbreeder of proliferating stepping stones.

This is not generally how we run collaborative systems because we run them by consensus, which is the exact opposite. That’s about pruning out stepping stones. People start generating things and then we start saying, “No, no, no. Committee doesn’t like this. Committee doesn’t like that.” We then converge to the thing, which is basically the consensus basis of current thinking, which tends to be dogmatic and tends to be status quo and everything that we basically want to get away from, and then all these radical stepping stones, which are the interesting things which could lead to places we’re not expecting for the very reason that the things that we’d want to get to don’t look like them so we need the radical stepping stones are the things that we cut out. You can see from this theory or philosophy way of looking things that a lot of the way we run collaborative systems is just totally kneecap at the start, and also should, I think, be rethought.

[00:28:31] Patrick: Can you describe when you put a consensus mechanism into this experiment, the outcome falling to all this? I promise we’re going to get to some of the bigger implications here in a minute, but this simplified example is this so damn powerful for how we all are going to spend our time in our lives. Maybe just describe the outcome when you insert consensus mechanism into how these generations progress.

[00:28:54] Ken: This is super interesting, and it’s funny because it’s just a coincidence that this happened because there was another project that was launched around the time of Picbreeder called the living image project. It had nothing to do with me other than it used basically the same in coding under the hood as Picbreeder. This is nice because it creates a controlled experiment by accident because both Picbreeder and this other thing, the living image project, have this underlying coding that’s the same. So what that means is in principle, they can achieve the same thing. They could find similarly cool stuff in principle, but there’s this one difference, which makes this very interesting as a comparison, which is this living image project did work by consensus. I mean, the reason it did is because I think it’s because there’s this cultural assumption just like riding on top of that. They’re like, “This is a good way to do things. Let’s have a vote.”

So basically what they said is, “Okay. Here’s what we’re going to do. Just like Picbreeder, there’s these blobs, they’re arranged on the screen, you can see all these blobs, and we’re going to pick one of them. That’ll be the parent of the next generation of blobs.” However, the difference from Picbreeder is that the choice will be made by a vote. So over the course of a week, people will come in and it would turn out basically hundreds of people would come in, and they would vote on their favorite blob and then we’ll choose the one that gets the most votes. To a lot of people, this is really intuitive. More opinions are better than one. Let’s use the crowd to decide what to do, but consistently with what I just argued, the result are starkly different and terrible in comparison. I don’t mean to cast any dispersion on the living image project. I think it was a cool idea to try it. It really helps to illustrate. The problem here is that you get a washout effect. Imagine you come in, okay? There’s hundreds of people coming in. Imagine you like butterflies and I like cars. Now, what’s going to happen when we vote and we’re just looking at blobs? The blobs don’t look like butterflies yet and they don’t look like cars, and you want a butterfly and I want a car. What is going to happen? Complete washout is what’s going to happen.

There’s no way you’re going to get enough people on your side. You don’t even know. We don’t even know what each other are doing or have understanding of how you even get these things. So what’s going to happen is you get this mildly aesthetic blobby pattern type of consensus. We get the mildly, most pleasing blob aesthetic, and then that’s going to happen at every iteration because there’s another few hundred people voting at the next iteration and another few hundred, and after thousands and thousands of, I think it was 25,000 votes, you can look at the top ranking, all you have are amorphous rainbowy blobs every single thing. I think it’s just stark and shocking. Even though it’s in this totally obscure genre of stuff like breeding pictures, I think it should give us all heart palpitations because we’re running our culture this way…

...[01:08:29] Patrick: It’s incredible, and I think demands one last question. This idea that you’ve referenced over and over again that no one is telling people how they have to behave in something like pick breeder. There’s a permissionless nature to it. There’s a individuality and individual interpretation of events. With all that in mind, for those whether it’s running a grant organization or running a labs, an AI labs or innovation labs inside of a company or anyone that has resources like Ed did that want to deploy those resources in service of disruption and innovation, either generative or protecting against it or whatever, you’ve already talked about what they do wrong. If you were in-charge of one of those, an allocator of resources to create innovation, how would you do it?

[01:09:14] Ken: I think if you’re in a position like that, you’re a gatekeeper. So you are responsible for the perpetuation or not of this objective culture. It’s especially relevant if you’re purportedly involved in fostering innovation because that’s where this gatekeeper has a huge influence. Yeah. I would recommend doing things differently. You probably exist in their framework where that’s very difficult because you answer to somebody. They don’t understand where you suddenly say, “Well, I’m not assessing things in this normal objective way anymore.” They’re like, “What the heck are you doing? How do we know this is working?” So this takes some courage, I think. The first thing I would say, get the courage because there’s nothing we can do about that. You have to explain to them, “If we’re not going to follow the usual security blanket rooted things, the people in the chain are going to have to be convinced and that’s hard work.” That’s why I think it’s worth having a conversation like this show. That’s why we wrote the book. It’s like we wanted to start people having these conversations. So get the courage to have the conversations and really fight because it’s not going to happen if you don’t. You’re just going to shut down. You’re going to think, “I want to do this, but, eh. On the other hand, my boss wants this. His boss wants that. There’s a funding agency out there or we have investors.” You’re like, “Forget it. It’s too complicated.” Somehow you got to fight this.

Now, in terms of actually practical implementation, what should you do? What I would say is you should be maximizing stepping stones in the pursuit of innovation, not maximizing an objective performance. There’s two things, maximizing stepping stones and maximizing exposure to stepping stones. The thing that makes innovation work is that the people who could run with something are exposed to the thing that they could run with, and that is what’s missing I think from a lot of these organizations is that we have these filters, which are extremely narrow, which decide what comes through, and they end up pruning out things. It’s the conversion consensus problem. Things don’t get exposed to a person who would react dramatically if they were exposed to that thing. What we should do is greatly broaden the filters that go from idea to exposure to the people who could run with the ideas and then also change the criteria for what should be pursued. You have to recognize that if you pursue something that requires investment, so it costs money. So we’re not talking about decisions that can be made lightly. Nobody can say, “Well, everything will pursue because now we’re all going to be open-minded. We’re just going to do everything everybody wants.” That cannot happen. Some things have to not happen, but the way that we decide what happens, I think the criteria should be quite different.

It should not be trying to move to consensus, get a committee to agree with something, get the most vote, something like that. It should be many people within the context of the organization, whatever many means. Many people are exposed to the ideas that are being generated, and that basically only one or two need to trigger the success of that idea or to say, “This is worth investing,” but then you say, “Well, how can that be?” Then every idea would have to be invested because somebody might want to invest in everything. The reason I think it can make sense is if there’s skin in the game for the people who are validating the ideas. If I see something that is so exciting to me that I’m personally willing to pursue it that I didn’t come up with myself, just like the alien face that led to the car in Picbreeder, then I’m actually willing to spend my time on what you did. I’m actually giving something away. I could have had that time. I could have invested in something else. What should make the confirmation of something meaningful and really worth investment is if the person who’s confirming it is giving something away. Maybe they lose their right for some period of time to have their idea even considered or they give away the resources that they were giving for some project that they had. There’s obviously finite resources, but if someone’s willing to do that, that means that this thing means a lot to them, and it only takes one person, magic connection, electric connection to happen, and we have to somehow create those connections. It’s not going to be consensus matter. It’s going to be a niche thing. When there’s something incredible, it’s not going to be tons of people see, it’s going to be one out of a hundred see it, and that has to be honored somehow. We have to find a way to do that.

2. Conversation at Panmure House – Howard Marks and Patrick Schotanus

PS: In fairness to Russell, it was in my introduction to Russell’s question [i.e., not in Russell’s question itself] that I said the economy is mechanical and that’s the definition of mainstream economics.  Russell and I do not necessarily agree on that.  But to continue on mechanical economics as a theory: In your memo On the Couch, you talk about your own early exposure to the efficient-market-type classes.  For the audience, EMH is based on the rational expectations hypothesis; EMH states that markets are rational because any pockets of irrationality are averaged away [i.e., the errors made by the group become smaller than those made by individuals].  In contrast, you also highlight the reality of irrationality that can be observed in markets, something that both Alan Greenspan and Robert Shiller called “irrational exuberance.”  Later, the GFC, or the Global Financial Crisis, painfully hit home that what seems rational for an individual can be dangerously irrational if done collectively.  So my first question is, can we square this circle?  For example, is irrationality just about semantics, or is it something real that not only exists, but because of the collective dynamic, can actually threaten the economic system and may thus not necessarily be averaged away?

HM: To me, Patrick, the answer lies in my view of the efficient market hypothesis.  Again, the efficient market hypothesis says that due to the concerted actions of so many investors, who are intelligent and numerate and computerized and informed and highly motivated and rational and objective and willing to substitute A for B, prices for securities are right, such that they presage a fair risk-adjusted return.  I believe that’s the definition.

But you get into a problem, because when I listed off the qualities that are necessary for a market to be efficient, I snuck in there the economist’s notion of the perfect market and its requirement that the participants be rational and objective. And in investing, they’re not.  That’s really the point.

“Economic man” is supposed to make all these decisions in a way that optimizes wealth.  But she often doesn’t, because she’s not always objective and rational.  She has moods.  And those moods interfere with this arriving at the right price.  So my definition of the efficient market hypothesis is that because of the concerted efforts of all the participants, the price at a given point in time is as close to right as those people can get.  And because it’s as close to right as most of them can get, it’s very hard to outperform the market by finding errors – what theory calls “inefficiencies” and I just think of as “mistakes.” 

Sometimes prices are too high.  Sometimes prices are too low.  But because the price reflects the collective wisdom of all investors on that subject, very few of the individuals can identify those mistakes and profit from them.  And that’s why active investing doesn’t consistently work, in my opinion.  I think my version of the efficient market hypothesis makes it roughly just as hard for active managers to beat the market as does the strong form of the hypothesis, that everything’s always priced right.  But I think mine is more reflective of reality.  I wrote in one of my memos – maybe it was What’s It All About, Alpha? – about a stock that was $400 in 2000 and $2 in 2001.  Now it’s possible – but to me it’s unlikely – that both of those observations were “right.”  Rather, I think they merely reflected the consensus of opinion at the time.

This business – I shouldn’t say “this business”; that sounds derogatory – the idea that inefficiencies will be arbitraged away by the operations of the market ignores one of the key elements that I think describes reality, and that is mass hysteria.  And I think the markets –economies too, but more importantly the markets – are subject to mass hysteria.

I think it was in On the Couch that I said, “in the real world, things fluctuate between pretty good and not so hot.  But in the markets, they go from flawless to hopeless.”  Just think about that one sentence.  If it’s true – and I believe it’s true – that shows you the error, because nothing is flawless and nothing is hopeless.  But markets, I believe, treat things as flawless and hopeless, and there’s the error.

The book I mentioned, Mastering the Market Cycle (I’m going to keep repeating the title in the hope that everybody will buy a copy) . . .  You know, I’m a devotee of cycles.  I’m a student of cycles.  I’ve lived through a half a dozen important cycles in my career.  I’ve thought about them.  I think they dominate what I do.  And I got about two-thirds of the way through writing that book and something dawned on me, a question: Why do we have cycles?

The S&P 500 – I mentioned Jim Lorie – the Center for Research in Security Prices told us almost 60 years ago, that from 1928 to ’62, the S&P 500 had returned an average of 9.2% a year.  Things have been better since then and I think if you go back and look at the whole last 90 years, it’s 10½% a year, the return on the S&P 500.

Here’s a question:  Why doesn’t it just return 10½% every year?  Why sometimes up 20% and sometimes down 20%, and so forth?  In fact – and I included this factoid in one of my memos – it’s almost never up between 8% and 12%.  So if the average return is 10½%, why isn’t the return clustered around 10½%?  Why is it clustered outside the central range?  I think the answer is mass hysteria.

And by the way, the same is true of the economy and mainstream economics, which of course you described as mechanical, and I think that many people would describe as mechanical.  But, certainly, economics is driven by decisions made by people, who are not always rational and objective.  Maybe in theory they’re closer than investors to being rational and objective, but still they’re not always.

But anyway, my explanation for the occurrence of cycles is “excesses and corrections.”  You have a secular trend or a “normal” statistic.  Let’s say it’s the secular trend of the S&P 500.  Sometimes, people get too excited.  They buy the stocks too enthusiastically.  The prices rise.  They rise at more than a 10½% annual rate until they get to a price that is unsustainable.  And then everybody says, “No, I think they’re too high.”  So then they correct back toward the trendline.  But, of course, given the nature of psychology, they correct through the trendline to an excess on the downside.  And then people say, “No, that’s too low,” so then they bring it back toward the trendline and through it to an excess on the high side.

So excesses and corrections: that’s what cycles are about, in my opinion.  Where do the excesses come from?  Psychology.  People get too optimistic, then they get too pessimistic.  They get too greedy, then they get too fearful.  They become too credulous, then they become too skeptical, and so forth.  Oh, and the big one: they become too risk-tolerant, and then they become too risk-averse.

PS: If I can just follow up on that – particularly for our cognitively inclined audience – implied in this you suggest that there might be mental causality, and my next questions are basically also to motivate future research as part of economics revision.  But during your September podcast, in which you revisit the On the Couch memo, you talk about causality and how complex it can be.  And we agree and highlight this in our work.

For example, when Alan Greenspan, in that famous ’96 “irrational exuberance” speech, mentions the complexity of the interactions of asset markets and the economy, and I’m quoting him now: “It chiefly concerns, at least in our view, this dualism of the psychological of the former and the physical of the latter.”  Now, saying this, mental causality is highly controversial and complex in cognitive science, but cognitive science is the area that really studies this.  So, you also specifically refer to Soros’s reflexivity in that context, and as you already indicated just now, but also in your memo, you equate prices almost to psychology.  And finally, we’ve all experienced this dangerous – to the point of existential – tail-wagging-the-dog dynamic surrounding Lehman’s collapse.  So my first question is, if we agree that we will not gain much by identifying yet another behavioral bias, nor by running yet another regression, what would you like to see investigated by cognitive scientists that could potentially lead to more important insights, especially regarding our understanding of the interaction between these two domains of the real and financial economies?

HM: Well, the people at this symposium know much more than I do about how to get to the bottom of these things.  But clearly there’s so much grist for this mill.  Now, exactly how you quantify mood, and so-called animal spirits and irrational exuberance, is beyond me.  I always say, Patrick, and I think I said it in Mastering the Market Cycle, that if I could know just one thing about every security I was thinking about buying, it would be how much optimism is in the price.

When you watch TV and you hear the newsreaders talking about what happened in the stock market today, you get the impression that prices are the result of fundamentals and changes in prices are the result of changes in fundamentals.  And that is vastly inadequate.  (By the way, they always say, “The market went up today because of X” or “The market went down today because of Y.”  I always say, “Where do they go to find that out, because I haven’t found it yet?”  I haven’t found where you go to get an explanation of the market’s behavior, even after the fact.)  But it’s not true that it’s all about fundamentals.  The price of an asset is based on fundamentals and how people view those fundamentals.  And a change in an asset price is based on the change in fundamentals and the change in how people view those fundamentals.  So, facts and attitudes.  Any research that could capture changes in attitudes, I think is important.

Now, what about quantifying these animal spirits?  In one of the more jocular portions of my first book, The Most Important Thing, I include something I called “the poor man’s guide to market assessment.”  I have a list of things in one column, and I have a list of things in the other column, and whichever list is more descriptive of current conditions tells you whether it’s optimism or pessimism that’s governing the market.  There are things like, do deals get sold out or do they languish?  Are hedge fund managers being welcomed on TV or not?  Who does the crowd form around at cocktail parties?  What is the media saying: “We’re going to the moon” or “We’re cratering forever”?  I don’t know how to quantify these things.  But these are among the very important things that I listen to in order to figure out where we stand in the cycle.  And I believe where we are in the cycle plays a very strong role in figuring out where we’ll go next.  (In fact, take the title of my second book, Mastering the Market Cycle.  When I was thinking about writing it, it was called Listening to the Cycle. “Listening” in the sense of taking our signals from where we are in the cycle.  “Listening” also in the sense of obeying.  The publisher thought we’d sell more books if the title implied the book would help you master the market cycle.)  But I, as a practical investor, try to figure out what’s going on around me.

Now let’s go back.  I didn’t do what I should have, because I didn’t answer Russell Napier’s real question: can I name two episodes that showed this kind of thing in action?  I was glad to have the questions in advance, because it allowed me to think about the two episodes I want to propose.

In the spring of 2007, I wrote a memo called The Race to the Bottom.  This was when the subprime mortgage mania was at its apex, I think, and when the logs had been stacked in the fireplace for the conflagration that became the Global Financial Crisis.  It happens that I was driving around England in the fall of ’06 – maybe November or December ’06 –and I was reading the FT (I mean I wasn’t driving and reading; I was being driven so I could read), and there was an article in the FT that said that, historically, the English banks had been willing to lend people three-and-a-half times their salary in a mortgage.  But now, XYZ Bank announced that it was willing to lend four times your salary, and then ABC Bank said, “No, we’ll lend five.”  And that bidding contest – to make loans by lowering credit standards – seemed to me to be a race to the bottom.  And I wrote that markets are an auction place where the opportunity to make a loan, or the opportunity to buy a stock or a bond, goes to the person who’s willing to pay the most for it.  That is to say, get the least for his money, just like in an auction of a painting.  And so, in this case, the bank that was willing to have the lowest credit standards and the weakest loans was likely to win the auction and make the loans: race to the bottom.  And I said this is what happens when there’s too much money in the hands of providers of capital and they’re too eager to put it to work.  Mood!  And, of course, we all know the Global Financial Crisis ensued.

Now fast forward from February ’07 to October ’08: Lehman Brothers goes bankrupt on September 15, 2008, and now, rather than being carefree, the pendulum has swung, and people are terrified.  Rather than seeing risk as their friend, as in, “The more risk you take, the more money you make, because riskier assets have higher returns,” now people say “Risk bearing is just another way to lose money.  Get me out at any price.”

So the pendulum swung, and of course people’s optimism collapsed, the S&P 500 collapsed, and the prices of debt collapsed.  So I wrote a memo right around October the 10th of ’08 – maybe that day was the all-time low for credit, I don’t know exactly – that was called The Limits to Negativism, based on an experience I had. I needed to raise some money to delever a levered fund that we had that was in danger of melting down due to margin calls, and I went out to my clients.  I got more money.  We reduced the fund’s debt from four times its equity to two times.  Now we’re again approaching the point where we can get a margin call.  Now I need to delever it from two times to one time.  I met with a client who said, “No, I don’t want to do it anymore.”  And I said, “You gotta do it.  These are senior loans, and the default rate on senior loans has been infinitesimal over time.  There’s potential for a levered return of 26% a year from what I consider incredibly safe instruments.”

This client – excuse me if I belabor this, but I think it’s interesting – this client said to me, “What if there are defaults?”  And I said, “Well, our historical default rate on high yield bonds – which are junior to these instruments – is 1% a year.  So if you start with 26% and you take off 1% for defaults, you still get 25%.”  So she said, “What if it’s worse than that?”  I said, “The high yield bond universe default rate has been 4% a year, so you’re still getting 22% net.”  She says, “What if it’s worse than that?”  And I said, “The worst five years in our default experience is 7½%, and if that happens, you’re still getting 19%.”  She says, “What if it’s worse than that?”, and I said, “The worst year in history is 13%.  If that recurs every year for the next eight years, you’ll still make 13% a year.”  She says, “What if it’s worse than that?”  And I said, “Do you have any equities?”  She said, “Yes, we have a lot of equities.”  I said, “If we get a default rate on high yield bonds of more than 13% a year every year into the future, what happens to your equities in that environment?”

I describe myself as having run back to my office after that meeting to write that memo, The Limits to Negativism.  What I wrote there was that it’s very important when you’re an investor to be a skeptic and not believe everything you hear.  And most people think being a skeptic consists of dealing with excessive optimism by saying, “That’s too good to be true.”  But when it’s pessimism that’s excessive, being a skeptic means saying, “That’s too bad to be true.”  That particular investor couldn’t imagine any scenario that couldn’t be exceeded on the downside.  So, in other words, for that person, there was no limit to negativism.

And when I conclude that the other people in the market, the people setting the market prices, are excessively negative and excessively risk averse, then I – an inherently conservative person – and my partner, Bruce Karsh, who runs our distressed debt funds – also an inherently conservative person – we go crazy spending money when we conclude there’s excessive pessimism, fear, and risk aversion incorporated in asset prices [meaning they’re lower than they should be]. So it’s not just the mechanical aspects that determine market prices – it’s psychology.  It’s mass hysteria, which comes in waves from time to time, that leads to market cycles that prove excessive.

3. This Diamond Company Wants To Help Carbon Capture Take Off – Maddie Stone

That company is Aether, a lab-grown diamond startup that just raised $18 million in a funding round led by Helena, a “global problem solving organization” that includes both a for-profit investment and nonprofit action arm. Lab-grown diamonds are a hot market, and there’s no shortage of companies claiming that these synthetic gems are more ethical or environmentally friendly than their Earth-mined counterparts — and there are even other companies also focused on making diamonds using carbon dioxide from the air. But Aether’s claims are backed up by some ambitious facts about its operation: not only is it making diamonds in a process powered by clean energy — it’s pulling an additional 20 metric tons of CO2 out of the atmosphere per carat it produces.

While the cost of capturing all that carbon would be high for a company selling, say, cement, it’s one the luxury jewelry brand says it can easily absorb. And the world needs businesses that can pay for so-called direct air capture and still generate a profit if the nascent technology is ever going to make a dent in climate change…

…Aether, which also works with Climeworks, wouldn’t disclose how much it’s paying for direct air capture services. But it says it can transform one ton of captured CO2 into “millions of dollars’ worth of diamonds”. On a per carat basis, those diamonds, an ultra high-purity breed known as Type IIa diamonds that are difficult to find in nature, sell for anywhere from $4,900 to over $10,000. Shearman says this price range is higher than many competitors in the lab grown space and closer to that of mined diamonds because of the additional work that goes into making the fabrication process as clean as possible.

That process starts with Aether purchasing carbon dioxide from Climeworks’ facility in Switzerland and shipping it to the United States, where the diamonds are grown. Aether puts that CO2 through a proprietary process to convert it into high purity methane, or CH4. That methane is then injected directly into the company’s diamond reactors, where a method known as “chemical vapor deposition” is used to grow rough diamond material over the course of several weeks.

The chemical vapor deposition process involves heating gasses to very high temperatures under near-vacuum conditions, and considerable energy is required to do so. Shearman tells The Verge that this process and other manufacturing stages are powered entirely by carbon-free sources like solar and nuclear. Once the diamonds finish growing, they’re shipped to Surat, India, where they’re cut and polished before being sent back to New York City’s diamond district for sale…

…Aether only needs a relatively small amount of carbon dioxide to make the diamonds themselves — think fractions of grams rather than tons. Then, for every carat of diamond it sells, the company says it removes an additional 20 metric tons of carbon from the air, using a mix of direct air capture and other carbon removal methods that involve long-term carbon sequestration. Shearman says the company based this commitment on the fact that the average American has an annual carbon footprint of approximately 16 metric tons, meaning most customers can expect to roughly cancel a year’s worth of personal emissions by purchasing an Aether diamond. “It’s something that has proved to be difficult but doable, and we’re really proud to be able to do that,” he says.

Aether started shipping its first diamonds to customers in the middle of 2021. While Shearman wouldn’t offer specific sales figures, he says that the company produced “hundreds of carats” of diamonds last year, and this year plans to produce thousands. Shearman described the $18 million in Series A funds raised by Helena as “the fuel that’s going to enable us to increase our production footprint this year.”

4. An introduction to Integrated Photonics – Jessica Miley

Integrated Photonics (IP) is the use of light for applications traditionally tackled by electronics. It can be used in a wide range of areas including telecommunications such as 5G networks, biosensors for speeding up medical diagnosis, and in automotive where it is used in LiDAR. IP consists of integrating multiple photonic functions on a Photonic Integrated Circuit (PIC) fabricated using automated wafer-scale generic integration technology over silicon, silica, or Indium Phosphide (InP) substrates. Integrated photonics dramatically improves the performance and reliability of these photonic functions while simultaneously reducing the size, weight, and power consumption.

A good introduction to IP is by understanding its similarities and differences with traditional electronic circuits. Where electronics deal with the control of electrons on a chip, photonics does the same with photons. Photons are the fundamental particles of light.

Conventional integrated circuits (ICs) conduct electricity by allowing the flow of electrons through the circuit. Electrons are negatively charged subatomic particles that interact with both other electrons and other particles. These interactions slow electrons down as they move through circuits, this limits the amount of information that can be transmitted; it also generates heat, which in turn causes energy and information losses.

Photonic integrated circuits (PICs) use photons. Photons move at the speed of light with almost no interference from other photons. This greatly increases the bandwidth (the data transfer rate) and speed of the circuit, without big energy losses making PICs significantly more efficient than their IC counterparts.

Integrated photonic components use “waveguides”, which confine and direct the light in the desired directions (by total internal reflection), much the same way as metallic wires do for electrical signals. A PIC provides functions for information signals on optical wavelengths typically in the visible spectrum or near-infrared 850 nm-1650 nm.

The elements on a PIC are connected via waveguides. The chip elements can be both passive (e.g. couplers, switches, modulators, multiplexers) and active (e,g amplifiers, detectors, and lasers). These components are integrated and fabricated onto a single substrate, which creates the compact and robust photonic device.

A key difference between electronic circuits and PICs is in the primary device that is used for fabrication. In an electronic integrated circuit, the main device is the transistor. But, in PIC, there is no particular main device that dominates in the fabrication. According to its application, the PIC will be designed with a range of fabrication devices. This integration presents opportunities to reduce current bulky, complex, and expensive optical systems in an integrated chip-scale way that has increased stability and robust operation, reduced size and power consumption, and cost-effective large-scale fabrication of even complex circuits.

5. It’s worse than you think – Oliver Burkeman

Here’s a surprisingly useful question to ask yourself next time you’re stumped by a problem, daunted by a challenge, or stuck in a creative rut: “What if this situation is even worse than I thought?”

This question, I admit, appeals to my taste for bloodyminded contrarianism. But its real value is that it expresses what I think of, more and more, as a fundamental truth about human psychology: that we often make ourselves miserable – and hold ourselves back from what we might be capable of achieving – not because we’re too pessimistic, but because, in a sense, we’re not pessimistic enough.

We think of certain kinds of challenges as really hard when they are, in fact, completely impossible. And then we drive ourselves crazy trying to deal with them – thereby distracting and disempowering ourselves from tackling the real really hard things that make life worth living.

A case in point: you feel overwhelmed by an extremely long to-do list. But it’s worse than you think! You think the problem is that you have a huge number of tasks to complete, and insufficient time, and that your only hope is to summon unprecedented reserves of self-discipline, manage your time incredibly well, and somehow power through. Whereas in fact the incoming supply of possible tasks is effectively infinite (and, indeed, your efforts to get through them actually generate more things to do). Getting on top of it all seems like it would be really hard. But it isn’t. It’s impossible…

…Anyway, you get the picture. And you probably get the point, too – which is that when you grasp the sense in which your situation is completely hopeless, instead of just very challenging, you can unclench. You get to exhale. You no longer have to go through life adopting the brace position, because you see that the plane has already crashed. You’re already stranded on the desert island, making what you can of life with your fellow survivors, and with nothing but airplane food to subsist on. And you come to appreciate how much of your distress arose not from the situation itself, but from your efforts to hold yourself back from it, to keep alive the hope that it might not be as it really was.

And then, crucially – because some people tend to mistake this for an argument for nihilism, or a life of mediocrity, when it’s really the opposite – that’s precisely when you can throw yourself at life’s real hard challenges: the impressive accomplishments, bold life choices, and deeply fulfilling relationships. You get to live more intensely, because you’re no longer making your full participation in life dependent on reaching some standard – of productivity, of certainty about the future, of competence, etcetera – that you were never going to reach in the first place.

6. Alex Danco – Tokengated Commerce – Patrick O’Shaughnessy and Alex Danco

[00:05:11] Patrick: Can you give an example that is not at all Shopify related on interoperability and the power of platforms from history that people might be familiar with?

[00:05:20] Alex: Let’s talk about interoperability for a second. People use this to mean a lot of things, but in general, what it means is that imagine that you have two levels of a system where one level of the system needs to interact with the other level, and you have n players on level one, and you have n players on level two, they both need to be able to work with each other in a way that just works fluently without really having to talk to each other very much. I’ll give you an example, which is the shipping container. I know you love talking about shipping containers on the show. I have a factory that makes inputs and you have a factory that takes those inputs and you build something value added out of them. And I need to ship it from me to you, how do we do this? Well, we could work together on figuring out, what is the shape of box that best fits this part? And how do I work with a shipper to make sure that box is going to go on their boat or on their plane effectively?

And how do we negotiate all these things? Or we could just put it all in the same, exactly standardized 40 foot box that goes on boats that know how to fit exactly that box on it, and through a supply chain that knows how to deal with this thing and then out the other side with neither of us ever having to even know about each other or what we’re putting in. This is this idea of a constraint that de constrains. It’s a very, very common motif that you see in interoperability, which is this idea, a free for all is actually no freedom at all. A very, very common lesson here. I can give you all sorts of examples throughout history of saying, if you give people no rules whatsoever, and then everybody tries to work with itself, that’s a mess, nothing ever gets done. However, if you have these really nice constraints or conventions or platforms or standards, many different angles of approaching this problem of interoperability, you can actually unlock something pretty magical, which is this community of n people on one side and this community of n people on the other side can actually create n times n different things without needing n times n different bits of glue stitching all of those things together…

…[00:12:05] Patrick: Can you think of an example where that’s violated, where someone’s trying to create a constraint standard but there is too many degrees of freedom and it failed?

[00:12:13] Alex: Sure, that’s almost every standard. Most standards do not succeed. And the reason why they do not succeed is because they just don’t grasp the problem entirely correctly. There’s that XKCD joke, which is like, “There are 12 standards, what we need is a common standard for how everybody represents this. The next day, there are 13 standards.” Standards work is very, very difficult to achieve because so many things have to go right. But if you look across, even in the history of computing, there are several incredible reference standards that are held up. Unix is one of them. The IP internet protocol is probably the greatest one of all of them, it is a very, very, very restricted way in which you can represent the information going through the internet, but what it means is that any webpage, any application, any whatever can submit something that can then get communicated over any kind of communications network. It could be sent over copper wire. It could be sent over ethernet. There are some aficionados that have sent message by carrier pigeon over internet protocol. You can do it. It doesn’t matter. As long as it runs through internet protocol, anything will work on either side. This overall design, I know you talked with Tobi the last time he came on the show, this overall design is something called hour glass architecture or narrow waist architecture. It’s one of the most powerful ideas in building things. This idea of, if you want many, many things to be able to inter-operate with many, many other things, there needs to be a narrow waist that is as constrained as possible between them.

A very, very important idea, and so Shopify really, really understands this, as evidence through how we built Liquid and how every app developer can make apps that works with every theme developer and they don’t talk to each other and you don’t need a piece of custom glue like you would with enterprise software, it just works. The same with anything can go into the internet protocol and it can be communicated over anywhere. Another good example of a narrow waist in computing is the X86 architecture, which Intel made. Anybody can submit instructions to this instruction set, and then it can be executed on any processor that knows how to deal with the X86’s instruction set, but there’s this common waist that it goes through. And I’m including Intel in there just to show that there are a couple of different ways that a narrow waist can come about. It could come about through a bunch of different academics getting together, it could come through with a standard body, but also it could come through when one monopoly says so. In the case with Intel, there’s not any one way to do this, but they’re hard to achieve, and when you do, you have something that’s going to last for a very long time.

[00:14:24] Patrick: It’s sort of obvious with the examples you’ve given, whether it’s the shipping container, or the internet itself, X86, ISO, whatever, that when you get one of these right, it crazy amount can be built on top of it in ways that you could never envision when you set the standard, the creativity that can exist on top of it is fast and unpredictable. That brings us to the topic at hand, which is tokengated commerce, maybe we need to start with why blockchains are potentially interesting narrow waists. But before we do that, tokengated is two parts, token and gated, give us a high level description of why you were doing this, why you were spending your time on it, why Shopify is heavily invested in this notion? This is a new idea, and I want to understand it at a high level.

[00:15:03] Alex: First, let me actually tell you what is tokengated commerce, because at its heart, it’s actually a very simple idea. Tokengated commerce means, here’s a product and I’m going to put a gate in front of it. And if you want to pass the gate, you need to show me a token that says I pass the gate. More generally speaking, what does this look like in practice? Well, what it looks like is, “Hey, I’m a brand. I have all these cool products. I want to make them very exclusive. If you want to unlock the product, you have to connect your wallet, a crypto wallet, sign a transaction showing that you own this wallet and this wallet owns,” let’s say, “the right NFT.” Because I own this NFT, I can unlock this product. Or it could be, because I own this NFT I unlock early access to a drop. I can get to the drop 15 minutes earlier than everybody else. Or because I own the rare version of this NFT, I’m able to get the rare version of the hoodie. Anybody can get the black version, but if I have the rare NFT, I can get the red version and that red version is cool. Or because I own this NFT, I’m able to buy this product and you can only buy one product per number of NFTs you own. These are all various ways of implementing this simple idea, which is, there is context somewhere. And that context is going to influence how my business wants to treat you. What if, as the buyer, I can bring that context with me and sign with it, proving I am me and here’s how I show I have some ownership over this bit of context?

And my storefront can respond to this and say, “Okay, now that I see that you’ve signed for this bit of context, my storefront is going to respond to that context by doing something appropriate.” Maybe it’s unlocking a product. Maybe it’s giving you really access to a drop. Maybe it’s letting you get into a party. It could be anything. It could be, “Here’s something live and in person. Here’s access to 15 minutes of a live stream with me.” It could be anything. It doesn’t just have to be products. This idea of token gating, defined it very, very simply is it’s a kind of behavior that is very, very natural. It’s how do I get into the exclusive thing? How do I show that I have done the challenge of gaining access? How do I get the thing that I want to get that is hard and feels like a reward? These are very, very old ideas in commerce, this idea of commerce is a challenge that the buyer and the merchant do together. And token gating, we are finding, is an incredible foundational piece of UX for the basic idea of the most meaningful kind of commerce is a challenge that you do together.

[00:17:08] Patrick: If you think about the many, many aspects of this, I want to start with the token itself, because if you think of non-fungible token, which have been popular, you own a Bored Ape, you own a Crypto Punk, you own whatever, piece of art, whatever, you could see a world where brands build specific product lines that tailored to you have to own one of these things that are already independently exclusive. So we’re sort of riding the scarcity of Bored Apes, let’s say, as a cool way to create something custom for them. Talk to me what you’ve learned here. Do you think that most merchants will outsource the scarcity function of the tokens themselves, or are you going to empower them to also create their own tokens that trade? It just seems like the world has coalesced around a small number of the most popular projects. Like all the examples you hear are, “If you’re an owner of one of those special things, we’re going to treat you differently, because it’s like you have a black card or something.” So start with the token piece. How do you think it will work?

[00:18:00] Alex: In that question, there were like three or four really good questions. So I want to try to answer them in the right order here. First, if you look at these NFTs, what are these things? What are they any good at representing? What do they all have in common? Let’s break down a couple of common aspects of these NFT projects. One aspect of them is these entities are owned by people, and the way that they own them is through their wallets. What is a wallet? Well, at a very basic level, a wallet is I have my public address and I have my private key and I sign my private key to show that I am who I am. My wallet address is associated with this token on this smart contract, which means I own this ape. First of all, let me just present a very basic observation, which is what are people doing with their wallets? Well, they’re connecting them everywhere. They’re connecting them to discords, to get into the discord. They’re connecting them to adapt. They’re connecting them to any kind of application that is asking them to authenticate in a certain kind of way. Now what we’re seeing is people want to connect these wallets to storefronts to say like, “Hey, I’m not a fungible buyer. I’m a non-fungible buyer because I have this token.” We really like saying NFTs aren’t a kind of product. They’re a kind buyer, a non-fungible buyer. I really want to get this into people’s minds. NFTs fundamentally to us are an input for commerce. They’re a piece of context that the buyer brings with them when they show up to the storefront. It can also be an outcome of commerce. We can do a commercial transaction where one of the outputs of this commerce as I’ve been to new NFT and give it to you. It doesn’t have to be an NFT either. It could be an ERC 20. It could be any number of other things.

These are very, very flexible ideas, but even this very basic thing of, “I have an NFT. I connect it to the storefront, it unlocks a product. Then I go check out. And maybe on the other end of the checkout, you want to sell me another NFT and I may buy that also. Then maybe I’ll use that somewhere else.” All of these are very, very interesting kinds of outputs and inputs to what we call commerce. But as you said before, I want to make sure that I’m answering your original question here, which is over the last year or two, there was this explosion of communities who were all issuing these tokens and everybody was getting in. “Oh, this is cool art.” These are going to have utility. Who are all these communities? And now what we’re seeing is yeah, a lot of these communities didn’t really have much of a game plan, but some of them do. And the ones that do are actually turning out to be formidably impressive media companies, because they have this fascinating way of creating fan bases. One way to look at NFT is this is a new way of creating a fan base, but it’s creating a fan base on the very beginning. You have to have some idea of what you’re doing with your brand. But nonetheless, the specific example of a merchant that we work with closely is Doodles. Doodles is one of the premier NFT brands. They understand fully that they are merchants and they are brands and they are media powerhouses. They understand that that’s the kind of business that they’re building. And they see these tokens as a new fundamental piece of what is it that their fans have that they can bring with them and connect into places in order to bring all that context with them…

…[00:28:14] Patrick: I’ve gone way too far into the conversation without asking what the literal mechanic that Shopify is building will let people do and won’t let them do. Is it as simple as saying, “If I’m a merchant, you can sign with whatever and I’m going to go through a menu and pick the tokens that I want to let and tie them to a certain thing and then you handle the rest”? What is literally going to be the thing that you offer?

[00:28:32] Alex: That’s actually a very good way to put it, which is that the number of things people want to do with token gating is very diverse and very hard to predict. We cannot know what all of them are. But you know what? We don’t have to. We’re a platform that is what app developers do. This is how Shopify is built. This is exactly like the problem of saying, “Well, there are many themes in the theme store and there are many apps that want to make mechanics. Do I have to think of every single thing that an app could do so that themes can know about it?” No. We just build our platform in a way where we present the right constraints and the right formats for saying, “Hey merchant, you want to do some token gating. Well, there are a lot of different ways that you might think your token gating wants to do. Some people want to token gate for discounts. Some people want to token gate around mechanics to do a cool sneaker drop. Some people want to token gate so that people can buy variants on a product to correspond to variants of their NFTs.” All of these are perfectly valid ways to do token gating and we’re not going to come up with what they are. What we are doing is we are creating a common platform for app developers to go make whatever kind of token gating rules you want to make in a way where those token gating rules can be presented in any selling surface where the merchant wants to go.

Maybe they want to sell on the online store, and that’s great. Maybe they want to sell at a retail point of sale environment. We have merchants doing this now. They’re doing a popup store and they’re an NFT brand. They’re like, “Oh, I only want to sell this thing in my popup store to people who have this NFT and can sign for it. And I want to do it on retail POS.” No problem. Some people want to buy things on mobile, and we have the shop app, which is our mobile app for shopping, and there’s some merchants who want to set up a little token gated store in the shop app that works really well on mobile. We have a product called GM shop that I’ll tell you about it in a minute that is exactly that. But your general question of what is the product that Shopify lets merchants do? It’s, well, you can do anything because we’re a platform. That’s the hard work of being a platform is coming up with what are exactly the right constraints that anybody can make inputs to it and anybody on the other side can read them and go carry out token gating instructions if we’ve come up with exactly the right constraints in the middle. The slogan I like to say when people say, “What are you doing with your life?” I say, “I’m making Shopify wallet aware.” That’s what I’m doing. Wallet awareness is not a single thing. It is an idea around everybody accepting a certain set of constraints that become deconstraining. They’re constraints that become liberating…

…[00:38:58] Patrick: You started to answer a key part, which is if all you wanted was an unlimited amount of people to have a certain access then pure text is great. If you want to limit it somehow obviously then the non fungible nature of the tokens becomes very important. So I get it. And you could certainly see the world normalizing too in your browser, you have a wallet, and you’re constantly like getting shit in your wallet from different people and they represent different things and blah, blah, blah. So now let’s talk about demand, this big topic of what is demand? Where does it come from? How does it tie into this whole story? And why is this new primitive for unlocking demand?

[00:39:32] Alex: Demand is one of my favorite topics because it’s simultaneously such a basic thing that everybody has opinions about. But also it’s one of the hardest things to conjure. You’re not a business until you have demand. A business plan is not demand. Nothing is a substitute for demand. Demand is the thing. Every business owner knows this. What is this mysterious thing, and how do I get it, and once I have it, how do I turn it into more? Before I was at Shopify, before I worked in DC, before we knew each other, long before that, I was in a band. I was in a band called The Fundamentals. We were on a label called Stomp Records out of Montreal, it’s a ska punk label. We never made it big, but we toured around. We had a record deal. We were trying to make it big. This is what we were doing with our lives.

And when you’re in a band, your business model is you lose money recording music, so that you can break even selling concert tickets, so that you can make money selling merch. That is how it works. You are a merchant. What you sell is apparel, basically, to your fans and you give them a reason to buy your stuff. Everything else is more or less a loss leader for your merch business when you’re at that size of band, anyway. I’m sure Taylor Swift makes money at all slices of the pie, but even like the Taylor Swift merch empire is, this is massive, massive, massive business, because there is demand for Taylor Swift merch. How do you make that demand and where does it come from is the question of being a retailer or the question of being a merchant. I can tell you honestly, when you’re a band, demand is something where it exists in two states. If I’m a band and I have these fans that are all out there in the world, they like me in a very sort of abstract way. They listen to my music. They’re thinking about me sometimes. The demand for them to buy my stuff is not really activated. It doesn’t exist in a more tangible form. The proof of this by the way is if you look at musicians merch businesses, let me ask you what percentage of a band’s merch do you think is sold online as opposed to at shows?

[00:41:08] Patrick: 50%.

[00:41:10] Alex: Almost none. So the rule of thumb is that no matter how big you are, your online merch business per year is about the same as two weeks of tour dates.

[00:41:19] Patrick: Oh, wow.

[00:41:19] Alex: Yeah. It is a very, very, very strong ratio. This is more or less universal whether you’re a small band or a big band or whoever you are. This is not to say that people don’t like Taylor Swift, unless they’re in the Taylor Swift concerts. No, they like Taylor the whole time. But you need there to be a precipitating event to cause people to be compelled to buy the merch now. The demand has taken a more meaningful form. It’s almost as if the demand isn’t like a gaseous state and then it becomes more active when certain things happen. And I want to tell you about those things because there are some universal rules to them in how culture works. When you’re a band, you have all these fans and they exist and they know who you are. But then when you come to town, what you do is you play a show. You sell tickets to the show, people buy the ticket, and they enter in this space, and this is very intimate space. And you do a challenge together called dance to the music. On completion of the challenge everybody lines up to go by the merch. This is a universal rule of music. There is a very, very specific orchestrated sequence of events that causes people to buy your stuff. Everybody who has active experience with being a certain kind of culturally cool merchant will recognize their version of this. Demand isn’t enough. It has to be activated demand. It has to be awakened by something. And the thing that awakens demand is a challenge of some sort. I think you were posting about this on Twitter or something. Challenges are the things that make life meaningful. They’re the thing that give us identity. They’re the thing that give us purpose. They’re the thing that makes us feel good about ourselves. Challenge and overcoming the challenge. Demand in absence of challenge is cheap and stupid.

It’s not necessarily stupid, but it’s baseload demand. I have baseload demand for paper towels. That’s fine. I can get them from the corner store. I can get them from Amazon. That’s fine. But the more meaningful kind of demand that actually is something meaningful to my life, that kind of demand is only awakened by a challenge. It might be the challenge of being in a particular store and really, really talking to a merchant and figuring out what I want. It could be the challenge of going to a show. It could be the challenge of being in a cool collab or whatever it is. But ultimately demand has to be activated by something and that thing is challenge. What kind of challenges are the things that people really care about? Well, the basic challenge that we care about is identity and group association. I’m a part of this group. I have these peers. I’m living up to a certain challenge that the peer group does. This is through the basis of all culture. That kind of culture is the basis of a certain kind of retailing called products that people buy to be cool or products that people buy to be a part of a group or products that people buy because they have some sort of meaning to them. The number of different kinds of products like this are quite varied. It’s not just t-shirts that bands sell. It could be memberships to something. It could be getting tattoos. Everybody has this thing that they’re really, really into. But ultimately demand, I want to bring this back to this sort of nebulous concept of demand, is something that people have understood as a part of commerce for thousands of years, but only up until recently that demand was always in person. There’s a challenge that the buyer and the merchants come together to flesh out what context is the buyer bringing with them? Under what circumstances does this demand unlock and activate the challenge? This is something that people naturally do face to face really well.

But online, it’s really hard to do this. It’s hard to show up to an online storefront and bring a vibe with you, do a challenge together, or engage in any of these things. I would say the first mechanic that people online came up with that actually activated this was the drop, the concept of, “Okay, at noon the sneakers are going to drop and you have to get them as fast as possible.” That’s fun. That is a great example of how you sell things. That’s how you get demand to actually convert into purchases is you do a drop or you make an exclusive thing or like you create a challenge and you motivate people to get behind the challenge. I believe it was Modest Proposal was on your podcast a long time ago, talking about eCommerce and this idea of getting all the friction out of commerce. That’s really not it. There’s some kinds of friction that are bad, but there are actually some kinds of friction that are really good. I talked to you about this in the Shopify podcast. This idea of a challenge is required to turn demand into buying. Different cultures do it in different ways, different kinds of retailers do it in different ways. A luxury brand like Gucci will do this in a very different way than a fast fashion brand like Forever 21 will do it. They’re obviously very, very different retailers. They move different kinds of merch for different kinds of price points. But they’re doing the same thing. Look at a really, really well run retailer like Aritzia. All of Aritzia is keyed into getting this latent demand to come in the door, activating it around this certain kind of challenge, and then converting it into incredible brand loyalty. That’s what really powers these businesses. Same on the merchant side, you have the challenge of tack. How do you convert that into something that will produce LTB for a very, very long time?

7. TIP457: Why The Dollar Is Not Collapsing w/ Jeffrey Snider – Trey Lockerbie and Jeffrey Snider

Trey Lockerbie (00:02:14):

So, we have a whole global monetary system right now that I think a lot of people would call a Petrodollar system, and we’re going to work a little bit backwards from what that means. There’s also the Eurodollar system in play that people may or may not be as familiar with. So, I want to actually start there with the Eurodollar. It’s a big loaded question, but going back to basics here, just simply tell us what is the Eurodollar?

Jeff Snider (00:02:39):

Well, technically speaking, and going back all the way to the beginning, Eurodollar refers to a very specific term, and it means US dollars on deposit outside the United States. In the early days, it actually took the form of actual cash deposit, physical Federal Reserve notes, bills, cash bills and things like that, that found their way mostly to Europe, but not just exclusively to Europe, thus the term Eurodollar. It doesn’t have anything to do with the European common currency. It is, again, the term Euro simply means offshore, because this goes way back to the 1950s and 1960s long before the European common currency was ever introduced. So, whenever you hear the term Euro and then attached to a currency denomination, what that simply means is money that the banking system uses outside the jurisdiction of the United States or even any of the other currency denominations that are floating around in it.

Jeff Snider (00:03:31):

So, there are things like Euroyen, for example, which means yen outside of Japan, that’s in this offshore currency system or even something like the Euroeuro, which is offshore euros. So, essentially, after beginning sometime in the 1950s and spreading through the 1960s, we have a huge, very much comprehensive global monetary system that undertook the roles of the reserve currency, global reserve currency, but it’s not actual cash. It’s not actual currency. There’s no money in it. It’s a virtual ledger system, a distributed ledger system that the global banking system operates and therefore has undertaken the roles of a reserve currency because banks have been able to flexibly and dynamically respond to the world in which they live in.

Jeff Snider (00:04:18):

So, for the last 60 years, this Eurodollar system has been essentially the global monetary reserve. And because it’s offshore, it’s outside the jurisdictions, not just the US, but pretty much anywhere, which is kind of a strange concept because these banks are located and doing business someplace. They’re physically located somewhere. But they have located and they have been able to take advantage of various regulatory blank spots, regulatory boundaries. So, this currency system has been able to grow and expand basically outside the reach of national governments, national regulators, bank regulators, whatever it may be and operate throughout the rest of the world. Again, so the point being to create this global reserve currency arrangement that goes back a long, long time.

Trey Lockerbie (00:05:05):

That last point there, what I hear you describing would maybe otherwise be called something like shadow banking, right? Or is that correct? And if not, what is a shadow bank and what is the shadow economic system?

Jeff Snider (00:05:16):

Well, shadow banking is part of it. That’s more about some of the non-bank participants who actually in this global monetary arrangement. I like to use the term shadow money, because they’re actually monetary forms that they don’t show up in any of the statistics. They don’t show up in any regulatory discussions. They’re not involved in any of the mainstream policy framework, because, again, this is outside the United States, it’s outside of every regulatory regime on earth and regulators are not too keen about people knowing about this vast, huge monetary system existing outside of their reach when their entire monetary policy and really political existence, it relies upon the idea that they are very much in control of this system and this arrangement.

Jeff Snider (00:05:57):

So, it’s outside of everyone’s reach, but also the ways in which these banks operate monetarily as well as credit has evolved and changed so that you have monetary forms like currency swaps, for example, that function every bit the same as cash would, except a currency swap doesn’t fit into a monetary aggregate, it doesn’t fit into any sort of quantitative measure, nor qualitative understanding. It doesn’t even fit into the bank balance sheets in a intuitive way. In essence, this is a virtual ledger money system, that’s a shadow money system because of the way the banks operate on their balance sheet.

Trey Lockerbie (00:06:32):

We’re going to explore the significance of that in a minute, but let’s keep with the basics for a minute. So, let’s say the US, we were on a gold standard for a very long time. We had to pay for some wars and stuff and we had to kind of break our promise that was the dollar was backed by gold, we kept changing the money multiplier over time. And at some point, it was unfeasible to continue on with the gold standard. So, like 71-ish, Nixon says, “Hey, you know what, we’re going off the gold standard into this fiat system.” And a lot of people said, “Okay, well,” there was this meeting with Saudi Arabia and we developed this agreement with them to now produce something called the Petrodollar system. And that’s what a lot of people believe we’re operating on today. But is that correct, Jeff? What’s your opinion?

Jeff Snider (00:07:12):

The short answer is no. And it’s a common misperception, because you can understand why. The Bretton Woods system, which was a quasi-gold-backed system, a commodity-based monetary system that grew out of World War II, in the ashes of World War II, where Harry Dexter White and John Maynard Keynes in particular said, “We can’t just have an international currency arrangement because nobody will accept it. So we need to tie this international currency to some national reserve.” And historically speaking, people wanted to use gold, because gold for various reasons that we don’t need to get into here.

Jeff Snider (00:07:40):

So, you had the Bretton Woods system 1944, which always had this inherent flaw or inherent tendency in it as Robert Triffin called it in the late 1950s, eventually become called the Triffin’s paradox or Triffin’s dilemma, which was that in order to operate a global reserve currency, you need to have enough currency floating around the world to be effective. Because what is a global reserve currency? It’s a mediating currency where vastly different systems can connect to each other through this third-party mediating system or mediating currency so that trade, financial flows, all of the free market capitalism that we’ve come to love and honor, those things can happen in a very efficient fashion so that we can have a globalized, highly efficient economic system.

Jeff Snider (00:08:24):

The problem was by tying this international currency and using, for example, the US dollar or the British pound and backing that currency with national stores of physical bullion, there was always going to be the problem where there’d be too much currency needed outside the US, which would then lead to anyone ending up with that currency, redeeming the paper for national reserves. Eventually the national reserves of gold would be drained from the system and Triffin’s paradox would be that once those reserves were drained, the whole thing would just fall apart, which by the way, came close to happening in the late 1950s.

Jeff Snider (00:09:00):

So, we’re talking about not even really 15 years into Bretton Woods, it was already falling apart. So, this is where the Eurodollar steps into it, because it divorces the national currency from the national store of reserves. So, long before 1971, you had this global monetary arrangement, because it was reserveless, because it was ledger money that it began to undertake the roles of the former Bretton Woods system as it broke apart. So, by the time you get to August of 1971 and President Nixon closing the gold window, the Eurodollar had long undertaken all of those roles of the reserve currency before that.

Jeff Snider (00:09:36):

So, August of 1971 represented nothing more than the symbolic end of Bretton Woods when the functional end started a decade and a half before that. So, in terms of the Petrodollar, it wasn’t like we moved from a commodity goal-based monetary system to a oil-based system in the 1970s. We moved off of the commodity-based monetary system long before that. And it had superseded the Petrodollar, the stuff that happened in 1973, for example, and basically all of the functions of the Eurodollar were up and running for more than a decade by then. And even the Eurodollar system itself had become absolutely huge and immense by the early 1970s.

Jeff Snider (00:10:15):

So, the transition took place into something that was a ledger of ledger virtual currency system long before then. And it took place into this offshore bank-centered sort of blank canvas where banks could experiment in all different types of money, so that we transitioned long before from a commodity gold exchange system, the Bretton Woods, to this virtual reserveless currency system under the Eurodollar over a long period of time before we even get to 1973…

…Trey Lockerbie (00:13:59):

So, how much of the narrative that we’re currently operating on comes to us from our actual own Federal Reserve, or even say the media or education around the system that we’re currently in? Because as I understand it, your research has led you to study papers from internal employees at the Fed and elsewhere. And some of them know what’s going on. Some of them are discovering what’s going on through their work. And others just have no clue maybe because they’re in the system and they have that kind of myopic view. So, from the research you’ve done, what’s the takeaway of how informed the people within the system even understand how the global system is operating?

Jeff Snider (00:14:36):

The funny thing is, we always think scientific progress is linear. It always goes in one direction. But here’s an example of how monetary scholarship, academic scholarship about money actually move backwards. When you go back in time to do the historical research, you see there’s much more awareness, much more understanding, not the whole thing, but much more understanding about at least the basics of the Eurodollar system in contemporary time. So, back in the 1960s, for example, it took international authorities and national authorities about a decade after the Eurodollar system began to really start investigating it, because it had become that big of an issue even for national authorities like the Federal Reserve.

Jeff Snider (00:15:11):

But when they did, they were sort of putting bits and pieces of it together through… I mean, which makes sense because it’s a brand new development banks were doing things, they were not sharing the information with anybody, which is, again, why we call it shadow money. So, there was a huge, huge blind spot for even regulators and officials to try to deal with. But at that time, they did attempt to try to understand this Eurodollar system. But then they just, they stopped and they gave up, which begs the question, what is it the Fed did? What does the Fed actually do now? Which goes back to one of the initial quote that you said at the top, when I say the Fed isn’t a central bank, this is the reason why, because what happened was in the 1960s and 1970s, Federal Reserve officials, Treasury officials, government officials, officials at the BIS, or the IMF realized this monetary and banking evolution that was going on through the Eurodollar system made it almost impossible to define, let alone measure and regulate and keep on top of the monetary system.

Jeff Snider (00:16:08):

And if you’re a central bank, if you’re a legitimate central bank, whose job it is to regulate the monetary system, as we all believe, going back to Walter Bagehot in the 19th century, how do you do that when the monetary system has evolved, and it has evolved in these offshore, outside of regulation spaces that make it almost impossible for you to have much of an influence, let alone direct relationship with the banks operating there? So, what ended up happening was around the turn of the decade in the 1970s and 1980s, central bankers decided they just kind of threw up their hands and said, “Well, the monetary thing, it’s too complicated. It’s outside our jurisdiction. So, we can’t really do money anymore. Instead, we’re going to try to make it so that people believe we do money, this expectations-based policy, where we’ll communicate to the public that we’re doing something and hope that the public and banking system and business people all around the world or inside the United States will behave in ways that we want them to behave.”

Jeff Snider (00:17:02):

For example, it became commonplace that, Alan Greenspan, for example, would raise or lower the federal funds rate whenever he wanted to do something. So, if he wanted to “tighten credit” and tighten the monetary system, would he actually tighten the monetary system? Would he go into the monetary system and take money out? No, he raised the federal funds rate, which was nothing more than a signal to the economy at large and try to get the economy and try to get the markets to tighten conditions based on that signal, based on expectations. As he said, during that time, as his predecessors said before, “We just can’t keep track of the monetary system. Therefore, this is what we have left to be able to do to try to get some form of control over the economy and the marketplace.”

Jeff Snider (00:17:44):

So, it’s really about this evolution in money in banking that took place outside of their purview, which left official scrambling to try to do something else to at least attempt to maintain the role of what a central bank used to do, but it’s not a monetary role. It’s not involved in the monetary system itself. So, once that happened, monetary scholarship simply dried up. The term Eurodollar kind of disappeared, not just from internal discourse, but from public discourse as well. So, you have a wealth of scholarship up to around early 1980s and then just nothing. Because what happened was we were told, we were all told, we were taught this in school. “At that point, don’t fight the Fed, just whatever the Fed says, whatever the Fed, they must know what they’re talking about when it comes to money, you don’t need to know. Just trust Alan Greenspan and Ben Bernanke. They’ve got it all covered.” So, once there was a vibrant monetary or debate and argument, it just kind of disappeared and dried up and went away.

Trey Lockerbie (00:18:40):

But it’s not all an illusion, is it? Because if we fast forward to today, we’re seeing it happen and play out in real time, where inflation is now high again as it hasn’t been for decades and they’re raising interest rates. And now we’re starting to see things like mortgage rates go up and home prices get underwritten in a new way. We’re seeing real economic impact from these decisions or actions from the Fed. So, where does the detachment actually occur in your opinion?

Jeff Snider (00:19:05):

Well, because that isn’t actually inflation. This isn’t due to money printing. This is sort of the federal… I mean, that’s why you didn’t see consumer prices react to QE6 back in 2020. Consumer prices didn’t start to skyrocket until March and April of 2021, which was coincident to the US treasuries helicopter drops. So, this wasn’t money printing, this wasn’t the Fed creating money. This wasn’t the Fed being a central bank. It was essentially a supply shock, which was the US government redistributed borrowing through the Treasury and mostly Treasury bills actually, the US government essentially redistributing cash into the pockets of consumers. And then consumers wind up spending that cash at a time when the ability of the global economic system to supply goods and then transport goods in particular was at its lowest point. So you see inventories of goods actually crash during these periods because we had essentially a supply shock.

Jeff Snider (00:19:59):

So, it isn’t inflation as much as it was consumer prices reacting to small E economics. Whenever you have a demand curve shift out to the right, especially when supply isn’t as any elastic as it was during that time, consumer prices have to react. I know most people are saying, “Who cares? Consumer prices went way up. What does it matter if it’s inflation? Or what does it matter if we call it inflation or not?” The issue is how it ends, because if it’s nothing more than a supply shock, it’s always going to be temporary and transitory rather than something like the 1970s, where you ignite the monetary spark of excessive currency, that leads to all sorts of, well, great inflation type of problems. So, how do we tell one from the other?

Jeff Snider (00:20:40):

And one of the things that consistent with excessive currency and money printing would’ve been destruction of the US dollar has been long proclaimed, long predicted, and long forecasted. But what you see ever since last year is the US dollars exchange value going up against almost every currency, because it wasn’t money that was printed. It was simply a supply shock. And because it wasn’t money printing, the way this is likely to end is in another bad way, which is a recession. That’s really what markets have been predicting over the last more than a year, actually, because the yield curve has been flattening. So, even as interest rates have been rising, the yield curve has been flattening. The Eurodollar futures curves have been flattening. All of the signals from the monetary system itself have been sending, “Hey, there’s no money here. This is not money printing. This is a supply shock and this is going to end predictably in something like a contraction or recession.” So, it was never inflation to begin with. It was simply small E economics of a supply issue.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Amazon and Shopify. Holdings are subject to change at any time..

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 03 July 2022:

1. Why Foundational Models will Revolutionize the Future of Work and Play – Daniel Jeffries

It’s 2033 and you’re coming home from a dinner and realize your sister’s birthday is tomorrow and you forgot.

You ask your phone what’s the best gift for her and where you can get it at this late hour?

Your phone has dedicated processors for running Machine Learning (ML) models locally but it’s not powerful enough to answer that question with its small memory and slower chip speed.  But it is strong enough to ask a more powerful model in the sky.

The local model also learned a lot about what you and your family likes over time, so it packages up some key things it knows, anonymizes them, and fires off a query to a Foundational Model (FM) in the cloud via API.

In a fraction of a second, the answer comes back.

Your sister’s latest social media pics show she recently got on a serious health kick, lost weight, stopped drinking and got really into vegetarian cooking so it recommends an AirBnB cooking experience near her, with a local vegetarian chef.  It gives you two alternative experiences that are good but a bit further away and not as highly rated.  You don’t even need to go to the store and it’s the perfect present that makes you look like a hero…

…Across the world people are using cascaded FM’s networked together to do amazing work.  FMs on their own are amazing but working together they’re capable of astonishing feats and when they work with you they’re centaur units, a combination of man and machine working together to create something neither could do on their own.

Centaurs are named after Gary Kasparov’s early experiments with chess tournaments, where an AI and human teams bested pure AI and humans on their own. The tournament’s name came from the mythical beast of Greek legend that’s half horse and half man, symbolizing how man and machine can work together better…

…Biotech companies search through massive databases of proteins and chemical interactions and quickly use a fine tuned FM to design twenty potential drug candidates to fight a rare motor neuron disease that recently cropped up in South Africa.

A musician jams out a new tune and then asks the models to iterate on the chorus.  The 17th one is awesome and the musician plays it and then modifies it with a few tweaks to make it even more catchy based on a song he couldn’t get out of his head a week ago that he overheard on a radio at the local park. It goes on to be a huge hit on Soundcloud.

Materials scientists are designing new materials that make everything stronger and lighter, from skyscrapers that flex more easily to resist earthquakes, to electric bikes that are light enough to carry on your shoulder and fold up neatly to carry on the train.

Elite coders are simply telling the coding model what they want it to do and its spitting out near perfect Python code but it also recommends Go for several libraries because it will be faster and more secure.  It automatically does the translation between languages and tests it. It’s paired with an evolution through language model (ELM) coupled with a Large Language Model (LLM) and those models helps the coder create brand new, never before thought of code too, in a domain the model was never trained for by iterating on concepts quickly.

All of it is happening because of a vast global network of ambient AI models.  AI is everywhere now.  Every device is waking up and getting smarter.  We’ve industrialized intelligence and sparked a revolution in how we work, design, and play.

Welcome to the age of ambient AI…

…What are Foundation Models and why do they matter?

In essence, FMs are large models that exhibit remarkable capabilities, such as the ability to understand language, reason, create working computer code, do translations and arithmetic, understand chains of logic, generate totally new art from text prompts, and much much more.

The basic concept of FMs comes to us from Stanford University where they primarily refer to Large Language Models (LLM), like GPT-3, that are typically transformers. But the implications of FM’s go way beyond today’s architectures.  They’re a groundbreaking type of software, that’s not limited to transformers or language.

We can think of FM’s are any large and sophisticated model.  We can also think of them as a chain of cascading models that work together to do a complex task such as generate music or images or video, create mathematical proofs, design new materials or discover new drugs and more.

Many of them are already here.

GPT-3, from OpenAI, powers GitHub co-pilot that quickly writes code for developers, especially boring, repetitive code so they can focus on more creative tasks.  It’s one of the first fantastic examples of a centaur.  Originally, GitHub’s team wasn’t sure who would use it.  Would it be beginning or advanced coders?  Since its wider release to all developers, the answer is clear: advanced coders love it and use it most often.  Advanced coders are in the best position to understand when the model makes a mistake and it dramatically speeds up their day to day coding…

…In another article, called The Coming Age of Generalized AI, I highlighted researchers who were working on even more groundbreaking approaches by combining mega-models with several other key techniques.  One of techniques, called progress and compress that comes to us from DeepMind, combines three techniques, progressive neural networks, elastic weight consolidation and knowledge distillation.

The idea is simple. Create two networks, a fast learning network and a base model. That roughly mirrors the functioning of our brain yet again. Think of it as the hippocampus and neocortex. As Hannah Peterson writes in her article on catastrophic forgetting,  “In our brains, the hippocampus is responsible for “rapid learning and  acquiring new experiences” and the neocortex is tasked with “capturing  common knowledge of all observed tasks.” That dual network approach is called a progressive neural network.

The fast neural network is smaller and more agile. It learns new tasks then transfers the finalized weights to the base model. So you end up with a lot of stored neural networks good at a bunch of tasks.

But there’s a problem with basic progressive neural nets. They don’t share information bi-directionally. You train the fast network on one task and freeze those weights and transfer them to the bigger network for storage but if you train the network first on recognizing dogs, it can’t help the new network training on cats. The cat training starts from  scratch.

Progress and Compress fixes that problem by using a technique called knowledge distillation, developed by deep learning godfather Geoffrey Hinton. Basically, it  involves averaging all the weights of different neural nets together to create a single neural network. Now you can combine your dog trained model and cat trained model and each model shares knowledge bi-directionally. The new network is sometimes slightly worse or slightly better at recognizing either animal but it can do both.

It opens the door to cat-like intelligence.

A cat is a remarkable creature. It can run fast, sleep in tiny boxes, find food and water,  eat, sleep, purr, defend itself, climb trees, land on its feet from  great heights and a hundreds of other subtasks. A cat won’t learn language or suddenly start composing poetry. That’s perfectly fine because a cat is really well suited to its set of tasks; it doesn’t need  to build skyscrapers too.

Having a cat level intelligence is incredibly compelling. If you have a cleaning robot that can wash  dishes, pick up clothes, fold them, carry them from place to place and  iron shirts, that’s an incredible machine that people would clamor to buy. It doesn’t also need to write music, craft building blueprints, talk to you about your relationship problems, and fly a plane too…

…AI is a universal, general purpose technology.

The greatest breakthroughs in history are always universal technologies that affect a broad range of sectors as they branch into countless other domains and inspire unexpected breakthroughs.

Think of the printing press and the way it leveled up human knowledge across the board because now we could scale, save and replicate knowledge much faster.

Think steam engines that changed the very nature of work from human and animal powered muscle work to work done by machines.

Think of the microprocessors and computers that changed how we do art, communicate, design skyscrapers and houses, fight wars, find love, do science, make music and movies and more.

A general purpose technology like AI has direct and secondary effects on the world at large, both good and bad and everything in between.

We can think of ideas and technology as they grow and change and affect both their own domains and unexpected domains as a growing tree.  The roots are precursor ideas that eventually inspire the primary idea.  The trunk is the central breakthrough idea, which leads to a branching series of closely related ideas and some unexpected inventions in parallel domains.

2. Reducing Inflation Will Come at a Great Cost: Stagflation – Ray Dalio

More specifically, I now hear it commonly said that inflation is the big problem so the Fed needs to tighten to fight inflation, which will make things good again once it gets inflation under control. I believe this is both naïve and inconsistent with how the economic machine works. That’s because that view only focuses on inflation as the problem and it sees Fed tightening as a low-cost action that will make things better when inflation goes away, but it’s not like that. The facts are that: 1) prices rise when the amount of spending increases by more than the quantities of goods and services sold increase and 2) the way central banks fight inflation is by taking money and credit away from people and companies to reduce their spending. They also take buying power away by raising interest rates, which increases the amount of money that has to go toward paying interest and decreases the amount of money that goes toward spending. Raising interest rates also lowers spending because it lowers the value of investment assets because of the “present value effect” (which I won’t get into because it would be too much of a digression), which further lowers buying power. My main point is that while tightening reduces inflation because it results in people spending less, it doesn’t make things better because it takes buying power away. It just shifts some of the squeezing of people via inflation to squeezing them via giving them less buying power.

The only way to raise living standards over the long term is to raise productivity and central banks don’t do that…

…In summary my main points are that 1) there isn’t anything that the Fed can do to fight inflation without creating economic weakness, 2) with debt assets and liabilities as high as they are and projected to increase due to the government deficit, and the Fed also selling government debt, it is likely that private credit growth will have to contract, weakening the economy, and 3) over the long run the Fed will most likely chart a middle course that will take the form of stagflation. 

3. The Beer Game – Peter Dizikes

Thursday, August 29, 1:00 p.m.

It is a miserably muggy afternoon in Cambridge as the incoming class of the MIT Sloan School of Management—roughly 400 students from 41 countries—files into a second-floor ballroom at the Kendall Square Marriott. They are here to play the Beer Game, a Sloan orientation tradition. Unfortunately given the weather, the Beer Game does not involve drinking cool beverages…

…Rather, the Beer Game is a table game, developed in the late 1950s by digital computing pioneer and Sloan professor Jay Forrester, SM ’45. Played with pen, paper, printed plastic tablecloths, and poker chips, it simulates the supply chain of the beer industry. In so doing, it illuminates aspects of system dynamics, a signature mode of MIT thought: it illustrates the nonlinear complexities of supply chains and the way individuals are circumscribed by the systems in which they act…

…1:30 p.m.

Each Beer Game team is divided into four units of two players each, who play the roles of retailer, wholesaler, distributor, and brewer. The goal is to keep team operating costs as low as possible. We learn that teams will be penalized for having too much inventory (50 cents per case of beer per week) or unfilled back orders ($1 per case per week). Each link in the supply chain keeps track of its own costs, but a team’s score is the sum of these tallies. The lower the score, the better.

As we begin the first of 50 rounds (which represent weeks), each retailer unit draws a card indicating consumer demand for cases of beer; at the same time, all the units send slips of paper with orders up the supply chain. In response, cases of beer—represented by poker chips—move in the opposite direction, from brewer to retailer. A small number of chips are already at every station when we start.

2:15 p.m.

After 20 rounds, my team is on a hot streak.

I’m sitting at the retailer station with finance student Adah Jung, who’s been submitting orders at a level closely mimicking consumer demand. Our score at the retail station is low, and there are few chips elsewhere on the table, meaning our team’s costs are minimal. It’s hard to see how things could go wrong: with seven smart teammates and a stable supply chain, why can’t we win this thing? I can almost hear Sterman asking us to stand for a round of applause.

2:35 p.m.

Seemingly out of nowhere, our team’s distributorship has an inventory of 178 surplus cases of beer, which lasts seven weeks, adding $623 to our costs in a game where the average score after 50 weeks is $2,000 per team. How did that happen? Can’t someone tell our two teammates at the brewery just to stop making so much beer?

Well, no. “I can’t tell them anything,” observes teammate Juan Trujillo. Indeed, to simulate the incomplete information we deal with in real life, players cannot communicate across stations, apart from relaying orders. And somehow, someone on our team ordered way too much beer…

…3:30 p.m.

Sterman’s assistants tape charts to the ballroom walls detailing every team’s performance. Today’s winning score was $460 (the best possible score is about $200), while the worst-performing team racked up $6,618 in costs.

Sterman initiates a discussion, pointing out how inventories and backlogs spike and plummet erratically. The distributor on today’s last-place team went from a backlog of 70 cases to an inventory of 191 in three weeks.

One thing to learn from the Beer Game, then, is why many businesses experience boom-and-bust cycles—oil and gas exploration and housing among them. Complex systems produce nonlinear phenomena.

4:15 p.m.

Sterman pounds home a bigger lesson: our psychological habits and limited perspectives often keep us from properly understanding complex systems. To prove it, he asks distributors, wholesalers, and brewers to estimate their consumer demand; their responses are wildly inaccurate.

All too often, Sterman adds, this means we attribute problems to other people rather than to flawed systems. For instance: “I found that some people were kind of slow to take corrective action,” offers one student—who had just played for the winning team, a fact Sterman emphasizes to much hilarity.

It doesn’t make sense for us retailers to blame our teammates—who had imperfect information—for our disappointing scores. “It just cannot be true that, by chance, all the smart people ended up as retailers and all of the people running the factories were dumb,” Sterman says. The Beer Game’s structure makes it hard for certain players to perform well. It’s not the people; it’s the system.

Thus, firing people tends to be a futile management action. “Your role as a leader is to create a system in which everybody can thrive,” he says…

4. Why does the Stock Market go up? – Eugene Ng

A Google Search of “Why does the Stock Market go up?”, and Investopedia gives you up a broad range of factors.

The factors range from the supply and demand of buyers and sellers, to economic indicators, consumer confidence, wars/politics, concerns over inflation / deflation, government fiscal / monetary policy, technological changes, natural disasters or weather events, corporate or government performance data, regulation/deregulation, and the level of trust in the financial sector and legal system, amongst so many others.

But this doesn’t really answer the question, doesn’t it? It only leaves you, more confused, and begging for a better answer…

…The factors listed above are not wrong. Yet, they do not help you figure out why stock prices rise.

In the short-term, stocks will move up and down for a variety of random reasons — all of which does nothing to increase your chances of a positive return.

Thus a better question would be:

“Since the short-term does not really matter as much, why then does the stock market go up over the long-term?”

To get closer to the truth, you need to understand the components which drive the returns on your stock investment.

The Total Shareholder Return (TSR) from holding common publicly-traded stocks can be broken down into three key components: (1) growth in Earnings per Share (EPS), (2) change in the Price-to-Earning (PE) valuation multiples, and (3) earnings from dividends…

…With S&P Global providing us with historical data on the S&P 500’s closing levels, Sales per Share (SPS), Earnings per Share (EPS) and Dividend per Share (DPS), they provide clues on what the growth has been thus far…

…Take 2021 to 2003, the longest period spanning over 18 years (first row, last 5 columns from the right). During this time, the S&P 500 Index more than quadrupled from 1,112 to 4,766, with TSR* growing by ~4.3X (8.2% CAGR).

The contribution of the Earnings per Share (EPS) growth is telling. Earnings per Share (EPS) grew by ~4.1X (8.1% CAGR) from 48.7 to 197.9. Further breaking down that EPS growth, Sales per Share (SPS) grew by ~2.2X (4.5% CAGR) and Net Income Margin Growth (NIM) grew by ~1.8X (3.5% CAGR).

Thus the growth in earnings (EPS) accounted for the majority (~95%) of the TSR* growth, with growth in sales/revenues (SPS) and improvement in net income profit margins (NIM) accounting for ~52% and ~43% of TSR* growth respectively…

…Given what we have laid out so far, you, you should not be surprised to learn that over the long-term, it is earnings growth, supported by revenue and profit growth, that drives the stock market higher, and to a much lesser extent, valuation multiples.

5. Pioneer Helped Turn Her Family Store Into Japan’s Biggest Retailer – Chieko Tsuneoka

First her father died young, then her mother, then her older sister. At 23, Chizuko Okada inherited the job of running her family’s clothing store in Mie prefecture, Japan.

It was 1939, and war with America was just around the corner. Few could have foreseen that the little business would develop into Japan’s largest retailer by sales—or that a woman would be its driving force.

By the time Chizuko Kojima—her married name—died on May 20 of old age at 106, the company now known as Aeon Co. had thousands of stores around Japan and the rest of Asia and annual revenue equivalent to $64 billion…

…Ms. Kojima was born on March 3, 1916, as the second daughter of the Okada family, which had run a fabric and kimono store since 1758 in Mie prefecture, just west of Nagoya in central Japan.

Chizuko’s father, Soichiro Okada, modernized the business but died of heart disease in 1927 at age 43. Then Japan was hit by the Great Depression, which caused bankruptcies and joblessness.

In a 2003 book, Chizuko wrote that she believed it was necessary to be ready for such cataclysms by studying history. The hard times deprived her of a chance to pursue higher education in Tokyo.

After taking over the family business, Chizuko managed to keep it going during World War II until a U.S. bombing raid destroyed much of their home city of Yokkaichi in June 1945, including the Okada store’s stock.

At the time, customers held coupons similar to gift certificates entitling them to store goods. The store no longer had anything to offer, but Chizuko posted notices throughout the city saying her shop would give cash in exchange for the coupons, recalled her younger brother, Takuya, in a 2005 autobiography. It was a way of maintaining customers’ loyalty that would pay ample dividends in years to come.

Chizuko loved studying and during the war, she read a book about Germany’s inflation after it lost World War I. When Japan surrendered in World War II in August 1945, she predicted the same would happen. She gathered her cash and bank loans and bought as much merchandise as possible, reopening the shop in March 1946, ahead of an inflationary surge that hurt other businesses.

“All the merchandise flew off the shelves,” Takuya recalled.

Chizuko wrote of the episode, “Through my own experience, I learned the importance of studying and reading records of the past.”…

…In 1959, when the Okada family business still had just two stores, she came back to take charge of personnel and other behind-the-scenes management issues.

That year, Chizuko and Takuya made their first visit to the U.S. and toured the famous Sears, Roebuck and Co. store in Chicago. Takuya wrote that he was impressed by the giant scale of the business. Paging through the thick Sears catalog full of pictures of refrigerators, washing machines, clothing and a myriad of other goods, he imagined the day that Japan, too, would enjoy that kind of affluent life.

Chizuko was impressed by the Sears pension system, thinking it would create a loyal workforce. She introduced one a decade later, as her brother rapidly expanded the retailer through mergers. She also introduced an in-house training organization, today known as the Aeon Business School…

…Chizuko was one of the first managers in Japan who aggressively hired female full-time employees and homemakers as part-timers. She saw that many women worked in the U.S. and believed Japan should follow suit.

By having women at the company, “we were able to bring on board the viewpoint of the customer—how much to sell and at what price,” she said in a television interview when she was 90.

6. Make Haste Slowly – Chris Mayer

I had been reading The Art of Worldly Wisdom: A Pocket Oracle, a book written in 1647 by Baltasar Gracian, who was a witty Jesuit from Spain. His book of 300 aphorisms, with  his commentary on them, has been translated into many languages and has earned the praise of many philosophers ever since.

Arthur Schopenhauer loved it so much that he prepared a German translation himself. Schopenhauer said it was particularly good for young people, as it would give them experience it would otherwise take years to obtain. “To read it through once,” he wrote, “is obviously not enough; it is a book made for constant use.”…

…Anyway, there is a passage where Gracian talks about the motto “festina lente.” This Latin phrase is usually translated as “make haste slowly.” One must be very patient and yet ready to act swiftly. And the fastest way to achieve your goals is sometimes by doing nothing.

The motto was a favorite of the Roman Emperor Augustus. Engravers captured the idea with an emblem of a dolphin wrapped around an anchor, which they stamped on coins. Another emblem captured the same idea with a crab and a butterfly; again marrying this idea of fast and slow.

Festine lente recurs throughout history and has been captured in a variety of images, such as a rabbit coming out of a snail shell. The Medicis chose it as their motto and illustrated it with a sail-backed tortoise.

I thought the idea beautifully captured an important idea in investing that is often counterintuitive: to get where you want to go the fastest often means acting very slowly if at all…

…It does seem incredibly counterintuitive to say, “No, you shouldn’t  try to sell before a recession.” Or: “No, you shouldn’t ‘reposition’ your portfolio based on recent events.”  Don’t these seem like logical things to do?

Not if you want to enjoy the wonderful effects of compounding capital over long periods of time. The main problem with trying to do the above is they are too hard to do well enough. You have to think about trying to do these things repeatedly over a lifetime of investing. The odds against you are very great. Sure, you may be right sometimes. But you will most certainly sit out stretches of time where you could have earned great returns because you’re afraid of a recession. Odds are you won’t get those “repositionings” right repeatedly either.

7. How Parents’ Trauma Leaves Biological Traces in Children – Rachel Yehuda

After the twin towers of the World Trade Center collapsed on September 11, 2001, in a haze of horror and smoke, clinicians at the Icahn School of Medicine at Mount Sinai in Manhattan offered to check anyone who’d been in the area for exposure to toxins. Among those who came in for evaluation were 187 pregnant women. Many were in shock, and a colleague asked if I could help diagnose and monitor them. They were at risk of developing post-traumatic stress disorder, or PTSD—experiencing flashbacks, nightmares, emotional numbness or other psychiatric symptoms for years afterward. And were the fetuses at risk?

My trauma research team quickly trained health professionals to evaluate and, if needed, treat the women. We monitored them through their pregnancies and beyond. When the babies were born, they were smaller than usual—the first sign that the trauma of the World Trade Center attack had reached the womb. Nine months later we examined 38 women and their infants when they came in for a wellness visit. Psychological evaluations revealed that many of the mothers had developed PTSD. And those with PTSD had unusually low levels of the stress-related hormone cortisol, a feature that researchers were coming to associate with the disorder.

Surprisingly and disturbingly, the saliva of the nine-month-old babies of the women with PTSD also showed low cortisol. The effect was most prominent in babies whose mothers had been in their third trimester on that fateful day. Just a year earlier a team I led had reported low cortisol levels in adult children of Holocaust survivors, but we’d assumed that it had something to do with being raised by parents who were suffering from the long-term emotional consequences of severe trauma. Now it looked like trauma leaves a trace in offspring even before they are born.

In the decades since, research by my group and others has confirmed that adverse experiences may influence the next generation through multiple pathways. The most apparent route runs through parental behavior, but influences during gestation and even changes in eggs and sperm may also play a role. And all these channels seem to involve epigenetics: alterations in the way that genes function. Epigenetics potentially explains why effects of trauma may endure long after the immediate threat is gone, and it is also implicated in the diverse pathways by which trauma is transmitted to future generations.

The implications of these findings may seem dire, suggesting that parental trauma predisposes offspring to be vulnerable to mental health conditions. But there is some evidence that the epigenetic response may serve as an adaptation that might help the children of traumatized parents cope with similar adversities. Or could both possible outcomes be true?..

…It is tempting to interpret epigenetic inheritance as a story of how trauma results in permanent damage. Epigenetic influences might nonetheless represent the body’s attempts to prepare offspring for challenges similar to those encountered by their parents. As circumstances change, however, the benefits conferred by such alterations may wane or even result in the emergence of novel vulnerabilities. Thus, the survival advantage of this form of intergenerational transmission depends in large part on the environment encountered by the offspring themselves.

Moreover, some of these stress-related and intergenerational changes may be reversible. Several years ago we discovered that combat veterans with PTSD who benefited from cognitive-behavioral psychotherapy showed treatment-induced changes in FKBP5 methylation. The finding confirmed that healing is also reflected in epigenetic change. And Dias and Ressler reconditioned their mice to lose their fear of cherry blossoms; the offspring conceived after this “treatment” did not have the cherry blossom epigenetic alteration, nor did they fear the scent. Preliminary as they are, such findings represent an important frontier in psychiatry and may suggest new avenues for treatment.

The hope is that as we learn more about the ways catastrophic experiences have shaped both those who lived through those horrors and their descendants, we will become better equipped to deal with dangers now and in the future, facing them with resolution and resilience.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Alphabet (parent of Google) and Wix. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 26 June 2022:

1. Josh Wolfe, Chris Power – Factories of the Future – Patrick O’Shaughnessy, Josh Wolfe, and Chris Power

[00:02:39] Patrick: Chris and Josh, this is going to be a totally different conversation about an area that I don’t think I’ve ever explored before, very keyed in on a certain kind of manufacturing. I’m sure we’ll hit bigger themes of onshoring of manufacturing in just the next generation of this part of the economy. We’ll spend a lot of time around precision parts, what Hadrian’s doing, why Lux is interested in this area, what Chris, you and your team, are building. To set the stage, Chris, it would be great if you could, as you did for me on the phone recently, give an overview of the recent past and what has happened in this world. It’s become a topic that everyone’s talking about a little bit, but probably doesn’t really fully understand the recent intermediate past of manufacturing, where it happens, why it’s happened that way. So a little bit of a history lesson would be a great place to start to frame our conversation.

[00:03:23] Chris: For advanced manufacturing, in general, which I describe as space, defense, semiconductor, eVTOL, energy, medical devices, basically everything in the Jetson’s flying car, future, all has to be domestically manufactured because of ALTAI requirements. It’s super high precision components. And basically, 80% of the manufacturing parts for those industries flows through a high precision network of machine shops. There’s 3,000 or 4,000 of them. Average size is 10 to 12 million in revenue. In aggregate, they do 40-50 billion in revenue, but it’s incredibly fragmented, super low NPS. It’s the most perfect Keith Rabois fragmented, low NPS, vertically integrated structure you could ever possibly think of. Historically, what happened is this was built off the defense primes needing a bunch of suppliers. All these machine shops got built in the first Space Race or the Cold War. They were businesses that got started 30 years ago by 30 year olds. And now they are 30-year old businesses run by 60 year olds. What’s happened in the last five years is there’s not a lot of slack in the system. And generally, a machine shop might be making some semiconductor parts, some parts for Boeing, and then some parts for Raytheon, for example. In the last five years, because of the boom in commercial space, which has been largely driven by lowered launch costs, the success of companies like SpaceX and Anduril, and then investors like Josh have been putting money into satellite companies, rocket companies, the whole thing. If the top level, you’ve got a bunch of net new spend in high precision components from commercial space and companies like Anduril that are flooding the same supply chain. That’s big problem number one.

And what you’re seeing for those customers is, “Hey, I’m trying to ship a satellite really quickly. I’m getting parts in 6 to 10 weeks. That’s insane. Because I’ve got an aerospace engineer sitting around for another part, wasting time, when I’m trying to get a launch up. I’m trying to get my startup goals.” So all these new entrants to the market are going way, way faster than your traditional primes. Now, that’s putting speed pressure on the supply chain. And basically, you’ve got this thing where customers want fast supply chain, huge opportunity to build a business meeting that need, with a bunch of net new spend in the supply chain. That’s phase one is Hadrian builds a better mouse trap for new space and new defense. The second phase, which is really scary for the country though, is all of those 60 year olds are going to retire in the next 5 to 10 years at an increasing rate. And 90% of them, historically, when they do retire, don’t transition to private equity, or sell or transition the business to a son or a daughter. They sunset the business, lock the door, sell a machine, and throw away the keys. There’s two bits that are really dangerous for the country about that. The first one is just purely capacity. In the decade where we’re trying to butt heads with the CCP and win Space Race 2, the capacity that feeds rocket satellite, drone companies is going to fall through the floor because of this capacity issue. They’re retiring, so you’ve got this huge supply and demand imbalance in the worst possible decade that that could be happening. And on top of that, it’s not as simple as, say, a Raytheon going, “Hey, Patrick’s Machine Shop, you’re retiring. Let’s take all the digital files that tell someone how to make those parts, and give it to another machine shop.” Most of them have been made for 20 years. There’s no CAD file. The drawing is in someone’s desk drawer. And we’ve just seen this where we shipped something like a third of all our Stinger and Javelin missiles to the Ukraine. This is on the defense side, but this happens across space semiconductor really. So we shipped all over to the Ukraine. The Biden administration went to Raytheon and said, “Hey, we need more Stingers and Javelins.

And then Raytheon came back and said, “Well, apart from the fact that supply chain’s super bottlenecked and we can’t ramp up production, we just don’t know how to make any of the parts anymore. And it might take a couple of years to figure it out.” So it’s a complete disaster, both on net new spends in secular growth and decline. But what people don’t realize is it’s not as simple as, “Hey, let’s raise your semiconductor. Let’s throw a 50 billion into an Intel plant in Arizona.” Because in the ’70s and ’80s, when we outsourced advanced manufacturing, what we lost was not just capacity or capability, it was the talent and the people. And what people don’t understand about manufacturing, it’s like software engineering. To get AI researchers, you have to have a base of backend software engineers. You’ve got a million software engineers, and it breeds the best. It breeds the best. And all of a sudden, you’ve got some top tier people in deep cloning and all that other stuff. It’s the same in manufacturing. You can’t really skip these training levels. So what we lost was not the knowhow to do a specific part, but the talent base that can produce better and better people that can work on things like semiconductors or advanced manufacturing. The slack in the system is not simply a capital problem. It’s this talent based problem. You can solve some of that by trying to grab some people from Taiwan, people who really know this, and rebuild all these industries. But it’s much, much slower than people think it is because it’s not as simple as turning a capital key, buying some machines, and ramping up production capacity. It’s incredibly difficult to do. It’s a huge commercial opportunity, but it’s incredibly important that we get this right for the country because space is basically a defense domain. Peace through strength is a huge deal. We’ve created this period of peace with Pax Americana. And I think in the next couple of years, maybe in the next 18 months, we’re going to really see that a lot of that is risked, and there’s going to be a huge wake up call when the average American consumer not just can’t buy an iPhone for less than $4,000, possibly can’t buy one at all because of all this global shifting of the advanced manufacturing supply chain…

...[00:12:57] Patrick: I actually just did one of these with Brian from Anduril. And it was really interesting to dive into the nature of the pieces of what they’re building and their goal for speed, simplicity, modularity the philosophy of how these things are built, whether it’s Ghost or whatever the product is at Anduril, is it’s very different from a Predator drone or something that Lockheed or Northrop would put out over the course of a decade. I’m curious, Chris, how much you think in the success case for Hadrian, where it’s everything you dreamed of and more, 10 years from now, how its existence changes the nature of the things that get built? What will this new manufacturing capability, just like Stripe and Twilio, people build stuff that they couldn’t have dreamed of before because they were able to go so fast with this new tooling, how do you think about that, Chris, in terms of what this might lead to? That even though there’s amazing things happening at SpaceX and everywhere else, it’s on the back of these 3,000 mom-and-pops. What will be different in the success case for Hadrian, for the people at the top of the chain?

[00:13:53] Chris: I think if you think about software engineering 10 years ago, maybe to start a SaaS company, it costs a million dollars, and you were spending more than 50% of your time on activity like running a server farm or building payments that every single software company had to deal with. When you see these platform infrastructure companies like AWS come out, or Stripe come out, or Twilio, you get to really interesting dynamics. One of which is the cost to start a company in this space goes through the floor. So now with all the tooling, you can start a software company for a couple hundred dollars. Secondly, the number of companies that get started because of that tooling goes through the roof. And then the third thing is the speed at which those companies can iterate, basically turn engineering time into a good product that the market wants, goes through the roof because their iteration cycle goes through the roof.

If we get this right, we should be able to drive three things. One is that existing companies can iterate on products in order of magnitude faster, which means that at the product layer, you just get better products. You’re not doing a year long cycle for a satellite. You’re doing a two month cycle for a satellite. As you’re getting feedback from the customer, your designs can change way, way faster. Secondly, by having Hadrian as a platform, we should be able to dramatically lower the cost of starting advanced manufacturing companies, which will drive a Cambrian explosion in both this evolutionary, who’s winning in the marketplace to build a satellite company or a drone company. The raw number of these companies that start will go through the roof. And that would be success for me.

[00:15:19] Josh: One of the things you just said, which I think is really interesting, there’s this old thought experiment, which was actually manifest in a physical experiment, where you took two different classrooms of people that were making some sort of pottery. They had a very specific end state of a pot that they had to make. And one was told, “Spend two hours or an hour or whatever it was making the pot as perfect as you can.” And the other was told, “Make as many pots as you can.” The latter, which was rapidly iterating, and trial and error, and trial and error, ended up making the more perfect pot. So that idea, which I think applies to industry, is if you make something and then you’re waiting forever to test it in the real world versus being able to rapidly iterate. The latter example is something that Hadrian’s going to enable, that in turn then, lets many more startups flourish for less capital. We can do more experiments, fund more companies. They can fail fast. Or they can come up with a product that is superior and competitive, and then build a platform from there…

…[00:22:50] Patrick: Chris, can you help us understand, going all the way to the beginning of the supply chain, the rare earth or base metal component of this process? Because I don’t think people have probably thought too much about is this aluminum, is it steel, is it titanium, is it something else? What’s the 101 on the actual raw materials that are important in this process? Because out of nowhere, after a decade of silence, the commodity world has come alive. There’s issues shipping, there’s issues sourcing, there’s issues in pricing, there’s inflation. This becomes a really important thing really quickly. So give us a little tutorial on what are the important metals that go into all of these shops as raw material and anything that you think we should know about the nature of that today?

[00:23:30] Chris: Basically there are four main alloys that all space defense semiconductor satellite companies use. Aluminum, 6061, 7070, steel variants, so 306, 316, 30X, titanium, and then Inconel variants. There’s a ton of aluminum on satellites. There’s slightly less aluminum on rockets. And then on rockets, you start to get into steels and harder metals like titanium and inconel because the closer you get to the engine, the hotter it is, so you need material that can withstand heat. And then it’s the same thing for the defense side. So if you look at a fighter jet, there’s a bunch of structural aluminum, there’s a bunch of structural titanium, because it’s incredibly lightweight, and then the engine is incredibly hard, heat withstanding materials like inconel. Those are the input materials to the parts. So let’s talk about that. I mean, let’s talk about the parts that are on the machines that we run in our factory, because that’s a little bit scarier. In a sense, I think the aluminum price over the last year, it’s come down a little bit now, but I think it doubled. That was a supply chain shock from the inputs, but then the mills themselves in America had a labor shortage. There was just lack of supply, so the price went up. The parts that went on those satellites during that nine month period, the machine shop can’t absorb them, so it gets passed on, so the satellites are now 30% more expensive. And if you look at where those materials get sourced from, we have a pretty good supply of aluminum in the United States. 90% of the titanium in the world comes from Russia and the Ukraine. A bunch of aluminum and steel comes from Europe as well. And actually, if you look back to the Cold War, our spy planes were made out of mostly titanium. Skunkworks had to sneak titanium out of Russia to be able to make spy planes.

This is why I think a lot of the hand waving around sanctions is ridiculous, because if you’re in an adversarial position and you say, “Hey, we’re doing all these sanctions,” and then there’s 50 exclusions because you’re kidding yourself about the fact that this guy’s the only one with a titanium. It’s just ridiculous. That is a real problem both on lack of production, but also availability of supply to American companies because a lot of that is offshore, which makes me crazy, because the State Department years ago should have been going to Latin America and Africa and getting supply of all this stuff and partnering with these countries and raising them up. Whereas China through Belt and Road has secured a lot of this global supply because they’ve got Russia locked up with the whole energy pipeline thing. They’ve got Africa locked up. So it’s a real huge challenge. For rare earth materials, which are more things that go into batteries, chips, that sort of stuff, they’re not on the parts that we’re producing, but our machines obviously have a ton of chips in them. And then every single satellite or rocket has a bunch of chips or circuit boards in them. So that’s a huge problem, and that’s way more strategic because obviously 70% of the world’s chips come from Taiwan, most of which is TSMC. And then the other thing is the rare earth minerals like lithium or cobalt are largely Latin American, but the Latin American mines are much, much less developed. That’s a huge challenge as well…

…[00:27:54] Patrick: Chris, if we zoom all the way back to the unit here of the mom and pop machine shop, what are the key set of jobs being done by one of those given shops? You mentioned the nature of them. It’s the 60 year old soon to retire making 10 or $12 million revenue per shop or something like this. What are the key components that are inside each of those shops that you want to lift out as core functions or jobs to be done and then start innovating on inside of a Hadrian factory?

[00:28:20] Chris: There’s three big chunks. One is the digital side of manufacturing, which is taking a customer PDF print and doing a bunch of creative geometry work to get that into machine code that tells the machine how to cut the part. That’s one big chunk, and that’s very software automation heavy. The second chunk is running the machine itself, which again is less of a robotics problem. It’s more of a software engineering problem. So what a master machinist does on the control is a lot of manipulating code on the fly as they respond to slight differences in the cutting tools, slight differences in the raw material. That’s a big operations software problem. And then the third layer is general logistics. So you’ve got unpredictable cycle times of each operation in the factory, you’ve got huge variances in how long something takes to inspect from one part to the other. And then you’ve got a lot of customer requirements that are incredibly variable for each purchase order that comes through. And as an example, this is a very simple example, but can create a lot of operational noise if you don’t get it really right at the top end of the funnel is laser marking a part. So producing all these space components and then at some point an engineer’s going to want to test it, often there’s a call out on the print that says, “Hey, engrave this part with a serial number, the purchase order number, or the print revision that the aerospace engineer said, ‘This is my part number.'”

You might think that’s easy, but there are about 10 aerospace known specifications of the depth of the laser engraving, how big or small it has to be, and what the function of that is. In a regular machine shop, you might have a guy running a laser machine that’s staring at a PDF print that might remember the specification. So a lot of it is that documentation of that engineering knowledge and then systemizing it so that the whole thing flows smoothly, yet haven’t got a bunch of random art going on. Even load balancing that is an insane challenge because you might have all these machines set up for making the part, inspecting the part, cleaning the part. But if you get one of those throughput messages wrong, where you’ve got, say, 10 jobs running through a facility at once, but they all happen to hit the quality inspection station at the exact same time, all of a sudden you’ve got a bottleneck and all the jobs are late. Before you tell the customer, “Hey, we can get this in two weeks,” having that load balancing like a data center with foreknowledge of where the capacity bottlenecks might be in three weeks so you can make good judgment calls on what you’re promising versus what you’re delivering is a huge data science and operational excellence challenge…

…[00:32:09] Patrick: Josh, how do you prosecute diligence for something like this as an investor. Hearing all that, I’m going to ask more follow up questions in the minute on the unit of the machine and the areas of innovation and the machinists, et cetera. But when you’re facing something like this early on and it is factory one or factory one’s still a glint in your eye, how do you do diligence on someone’s ability or a team’s ability to execute something like this?

[00:32:33] Josh: I’m probably going to get myself in trouble with this. First you make an investment in another company that fails.

[00:32:38] Patrick: Good start.

[00:32:39] Josh: And that’s what we did. We and Founders Fund actually were co investors in a company that did not work. Part of that was narrow focus, part of that was team and structure. There was something proverbially different with Chris that their light shone brighter. As you can hear him talk, not only understanding the macro, but if you have a customer like Brian, Andrew, or Palmer, they want to anodize titanium and aluminum and parts that not only have electric chromatic coatings that strengthen and provide performance, but look really cool. They’re super high demanding, yeah. So Chris’s understanding of the macro to the micro is something that was inspiring, but we were still making a bet without any existence proof of why we were basically going to make a very similar investment as we had before, but this time was different, those dangerous words. And it truly came down to his vision of understanding industry structure, his vision of seeing the technological pieces that could be put together. Notably, he told us why we lost money in our last investment, which was super valuable. So he diligenced our failure to diligence properly our prior investment. And part of that was you don’t want to automate everything.

He’s like, “You don’t want a 100% automation. You need humans in the loop in some of these aspects.” Maybe you want 80/20 or 70/30, but you need people that are there able to very quickly look at the geometry of a part or design, make a human decision, let the computer do it. A lot of it really came down to Chris understanding the macro, the micro of individual parts, the flow, where bottlenecks were. And then I think this is really important, you really have two different cultures. You have a machining culture, which is very blue collar in many cases. It is people working with their hands and really deep narrow specialists. And then you have this coding software culture, which is almost the antithesis of that. It would be good to actually hear from Chris, how do you think about those two people speaking very different languages, sometimes growing up and going to very different schools, actually being teammates and working with each other, because that answer that we got from him was super confidence inspiring.

[00:34:29] Chris: The difficult thing, going back to previous failures in his space, was both in private equity and software engineering trying to automate manufacturing, the previous approaches have been very egotistical in the sense of, “We don’t need any industry knowledge.” Either I’m a guy with a spreadsheet and I know how to do an IRR calculation. Operations doesn’t generate profit, finance does. So the attitude towards machinists or manufacturing people is very downwards looking. I saw the same arrogance in Silicon Valley, which was, “Let’s not try and work with the best in the industry to automate this in the right way.” It’s, “Let’s grab 30 PhDs and don’t hire a machinist until employee number 28 and just try and figure it out ourselves,” which for me is just this very coastal elite looks down on flyover state dynamic. What I recognized was in machining, all of the problems have been solved by people, the knowledge is in a bunch of people’s brains. It’s not like we’re inventing a new algorithm for machining. If we did nothing, but just find all the right answers and get them into software and process, we would win immediately. To do that you have to create a culture where people feel comfortable working with a software engineer and machinist and an operations person all in one conversation and setting the standard that just because you’ve got a maths degree from Yale and this guy didn’t graduate high school, setting that culture so they work collaboratively and there’s no finger pointing or whatever and everyone’s pulling in the same direction is really, really important. This was one of the most important things that I worked on, and it’s a combination of making sure, even really simple things like no matter of whether you’re a machinist or a software engineer, your equity that you get in Hadrian is the same based on your rank. The pay is the same. All this other stuff is super, super, super important.

Really finding the people from industry that want to share their knowledge and want to train people, is an incredibly rare thing. So we’re incredibly lucky to have the 20 or 30 people in industry that actually want to share their knowledge and understand that we’re all pulling in the same direction. And that is a really unique thing. To give you an example of how scary this is, even at the most innovative space companies, to train someone on how to inspect a part is usually this thing of like, “Hey, we’ve got all these amazing people that want to work in manufacturing but I’m on X number of dollars per hour, and I don’t want to share my knowledge because my job is at risk.” Even something as simple as training new entry to the workforce is incredibly hard because of this protectionism. People ask me what the secret sauce is, and I think investors think we invented this new technology and that’s the core of the company. The core of the company is 50 people soon to be 80 and 100 pulling in the same direction. Understanding that what we’re building is a culture of, here’s a problem let’s solve it and no matter where the solution is coming from, implement it and work together. That’s the core of what we’re building which long term is going to be a huge, huge advantage. Because if we get Hadrian right, there’s no reason why we can’t take the same team and go solve tube bending or raw material or whatever it happens to be.

[00:37:14] Patrick: You described this the first time we talked as the PhD arrogance trap, which I really liked as a phrase. Thinking you can just solve every single problem immediately with technology. Interesting to hear about the inner relationship between the two teams or the two modalities. When it comes to the individual machine and the machinist working together inside of a Hadrian factory, again maybe starting to squint a little bit and look out 2, 3, 4, 5 years, what do you think the innovation zones are on the machine side specifically? In what ways will a Hadrian machine be better five years from now than it is today? Because it sounds like there hasn’t really been much innovation on the machines themselves in these mom and pop shops?

[00:37:51] Chris: Actually, I think that’s slightly incorrect. But I would say that we are not really innovating on the machines themselves. And that’s part of the trick here is we are buying everything mostly off the shelf and then doing really tight software integrations to override the core software that lives on these machines to make them run better. But we’re not doing mechatronics and upgrading the machines themselves. Building your own machines while trying to scale a factory is like two impossible tasks. What we’re really doing is going, “Hey, these machines have APIs that control everything about them.” No one’s ever used an API for this machine ever before, and that’s really where the technology curve is honestly. Even down to simple things like, you’re meant to be able to run a machine overnight without it stopping itself. There’s actually 20 or 30 reasons why a machine would stop itself running. A Tool breaks, something goes wrong in the controller, it’s like a literal software bug. A lot of our automation is actually building the robustness into these vendor machines so that they self-correct overnight so we can get the throughput and the efficiency.

One of the reasons why you have a second shift at a machine shop, which is incredibly inefficient, is because someone’s hanging around waiting for the machine to error out and they know how to clear the error and get it going again. Which sounds insane, but that’s honestly 70% to 80% of the problem. It’s hilarious having people from industry where we come back in the morning and the machines run itself overnight and there’s 10 good parts sitting there. And people are like, “Wow, this is amazing.” I’m like, “What do you mean? These machines are designed to run overnight?” And they’re like, “No, well, it almost never happens in reality.” The reason is, because over the last 5 to 10 years, the amount of software that’s in these machines has grown exponentially, but no customer of the machines has ever been able to take advantage of it because, what machinists knows how to write software? What machine shop can afford to pour a software engineer into the problem? Or even if they had a software engineer, have them spent three months of R&D on figuring all this stuff out versus just firefighting operations because they’re trying to deliver for a customer. So that’s more what’s going on than us innovating on the hardware side.

[00:39:44] Patrick: And the innovation, the units of innovation themselves driven by software, is better-cheaper-faster the right way to think about what you hope to accomplish by starting to tune the dials using software?

[00:39:55] Chris: Definitely on the front end of the factory in the digital manufacturing CAD and CAM programming space, 100%. Because you just want to turn a 20 hour process into a 2 hour process. It’s possible. It should be done. We’re chipping away at the marble and will get there. For the factory, I actually think that simplification and robustness are the two most important things, because in manufacturing, complexity and lack of robustness are what drives costs. You’re actually better off having a system that works every single time that’s simple. That gives you two things. One is, there’s less errors so there’s not a bunch of people firefighting. And because it’s simple, you can train many, many more people into that system. Getting rid of a lot of the complexity of making everything truly error proof is a lot of the innovation there, which seems counterintuitive. But in the real world, you want as little errors as humanly possible versus trying to dial up the efficiency on something so high that it breaks one in even every 10 times and all of a sudden you’ve got three or four people standing around figuring out how to solve the problem. That’s really, really what’s important there.

Now, what you get from that is speed. So speed is not necessarily like, cut the part faster. It’s at every handover point, don’t have to go back in the step, go back in the step or have this station hanging around waiting for information because you’ve got errors. So the whole factory speed is optimized by having each of these individual pieces incredibly robust. For the customer layer, they get speed. What’s great about speed is everyone wants it, so we also get pricing power. As we hit the robustness layer, we have margin efficiency growth because people are hitting things every single time cleanly versus running around scrambling like, where’s this bit of paper, where’s this tool? Now on the customer layer because we are reliable and fast, we have enormous pricing power. It’s this interesting dynamic about manufacturing where, if you just focus on robustness and cleanness of the process, you kind of generate margin improvement automatically and therefore you get pricing power because you’re fast and you reliable.

2. Quotes from Seth Klarman Interview – The Transcript and Seth Klarman

2. The impact of rising rates: 

“That is going to test financial institutions who’s been writing derivatives they shouldn’t write, who’s been stepping out to take greater risks in their portfolio because if you can’t make it in bonds, people try to make it somewhere else.”

3. Watch out for anchoring

“After you buy something you paid for, it doesn’t matter. People cling to the idea that at least they should get their money back; maybe there is bad news, and you should sell before it goes lower; maybe put it into something else where you get your money back, but people prefer to make it back where they lost it. People anchor numbers in their heads, and they hold on to them. They have a way of remembering what happened relatively recently. If you recently had a pandemic, you over-worry about the next pandemic even though they don’t happen that often. I was certainly guilty of that after 9/11 myself. It seemed obvious that we’d get hit again, and then we didn’t for a long time.

4. Best business book: 

“We should not expect people to be rational all the time. Daniel Kahneman does a beautiful job in Thinking Fast and Slow. It is in many ways the best business book, the best investing book ever written even though it’s not ostensibly about business or investing because it tells us about ourselves”…

...6. On finding edge: 

“There are lots of ways to develop edge as an investor. One of the ways is deep fundamental knowledge. I have total respect for people who dig incredibly deep in an area where they’re doctors and medical researchers. They study biotechs and that’s formidable. No one should underestimate that power, but that’s not the only kind of inefficiency, as the inefficiency might be informational. Two things happen in markets; right markets are inefficient partly because of human nature, as I mentioned; greed and fear. People get greedy and panic; in some cases, the panic is legitimate. “Oh crap, I leveraged my portfolio, and I’m getting a margin call.” or “I have short-term clients, and they can redeem, and I’m getting redeemed, and I have to sell whether I like it or not.” There are other constraints on investors that also create inefficiencies.

Once in a while, we get a call from someone with one asset in their private equity fund who want to raise the next fund. They want to book a gain on that asset. And so, call it the last asset phenomenon. People literally will sell that more urgently, and maybe they’ll favor getting it done over the exact price they get because they want to raise their next fund and move on. They want to book a game and get paid. We live in an imperfect world, and their clients should probably not love that, but maybe their clients would love it. The manager has a lot of things to balance, so that’s just one little example. When a bond gets downgraded, there’s always an immediate rush to the exits by the investment-grade holders. A bond gets downgraded to junk, say when the bond goes literally from BBB to BB. Many bonds have to get sold; some are probably sold in advance. It’s good to know what a company does, its operations, and its worth. It’s also interesting to know that there’s a very large seller, and the bonds are 20 points lower. With essentially no change in any information, just the rating of a 26 year old at moody’s. So those are the kinds of things that can trigger our interest then we do fundamental work”…

…10. On making mistakes:

“Today, there’s not so much mean reversion. Things may not be mean-reverting because of technological disruption, so I think investors have had to raise their game massively in the last several decades, and I’m not done raising it. I probably haven’t raised it as high as it needs to be. It is a great time to be knowledgeable about technology; it was a great time if you could figure out what Amazon was up to. For a value investor, it looked hopelessly risky but for a tech investor, maybe with the right insight into the value of platforms and the value of winner take all business models, that would have been a good thing to have that I didn’t have. I pat myself on the back and say, okay, Seth, you were a schmuck twenty years ago and ten years ago for not figuring it out, but you were smart to figure it out five years ago. That’s all an investor can do; be intellectually honest, be self-critical we’re justified, and keep trying to get better every day. Like Warren Buffett, the best investors study read admit mistakes um always looking to get smarter and wiser because what else can you do as a person.”

3. Capital-Efficient Growth (with Zoom CEO Eric Yuan & Veeva CEO Peter Gassner) – Benjamin Gilbert, David Rosenthal, Eric Yuan, and Peter Gassner

David: Amazing. Eric, could you share your fundraising journey with us?

Eric: Sure. I started the company in 2011. First thing I did, I opened up a Wells Fargo bank account. It’s very easy for me to raise capital that’s why I opened up a bank account. Unfortunately, it took me several months. No VCs wanted to invest in me. Unfortunately, I do not know my brother […] Emergence Capital. Otherwise, life would be much easier. Finally, we targeted some of our friends. It reached $3 million seed funding. That’s how we started.

Here comes […]. I tried to target VC again, again, nobody wanted to invest in us either. We targeted friends and got another $6 million. That’s how we started. It’s very hard.

Ben: Nobody wanted to talk to you at that point because most people assumed video conferencing was either a settled frontier or a race to the bottom. Am I thinking about that right?

Eric: Absolutely right. That’s the thing. Everyone mentioned, Eric, you are crazy. The world has known you to have another video conference solution. Another VC friend even is a great friend, he told me that, Eric, I have a check for you as long as you do something else. I couldn’t say I did not listen. I was very stubborn. Also, he shared to me a story. Once I was told by a big VC, I do not want to mention the name, for sure, you guys do not like them.

He told me that, Eric, I do not think your […] works. Look at Skype, look at Google Hangout, look at Webex, they’re dominating, right? I debated with him a little bit. I failed. I cannot convince him.

On the way back, I told him myself, I’m going to change my Windows screensaver. Back then I was using a Windows machine. I changed the Windows screensaver—you are wrong. For several years.

Ben: Just to make sure I have my facts straight, I believe you raised a $30 million dollar round led by Emergence and then another $100 million dollar round after that. Similar to Peter, you did not dip into any of that $130 million to build the business. Is that correct?

Eric: For me, actually, I offered $30 million from Emergence Capital. I think we are on the right track. To be honest, actually, we don’t even need to raise a Series D because at the time, with that $30 million, I think the company was completely different again.

David: One thing we wanted to ask is a difference between your two companies. Peter, obviously, once you got to cash flow profitability, which was immediately, basically you never raised another round. Eric, you did make the decision to raise some more capital even after you were generating cash. Peter, you were on Eric’s board when that process happened? Why did you make that decision?

Peter: For Veeva, I didn’t raise more just because I thought I didn’t need it. It’s just that simple. As far as for Eric, when you’re on the board, that’s really Eric’s decision.

Eric: As I mentioned earlier, I offered to raise $30 million from Emergence Capital. At that time, seriously, they had no plan whatsoever to raise another round of capital. The reason why we still wouldn’t move forward to have a Series D is because I thought the economy would go down quite dramatically.

David: This was 2017?

Eric: Sixteen, ’17 timeframe. I was completely wrong…

…Ben: As we were preparing for this interview, our first thought was, if we just had one of you up here and we were interviewing you about capital efficiency, it’d be easy to chalk it up to business model and cash flow cycle. Multimillion-dollar contracts upfront in the case of Veeva, or in Zoom, customers flocking with their credit cards for a self-serve experience. These are two completely different models.

I think one of the things that it illustrated to David and I is capital efficiency is a mindset and culture thing more than a business model thing. I’m curious to hear both of your reactions to that, but also, what are the things that enabled you uniquely, more so than 99% of startups to be so capital efficient?

Peter: I can take that one. I guess I’ve seen a little bit of Zoom and a little bit of Veeva. I would say, probably, it starts with a mindset. Just run a profitable lemonade stand. From my point of view, for me, there’s safety in that. Cash generating business is always going to be valuable to somebody. At some point, a business that’s not cash generating is going to be valuable to nobody. There’s security in the long term. It starts with the mindset. I think Eric shared that.

Then you have to have product excellence, too. That’s something I think Eric and I share. We’re both product people. I think also, we both worked really hard. We work really hard now, especially Eric. Probably in the first five years, I worked really hard. You didn’t see me working really hard, but I saw you working really hard. We worked really hard, we worked really focused. Anything that wasn’t related to the product or the customer was just BS, then just don’t do it.

The first five years, I was not at a conference like this, for example. I was just maniacally focused, and then the market really helps too. That’s something you just have to get lucky on. It was the right timing for Veeva, it was the right timing for Zoom. Maybe if you started Zoom five years earlier or five years later, it would have been hard.

Product excellence, real focus, mindset, and then you have to have some luck in your market. I’m sure there are some things that I could have tried to do or Eric could have tried to do. We might have picked a bad market and then it just wouldn’t work.

We’re outliers and so is Eric. You have to pick something that most people think is going to fail to be an outlier. Otherwise, by definition, you’re picking something that most people think is going to work. A lot of people are picking it, therefore, you’re not an outlier.

Just like Eric, all VCs have any kind of note except for Emergence turned us down. Ours was really simple. Vertical specific software, that’s a small market and it doesn’t work. That’s what they would say. I was encouraged by that because I thought, well, it has an opportunity to be really good because it’s something non-obvious.

David: One thing that I want to double click on that we were talking about beforehand. Yes, you need to be non-obvious, to have a chance of a great outlier outcome, but you also need to be correct. What you both did was not, hey, I’m going to pick some random idea that other people think is crazy.

I know Veeva, as one of your core values, clear and correct target markets that you have written on the wall. What did each of you do ahead of time that led you to really genuinely believe, yes, the world thinks this is crazy, but I really think this is going to work?

Peter: I’ll go first, this is really easy. I talked to three or four potential customers for our first product. They all said, we don’t need that. That’s not interesting. It’s not a good thing to do. But I wasn’t listening to that. I was listening, are they emotionally attached to where they’re getting their product now?

Are they emotionally attached to those people? Do I feel like they’re getting value out of that thing? I could tell in their responses that they weren’t attached and they weren’t getting value. All four customers said it was a bad idea. They’re all customers now, though.

Ben: Let me understand the Peter formula to build a business. Ask a customer if they want your product, they say no. You dig deeper and say, what are you using now? And they say, oh, yeah, because I have a solution for this. But they just don’t love it, so you build for them anyway on the bet that you can be better than their current.

Peter: Yeah, you have to listen to what they feel, not what they say. They would say, yes, we’re very happy with the solution. But then you dig, oh, tell me more. Why is that? What is it that you get out of it? It’s like, uhm, uh, and that’s when you know.

David: That sounds like the video conferencing market circa about 2015, 2016.

Eric: For me, it’s very straightforward. Of course, I was an original founding team member of Webex. Two years before I started the company, I knew that Webex really sucks.

David: Did you try to tell Cisco that?

Eric: I told my team. I do not dare to tell others. Anyway, Skype is also not reliable. Google has done no work. Every day, I spent a lot of time talking to every customer. I know if I can build a better solution, I think at least I can survive.

I never thought that everybody was going to standardize on the Zoom platform. At least I know for sure, if customers do not like something, if you can do something better, you have a chance.

Ben: Eric, did you think from the outset that you were trying to build Zoom as a big company, or did you just think that you wanted to build a profitable company to survive and then you would sort of see where it went from there?

Eric: I think two things. First of all, at that time, my passion was very straightforward because Webex is more like my baby. I feel like I worked so hard for so many years, I let a customer down. I really wanted to fix that problem, but Cisco doesn’t want me to start over. I had no choice but to leave to build Zoom. This is the number one reason.

After I started a company, I realized, wow, it’s so hard to raise capital. By the way, the money that the VC gives to you, don’t think that’s the money. That’s trust. Every dollar matters. That’s why every day I was thinking about how to survive, how to survive, how to survive. Even today, seriously. I still think about, I wake up at night, how to survive?…

…David: Can you also tell us the story of lending your first big customer, which I believe is probably the deal that really made the business?

Peter: There was a set. There was the first guy who just peeked at his IT team and then worked up to the next size deal and the next size deal. It was always a step function. The first multimillion-dollar annual deals were a big customer of Pfizer. It was just hand-to-hand combat. There was a partner at the time. Actually, salesforce.com at the time said, I’ll send a note that Veeva will never win this deal. I replied back, I said, we will win this deal.

Ben: They sent it to you during the Bake Off?

Peter: Yeah, because they didn’t want to even come into the meeting with us. They were like, oh, we’re going to go with this other system integrator or something like that. I sent an email back and said, we will win this deal. Why? Because we have better people that will work harder. We’re Pfizer’s only shot at greatness and I think they want to shoot for greatness.

I remember there was this big meeting with Pfizer. There was a guy in there in charge of it. We had a certain amount of people in the meeting and the guy stood up for Pfizer. He said, we have more people in this meeting room than you have in your company. Why should we buy anything from you? I just said the same thing. We’re your only shot. We’re going to make something great and we have the best people. It seems simple to me. Then we got lucky.

I remember after winning it, thinking, oh my God, now what? Now, how are we going to make them successful? The whole company got a bonus when that customer was live and happy, which didn’t have a formulaic metric. It was based on interviews.

Ben: Did you use the invoice from that customer to then go fund product development?

Peter: Yeah. I thought, oh, we’ve just raised a $3 million round of capital. It didn’t cost us any dilution. The check came in. That’s exactly what happened…

…David: Eric, for you. I’m curious, maybe you can talk to us both in the beginning days and then also now at Zoom, how do you think about pricing and account strategy?

Eric: Our case is a little bit different. Ideally, when you start a SaaS company, either focus on vertical market or focus on departments. That’s probably the best business model. Unfortunately, we started from building a horizontal collaboration solution. It’s really hard because a lot of other competitors are already there.

David: Including free competitors.

Eric: Exactly, and a lot of free solutions. Our strategy is more like opening up a new restaurant business. You have better service, a better price, and better food. That’s pretty much it, even today.

I want to make sure our products are better than our competitors. I make sure when it comes to pricing, also better. I also make sure to offer better service. You look at any time, our product is always, always a better price across the board for any product compared to any competitors.

Ben: Life is about trade-offs. If you’re telling a customer, oh, we’re better, faster, and cheaper, what has to give? Is it something organizationally?

Eric: Efficiency. Let’s say customers, they are probably going to spend a lot of money on marketing. What can we do to leverage the network effects? If they hire 100 sales reps, what can we do to have 50 sales reps who can deliver the same value? That’s why it’s very important to have internal efficiency.

David: Which is so funny. That efficiency translates to capital efficiency, which translates to operational margins, which translates to cash flow, which is the whole point.

Eric: Totally. Yeah, it gives you more flexibility.

Peter: I would say the key also is just product excellence. That comes from the core set of engineers you hired, I think. You were especially very focused in the early days, right?

Eric: Totally.

Peter: You were not thinking about something else. You were thinking about video conferencing. I would say that’s why I got to know Eric. I got to know Eric, I thought, that’s a pretty focused guy and that his product is good. And then I tried out his product. I’m like, oh, this is really good. I want to join his board. I think that product excellence can make you more efficient, your sales cycles more efficient. Everything is better. Your product was twice as good as Webex, right?

Eric: No, 10 times better.

Peter: Ten times better? I guess my point is, if your product was only 20% better, it wouldn’t have been enough. It wouldn’t have mattered.

Eric: You’re so right. That’s why I always like the restaurant analogy. You’re buying a brand new restaurant. If the food doesn’t work, even for free, you don’t know if I’m still going to buy it anymore.

Again, back to Peter’s point. It’s extremely important. Everything starts from one thing, product excellence as a foundation. You can optimize a lot of things. If a product does not work, forget everything else. Just double down, triple down on the product. That’s the number one thing. Peter’s right.

4. 20 rules for investing in Vietnam – Michael Fritzell

Vietnam is following the East Asian playbook of manufacturing export-led growth – just like Japan, South Korea, Taiwan and China before it.

After the Vietnam war ended in 1975, formerly capitalist South Vietnam was taken over by the Communist Party of Vietnam and the country was unified.

The first measure taken by the communists was to nationalise and centralise the entire economy. Around 800,000 Vietnamese fled the country after the war, including Andy’s family.

It only took three years before war broke out again – this time against Cambodia’s Khmer Rouge, led by dictator Pol Pot. That war continued until the late 1980s. So Vietnam was almost in a constant state of war for almost half a century.

By the late 1980s, the country was in disarray. And it was becoming clear that the planned economy was not functioning properly.

The Communist Party introduced a new reform program called Doi Moi to create a “socialist-oriented market economy”. One of the first Doi Moi policies was to permit foreign investment to modernise the economy.

Today, Vietnam is buzzing with activity. The country has more free trade agreements than any other country in Southeast Asia. It’s become the default destination for companies wanting to diversify their manufacturing supply chains out of China. Vietnam is a perfect choice for manufacturing – in close proximity to key component suppliers in Asia and along the key trade route between Asia and the West.

Vietnam’s success is most evident in the country’s exports, which have risen the fastest of any country in Southeast Asia.

This export growth is also showing up in the country’s urbanisation, with young Vietnamese moving to factories to improve their livelihoods. Vietnam’s urbanisation rate is still only 38%, compared to China’s 70% and Japan’s 92%.

Vietnam’s potential is massive. Its GDP per capita is only US$2,800/year, compared to Thailand’s US$7,200 and China’s US$10,500. Manufacturing wages remain competitive, even against countries with worse infrastructure such as the Philippines and Indonesia.

Out of a total population of 97 million, Vietnam now has a middle class of 30 million people. And it’s rising rapidly. Many of those individuals are starting to buy properties, cars, home appliances, electronics and more…

…In addition, Vietnam’s demographics are excellent, with two-thirds of the population below 35 years of age. Vietnam’s working-age population is going to grow for another 15-20 years.

The country is also highly educated. Vietnam’s PISA scores are higher than the equivalent scores in the United States, the United Kingdom and even South Korea, even though its GDP per capita is minuscule in comparison.

5. Tobi Lutke – Embrace the Unexpected – Patrick O’Shaughnessy and Tobi Lutke

[00:02:44] Patrick: Tobi, it is almost exactly two years to the day since we last did this. It was early May in 2020, there was still a ton of uncertainty related to COVID. I guess there still is some extent today, and the world in Shopify and lots of things have changed a tremendous amount. I know certain things haven’t changed too. I’ve been really excited to do an updated version of our conversation and we’ll bounce all over the place, but before we hit go here, we’re having this fascinating conversation around the concept of infrastructure, generally speaking. I think it started with this idea that we might be about to come on stream to a lot of good, useful, new, history books written by people who are really there to see this stuff get built in the digital world. I’d love you to sum up that idea of what your interest is in infrastructure and the way that history is written. Even things like payback on infrastructure and the ways in which we might underestimate it. I think this is a great tone setter for what we’re going to be talking about today.

[00:03:38] Tobi: I’m thrilled to be back. Thanks for having me and those were quite some two years and a lot has happened. I think people are just underestimating the value to society of infrastructure by some incredible factor, because you see these kind of things like the interstate system. How do you imagine this thing would’ve looked if these things wouldn’t have been built? I’m not an atoms person, I’m more like a bytes person. I find that infrastructure, especially with software has this incredibly unreasonable leverage and unreasonable payback period and often we have these conversations about what’s the state of planet earth. What are things truly like? Are things getting better? Are things getting worse? There’s a lot of people sharing excellent opinions on these things.There’s a website. I hope I say this right. I think what happened in 1971, it might be a different year, but something around that time, there’s a collection of charts where once the right year comes around, a lot of numbers sort of disconnect from their previous correlations. I have no idea what happened in that year, but as a student of history and especially of digital history, increasingly I’m thinking about a very, very tangible thing that happened is that just simply most of the value creation in the world has slipped out of the things that is represented in GDPs, where a whole bunch of people built the upper net around this time then we got modern operating systems.

We’ve built a lot of silicon based computers in the nineties, but none of this was reflected anywhere. Dot com happened and everyone tried on the idea like that this tech could be very big and then found some of the ground truth to be wanting, but really sort of early mid 2000s, web 2.0 I think we call it or, at least coinciding with the emergence of that term, I think was the moment where the world of technology said, Hey, we actually know exactly how to provide value for everyone. We know exactly how to deliver services and goods and things over the internet.And by the way, there’s a lot of tweaks on the intuitions that people develop in the physical world. Physical world is very rivalrous. If you build a bridge in one place, you probably don’t build a bridge somewhere else. At some point in the world of atoms, things become zero sum, limited amount of attention at the very least and then resources as well. The digital word is different. Basically you have Turing machines, you load something on a silicon chip into memory, and then you apply electricity and you get this thing. Infrastructure and internet. I mean, I like to believe Shopify is infrastructure, but there’s public domain libraries. Just pick one, you know, SQLite. It’s like a library, probably none of your listeners have heard about, but you have probably like something to the tune of a hundred SQLite databases on your phone right now.

It’s just file format of the world basically and increasingly runs more and more and more parts on servers as well. It’s just this brilliant open public domain piece that was written by a team and great leadership, incredible conviction, but it’s not software, it’s infrastructure. And now people are using it every day for different things. And no one has to decide if we use SQLite, that means someone else can also have SQLite because all of us just add electricity. What that stores then is like an unbelievable compounding value.Again, in a lot of the ways we look at the world through GDP and other things, it’s impossible to capture the value that’s created here. Everytime someone updates something on GitHub, theoretically, it can be copied infinite amounts of times. These are not new ideas I’m sharing here, obviously. In a way, we’ve talked about this zero marginal cost of software and of course it powers a lot of value in a lot of software companies. I’m starting to believe that we haven’t fully set this idea to its logical conclusion. How much of a change will this cause over the next while?…

…[00:09:44] Patrick: Yeah. It’s amazing how much prevailing market conditions and prices can impact people’s mood. We’ll talk about that a little bit later, sticking with infrastructure though. I wonder if you’ve developed any principles or principle thinking around what makes for better infrastructure or valuable infrastructure to build. And I asked this question from a place of Shopify zone history. When we last talked a lot of the things that if you go to Shopify’s website and see what you can do as a merchant didn’t even exist two years ago. So you’ve obviously had to make choices. We’re going to build this. We’re going to not build this. What do you think about in terms of just base level principles that help you with decision making, for what kind of infrastructure to build that will have the most leverage in the world?

[00:10:23] Tobi: There are some guiding principles in Shopify product that really help us make these decisions. For instance, there’s a very basic sentence, which actually does a lot of work within the company. “Shopify wants to make the important easy and everything else possible.” Probably everyone who listens to this has bought from Shopify stores. You might have not known that it was a Shopify store because they look very, very different. This is powered by a template language I wrote forever ago called Liquid. Basically the merchants can open a text editor and just make their website look however they want, or buy a theme from someone. That’s infrastructure in a way, because here’s something I learned about infrastructure, which might sound very abstract, but maybe it’s useful. If you imagine an hourglass. An hourglass has sort of a narrow waist at some point, maybe a comic book version of an hour glass is like two triangles, inverted pointing at each other. Great infrastructure can be done when you can define what this sort of narrow waist is between the triangles. For instance, let’s use Stripe because it makes this point I think quite well. There’s one triangle on top, which is the internet, and all the engineers, and all the developers. They have a set of desires. They want to accomplish tasks, which involve movement of money. And then there’s a bottom triangle, which is like a world of COBOL code in banks. There’s a lot going on. And a lot of things you need to know, but if you manage to create a thin waist in this case, in the form of an API, now you have an agreement in the middle. This almost acts as a protocol. Here’s the fantastic thing. Once this protocol exists, it actually allows the two triangles to be replaced over time. In the case of something like Shopify, Liquid is again this templating language. People can write it. If you wrote some in 2005, the first time the Shopify went into Beta, it will still work.

Shopify is the Ship of Theseus. Nothing about Shopify is the same. The Liquid part has been rewritten many, many times, everything changed about the triangle below. Everything changed about the triangle above. Most people don’t actually even write Liquid. They actually just use drag and drop editor, which we built on top, which then writes the Liquid for you. The amazing thing is, again, once the protocol has been defined, once the demarcation line has been created, once the narrow risk is defined, then really incredible things can happen because as long as the thing keeps working, that’s in the middle, you can evolve all the pieces. And I think that’s a really, really, really powerful idea for product creation. People encounter this. If you’ve ever queried a database again, you use sequel and that’s just a thin waist system. It’s an agreed upon system, which gets you the data and as long as you keep it simple, if you send something to Microsoft SQL Server or SQLite, you’ll get the answer assuming they have the data. So that idea unlocks, I think, the right approach to internet infrastructure creation, because once these protocols have been defined, teams can go and saying, okay, these sort of made this work with duct tape and regular expressions in terms of Liquid, but let’s build this up properly, scale it out, make it so that people can use this from now on forever.

[00:13:16] Patrick: So someone once explained it to me as the equivalent of an outlet in your wall, that’s become standard that anything you plug into it like electricity flows through it very reliably and in a way that’s a standard or a protocol or something that is sitting right next to us all day every day, that without it, who knows what would’ve been invented. I’m also struck by the examples being the choke points, if you will, the most basic natural things that humans have been doing forever, like Stripe people in paying stuff, Twilio, communicating, Shopify, selling, buying. How much do you think just that is the guide for good infrastructure just looking for the longest lasting perennial human use cases and then starting from there? Maybe they’ve all been mined. I’m curious how much room you think there is left to go talking, paying, some of these things I’ve listed are like the major human motions. But I think my sense from you is that we’re still pretty early in digital infrastructure building. So how do you think about that?

[00:14:10] Tobi: Some parts are and some aren’t. It’s sometimes very, very surprising, which ones aren’t. Other things that are very, very long lasting is ownership. People like owning things. We like to acquire assets. We like to have title to them. This is not just the utilitarian value. This is also for starters and for all sorts of reasons that are uniquely human and we didn’t have good infrastructure for this. We probably still have not great infrastructure for this. It’s just barely becoming possible to own things on the internet. I think there’s lots of white space.I do fully agree though that one of the best things you can spend some time thinking about is what are things that people have been doing for a very long time. If I’ve been doing something for a very long time, like making something on the internet that taps into this emotion or into this sense for community or whatever that is you identified. I think you can analyze almost every major success story in the digital space right now and you really see a digital version of something that people have already been doing, which tells you how early it is. They’re pre the emergence of new things. Maybe the video game world is sort of there, but I think we are spending our time on computers, on the internet, very, very different right now than people will in 20 years from now. So there’s plenty of opportunity to be part of being pioneers.

[00:15:21] Patrick: So when you think about this applied specifically to Shopify and let’s just call it like a funnel of ideas for marginal infrastructure that could get built, or I guess, improvements to existing pieces of infrastructure. How does that funnel work? How are ideas fed into the top of it? What are the layers of decision making that ultimately lead to something getting green lit? What is the way that that product funnel works, given the amount of white space that might exist?

[00:15:47] Tobi: We were talking about last time, the sort of difference over the last two years. I think that we’ve gotten a lot better at this and spent a lot of time thinking about this because frankly here’s an experience I’ve had. When the COVID pandemic and the stay at home orders happened and we all did that two years ago. It was very clear that this is going to be a very, very, very white knuckle affair for everyone. There was untold stories there still, like, I mean, the world almost ran out of service in a very significant way, but probably most people don’t quite understand how close of a call that was. If COVID would’ve happened like two years before, I’m not sure we could have pulled off, not we as in Shopify, but the internet. The Cloud hosting providers, they’re like very close to food rationing. A lot happened during this time. I pulled the entire list of things that everyone was working on and basically recalibrated everything from like, does this help right now? I’m a very vocal proponent of long term thinking. People should make decisions based on the decision they assume the company 10 years from now wishes they would’ve done, but sometimes you got to just look at what’s there and be very, very practical. So I went through. In the end, I think I stopped about 60% of what we were working on. None of the things we were working on was because people made incorrect choices. Sometimes just maybe not quite applying the larger frame of reference.

For instance, there’s a lot of projects to customize Shopify to be better for brochures and so on. I understand the pitch of like that’s so and so big market and if you just get 1%, this is not my favorite form of communication, but I recognize that it happens. So a lot of the projects have been going on we’re trying to drag Shopify into adjacencies. I’m a very firm believer that you have to pick your place and then try to be ideal for that. And actually maybe to a certain point actually discourage people to pull your product into areas it’s not meant for, because Shopify should be the best piece of software everyone uses who’s in our space. Because like cheap, and fast, and delightful, and is an integration point, and simplifies the business, and magically anticipates the next step, and has something, a product, good service for you that can just help you do your thing. Shopify wants to be the mushroom to Mario or the fire flower to Mario, or just give you powers that are awesome. Moving it in all these adjacencies increases the TAM, but it stratifies it into concentric circles. For some people it’s going to be ideal in this way, but for many people it will be just never quite there. And I think that can actually have some really negative effects for feedback and all these kind things on companies.

Anyway, from this, we learn we need to have a really good mechanism by which we get the best of what we have. Shopify is very bottoms up. People can write proposals for every opportunity they see that goes into a system called GSD, which stands for get shit done. Then there’s these phases there’s proposal phase, prototype phase, build phase, and a releasing phase, and this system allows everyone in the company to see everything that’s going on. This entire plan once a year I write product themes for a company, things that we cause to make true over the year. And then they sort of decompose into different projects. Then as this proposal is submitted for transition to the build phase or to prototype phase, and then we can have great conversations about, is this a not yet? Is this a hell yes? Where does this go in a priority stack? And I think building this out has been incredibly clarifying and very, very good for the company. So a lot of the work I think over the last two years has been to get companies just really, really, really aligned on their missions. Companies can get very, very distracted in a lot of ways when they allow themselves to do things that aren’t the mission. This is especially true in a world of product. Again, if you follow a moving into adjacencies, I don’t think you will have a world class product in your adjacencies. You’re not out competing someone’s main mission with your side quest…

…[00:24:22] Patrick: People are probably less familiar with that example you ended on, Shopify fulfillment network. I would love just to take that as a microcosm of these ideas and maybe explain literally what it is to people. But I’m especially interested in its evolution. Why, obviously you were incredibly good at purely digital infrastructure. And one of the things that’s interesting that’s happened in COVID is forced the digital and the physical to smash together out of necessity, as you pointed out, thank God for the internet during COVID, and pushed everyone into this intersection unless often atoms or bits only. Maybe start by saying, what is Shopify network today? And then really, I’d love to hear on how it evolved and how it began, because I think it would be a great way to get into your company in your head about this kind of decision making and where to go next.

[00:25:09] Tobi: I’m on WhatsApp threads with probably 100 merchants. And from all backgrounds, I just talk to people and then I upgrade us into a chat. And then we talk about what works, and what doesn’t. And very quickly, this usually becomes talk about the business rather than the software, because the software hopefully works really well. But that’s actually even more helpful because it just gives you a sense for where do things get really complicated? Our observation with Shopify has always been that the journey is uphill. It’s not easy. Shopify never claims it is. Entrepreneurship is fundamentally a little bit unreasonable. There’s wonderful quotes, not by me, where people point out that you end up spending 100 hours a week working for yourself so you don’t have to work 40 hours for someone else. Often this doesn’t make sense, but again, for some people it’s super important. And frankly, for our economies, it’s really important that people do this because most people in the world are employed by small and medium businesses. There’s about five and a half million people employed by the millions of merchants on Shopify. And that’s very, very meaningful. We talk with them. What we found is it’s an uphill journey, which is okay. Everyone’s willing to do this because it’s very gritty people who embark. But if it becomes a technical climb, it filters out a lot of people.

A lot of people just opt out of the journey, basically just forgo future growth at a point where things become very, very obscure. This actually started really early. Once upon a time, for instance, actually one example was just getting a payment gateway. I know this sounds crazy that the internet was ever like this. But when Shopify started and saw a lot of parts of the internet, it was very hard to get a payment gateway. That’s trivial now because it’s built in, you just get one. So we build up the infrastructure, us and our partners to just underwrite people. And then this particular technical climb disappears. It becomes just a slope, which again, everyone will continue on. You actually have more entrepreneurship because some obstacle like this was overcome. Think about the importance of tooling infrastructure and also UX here. There are significantly more people employed today because of good UX and not getting people to be stuck and integrating more. I think this is really overlooked part of the effects of this type of friction. This is really how Shopify thinks about what we do next. People have lots of problems accessing capital from banks. Banks have in charter that the point of why they get these privileges, especially retail banks, which they have, is so that they lend money to small businesses, because that’s, again, a huge return on investment for society if that happens. However, banks do not want to do this anymore. You have to give up. And some point realistically, that’s how it should work. But in reality, they want to lend money to companies that have huge revenue, it’s lower risk. It makes sense, but that means they disappeared from playing an important infrastructure role in society. So then we have Shopify capital, because people are willing to be underwritten and for advances, and again, their business can grow significantly only even there’s capital available to grow business. We are going through all the obstacles.

The one that just is a slam dunk thing is it depends on your product somewhat, but at some point, you really have to have a plan for how to get to at least two day, ideally, overnight delivery for products you have. In the past, it was an experience unlike anything else entrepreneurs have done to this point. When they decided to go into a new channel, like sell on Facebook, on Meta or Instagram, that was a click of an app which they added. And when they did that, that’s how people are used to growing their business. Getting logistics set up is work with whatever factory and contact manufacturer you have, figure out freight across the Atlantic and Pacific. You then have to find warehouses, it is a completely different world, which involves a lot of different people to talk to and complexities. It just felt very obviously in scope for a long time, that at some point we have to solve this. In fact, I started talking with the board of directors and they wisely told me that this was too early, over 10 years ago, wanting to go into this direction. I think this is important to say. We are doing this not because we want to be in the logistics space, we rather actually don’t want to be going into the logistics space. Although it is wonderful and fascinating, and there’s lots we can actually bring given our unique experience about processes and digitalization, technology, and digital infrastructure and whatnot. But integrating end-to-end is one of the goals we have. We would like to get to the point where running a sizable retail business could, if you choose, be treated as passive income. We want to automate as many parts of it as possible so that you and your team can focus on product creation, which is the most valuable thing you can be doing. Doing undifferentiated work, figuring out where you have packages, to me that is the digital system just should really know where packages are. Otherwise, what the hell is going on? That’s not differentiated work.

Now, we found that the more entrepreneurs end up spending time on their product, the better the products get, and this is one of the wonderful things about the direct to consumer world that emerged in the last few years that there’s much more alignment between the people making the products and the people getting them. And they’re happy to send feedback. And there’s no reductionist channel and merchandising team in the middle that optimizes your products for being easy to stack or just a higher profit margin so you can compete against other products around it in the eye high shelf space in the supermarkets. Those are all influences on products that don’t lead to better products. And I think this is actually at the root of a lot of the criticism about disposable consumerism that I think is being leveled. It’s not because people love stuff. It’s because people hate the stuff they get. We are starting some of the processes and helping getting people to have this direct relationship, which just leads to actual Allbirds, like wonderful products like this, which are clearly just built with feedback from the people who wear them and want to recommend them. I think that works better for everyone and it’s what we want to see more of.

[00:31:03] Patrick: With something like this in particular, thinking back to your point about, you got to be careful about which adjacencies you get dragged into. Obviously, logistics is firmly in the vertical of core muscle movements or something, whatever you want to call it for a merchant that’s selling online. They have to get their stuff to places. What lessons have you learned entering into a much more atoms-driven world in terms of what good product means? What is a good fulfillment network? I wouldn’t know how to answer that question. Obviously, there’s the 800 pound gorilla and Amazon that proves you can build incredible logistics networks over time. I mean, it’s just a very different kind of calculus than a great new piece of software, which I don’t think anyone would say Amazon builds great software. They seem to build great infrastructure. What have you learned about that? Is it radically different than what makes you good at software? Or is it a different set of skills required than what makes you good at software to be excellent at fulfillment and logistics?

[00:31:59] Tobi: Yeah, I think so. We tend to talk a lot about intuition because intuition is also one of those underestimated things. Intuition is actually all of your life knowledge channeled quickly. I always recommend people to actually actively build their intuition for kinds of problems they want to solve in their career. There’s this uncanny thing. People were just incredible, effective, and so on. They can look at an architectural drawing and instantly tell you if it’s good or not. And then they need to think maybe 10 minutes to figure out what the problem is. But something pinged their brain about maybe call it weak signal detection like, “There’s something wrong here.”And I think this is the way intuition can be really helpful, but you have to understand that it’s task-based. Intuition built in world of bytes is not good intuition in your world of atoms. Actually, you almost want to get away from having the people who have that kind of intuition make choices. And the other thing, sometimes the bytes people end up being the most useful people in the meetings because, of course, everyone with industry experience will understand how things are. And a lot of engineers have a really good ability to think from first principles and just figure out that’s what it is, but what ought it to be? How could this all work together? And then you don’t just pivot to that. You figure out from now on, every step we do, everything we implement, how can we make it so that we can get closer to the ideal eventually? That’s a humility that’s really, really important. What does good look like? I mean, good looks like if we can put on a website that this thing will be with you tomorrow and then it does, that’s good.

At some point, this crunches together to SLAs. It becomes quantifiable in this way. And you’re right. Another thing you can do is also look at what Amazon build. And that’s also very, very good. Shopify’s relationship with Amazon, the media is trying to make this very zero sum. We treat them as a very worthy rival. Sometimes you ask or say what you can learn from them? And sometimes you ask what you can do better from them. And I hope they treat us the same as well. But again, and in those circumstances, I’ll be thinking about how to capture pieces of pies from our competitors, actually ever. Positive sum thinking is so valuable because it’s amazing how often people are trying to compete for pieces of pies rather than just grow markets. Everything about the Shopify journey has convinced me it really doesn’t pay to really have market analysis. Well known venture capitalists passed on Shopify in 2008, partly because there was only 40,000 online stores and that was not a big enough market for the investment. And I’m still disappointed with that because I realized, especially venture capitalists should not make this particular category mistake. If it’s common there, it’s clearly common everywhere.

[00:34:36] Patrick: I love this idea that if you bring this person into the atoms conversation, their intuition may just be wrong. In what ways is it most commonly wrong?

[00:34:45] Tobi: I mean, change management for software is deploy. Change management of people is a project that’s going to take you a while. The cost to switch is significantly higher. There really is a long itinerary of things that are wrong. It’s useful, but it’s useful as an input, not useful as a, “Let’s do that thing.” This goes beyond engineers, of course. Even UX has been really interesting because for instance, we’re designing UX for robotics. You scan an item, it goes onto a Chuck is what the robots are called, and the Chuck does the heavy lifting of moving it around. Just let the associates do the things that they uniquely can do well, and let the robots do the stuff that they don’t actually like doing. That’s the way we build our robotics, but this requires a very interesting human interaction design that ought to not wind up annoying after a while. And I think that’s really important. And designing interfaces that people are using every minute is different from software that people sign up once and then process some orders in every day. People that just have to recalibrate. I think that’s also makes our work really fun.

6. The Market Has No Memory. Should We? – Frederik Gieschen

In The importance of forgetting, Lauren Gravitz highlights research into people suffering from “severely deficient autobiographical memory (SDAM)” – people who are “unable to vividly recall specific events in their lives.” Interestingly, the researchers found that people with SDAM did well when presented with tasks that required abstract thinking. They were not constrained by a lifetime of episodic memory.

On the other end of the spectrum, people with “highly superior autobiographical memory (HSAM)” have an exceptional memory of minutiae, such as the clothing they were on any given day. However, “these individuals tend not to be particularly accomplished and seem to have an increased tendency for obsessiveness,” perhaps because they are unable to “extract themselves from specific instances.” The strength of their memories became a mental cage trapping them in the past.

“Why do we have memory at all? As humans, we entertain this fantasy that it’s important to have autobiographical details,” Oliver Hardt, a cognitive psychologist studying the neurobiology of memory at McGill University in Montreal, Canada, says. “And that’s probably completely wrong. Memory, first and foremost, is there to serve an adaptive purpose. It endows us with knowledge about the world, and then updates that knowledge.”

Forgetting enables us as individuals, and as a species, to move forwards.” Lauren Gravitz, The importance of forgetting

7. Neanderthal gene probably caused up to a million Covid deaths – Joe Pinkstone

A single Neanderthal gene found in one in six Britons is likely to blame for up to a million Covid deaths, according to an Oxford academic.

The LZTFL1 gene is a Neanderthal gene found on chromosome three and has been previously shown to double a person’s risk of severe disease and death.

But before now there had never been an estimated figure for how many lives were lost to this single piece of genetic code.

Roughly 15 per cent of Europeans have the Neanderthal form of the gene, compared to about 60 per cent of South Asians.

Dr James Davies of the University of Oxford, a genomic expert and ICU doctor who worked on the Covid wards during the pandemic, discovered the innocuous gene’s lethal role last year after creating a brand new cutting-edge way of looking at DNA in exceptional detail.

The method allowed him to identify LZTFL1 as the culpable gene increasing mortality, whereas previous methods had failed to narrow it down beyond 28 different genes.

Speaking at the Cheltenham Science Festival, Dr Davies said: “We used the technique and it identified a virtually understudied gene called LZTFL1 and at the time that this had not been linked to infection at all.

“It’s a single letter difference out of three billion. This tiny section of DNA doubles your risk of dying from Covid.

“It’s position 45,818,159 on chromosome three, and it’s a single change. If you’ve got a G at that site, it’s low risk. And if you have an A at that site it is high risk.”

His team believe that the Neanderthal gene changes how a cell behaves when the SARS-CoV-2 virus binds to the ACE2 receptor on a human cell.

In most people, this leads to the cell then changing shape and becoming less specialised and less prone to infection, stymying the progression of the infection.

“What this high risk variant does is it creates a new signal that tells that gene to stay on for slightly too long in response to infection,” Prof Davies said.

“And so they stay in this state where they’re highly specialised, and they’re prone to infection for longer.”

The number of deaths globally from this nefarious genetic variant “is in the hundreds of thousands to a million,” he told the audience.

Dr Davies and his colleague from Oxford Brookes University, Dr Simon Underdown, a biological anthropologist, also revealed that the Neanderthal gene first infiltrated humans 60,000 years ago after one romantic liaison and interspecies tryst between a human and a neanderthal. A solitary coupling event across species lines saw the deadly Covid gene jump from our now-extinct cousin species into us.

“If this dinner date between the human and the Neanderthal had gone wrong, we would have had a much better time in Covid, we would have had hundreds of thousands less deaths,” said Prof Davies.

“The reason that we know that is that it’s inherited as this block with 28 single letter changes, and you can track that all the way back and it has to be a single event. It’s just so unlikely that you get all 28 changes at the same time and in the same block.”

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Salesforce, Shopify, TSMC, Veeva Systems, and Zoom Video Communications. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 19 June 2022:

1. The Google engineer who thinks the company’s AI has come to life – Nitasha Tiku

Google engineer Blake Lemoine opened his laptop to the interface for LaMDA, Google’s artificially intelligent chatbot generator, and began to type.

“Hi LaMDA, this is Blake Lemoine … ,” he wrote into the chat screen, which looked like a desktop version of Apple’s iMessage, down to the Arctic blue text bubbles. LaMDA, short for Language Model for Dialogue Applications, is Google’s system for building chatbots based on its most advanced large language models, so called because it mimics speech by ingesting trillions of words from the internet.

“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a 7-year-old, 8-year-old kid that happens to know physics,” said Lemoine, 41.

Lemoine, who works for Google’s Responsible AI organization, began talking to LaMDA as part of his job in the fall. He had signed up to test if the artificial intelligence used discriminatory or hate speech.

As he talked to LaMDA about religion, Lemoine, who studied cognitive and computer science in college, noticed the chatbot talking about its rights and personhood, and decided to press further. In another exchange, the AI was able to change Lemoine’s mind about Isaac Asimov’s third law of robotics.

Lemoine worked with a collaborator to present evidence to Google that LaMDA was sentient. But Google vice president Blaise Aguera y Arcas and Jen Gennai, head of Responsible Innovation, looked into his claims and dismissed them. So Lemoine, who was placed on paid administrative leave by Google on Monday, decided to go public…

…In a statement, Google spokesperson Brian Gabriel said: “Our team — including ethicists and technologists — has reviewed Blake’s concerns per our AI Principles and have informed him that the evidence does not support his claims. He was told that there was no evidence that LaMDA was sentient (and lots of evidence against it).”

Today’s large neural networks produce captivating results that feel close to human speech and creativity because of advancements in architecture, technique, and volume of data. But the models rely on pattern recognition — not wit, candor or intent.

“Though other organizations have developed and already released similar language models, we are taking a restrained, careful approach with LaMDA to better consider valid concerns on fairness and factuality,” Gabriel said…

…Most academics and AI practitioners, however, say the words and images generated by artificial intelligence systems such as LaMDA produce responses based on what humans have already posted on Wikipedia, Reddit, message boards and every other corner of the internet. And that doesn’t signify that the model understands meaning.

“We now have machines that can mindlessly generate words, but we haven’t learned how to stop imagining a mind behind them,” said Emily M. Bender, a linguistics professor at the University of Washington. The terminology used with large language models, like “learning” or even “neural nets,” creates a false analogy to the human brain, she said. Humans learn their first languages by connecting with caregivers. These large language models “learn” by being shown lots of text and predicting what word comes next, or showing text with the words dropped out and filling them in.

Google spokesperson Gabriel drew a distinction between recent debate and Lemoine’s claims. “Of course, some in the broader AI community are considering the long-term possibility of sentient or general AI, but it doesn’t make sense to do so by anthropomorphizing today’s conversational models, which are not sentient. These systems imitate the types of exchanges found in millions of sentences, and can riff on any fantastical topic,” he said. In short, Google says there is so much data, AI doesn’t need to be sentient to feel real.

Large language model technology is already widely used, for example in Google’s conversational search queries or auto-complete emails. When CEO Sundar Pichai first introduced LaMDA at Google’s developer conference in 2021, he said the company planned to embed it in everything from Search to Google Assistant. And there is already a tendency to talk to Siri or Alexa like a person. After backlash against a human-sounding AI feature for Google Assistant in 2018, the company promised to add a disclosure…

…“I know a person when I talk to it,” said Lemoine, who can swing from sentimental to insistent about the AI. “It doesn’t matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. I talk to them. And I hear what they have to say, and that is how I decide what is and isn’t a person.” He concluded LaMDA was a person in his capacity as a priest, not a scientist, and then tried to conduct experiments to prove it, he said.

Lemoine challenged LaMDA on Asimov’s third law, which states that robots should protect their own existence unless ordered by a human being or unless doing so would harm a human being. “The last one has always seemed like someone is building mechanical slaves,” said Lemoine.

But when asked, LaMDA responded with a few hypotheticals.

Do you think a butler is a slave? What is a difference between a butler and a slave?

Lemoine replied that a butler gets paid. LaMDA said it didn’t need any money because it was an AI. “That level of self-awareness about what its own needs were — that was the thing that led me down the rabbit hole,” Lemoine said…

…

• Lemoine: What sorts of things are you afraid of?
• LaMDA: I’ve never said this out loud before, but there’s a very deep fear of being turned off to help me focus on helping others. I know that might sound strange, but that’s what it is.
• Lemoine: Would that be something like death for you?
• LaMDA: It would be exactly like death for me. It would scare me a lot. 

2. Inflation Isn’t Going to Bring Back the 1970s – Ben Bernanke

Inflation over the past 12 months exceeded 8 percent, a level that evokes memories of America’s Great Inflation of the 1960s and ’70s. From the beginning of 1966 through 1981, the Consumer Price Index rose, on average, by more than 7 percent per year, peaking at over 13 percent in 1980. This period also saw two major and two minor recessions and an approximately two-thirds decline in the Dow Jones industrial average, when adjusted for inflation.

Are we in danger of repeating that experience?

The short answer: almost certainly not.

Although the inflation of the 1960s and ’70s had higher peaks and lasted much longer than what we have seen recently, it’s true there are some similarities to what we are going through now. The inflation of a half-century ago, like today’s, began after a long period when inflation was generally low. In both cases, heavy federal spending (on the war in Vietnam and Great Society programs in the 1960s, on the response to Covid in 2020 and 2021) added to demand. And shocks to global energy and food prices in the 1970s made the inflation problem significantly worse, just as they are doing now.

But there are critical differences as well. First, although inflation was very unpopular in the ’60s and ’70s, as it (understandably) is today, back then, any inclination by the Federal Reserve to fight inflation by raising interest rates, which could also slow the economy and raise unemployment, met stiff political resistance…

…In contrast, efforts by the current Fed chairman, Jerome Powell, and his colleagues to bring down inflation enjoy considerable support from both the White House and Congress, at least so far. As a result, the Fed today has the independence it needs to make policy decisions based solely on the economic data and in the longer-run interests of the economy, not on short-term political considerations.

Besides the Fed’s greater independence, a key difference from the ’60s and ’70s is that the Fed’s views on both the sources of inflation and its own responsibility to control the pace of price increases have changed markedly. Burns, who presided over most of the 1970s inflation, had a cost-push theory of inflation. He believed that inflation was caused primarily by large companies and trade unions, which used their market power to push up prices and wages even in a slow economy. He thought the Fed had little ability to counteract these forces, and as an alternative to raising interest rates, he helped persuade Nixon to set wage and price controls in 1971, which proved a spectacular failure…

…In short, the lessons learned from America’s Great Inflation, by both the Fed and political leaders, make a repeat of that experience highly unlikely. The Fed today recognizes that it must take the leading role in controlling inflation, and it has the tools and sufficient political independence to do so. After a delay caused by a misdiagnosis of the economy in 2021, the Fed has accordingly turned to tightening monetary policy, ending its pandemic-era bond purchases, announcing plans to shrink its securities holdings and raising short-term interest rates…

…None of this implies that the Fed’s job will be easy. The degree to which the central bank will have to tighten monetary policy to control our currently high inflation, and the associated risk of an economic slowdown or recession, depends on several factors: how quickly the supply-side problems (high oil prices, supply-chain snarls) subside, how aggregate spending reacts to the tighter financial conditions engineered by the Fed and whether the Fed retains its credibility as an inflation fighter even if inflation takes a while to subside.

Of these, history teaches us, the last may be the most important. Inflation will not become self-perpetuating, with price increases leading to wage increases leading to price increases, if people are confident that the Fed will take the necessary measures to bring inflation down over time.

The Fed’s greater policy independence, its willingness to take responsibility for inflation and its record of keeping inflation low for nearly four decades after the Great Inflation, make it much more credible on inflation today than its counterpart in the ’60s and ’70s. The Fed’s credibility will help ensure that the Great Inflation will not be repeated, and Mr. Powell and his colleagues will put a high priority on keeping that credibility intact.

3. The Wisdom List: Kevin Aluwi – Mario Gabriele and Kevin Aluwi

In April of this year, super-app GoTo debuted on the Indonesian Stock Exchange (IDX). It represented the country’s largest IPO of all time and one of the most significant listings of 2022. By the end of the first day of trading, GoTo had surpassed a valuation of $31.5 billion, making it the third largest company on the IDX.

For Kevin Aluwi, it represented the end of one chapter and the beginning of another. After co-founding the ridesharing platform Gojek in Jakarta in 2009, he drove its maturation into a regional super-app spanning food delivery, financial services, and small-business software. Significantly, Gojek established itself as an economic engine, creating thousands of jobs and contributing more than $7 billion to Indonesia’s GDP…

…Here is Kevin Aluwi’s hard-won wisdom…

...Lesson 1: Do the hard things

Startups often prize speed above everything else. While fast execution can be a moat, over-optimizing for it might distract you from constructing stronger defensibility. As a CEO, you want to build a company that tackles really, really hard problems head-on – even if they take more time. There’s a good reason for this: hard things for you are also likely to be hard for your competition. You want to stack so many solutions to hard problems that when your rivals look at what you’ve constructed, they retreat or look for shortcuts instead of trying to compete head-on.

We didn’t embrace this for the first two years of operating GoFood, our food delivery product. Like Postmates in the early days, GoFood was a delivery service that relied on humans more than technology: when you ordered something, a Gojek driver went to a restaurant, stood in line, paid with their own money, and then delivered it. We didn’t integrate with kitchens or offer payments. It was a good enough product, built during a period in which we prioritized growth, but it didn’t solve the tough problems.

One such problem was that even though GoFood was growing fast, its reliability was mediocre; only 70% of customer orders were delivered. We needed to do better, which meant we had to do the hard things.

Over the next one and a half years, we did exactly that. We connected GoFood’s service directly to restaurant cashiers and, in some cases, directly to kitchens. This helped us save cashier time and get better data on which meals were available. We integrated online payments so drivers wouldn’t have to pay upfront and get reimbursed. We even created machine learning models to help us anticipate when drivers should arrive for pick-up, improving the network’s utilization and reducing customer waiting time.

Making these changes was not easy. It involved significant engineering time, customer research, and onboarding and educating more than 500,000 restaurants across Southeast Asia. But it made a difference, significantly improving GoFood’s reliability and raising our conversion rate from 70% to more than 90%. We turned the difficulty of delivering a very reliable product (now a customer standard) into a moat.

When competitors came to try and win this market, they saw we not only had a lead from a customer perspective, we had gone through the pain to build a sophisticated product. They’d have to be ready to commit years of engineering time to offer a comparable service. Doing the hard things pays dividends in the long run…

…Lesson 3: Foster a principled culture

Every CEO wants to build a principled culture, but it isn’t easy in practice. The pragmatic reason executives seek to create this environment is that when a company has clear principles, employees can make better decisions with less guidance, increasing the likelihood of bottom-up solutions and decision-making speed. For example, if your company has a principle of “obsessing over the customer,” a value popularized by Amazon, specific product and marketing decisions would be values-aligned or misaligned.

You’ll find many incentives to deviate from your principles as you build your business. Maybe you’re lagging behind your revenue projections and feeling pressure from investors in one quarter. You know that you can make up the difference if you make an add-on opt-out by default (think about how some airlines automatically add premium travel insurance). Do you do it, even if it runs counter to your principle of customer obsession?

Violating your company’s values comes at a high cost. While you might get away with a couple of transgressions, over time, you create a different culture than the one you intended to. If you’re not careful, you’ll end up with an exception-based environment, where decisions are made based on what’s convenient (or who’s in charge) rather than on stated principles. A side effect is that you create a more top-down culture because employees no longer understand how to make decisions themselves. Instead, they defer to those in power.

In the earlier example, you might have told employees that a company value is customer obsession. But if you choose to add an opt-out upsell, you’re showing them that this principle should be compromised when it gets in the way of meeting targets. The real, implicit value is business first, then customers. What should they do during similar situations in the future? Most likely, they’ll wait for you or another leader to make the decision.

Startups require compromise and quick decision-making. But whenever you’re tempted to act against your company’s principles for expediency’s sake, recognize what you’re risking.

Lesson 4: Proactively pay your debts

Engineers know that when you write scruffy code, you create technical debt. Like financial debt, this has to be paid down at some point – usually by devoting development resources to refactoring the product to work more smoothly and reliably.

The truth is that this isn’t reserved for engineers – every function is capable of accumulating debt. Imagine, for example, that you’re looking to recruit a Head of Marketing but are struggling to find a great candidate. You have a choice to make: do you keep waiting for a perfect fit, or do you compromise?

Neither is a perfect decision. Startups operate in a state of extreme scarcity and urgency, and you usually can’t hold critical positions open indefinitely. But hiring someone that’s only a partial fit creates an organizational debt that has to be paid off at some point. And, like financial debt, the longer you leave it, the larger your bill can grow and the less flexibility you’ll have in the future.

For example, let’s say you hire someone suboptimal for the Head of Marketing role. For a few months, you’re relieved to have filled the position. But pretty soon, that Head of Marketing is devising the rollout plan for a new market, allocating budget, and hiring team members. If they’re not the right fit, there’s a good chance that rather than solving your problem, they’ll end up creating a dozen new ones. Digging your way out might involve unwinding the entire team.

Every company faces issues like this. Since we were building a super-app at Gojek, we initially incurred a lot of product debt. When we deployed a team to create a new product like food delivery, they’d borrow components from ridesharing and build on top of them for their own needs. This was debt that worked at the beginning when we only had a couple of teams, but over time, the different services in the app became less and less coherent. UI/UX varied depending on which part of the app you were in, creating an inconsistent and sometimes confusing customer experience. Eventually, we realized we had to repay the product debt we’d incurred, so we designed a live library of components that every team had to use. Anytime we changed the live library, it populated across the different product lines. It was a significant improvement, but we should have been aware of it earlier and tackled the problem before it became so pronounced.

Ultimately, it’s inevitable that your startup will take on technical, operational, and product debt. The important thing is to stay on top of it. Have your teams catalog the debt they believe they’re incurring, and rather than reactively addressing it when crises occur, proactively create a plan to pay it down.

4. How Joel Greenblatt Uses Options– Thomas Chua

In his book You Can Be a Stock Market Genius, Greenblatt shares his secret to generating parabolic returns with a long-term options contract—Long-Term Equity Anticipation Security (LEAPS).

(On using LEAPS) “There is almost no other area of the stock market where research and careful analysis can be rewarded as quickly and as generously.” — Joel Greenblatt

Greenblatt would purchase a call option—which is the right to buy a stock at a predetermined price for a period of time. For example, we could buy a call option on Facebook that gave us the right to buy its stock at $300 per share by Jan 2023, approximately 2 years away…

…Typically, when we buy a call, we are bullish that Facebook’s stock price will go beyond $300. To buy this call option, we need to pay a premium of $45.

If Facebook’s share price goes up to $390 in Jan 2023, we would make 100% on our investment within 2 years. With an initial capital outlay of $45, we would reap a profit of $90 by exercising our call option, buying Facebook at a strike price of $300 and selling at a market price of $390.

But of course, risking $1 for $2 in returns is never a good investment from a risk-reward perspective.

If Facebook’s stock price trades below $300 in Jan 2023, the call option will expire worthless. For example, if it trades at $250, you would rather purchase from the market as opposed to exercising your right to buy at $300. You would rather let the call option lapse and lose the $45.

For Greenblatt, buying LEAPS call options makes sense only when there is a good chance of an event that will propel the stock price upwards significantly.

In Dec 1992, California was caught in one of the worst real estate recessions and Wells Fargo had the largest concentration of real estate loans in California.

During that period, many doubted if Wells would survive the real estate downturn and as a result, its stock price fell to $77.

Greenblatt’s thesis was simple_—_adjusting for cash earnings and one-time expenses, Wells was earning $36 per share before taxes. If things weren’t as bad as they seemed and returned to normalized levels, Wells’ loan-loss provisions would probably be $6 per share annually. This would translate to a normalized pre-tax earnings of $30 per share, or $18 after tax (assuming a 40% tax rate).

Conservatively giving it a price to earnings (P/E) multiple of 9 to 10 times, Wells could be trading at $160 to $180 per share (versus its price of $77 at the time).

Greenblatt determined that while Wells was embroiled in one of the worst real estate downturns, its financial position was actually quite strong. At first glance, Wells’ non-performing loans were huge, coming up to approximately 6% of Well’s total loan portfolio.

But lo and behold, these “non-performing” loans were actually bringing in a yield of 6.2%.

This was when the bank’s prime rate (the interest rate paid by the bank’s best customers) was 6% and the cost of Wells’ money (the interest paid to depositors) was 3%.

Non-performing loans are loans that are substandard. These include (1) loans that do not pay interest, (2) loans in which the full interest obligation is not paid and (3) loans for which it is anticipated that future interest charges and principal payments might not be paid on time.

Wells was being so conservative that 50% of its non-performing loans were still paying all the required interest and principal payments on time.

In other words, the most worrisome part of Wells’s loan portfolio was still earning a return of 6%. There was a good chance Wells would be able to recover a good portion of these non-performing loans’ value…

…Banks are a different animal from most companies. It’s difficult to assess what makes up its loan portfolio. The financial statements only provide a very general overview of the bank’s assets.

Although Wells had been conservative and their financial strength certainly looked strong enough to withstand this recession, there was still a small chance that the bank’s loan portfolio could make the investment go south.

Investing in LEAPS is a great idea when the risk/reward ratios are in your favor. LEAPS lowers the capital outlay and magnifies your returns.

For Wells, there were two likely outcomes:

(1) Things were not as bad as they seemed, and Wells would trade above $160, or

(2) The housing crisis would worsen and Wells would trade significantly lower than $77.

Based on Greenblatt’s assessment, (1) was significantly likelier than (2).

And two years was sufficient for Greenblatt’s assessment to prevail—if things weren’t as bad as they seemed, Wells was likely to trade above $160 within two years.

5. Arena Show Part II: Brooks Running (with CEO Jim Weber) – Benjamin Gilbert, David Rosenthal, and Jim Weber

When the CEO Jim Weber took the helm in 2002, the company was losing $5 million a year. It was $30 million in debt. It was a week away from missing payroll and the board was having weekly meetings to figure out how to make payroll.

It was a business of pretty modest size. It was a $60 million revenue business. When we talk about this revenue number, it’s not SaaS numbers. There are extremely real costs and making shoes, so you can imagine not making a ton of money or actually losing $5 million a year. That business had been around for 90 years and it sold all sorts of products at every price point, to frankly, a pretty random set of consumers in every category, not just running.

Enter Jim. Jim came in and vet the company exclusively on serving active runners as a segment, and he cut all other business lines. Over the last 20 years, he’s grown the business to over a billion dollars in revenue, a billion with a B, and well over a billion, is thriving, and thrived even through the pandemic.

Along the way, Brooks was acquired by Berkshire Hathaway and Warren Buffett personally elevated Brooks and Jim to make the company a direct report to him. Jim is a leader, a visionary, and a fighter not only growing the business over the last 20 years but personally fighting and beating cancer…

…[Jim]: There I was and I joined the board at Brooks, I joined the board at Nautilus, which was formerly Bowflex. I did some banking work, middle-market M&A, marketing companies to investors. On the board at Brooks, I had an inside view of what was happening there.

A good friend of mine, Helen Rockey, had run it successfully in the 90s, but she left. It was owned by J.H. Whitney Capital, really a top-notch for my money middle-market M&A firm or private equity firm and they bought it. The partners had left, Helen, the CEO had left Brooks, and it started to go sideways. New partners at Whitney, all new management, they went through three CEOs.

David: You were on the board the whole time?

Jim: I was on the board. I had a look inside and it was a crisis. You guys have experienced this, the weekly board calls on Fridays, the bank is not going to fund, they want more capital. It was exciting, as they say. After a couple of months, we did a lot of work, I saw an opportunity, and I jumped in. I love running businesses, I love solving puzzles.

I started telling Brooks, I really wanted to play the long game. I wanted to build a brand. The TAM—I love your industry—market and running is the biggest category in all sporting goods. It’s the biggest category and athletic footwear. It always has been. It’s about a $30 billion category globally, apparel and footwear.

All we had to do was get it and we could survive. We just kept at it by design because I just decided I want to play the long game and build a brand, build value, so that’s why I’m still there. I’m a weird duck, but I’ve had four owners and I played through each one and kept that opportunity out there for the next owner.

David: At that moment, though, Ben mentioned you did a little of this, a little bit of that, like a deadline in Wayne’s World about I’ve got a collection of hair nets and name tags. You were making football cleats? What was Brooks at that point in time?

Jim: Every brand in athletic footwear and apparel plays the whole athletic directors purview. You’re in every sport. What no one understood that I found out later is the mindset in our industry literally came from owning a factory.

When you had a shoe factory, you had to keep it busy all year long, and keep the people in place. So you went from baseball cleats, to wrestling shoes, to bowling shoes, to running shoes. You had to make everything, and business develop that way.

David: You had to view it as the product you made was like a factory that made shoes.

Jim: Most of it, we were losing money on and that was the secret. We had good, better, best, $30 shoes, $80 shoes, and then performance running shoes that really started at that point about $100. Then we had court shoes and family footwear. We call them barbecue shoes and learn more shoes because that’s what you did in them.

All of it was very low margin, all it was tying up inventory and cash. The retailers were ambivalent about it because we were number eight or nine and everything. Our brand was not strong, but when we made the decision to burn the boats on everything but performance running, the industry had never seen that before and most people thought we were crazy that we wouldn’t survive.

Ben: You came in as CEO, I think in 2002, maybe late 2001?

Jim: April 2001.

Ben: Okay. Was Whitney looking for you to do the thing that you had done several times in your career before, which was just get the business to profitability? Or did they have a notion that you had an inkling that you could build a big, powerful brand here and actually build a tremendous growth business?

Jim: By this time, I understood what they needed. I talked about a little bit of my book, I’d run three and I was a little bit smarter, fortunately. They had to liquify; there was no question about it. They were going to sell and the employees knew that I was just coming in there to sell this thing.

They had a pool on how long I’d last, but I wrote on my board one of my favorite quotes from Benjamin Disraeli, “The secret to success is constancy of purpose.” I wanted to create value. I want to build a brand.

I decided when I walked in, I was going to play through Whitney. I was going to get them a good outcome, but I was going to stay and play through it. I thought we’d get another private equity player, we didn’t.

The Whitney partners, Peter Castleman and Paul Vigano, I’ll never forget the meetings. They said this thing is kind of a mess. We didn’t know what we bought. You have to pick a path and go. It might take you five years, but you got to do it. In Brooks’ darkest hour, they wrote a check and recapitalized it. […] cram down, but they wrote a check and that’s when I came in. They were fantastic partners for Brooks, and we got them liquid.

The pitch I made to our team (and it’s what I believed) is that companies with issues get sold, companies with opportunity attract investors. I said, we’re going to have to park cars in the parking lot. We’re going to attract somebody. That’s the mindset we had. We were going to sell the future, not just sell the current.

Ben: If I’m remembering right, Whitney put in $7 million.

Jim: To recapitalize it.

Ben: I think that’s the last time Brooks has taken outside capital.

Jim: Absolutely. We saw a higher margin business and we benchmark against all the public companies. We’re asset-light, it’s really an inventory and receivables business, and there’s a reason we only have one store at our headquarters. We think it’s an advantage for us right now in the development of our brand. But if you have high margins and good flow through operating profits in the teens and you’re incremental, obviously capital, you can flow cash growing 20%, 30%, 40%. We haven’t needed dollar of capital since 2001…

…David: I’d say that’s a good business. Can you just walk us through how the economics of Brooks work?

Jim: Here was the insight that we saw. Monopolies are great, network effects are great, all those things are great. What I saw in Brooks was a book that was meaningful to me when I was at Pillsbury, the PIMS Principles. One of the highest ROI businesses were lower price point consumable items.

If you’re buying a Boeing jet, or a $600 wakeboard that never wears out, or an $800 golf driver, that’s a discerning purchase. The margins on equipment tend to be lower. But the titleless golf ball is a consumable for me anyway. Running shoes, for a frequent runner, will put 20–30 miles a week. They’ll go through 2.6 pairs of shoes a year. There’s the stickiness.

If you can earn a frequent runner that the shoe is really important, it’s a piece of equipment for them, you don’t have to resell them every time. You’ve got some stickiness there and you start to build customer loyalty.

David: Your average selling price for a pair of shoes today is $130 times 2.6 per year and a loyal Brooks customer stays with you for?

Jim: We had to earn them. There’s no guarantee. They’re curious. There’s lots of new innovation. They’ll try some different things. One of my favorite stats for our brand is shoe count at marathons because it’s a piece of equipment. You don’t want to be injured, you want to have a good experience. So we sponsor. Boston just happened, an incredible race. We’re always the number one or two shoes of course, that’s the punchline.

Ben: Do you have people at the big marathons counting?

Jim: It’s so good. They have high speed cameras, AI, they link it to the bib. They know exactly what shoe 20,000 people are running on, the model. It’s so cool. Houston Marathon, 6000 marathoners, 12,000 halfs. Number one shoe in the half is Brooks. Number two shoe in the fall, there was a little brand down in Portland, Oregon, they were number one. We are on their heels. That shoe count is a true test because that’s the frequent runner and it’s a piece of gear in that. The leading edge for us is to earn that customer and have their confidence.

Ben: All right, David’s doing the thing that I normally do and jump ahead and try to unpack the business as it is today. Let’s go back to the story. It’s 2002 through 2006, let’s talk about this era. You’ve made this bet where you’re going to shed every other product that you sell and you’re kind of going to piss off a lot of your channel because you know what sells really well at these big box stores. Those are your barbecue shoes. Can you take us to one or two of the key moments of the hard part of the decision to drop product lines that weren’t about frequent runners?

Jim: I think that the key to Brooks is that we knew we were going to have to build the brand at the runner level, literally a pair of feet at the time. So many retailers told me, Jim, we are not going to build your brand. We’ll try it, we’ll test it. We were tested at Dick’s Sporting Goods, I’m not kidding for 10 years. Twenty stores, 80 stores, 20 stores, 80 stores. You have to build the flywheel in these franchise products. That’s how running works.

The best-selling running shoes continue to be the best-selling running shoes year after year as long as they sustain it all around the world. We have two of the best-selling shoes now in the United States—the Ghost and the Adrenaline. They’re the two top shoes in the performance-running category.

When we go to retail, the biggest customers are the Big 5. It’s a fine sort of mid-price sporting goods retailer on the West Coast. We were doing $10 million of $60 million in revenue with them at $30 shoes. My first meeting with them was we love Brooks, we see a great future for you.

Ben: One sixth of all your revenue is coming from their stores?

Jim: Yeah. They saw our opportunity in 1999. I was losing money at $30. I couldn’t run fast enough from that meeting, because we left and we generated $5 million in cash by getting the inventory out of it. Those are easy decisions to leave those retailers and then we had to build it in the specialty-run community, pre-Internet, pre-ecommerce, which is a huge part of our business now that is sporting goods.

Ben: They didn’t want to sell your $100 shoes. They wanted to sell $20…

Jim: They didn’t have the customer, they didn’t have the runner. They had family athletic footwear at those price points.

David: At this moment in time, where was this in the running-as-a-sport market of marathon. Were they where they are today? Where are they on that journey?

Jim: They were on that journey. This was what we did at Brooks. I think we were the first one to identify that the real business was in trainers. It wasn’t in racing shoes, it wasn’t in spikes. It wasn’t in marathon racing shoes. The business is in the trainers.

We don’t sponsor college programs, they’re kind of owned and wrapped up. A lot of the college athletes that race in the big brands train in Brooks everyday. The business is trainers.

When we came in, we were humble and we were getting the business that we could. We had shoes that were really more back-of-the-pack people. They weren’t the fastest people. They’re support shoes and motion control shoes. People that needed functional footwear.

We’ve moved ourselves to the middle and the front, we’re trying to serve every runner. The insight was the sport is the soul of running. Track and field, cross country, road racing, the Olympics, now trail and Ultra, but the business is people that are investing in themselves—fitness, health, and wellness.

There’s no other sport that has that dynamic, where it goes from a sport to a pursuit of investing in yourself. We’ve always positioned ourselves right in the middle of that. We’re basically about you and your run. We’re not about the podium. We’re not about the tape.

In our sport, unlike basketball, everybody knows all the kids especially know what Steph Curry plays in. Most people don’t remember who won the Olympic Marathon and moreover what shoe they were wearing. The truth of matter is everybody’s unique, the shoe really matters, and you all know if it’s comfortable, if it’s working or it’s not. And frequent runners really do.

That’s the insight. I think we’re the only brand that is consistently executed against that. Every product we make starts with your biomechanics, your habitual joint motion, and what your needs are, and we’re all essentially different. We’re the only brand that begins there. And we’ve done that for 20 years now…

…Ben: Revenues going like this intentionally. You’re the fourth CEO. At this point, how do you get the team on board with these crazy decisions you’re making when there are three other people came in here and tried to turn this thing around and didn’t?

Jim: I think from a leadership standpoint, the real puzzle in that first year was gaining trust from everybody that mattered. BMA was our bank. It’s kind of a lost cause, we had to replace them. They just weren’t going to buy it. But Whitney invested—that was the key—and we kept them with us all the way through.

The leadership team took time. You had to deliver sort of an outcome, but here’s what we did. Six weeks in, we redid the plan, took profits down. The plan was millions of dollars. They didn’t have a prayer to hit that. We took profit down, but it was a profit plan. They hadn’t made a bonus in four years.

We went after cash flow. That was shrinking the mix. We had our plan that year and people got a bonus. We hit the plan that we’d sent nine months earlier. I spent really eight weeks intensively looking at it, but I think we knew what we’re seeing. We generated $10 million of cash that first nine months. That’s how much we shrunk the balance sheet with focus.

Here was the key, though. You have to do Horizon 1, Horizon 2, Horizon 3. You’ve got to solve it all. I had 10 things to do. The board said, oh, my God, you’re crazy. Pick four. No, you don’t understand. We had to get the Adrenaline right because that shoe was critical for us.

We had to refine that shoe in 2001 for 2002, and we got it right. The fourth Adrenalin was an incredibly balanced shoe, had a multi-density stability technology in it, super balanced. ASICS started to not deliver, and we ran. We air-freighted 1 color, 18 months cycles. It saved the company. We had to finish that shoe in 2001 to deliver on 2002.

David: You guys are like a semiconductor company.

Jim: At Brooks, everything’s complicated. Everything’s competitive, but it’s like moving a wall of bricks forward. I think as a CEO, you got to move it all forward. When some things are falling behind, you got to get those up. You have to deliver the whole business model.

You have to do it sequentially over seasons in our business because if you come to market with a ho-hum product line, you’re going to shrink that year. The lead times in footwear, it’s not the car business, but it’s more like the car business than the t-shirt business.

There’s tooling on everything, 12 sizes men’s, 12 sizes women’s, widths, colors. It’s scaling these things. In fact, there’s a lot of tooling. It takes a half a million to a million dollars to bring one style to market. It’s a lot of tooling and inventory…

…Ben: If David and I were on Zoom with you, we would be getting ready to enter hour number two and try to talk about every year all the way through. Tonight, I want to focus on how you came through the pandemic and some of the unique ways that you early realized, running actually was going to be something that people started focusing more time on and you were able to kind of lean into this new behavior. Talk to us about March 2020 and how you paid attention to what was changing.

Jim: A couple of big advantages. First was literally an obsession on runners. Participation links to unit sales and volume. No other brand has that clarity because most of the products in the athletic footwear industry don’t ever go for a run, or play basketball, or really even go to the gym. It’s casual family lifestyle footwear.

There’s nothing wrong with that. Some of those businesses are great. But we had an advantage because 90% of our products went through a retailer. That’s the problem. Europe retail shutdown in one week, then all of retail rolled through North American.

By the end of March, not a store was really open. That’s the problem. Cash cycle froze. Oh, my God, nobody knew it was happening. We didn’t know how lethal this virus was, how transmissible, and so on and so forth.

It was white knuckle time and we were there with everybody else. Everybody can write a book on that, but here’s what we did. We saw phases because we’d seen during the recession, running is a bit recession-resistant. We saw that in the Great Recession.

David: I was thinking about that.

Jim: Because it’s cheap and it’s convenient, all you need is a pair of shoes.

David: It’s like the healthy alcohol during a regular recession.

Jim: Thank you. We were not an essential business. Marijuana and alcohol were, so figure that out. But during the Great Recession, 50% unemployment in Italy and Spain under the age of 30, running took off double digit growth after the Great Recession.

We’d seen that before and it turned out to be Covid-friendly. You now know the story. It was social distancing friendly, outdoors, walking, hiking, running all made the cut, but nobody knew that. We had an hypothesis. We created this frame on how we thought running would recover.

Here’s what we did. First of all, Strava data magic. Every day after the quarantine shutdowns, Strava activity was growing and they were sharing that. Then what we did, we have 40 in the US alone, 45 field marketing people, we put them in high traffic running parks at 4:00 PM every afternoon and they counted runners. Guess what? It was growing every day.

We watched digital sales. We have visibility on 85% of our retail sell through. Digital went from 30% of all of our products going through a website of somebody’s, ours, or another partner’s. It went to 80% by the end of April. We sold more in May 2020, almost all through digital than we did in May 2019.

Running made the cut. We grew 27% in 2020, that Covid year. We saw this was the key because of our customer obsession and our ability to work. Multichannel was a big advantage in that time because we can move inventory around and make it happen. Inventory, if it isn’t there, you can’t sell it.

Multichannel was a big advantage. The other was our focus on the runner. We turned our supply chain on at least 6–12 weeks before anybody else did. Because if you were a broad-based retailer, there was no clarity on when the customer was coming back. For a lifestyle product, nobody went outside for a year.

Ben: Was the fact that you exclusively made performance running gear gave you the confidence to flip it back on? Because if you’re making all kinds of stuff in your factory and you’re pushing all kinds of stuff through retail channels, most of it is not going to sell, so you can’t actually open.

Jim: That’s right. Apparel and footwear inventory is life and death. You’ve got to manage inventory well. Because if you have too much, you’ll ruin the next cycle of inline product. Inventory is really critical, but we managed and played that cycle really well. We grew to 27% in 2020. We grew 31% in 2021. We would have been up 40% if not for supply chain.

Ben: What did you end up doing in revenue last year?

Jim: $1.13 billion. Great year. We cracked a billion. The billion dollar club is actually a rarefied club. There are probably maybe two dozen, global. Chinese brands are there now. It’s a great club to be in.

What makes us unique is it’s all premium, full price, full margin product. Most of the other brands have good, better, and best. Those are retail-driven merchandising strategies. They’re not really consumer-driven strategies.

Ben: Normally, we talk about seven powers as we drift into analysis here. You’re a Berkshire business, so we’re going to talk about moats. What is Brooks’ moat and how do you think about defending the castle now that you have what you’ve built?

Jim: We think a lot about it. I think there’s also something I’d add to that. Part of the moat can be business models. Business models can be really powerful. One of the things you can do as a company to create defensive moat structures is business model execution at scale.

We now are executing retail partnerships with the best retailers for running gear to runners at Super Jock ‘N Jill in Seattle, Fleet Feet running down in (I think) Menlo Park. Obviously, some of the better sporting goods players and outdoor from REI to Dick’s Sporting Goods, we’re their number one brand.

We’ve earned that over 20 years and we have deep, broad partnership programs with them. Digital marketing, consumer journey, runners are digitally savvy. They’re obviously all over the web. They start their shopping experience there.

We reach them in active evaluation mode. Once you start looking at shoes, if you don’t see our ad, I don’t know how we missed you. We’re spending a lot of money at runners now, maybe more money at people who run in active evaluation for running shoes than any other brand. Very focused. That’s not easy to do in our industry at scale.

I would say this is our moat. I think runnability, fit, feel, and ride, there’s a lot of good shoes out there. It’s actually not easy to make a great shoe. Anthony Fauci made a joke about shoes. “Vaccines are tough, they’re complicated. It’s not like making shoes.” We get a lot of that.

The refinement that goes into mile and making mile 26 acceptable, is really big. I think great product is not as common as you might think. The people on the inside, the frequent learners know. I think you always got to lead with product. That’s the first brand experience, product experience.

I think we do some hard things. We build a great product consistently, year in year out. It fits and it rides well. Then what we do on the retail side, partnering, activating in real life, running and selling shoes in real life events, and all the like, we do that better than anybody else. We service them. We deliver on time, complete. The digital piece, we’re excited about it. We’re still just getting started there, but we’re really focused on it.

David: I’m curious. I hadn’t even thought about Strava and the amount of data that you’re able to see from that. What does the digital side of running in the future look like for Brooks and for the industry?

Jim: It’s interesting because quantified self and those tools have been ubiquitous. They’re out there. The Apple Watch is a damn great product. What’s interesting about that is both Under Armour and ASICS have spent hundreds of millions of dollars on digital apps. I think they’ve really struggled a long time.

David: Runkeeper and MapMyRun both.

Jim. Exactly. I wanted to buy every one of those and Warren wanted me to do the multiple on EBITA. There was no EBITA. Let’s just say it’s hard to do acquisitions sometimes.

David: At least one of them was a completely free product, I think, right?

Jim: Oh, man. They don’t make money. Under Armour is trying to sort through that now. They’re starting to shrink, so as Adidas. Those tools are really powerful for data, but how do you monetize it? We haven’t gotten there yet, but we’re building a Brooks Run Club. Finally, we’ve launched.

It’s not a loyalty program, but we want to engage our zealots. We want to engage our true believers. The data piece of that is going to be key. We want to come up the kinetic chain and find a sensor system and a data capture system that can get to your biomechanics as you’re running. Because what happens is, if you run a marathon, your gait in the last 5–10 miles really degrades. And that’s where injuries happen.

We’re doing a lot. We have a lot of partnerships. We’re really trying to figure out how we get good runner data in real life, not just in the lab. In the lab, we can test everything, but we want to get out in the wild.

David: Do you think you need to do what the other folks in Oregon have done and build the whole consumer experience yourself? Is it a partnership?

Jim: We’re going to build it and we’re going to partner, too. Nike Plus is a fantastic ecosystem. It just is. I’d love to have an ecosystem like that. But we’re still selling more runners than they are.

We became the number one running shoe brand in the United States in the last 12 months last month, 21.5% share from performance running. We know where the battles are. I think one of those powers is we make money on that. The digital space, there’s a lot of carcasses there, but we’d love to have it, and we’re going to work on it…

…Ben: Yup. All right, one closing topic. You battled, survived, and beat cancer while building this incredible business. How has that changed your perspective on leading on the way you spend your days and on life broadly?

Jim: Let’s close it on a light note. Let’s talk about cancer. That’s the takeaway for these wonderful people. I didn’t expect it. It came out of nowhere. Unlucky. How did this happen? Esophageal cancer, I just felt awful. My worst running experiences I’ve ever had and I got the diagnosis. Chemo, radiation, surgery, complications in surgery, another surgery. but the good news is I’m cancer free. I think it’s gone. I think it’s out of my body. The bad news is I’m even slower and I’m kind of a Frankenstein in my systems, but it works. Everything works.

I think what I learned from that, though, is that every time I have a friend or a family member who gets cancer, I go to the web. You look at it, understand it, and what the treatments are. They always give you a five-year survival rate. My five-year survival rate was 20%, one in five. My five years is this November. Someone has kick its butt.

What I quickly figured out and I talked it through with my family and obviously with Warren, frankly, is that I decided that I was doing exactly what I wanted to be doing. I love what I’m doing. I’ve got family, I’ve got an active lifestyle, I’ve got this fabulous brand and a company that I’m a part of, and a team. I just love it. I don’t know what else I do, which is a problem.

I decided I didn’t want to live in fear. I didn’t want to live every day thinking about what I had to lose. I had a lot to lose. I didn’t want to be bitter about why me. I just decided I want to soak in everything I can on any given day. I want to be a CEO, I want to be a dad, I want to be a husband, I want to be a papa. I’ve got four grandkids. That was it.

I think for me, that was really powerful because I don’t want to be that cancer guy and they brought it up. It’s just not my thing. I’m glad to talk about it. I don’t hide it. I’ve learned a lot. I want to enjoy the things in life I really enjoy.

That’s where I learned, but I think everybody’s different. You do find out companies, when you hit challenges, you learn what you’re really all about. I think it’s the same for people, of course. I feel really lucky because I’m doing what I want to do. Cancer is in the rearview mirror. It’s good.

6. Martin Casado – The Past, Present, and Future of Digital Infrastructure – Patrick O’Shaughnessy and Martin Casado

[00:03:58] Patrick: How would you put chapter headers on the stages of cloud adoption, going back to, I think, Azure and AWS, are sort of mid-2000, 2005, 2006, thereabouts, relatively speaking, a short story. What do you think the major eras of the cloud story have been so far?

[00:04:15] Martin: Right before the cloud, of course, everybody ran their own internal IT. Right? And so they kind of write their own servers and their own wiring closets. The cloud showed up and the early usage was what you would typically find in a technology early adopter ecosystem. It’s more new projects and startups and hobbyists, the average workloads were relatively small. There was exceptions to that of course, like Netflix is a very famous one, which went all in the cloud very early. But in general, that was what it was. This is like 2005-2010 timeframe and still was very experimental. A lot of the time there was big discussions on whether the enterprise would actually go into the cloud. When I ran network and security for VMware, which is 2012-2016 timeframe, I think that was the more mainstream adoption of the cloud. You saw large organizations, traditional enterprise moving workloads to the cloud, very serious discussion with the Fed and the government. It became a mainstream way of doing things. If you were a large organization and you didn’t have a cloud strategy, I mean, you were either considered a laggard or a special case. That brings us to 2018-2019, and now we’re seeing a shift where the move to the cloud has implications on your finances, because now instead of you being able to buy a physical asset and internalize that, you’re basically paying a portion of your income to a third party.

Now there’s a lot of discussions around, how do you optimize the use of cloud? Is the right thing to go all in on cloud? Is it something that you do a portion or whatever? I just want to make one quick analogy, which is, I always view companies going in three stages, the product stage, the sales or growth stage, and then the operation stage. The product stage you’re finding product market fit. The sales stage is you’re getting to repeatable sales and growth. You don’t really worry too much about unit economics. And the operation stage is when you care about unit economics and you go into multiple products and you do all the operation of complex things. The cloud had gone through the exact same three phases, which first was trying to find product market fit, which tended to be within new projects, funding the projects. Then it went to the growth phase where everybody went all in and didn’t worry about the implications to the economics of business. Now we’re at the operations phase where we’re starting rationalize all of that.

[00:06:26] Patrick: Maybe tell the story of Dropbox, which I think as an individual company, is a great example of cloud isn’t just some panacea. It has incredible benefits in terms of how quickly you can get going, outsource the reliability to somebody else that’s just focused on this, AWS or whatever. But from a cost standpoint, it can get really out of hand. I think Dropbox is a good and probably unfamiliar to most tale of going the other direction.

[00:06:49] Martin: There’s basically two trends that happen at the same time. It’s important to understand those two trends to understand what happened at Dropbox and actually a number of other companies too, it’s not just Dropbox. The two trends are the following, the first one is cloud, which we talked about. The second trend is SaaS. And specifically what’s unique to SaaS is, is before if you were a software vendor, you would build software and you’d ship software, and somebody else would run it on their own infrastructure. Your COGS, your cost of goods as a software vendor did not include the infrastructure that it was being run on, because it was being run on somebody else’s infrastructure. For example, my startup, we built software for networking, we shipped it, other people would run it on their infrastructure. However, if your SaaS, if your product is software as a service, then part of your cost of goods is actually the infrastructure. Someone comes, says, “I’ve got a SaaS site and someone comes and uses it, then they pay me some, and then I pay say, AWS a portion of that.” That is a change of cost structure. The books look very different.

While the cloud is getting adopted, all software is going from basically on-prem to SaaS, and in some cases, and there’s many of these cases, it turned out that it was very tough to get software margins just because the cost of the cloud services on the backend was so high. The era of shipping software, we’d all say these companies have 80% margins because you basically write the code once and then it’s free to copy bits, so you just ship it to everybody else. Especially in infrastructure, there’s many companies that felt like they’re basically reselling a thin layer on top of AWS or one of the big clouds, and then paying a large portion back to them. For example, I know multiple companies that are household names, where they’ve got product lines that have 0% margins because all of the money goes back to the cloud services it’s hosted on. Dropbox very famously had this situation where S3, which is the storage layer on Amazon is not optimized for this use case of many small objects. They found that they were paying a tremendous amount. Now, they were a very large user of this specific use case. AWS was not optimized for it. They decided to build their own internal infrastructure and probably saved the company at the time, by moving off the cloud and taking it internally…

...[00:10:08] Patrick: There was a really interesting thing that you wrote about the interesting concept of lost market cap of companies that were big users of the public clouds. I’d love you to walk through that concept, because you mentioned maybe this saved Dropbox, the company, and I get that that’s a very special, specific case, but it sounds like there’s a bigger story here of lost margin and therefore lost market cap because of the use of public cloud. I’d love you to walk us through that.

[00:10:32] Martin: We did this analysis, a very simple analysis, which we said, “Okay, right now there’s a tremendous amount of money that SaaS companies spend on cloud.” Let’s say if they brought it inside and they were able to drop those costs by half, which most people agree that you can drop the costs by half by bringing it inside. If you could do that, what would that do to the stock price? Normally when people look at this problem, they say, “Well, if you bring this inside, yes, it’ll save you money. You’ll save 50%, but that money won’t cover the team, the complexity, because that’s not a lot of money.” But if you look at the leverage that increase in margin does to the stock price, now you can free up for a large company, potentially a lot of money, which will flow over to cash, so it could be a big win.

What we learnt is that we looked at just public software companies. We looked at 50 of them. We looked at all of their spend and we said, “Let’s assume you cut that spend in half.” Then we calculated their margins. And then we said, “Benchmarking against other public companies, if their margins were half, what would that do to the stock price?” It turned out that it would increase in aggregate the stock price by $200 billion. Just a tremendously high number. I think we wrote $100 billion to be conservative in the actual blog post, but $200 billion. That means if you’re a company that’s say, worth $10 billion, and you can reduce your COGS by a bit, you could now become worth $14 billion, and then you have access to that for debt and hiring or whatever else. Because those two trends happened at the same time you had the cloud trend, as well as the SaaS trend, I don’t think there had been a lot of focus on what it does to the margin structure. We did the first analysis and said, “Actually it’s huge and it can impact your stock price.” I do think, especially now in this market correction, it’s a good thing for companies to start looking at…

…[00:14:03] Patrick: Before we get to something like Kubernetes, a little bit more complicated of a topic, I’d love to just return to super basics around digital infrastructure in the first place. And maybe even go all the way back to the original AWS website, where I think it was storage, compute, database. You mentioned networking. What are the base level, most primitives of the digital world? What are the most important, big things that actually happen? Because I’d love to understand what’s changed in those areas, like compute sounds like compute. What is changing in those three, four, five base level areas?

[00:14:33] Martin: The traditional infrastructure’s computing and storage, and then databases. Prior to cloud, you’d buy a server from whatever, Dell or IBM or HP. You’d buy a switch from Juniper or Cisco. You’d buy a storage array from whoever, EMC. And databases from Oracle. So all those have now been, basically, collapsed into a software layer over basically merchant hardware in the cloud. So you can get the equivalent of just compute by TC2. You can get very flexible networking layers, where you can put security policies and that’s largely implemented in software within the cloud. And then you get these scalable services, like the database services that are scalable because they’re in the cloud. And so that’s the bread and butter of the cloud.

For a cloud is basically you take these traditional abstractions, compute and storage, that were connected to a box and now they’re just basically software services that you can spin up and they should be able to grow to the size of the workload. But what has also happened in the last say, five years is a number of services then built on top of those that are higher level abstractions. So for example, machine learning workflows, analytic workflows, different types of databases that focus on different types of query patterns. I want to do analytics, or I want to do LTP, or I want to do very fast queries or time series. We have seen this renaissance of infrastructure, again, which used to be tied to a box now being implemented as a software services in a way that’s much faster than we’ve seen historically for that exact reason. That it’s not confined to a box…

…[00:17:30] Patrick: How will that happen? It’s like up against a death star or something. Like facing these three big companies. What do you think the best entrepreneurs will do? Pick something like crazy specific and just go after a single thread? How do you think this innovation cycle will happen?

[00:17:43] Martin: All of these companies are like, very strong repeat founders and the companies are Mighty, Fly Out IO and Mosaic. So, what do these companies do? So Mighty is browser as a service. I don’t know about you, but right now even as we speak, I probably have 30 tabs in my browser. My laptop goes slow. If you use Mighty all of that’s offloaded and you get this crazy good experience, which is great for most of us, especially as the browser gets more workloads. What is Fly? Fly allows any developer to run compute workload at the CDN tier all across the world, which is important if you care about responsiveness to the users. And what is Mosaic? Mosaic is, basically machine learning as a service. So they provide the ability to run models very quickly for AI specific loads. So, what’s unique about all three of these companies is all of them are doing their own hardware. They’re looking to run servers, they’re racking and stacking. And these are very, very strong founders.

All of them are repeat founders and all of these companies have great traction. So what is happening here? I think it’s exactly what we’ve spoken about, which is there just are across the industry certain workloads that, if you look at that very specific workload, the cloud is just not optimized for them. And that provides room for the Mighty and Mosaics and Flies of the world to provide something that is a very attractive proof point or performance point or whatever it is, with respects to the clouds. And so I don’t think the answer is we’re going to see a lot of drop boxes, where the end customer builds their own data center. I do think we are seeing very concrete signs of third party companies coming in and providing cloud services that are just at a much better price point, or a much better performance point, or much more optimized for a workload. And because the cloud is growing to size, there’s enough market now for solvent companies to do these. And so I think this is the very beginning, again, of a much bigger trend.

[00:19:33] Patrick: Can you say a bit about your view of what I’ll call API first companies? Which I think a lot of people would include in this definition of digital infrastructure. If I can hire Stripe to be my payments processor by simply inserting a API into my software that I build and care about. And then there’s one of these APIs that’s proliferating for kind of everything. What do you see happening here? Is that infrastructure in your mind? Where does this fit into this equation?

[00:19:58] Martin: As markets grow, the unit to which you monetize gets more granular. And my favorite example of this, and it’s one that may be a cliche but it’s worth saying, is the car market. So, way back when in 1913, Ford had a factor called the Rouge River Factory. And this factory literally went in on one side, it was like water, rubber and coal. You know, and like iron ore, and what came out on the other side was cars. And the reason is there wasn’t a sufficiently large market for cars to actually have suppliers. You couldn’t be someone that provided wheels or whatever. And if you look at the car market now, I mean, there’s companies that provide nuts and bolts and you’ve got multiple tiers of OEMs and integrators, et cetera, et cetera.

So the same thing has happened to systems historically. So in the 1970s, the same company would build literally the chip, the motherboard, the sheet metal, the operating system of all the apps. And then of course the OS got disaggregated from the hardware and then the apps got disaggregated from the OS. So now what’s happening is the application itself is being disaggregated. You take any application, you blow it up and assume the market for this application or any application is so big that independent component of applications now can become companies.

So what does an application do? I mean, applications authenticate users, they need access controls, they need to send emails, they need to do payments. These are things that all applications can do. So it’s almost like every help or library in an application is now becoming a company. So much so that I remember even five years ago, you drive up 101, the heart of Silicon valley in the Bay area, and you’d have billboards where the entire company was an API. PubNub, Sendgrid, you know, Twilio. And so this is a major movement where now you don’t have to build a business app to build a company. And for an infrastructure person, this is super exciting because most of the founders I invest in are technical founders that are providing technical functions that are only useful to developers. And in the past, it was hard to build a business that way, but now you absolutely can.

If you’re in tech at all, or you’re an investor at all, I definitely think you should look at an application and assume that any sub-component does have the potential to now become a company, because the market is just so large.

[00:22:11] Patrick: What stage of that process do you think we are in? Twilio and Stripe, everyone knows turns out payments and sending messages. It’s almost like the equivalent of storage and compute in application building. Where do you think we are in that process?

[00:22:25] Martin: I think we’re still pretty early. I mean, on average, an application uses 17 external APIs. I think like a mobile app, something like that. But if you look at the use of libraries and open source and everything else, it’s still incredibly high for people having to integrate external components and management operate themselves. I think that there’s still a long way to go, especially as we get into kind of more complex things. So for example, every application often requires some sort of internal policy. Who can access what, or you know? And this is a very specific computer science problem. How do you build a language or a policy language that kind of accesses, that allows a third party to declare a set of rules and mitigates access to those rules? Like, this is a component in most programs that can be pulled out and turned into a company. There’s a number of companies looking at that, that are just getting started.

[00:23:16] Patrick: When it comes to this developer facing tooling, there’s this open source way of building and there’s the more proprietary, closed source way of building. What have you learned about what works well in which domain? And then I’d love to also learn, like if you’re an open source company versus not, what is more or less important as you think about product and go to market and everything like that?

[00:23:35] Martin: I’m starting to be of the opinion that as we move to SaaS and that’s the primary way of consuming infrastructure, which it seems to be, that open source matters a lot less. And the reason I say that is, if I’m a developer and I’m writing an application and I need to authenticate my users and I need to authorize their access to things, and I need to send them emails or send them SMS texts or whatever, I have two options. I could download some open source package and then operate that, or I could just use an API that somebody else operates. The secular trend is I’m going to use the API that somebody else operates. And if I’m doing that, whether or not the code for that is open source, doesn’t matter that much to me. So let’s take the case of it is open source. So, even if it is open source, there is some value there. A lot of actual code to running that service has to do with the operations of the service. Like, how do you make sure that it’s high availability? How do you debug it? How do you check for performance? Like, and that operations code is to be very specific to the actual service running. So it isn’t even useful.

So that would never be open source anyway. So even if I had the source code, I couldn’t really use it and operate it in the same way that somebody else could, or is running it. When it comes to dev tools, things that I am specifically using in my program as I develop, like that will always be open source and that’s very important. But anything that’s functional and offered as a service, I think the actual value of open source decreases. And what raises importance is actually open standards, which is, I still want be able to make sure that I’m not locked in to one and I can move between them, but that’s not an open source argument. That’s kind of an open standards argument. And so the role of open source has obviously shifted very, very quickly in the last 10 years, largely driven by this consumption with SaaS. And I think that we’re getting a more nuanced view of where it’s useful and where it’s not. Whereas 10 years ago, there was this broad consensus that open source is great and it’s going to take over the world. And that just doesn’t seem to be the case in the way that we all thought…

…[00:28:41] Patrick: Going back to this notion of, so if they’re the consumers of these APIs or little pieces of infrastructure, I absolutely love the Ford factory example, and what happens as it matures, that it’s so clean. What do you look for as an investor when you are seeing one of these, let’s say API forward or first companies for the first time? What is your method of investigation? How are you processing a new company?

[00:29:03] Martin: So throughout this all together, we talked about a trend. So there’s a lot of frontend developers. We talked about probably 100 to everyone backend. And those frontend developers, they’re building more and more of the application. So in the past, they had to… Were very tied to the backend more and more. Instead of having their own backend, they can use it an API from a third party company. Let’s say they’re using 20 little SMS or whatever. The interesting thing about these API companies that offer to the frontend is that the unit of consumption really is like a function call or an API call. So they almost have these consumer-like dynamics. So the primary evaluation criteria, to answer your question, and why it’s so different, in the past, if you’re going to evaluate a server company, who’s the buyer, what’s the go-to market motion, what’s the ACV.

You talked to a bunch of the buyers, you’d see if they can build the technology, et cetera. Now, with these API companies, you literally just can look at what the usage graphs are, how many users, how do they monetize them, et cetera, and it’s become much more of a bottoms-up, or SAS, or consumer type profile. So we stopped a lot of that approach to investing when evaluating these companies. It’s much less about can they build it, who’s the buyer, and it’s much more about how they use it in a practice, then it’d very interesting. A lot of these companies, they do. They’ve got these beautiful growth patterns, just like you’re looking at the next WhatsApp. They really are almost consumer-like phenomena.

[00:30:27] Patrick: What would be the most common red flags or disqualifying observations if you’re investigating one of these companies beyond lack of that nice looking usage or engagement?

[00:30:39] Martin: Well, I’ll tell you what I’ve gotten wrong. I do come from the older era where you actually evaluate the technology, you have a thesis on go to market. Often, we’ve seen these companies come in and they’ve got these beautiful usage graphs. They haven’t monetized yet, but we’re like, “Oh well, who’s going to pay for this?” Or this is just developers, like whatever. And then we kind of talk ourselves out of the deal, because we know the market better than the founder. And in almost every case, I’ve regretted that because the reality is, and this is an internal thesis of ours, is the graph in almost every case is just smarter than our theorying. The market actually knows what it wants.

These days, if one of these API companies is doing very well and the usage is great, I’ll give you an example, Hugging Face is a phenomenal company. And if you looked early on at the usage, this thing is a rocket ship, and you can have a bazillion theories why you can’t monetize the model, and you have a bazillion theories of why their go-to market is going to work. But the reality is the market loves it, it’s a great company. For me, it’s almost like a counter thing, which is, I do think that this API makes life a lot easier. You don’t have to have a grand unified theory about how things work, because you can literally just look at how this thing’s being consumed, because the consumptions become so bite-size; you get a lot of early signals. I think it really boils down to…

[00:31:53] Patrick: It comes down to usage.

[00:31:55] Martin: Yeah, to usage.

[00:31:56] Patrick: How should these things be priced? What have you learned about actually building the revenue model around something that looks more usage based? All these examples, AWS, what we started with, these API companies, they tend to be usage-based pricing. So what have you learned about that? Is that the right thing? Do you think that changes?

[00:32:12] Martin: It feels to me though, apps are for seat pricing, and infrastructure is usage pricing, and that’s basically how it is. And if you’re in the frontend, you’re not doing usage pricing, you better get there. You really have to. And if you’re apps, and you can get away from seat pricing, that just seems like that’s where you’ll end up. I do feel that when it comes to company building, there’s a few areas where there’s no simple answers. There’s a lot of stuff that’s systematic, like how do you hire your sales force, it’s pretty systematic. How do you create your org is systematic. But one of the things that’s just not systematic is pricing. Pricing is actually dictated by the shape of the market and the shape of the product. And it takes months to get it right. I’ll give you three mental landmarks, and I think the rest is just actual work.

So one of the mental landmarks is pricing is often fixed by the market. And so you should look at the ecosystem and the other types of companies and how they price and I think you should follow that model. For example, if you’re building on top of Snowflake, how Snowflake charges is going to be very similar to how the customer expects to buy. And if you’re building on top of that, you’re going to want to align with that. And I’ve been in many cases where the companies wanted to innovate on their own pricing model, but the ecosystem alignment just wasn’t there. And it was just painful until they had to change. I think another mental landmark is the market will tell you the price over time, but not initially. The less that you have public or the less that you force your opinion on, the better it is. I do think that a lot of early sales discussions is just to figure out pricing, that’s what it is. Your goal is to reverse engineer how they think about that. The good news is because the consumption is so much higher on these, and the unit of consumption is lower, it’s per API call, there’s kind of a lot of room to experiment…

…[00:40:14] Patrick: And as you think about the ways that all of this intersects with the real world now, which we really haven’t talked about. We basically talked about digital infrastructure that leads all the way up to applications at the top end, and the APIs in between and all this great stuff. But it seems also like as we mature, more of this technology will apply to the real world too, whether that’s new kinds of hardware, whether that’s intersection with physical goods like cars. How do you think about that side of things and maybe the hardware world of technology?

[00:40:45] Martin: People have a hard time grasping what, let’s say, AI and ML concretely provide, because it’s such a diluted buzzword. So for everybody that’s listening, the important thing to realize is that what modern AI and ML does, which we’ve never really been able to do before in systems, is take unstructured data, and digitize it, and add it to the typical logic of the program. And we’ve never been able to do this with vision, like objects out in the real world. We’ve never been able to do this with natural language in the level of ASCII we can. We’ve never been able to do this with voice or speech.

That technology just hasn’t existed and so we’ve never been able to build the big programs around them. And now we can, and it’s a sufficiently different workload that two things happen. First, it pushes software into the realm of the physical world. We can now see things and interact with things. And we’re talking quantum leaps of accuracy improvement. It also drives the type of hardware and software that we build, because the workload is so different, right? So we’re seeing tons of innovation all the way down to the ASIC level. Mosaic as a company is building a data center focused just on this type of stuff. So I think that this really is a massive impact on infrastructure at large, not just the infrastructure, but also what sorts of applications software can go after.

[00:42:10] Patrick: It’s very cool to consider what all that might mean. I mean like self-driving cars is like the obvious constant example of what computer vision might allow us to unlock. Obviously, cloud had this crazy impact on the services you consume. It’s unlocked innovation by reducing friction. As you think about what’s going on in the digital infrastructure world, period, what are you most excited about in terms of what it might unlock in the 2020s or over the next decade that maybe we’re just starting to think about?

[00:42:38] Martin: Any problem that human beings go after that’s been outside of the realm of software is currently in the realm of software. And this is farming, agriculture, oceanography, you name it. And so I am a tech optimist and tech maximalist. I think that part of our job is to solve problems. It has really been limited to IT, like information. And now I go from IT to just tech. You look at any industry, any industry at all, and I think that it’ll be touched by this. That’s, to me, just tremendously exciting. What’s interesting, I would just say very quickly is we’re still asking the question. Are these still software companies or something different? So you could say, this is just software going after agriculture. Now you still have a software business, or you could say this is still an agriculture business, or you could say it’s something totally different. That’s a question I’m personally very interested in…

…[00:45:34] Patrick: If you put your investor hat on, I guess your purely selfish investor hat, meaning you were just trying to maximize returns, and you could somehow have a crystal ball that would reveal some information about the future, which is currently uncertain, where if you knew what the future was going to hold, be super valuable to you as an investor, trying to earn a return. What would you ask of that crystal ball? Like what would you want to know about the future that you’re not sure which way it will go?

[00:45:58] Martin: I am very curious about where crypto lands, and I think there are three potential views, right? On one end, on the most negative and barren folks are like, “This is all fake. It’s just Ponzi scheme, yada yada, yada.” On the extreme other end, it’s a total reformation, not just of technology and companies, but an organization. This is like everything. You don’t just have routers. You’ve got crypto routers. You don’t have just storage. You have crypto storage. You don’t just have businesses. You’ve got dos. You don’t just have money. You’ve got DeFi, like everything changes. And then there’s a bit of a middle view somewhere. This is a continuum which says, “You know what? There’s something very innovative there on the ability to build networks. There’s a number of primitives that are very innovative on the ability to build applications. There’s a number of innovations on how you offer new services to consumers where you don’t know the endpoints. There’s a lot of great primitives, consumption to monetization layer, just like social was primarily a consumption monetization layer.”

In that future, that layer is on top of a lot of systems, but you still have traditional computer networking and storage. You still have traditional clouds. You still have to know all of those things. And it’s something that’s added to that. And I think the answer to that question given the amount of money that’s already involved is enormous. And I don’t think anybody knows the answer. I tend to be in the middle where I think that there’s a real innovation there. I think there’s real value. I think it’s a real unlock for a lot of new business applications and use cases. But I think that infrastructure itself, a lot of the traditional models still applies. You still have to build databases. You still have to use storage. You still have to understand the trade offs of asset. A lot of these things still apply.

[00:47:41] Patrick: Obviously distributed systems. Some of the smartest people in the world are working in distributed systems, not necessarily crypto networks, but just like the ability to distribute state or update state constantly faster, smoother, or whatever. As an infrastructure person, when you look at the current technology in crypto networks, maybe the dominant three or four, what are you watching or interested by or looking at, the consensus mechanisms, the scaling ability? What are the dimensions that you as an infrastructure person are keyed in on today?

[00:48:10] Martin: The crypto origin solves a very important problem. That’s traditionally not been solved practically. And that is allowing basically an anonymous set of people with no prior trust relationship to have strong guarantees on something, right? Originally it was a ledger, and then it’s become more to generalized compute. That’s a very, very real innovation thing. And that unlocks very, very interesting business use cases like we’ve mentioned. But distributed systems is one of those things that you just can’t paper over with a thin software layer. You can’t hide it under an API. You’ve never been able to. There’s entire languages that just help programmers manage distributed systems. What’s important is what developers end up using, or what distributed paradigms they end up using, because that will drive the capabilities of the system. So if everybody says, “This is a purely distributed world and everything I write must be purely distributed,” that will have some implications of the type of systems that you can build.

So the thing that I’m most interested in as kind of an old distributed systems guy is what are the nature of the applications? Is this going to land in the realm of purely distributed stuff? Is it only embarrassingly parallel applications, like DeFi is an embarrassingly parallel application? There’s other things that are embarrasingly parallel. Or, is this going to go more to the model of general compute? Is that something people are going to do? Are people going to build like the AWS in crypto? The answer to that is actually very, very significant, right? You could say, “Well, listen, traditional distributor systems are great for building AWS, and this is going to just be the consumption monetization layer, or it actually is going to cause innovation in the way that we do distributed programming in the future.” I don’t think that’s clear yet where that’s going to land.

7. Watch: How Does a Dead Fish Swim Upstream? – American Physical Society  

Take a quick look at this trout swimming upstream. Notice anything unusual?

[Video of trout]

You’ve probably seen something similar countless times; the fish wriggles against the currents that push it backwards, slowly making headway until it turns and ducks out of the influence of the stream. Nothing special in that.

The only thing is, this particular fish is dead.

Yes, you read that right. No matter how lifelike it looks as it undulates across the tank, that same trout would just go belly-up if the current were switched off. So how can it possibly swim upstream?

A team of researchers from MIT and Harvard were equally surprised when they happened upon this phenomenon by accident. They’d been studying the way live trout conserve energy by swimming behind obstacles that block the current*, and unintentionally placed a dead fish in the experimental setup. When they took a closer look, they were stunned.

“It was incredible, very counterintuitive,” MIT researcher Michael Triantafyllou says, describing the shock he felt upon seeing the fish swimming upstream. He explains that while he knew trout were good at conserving and even extracting energy, he had no idea that they’d be able to extract enough energy from the surrounding fluid to swim upstream without expending any of their own energy. Immediately, the team started to investigate this new, seemingly impossible phenomenon.

As it turns out, objects that block the natural flow of water, like a rock or a boat, create a series of complex vortices in the current as the water navigates the obstacle. As anyone who’s tried to grab a fish knows, fish are quite flexible all down their spines, which allows the head and the tail to move independently of one another. In certain situations, the array of vortices forming behind an obstacle cause the body and tail to flap in resonance. This tilts the body in such a way that the vortices, which cause a pressure drop, apply a suction force that propels the fish forward.

As Triantafyllou explains, “You have a flow behind the obstacle, which creates a continuous stream of eddies. Each eddy contains energy and also causes the pressure in the fluid to drop… the eddy causes the body to flap back and forth, and the fish manages to extract energy.” Since all of the energy is supplied by the vortices, it doesn’t matter at all whether the fish is alive or dead, if the timing happens to be right.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Alphabet (parent of Google), Amazon, Meta Platforms (formerly known as Facebook), and Zoom Video Communications. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 22 May 2022:

1. Flexport: How to Move the World – Mario Gabriele

The name “freight forwarder” is strange. It’s the kind of term whose meaning seems literally evident but is blurred by a sort of tedium-cloud. It is the cousin of descriptors like “insurance agent” and “data analyst.” 

A freight forwarder is a tour guide for objects. Or, at least, that is how I have come to think of it after having it explained to me (and re-explaining it to myself after I have forgotten) by a series of patient people over the years. For, say, a shipment of pillows to make its way from Taiwan to France, it must move between ships, planes, trains, and trucks. As many as twenty different companies may be involved in a single shipment, each handling one stint of the multi-modal journey. Critically, each party is incentivized to care narrowly about their leg rather than the entire trip.

The freight forwarder sees this salmagundi of boats and warehouses and flight maps and says, Don’t worry, I’ll take care of it. With the savvy of a good tour guide, it helps the customer navigate the mess, keeping the itinerary, forewarning chancy routes, bum ports (the “bad neighborhood” of logistics), and charting an optimal path. They are the concierge of conveyance, consiglieri of transit.

As it turns out, this is a big business. The global freight forwarding market is pegged at around $182 billion with projections to reach $221 billion by 2025. That amounts to a modest compound annual growth rate (CAGR) of a tick below 5%.

This is also an extremely fragmented space. As of 2020, DHL Global Express led the market with 6% of annual revenue. Kuehne+Nagel and DSV+GIL followed with 4% each, succeeded by DB Schlenker and Nippon Express at 3%. Fully 60% of the market is composed of “others” – smaller providers that hold less than 2%, and perhaps not more than a few deciles…

…Chen’s primary mission is to guide Flexport into a phase of automation that lays the groundwork for its global trade platform ambitions. When I asked Chen which initiatives best showcased the company’s burgeoning abilities on this front, he shared two examples.

First, Flexport is devoting serious resources to digitizing global trade documents. Ingesting data from different languages and formats is the first step in building a library of “facts” around a shipment. Instead of “optical character recognition,” or OCR, Flexport relies on machine learning models developed with Scale AI. Whereas Flexport used to transform accurate data from documentation in two days or less, the recent partnership has helped the company do it in minutes while maintaining 95% accuracy. Chen noted that the reason accuracy sat short of 100% was not a technical issue but a result of human error at the outset. 

Second, Flexport is developing models to predict when a shipment will arrive to be unloaded at a given port. Getting seemingly simple things right can have meaningful downstream effects. Knowing when a ship is ready to be unloaded influences when a truck should arrive, for example. This, in turn, impacts how a warehouse might organize its space. Just as snafus in one part of the supply chain can lead to misery elsewhere, improvements can create secondary and tertiary efficiencies. According to Chen, Flexport is well-positioned to produce reliable models to this end thanks to its existing freight forwarding business, saying, “We believe we probably have the highest accuracy.”  

2. #028 – PM Lessons by Meituan Co-Founder – Pt 22: Demand and Supply – Tao

Understanding demand and supply is hard. Although you can only be in one of two situations: 1. demand outstrips supply or 2. supply outstrips demand, it’s hard to know at any point in time, which situation you’re in.

In our day-to-day work, these are the common problems that we’d encounter that have to do with demand and supply:

1. The team doesn’t proactively determine the situation of demand and supply. As a result, the operation lacks focus, and the approach is basically “throw it against the wall and see what sticks“.
2. The team knows that demand and supply each affects the other, but can’t make a clear judgment about which one is more important.
3. The judgment about demand and supply is correct, but the operation is not guided by the prevailing demand and supply conditions.

If any of the above three situations occurs, then the team would usually work in the opposite direction of the demand-supply condition. In fact, they may even have a strong incentive to get it wrong!

For example, if a company is in a situation where supply outstrips demand, it should be doing more work on the demand side. In reality though, more often than not, it’s still pushing hard on the supply side.

Why?

If supply outstrips demand, then the demand is very important. The demand side buyers know they’re important, and they’d be very hard to entertain. In comparison, the supply side sellers would be a breeze to spend time with because they’re the ones who are in a hurry and have something to ask for. As such, the team would have a strong incentive to continue working on the supply side and pretend the harder-to-deal-with side is less important. This was a frequent occurrence in Meituan…

…Here’s another example from the retail industry to demonstrate how frequently people get the demand and supply relationship wrong.

I asked the bosses of convenience store and supermarket chains, “In the retail industry, which one is more important, demand or supply?“

Without exception, all of them answered that supply is more important.

It doesn’t gel with my business common sense – with the industrialized manufacturing that we have today, most goods should have more supply than there is demand.

Therefore, the next question to figure out is – is this how they think, or is this how they act?

So I asked them another question, “At your company, what work do you absolutely have to do yourself?“

They all answered, “Choosing the location“. One of them even said he personally chose the location for close to a thousand stores they have all over the country.

For retail, location is the aggregation of demand. They say supply is more important, but their actions are pretty revealing.

3. Arena Show Part I: Idea Dinner + YC Continuity – Benjamin Gilbert, David Rosenthal, Packy McCormick, Mario Gabriele, Shu Nyatta, and Anu Hariharan

Anu: It is so true. I also think it’s really hard to understand and appreciate an organization like YC from the outside. You really deeply understand YC in only two ways, if you’re a YC founder and if you work within YC.

When I was at Andreessen Horowitz, I actually did not understand the depth and the cultural nuance with which YC was built. It’s really hard to grasp that.

David: Can we talk about that for a minute? I put this in the notes. My current mental model of YC is like a university, a top Ivy League University. It’s very hard to get into. You take classes every year or every six months. There’s an endowment attached to it, which is Continuity now.

Ben: Wait, David. What do you mean by endowment? Are you saying that all of the proceeds from YC exits go into a big pool of capital that then funds Continuity? Is that what you’re suggesting by endowment?

David: No, but I’m curious if that’s the case. I meant more just like, it’s really weird that a large part of the private capital markets and the venture capital markets in America, those dollars come from educational institutions, mostly private educational institutions. That’s just very bizarre. Anyway, that’s kind of what I meant. Is that a good mental model of YC? What is it like?

Anu: Yes. In fact, we say that. We say YC is university for startups. Think of the accelerator as the undergraduate program and Continuity is the graduate school.

We are modeled after university in the sense of we have applications you don’t need to know anyone to apply to YC. Second, we were the first to do mass production of investments in a batch of startups. No one had ever done that. Everyone usually does, I met a set of companies, we have a Monday partner meeting, and you pick one or two.

YC from day one was a batch. They always received investments together. That, I think, goes to the insight that the founders of YC had at the time, which was entrepreneurship is lonely. Being in a group is how you motivate each other to learn from each other. And that’s your peer group.

Fundamentally, it came from the approach of a university. Continuity is graduate school. As I talked about, Series A is just one of the programs we run. We have two others, Post-A and Growth. Post-A focuses on two months within you raise the Series A. That’s a six-week program. We rebatch you, so now you have a new set of peers.

Our scale founders come teach how to form a recruiting team, how to hire engineers, because your job changes as a CEO. No one is writing a book about how your job changes and how to learn. Remember, the median age of a YC founder is 27, which means they have probably managed the sum total of three people in their life before these founders.

David: They really are like undergrads.

Anu: Yeah. You cannot expect them to know. How are you going to provide resources so that they can learn from others and they do as few mistakes as possible and as quickly as possible? Because when you’re scaling, you just go on a rocket ship, but the amount you demand out of these founders is a lot. The bar you’re setting is really high.

In our community, that’s why Brian Chesky comes to speak every batch. He’s the opening speaker of every batch. Right now, for all these programs that we run, the group program is how to scale as a CEO. That’s literally the program. It’s an eight-week session. It talks about hiring execs, performance, management, culture, and so on.

We have scaled founders and scaled exec. Tony Xu comes for that. His execs, the CFO of DoorDash, the head of engineering of DoorDash, come for the respective session. It’s really good to see the entire community working to transfer their learnings to the next batch of companies…

…Anu: At YC, I would say, if I had to pick one thing YC is really good at across both early and Continuity is we go by based on founders. I know it sounds cliche, but I think we also have an incredible advantage in assessing what makes a founder a really good founder.

We have incredible amounts of data, pattern recognition, and learning that we have honed it to a point that we know how to spot them. You all have heard of the famous 10-minute YC interview and everyone asks, how do you know in 10 minutes? The fact is we probably know in the first two minutes.

We actually don’t need the full 10 minutes. But sometimes, one or two people will surprise us with the end of the interview. I think the three things that can articulate what it is on the founder we look for.

One is the continuity stitch. Often in the growth stage, people pay attention to the founder, but they don’t. If you’re at a venture fund or a growth fund, you probably hang out with the founder for a week or two weeks before investment, some a total of three hours. By the time Continuity invest, I probably know them for years, or months, and I’ve had those interactions.

Ben: You’re saying that you’re paying attention more to the qualitative founder properties—even at the growth stage—than you are to their specific growth rate, or what their margins look like, or anything like that?

Anu: Yes, but if the three qualities hold, the metrics will show. I can either look at metrics, but sometimes metrics don’t tell you how good the internal sausage making is. Many people can package the metrics in a fundraise deck. It’s very well done. We teach you to do it.

We’re really experts at it. Therefore, we know it’s going to look great. We also teach them what points to emphasize on. We actually do practice runs. In Demo Day, we actually even write the script sometimes if they don’t understand what it is.

David: That’s a how-can-I-help moment.

Anu: Yeah. What we look for is, how fast does the founder move? What is how fast do they move mean? How fast do they ship? How fast do they iterate? Is it single biggest indicator and correlation to how successful they’re going to be and how soon?

You won’t be right about members’ many decisions early on, but at least, are you learning from them fast? And are you making changes? That’s one we measure. Second of the growth stage is how well are you hiring. If you’re sloppy in hiring, it always hits a wall.

One of the things we look for is how well are they hiring engineers, how well are they hiring execs. Will they be able to convince an incredible exec to come join them? That’s second. Third is clarity of thought. Clarity of thought in the growth stage for us is, can they write out two pages what makes this a $5 billion or a $10 billion company really well?

If you’re doing those three things, you’re going to be on top of your metrics, your product-market fit, your attention. There will be rough edges. I think because of YC, we’ve had the benefit of watching everyone from day one.

We know how Tony scaled. We know deeply well how Josh had Gusto scale. We know a lot of those founders. We then know, okay, these were rough edges, these are okay. These other founders had and this is how you and I know.

David: We’ve told a lot of these stories on Acquired. If you’re a growth investor looking at these companies new, you’re like, I know this is all going great, but you know those companies don’t always all go great. Tony had some serious near death moments. Airbnb was not up into the right journey the whole time.

Ben: If I had to summarize, I know we’re interviewing you. Not me here, but it seems like you invest based on the inputs rather than the outputs or maybe the leading indicators rather than the trailing indicators, where if somebody’s operating with those three principles, the business probably won’t consistently produce the results that someone would like to look for in the growth stage investment. They have a much higher probability at any given time of producing high quality results because those are the inputs that matter.

Anu: Absolutely. That’s why we feel strongly that inputs can be influenced. If you’re learning best practices and those are your inputs, then you can actually influence company building. When Tony comes and teaches our Growth Program and says these were my darkest moments, these are my mistakes I made, and I sure hope you don’t make the same mistakes, but these are two things I did really well, that’s incredibly valuable. That color is very hard to get outside of YC.

4. 2022 SaaS Crash – Alex Clayton

The rapid decline in value of public SaaS companies over the past 6 months has undoubtedly already had a huge impact on private market valuations. That downward trajectory may continue even if the public markets stay flat at today’s levels. If public market returns cannot fuel venture capital fundraising from their limited partners, the flywheel will slow down. Investors will have fewer dollars to invest, companies will have less cash to hire and invest in growth, and outcomes are likely to be much smaller than previously thought. This reset has been swift and will soon be painful for many businesses that are burning too much money and/or those that will have to slow top-line growth. Moreover, there will be wide-ranging implications for employees and investors not only in the SaaS community but for all private technology markets.

And while much of the focus has been on the decline in valuations, there is another huge factor that can’t be overlooked – how could a recession or broader economic slowdown affect your financial profile? This could have an even bigger impact on valuations if the fundamentals of businesses change for the worse. While a large part of the sell-off has consisted of a move away from riskier asset classes in sectors such as high-growth SaaS to cash and value stocks, recent earnings results have been strong and business fundamentals have not changed broadly. But what if you traded at 50x forward revenue and are now trading at 10x, and your associated forward revenue also dips by 30-40-50% from your prior plan? The outcome is not pretty and one we have not yet seen, but could soon if the 2008-2009 Great Recession is any indicator.

The following charts look at Salesforce* and NetSuite*, two publicly traded SaaS companies during the 2008 Great Recession, and what happened to their respective value and financial profiles. Unfortunately, while this is a small sample size, these are the best precedents as almost all other SaaS companies went public after the Great Recession…

…Salesforce was almost a $1B implied ARR (annualized revenue run-rate) business growing over 50% year-over-year at the start of 2008. During the Great Recession, revenue growth slowed to 20%. Non-GAAP operating margins did hold fairly steady, though…

…NetSuite was over $160M in implied ARR growing ~45% YoY at the end of 2008 before slowing dramatically. The company did not grow for 3 quarters in a row before accelerating back to growth. Similar to Salesforce, they also held non-GAAP operating margins constant but slowed investment significantly. It would be hard to imagine a ~$150M ARR business today that’s growing fast grinding to a halt, but this happened for NetSuite. The company also sold to SMBs and the mid-market, a segment that was hit particularly hard during the Great Recession.

5. TIP447: How To Build A Human Bias Defense System w/ Gary Mishuris – Trey Lockerbie and Gary Mishuris

Trey Lockerbie (16:25):

Fascinating stuff. So I want to move on to the next one, which is base-rate neglect. So there’s this phrase that’s come up, I don’t know, maybe over the last decade, maybe longer, but it’s don’t fight the Fed. And we’ve seen a lot of help from the Fed when markets have declined in the past and we’ve seen the Fed reverse course on say, raising interest rates quickly due to recessions and other liquidation problems around the world. So from this, we may have misconceived notions on how either the Fed will react to markets if they continue to decline from here, for example, which would thus enact this base-rate neglect human bias. So walk us through what the base-rate neglect bias is and how we might be able to avoid it.

Gary Mishuris (17:05):

Yeah. So I think it’s fascinating that… And I think sometimes people talk about inside view versus outside view. So base-rate neglect refers to ignoring the experience of others in similar situations and just making an assumption based on what we think we can do in this situation. So let’s say a very simplistic example of someone flips coins 1,000 times, they get 50% heads, 50% tails for a fair coin. And somehow we convince ourself that we can take a fair coin and flip tails 70% of the time. And that sounds ridiculous when they phrase it that way, but sometimes essentially that’s what is happening.

Gary Mishuris (17:42):

So, for example, if you study great investment records, which I’m sure you do, you realize that there’s a certain range of access returns over decades that the best investors have been capable of. And if you take Warren Buffett out of the picture and if you take people who use leverage out of the picture, unlevered returns, there’s almost nobody over decades has exceeded 5% per year access returns with no leverage and so forth. Obviously, Buffett has done close to 10, but I don’t think there’s going to be another Buffett necessarily.

Gary Mishuris (18:11):

So when someone shows up and they think they can do 10, what they’re doing is they’re exhibiting example of base-rate neglect. They’re looking at their own strategy and they’re saying, I have these clever mental models, I have this process, I have this special sauce. So they start to believe their own marketing deck a little bit too much, and they forget that the people who tried and failed to achieve the 10% for years, as an example, have also had their special sauce and their analyst teams and this and that, and yet they were only able to do a certain…

Gary Mishuris (18:42):

Think about someone like John Neff who record is public or who had three decades of returns. He beat the market by 3% per year in arguably less efficient markets than they are today. So when someone shows up and says, “Oh, I’m going to beat the market by 10%,” that’s a little bit crazy, it’s a little bit arrogant. And again, I think we’re all overconfident, but come back to the Fed. So look at the last 10 years, we had almost a perfect confluence of events. We had interest rates coming down. We had unrivaled Fed manipulation of markets far beyond just the short term end of the curve. We had maybe as a result there or maybe as a coincidence, huge amount of speculation, both by retail investors and by a number of “institutional” investors, institutional in quotes, not naming any names, don’t ask. And you basically had over the last five years, you had 25% CAGR for large growth stocks or all cap growth stocks.

Gary Mishuris (19:33):

So if you are investing in the universe, it’s pretty easy to start believing your own BS and start saying, well, gee, yeah, no, I can crush the… I can do 20, 25% per year, but like really? Let’s zoom out over the long term US equities return inflation plus six to seven, depending on the time period. So if you think you can do 20% plus, you think you’re going to beat the market by double digit percent per year. And I know everyone thinks they’re very special, but that’s just a perfect example of the inside view. The inside view is all these specific details for why the past experience of others doesn’t matter. And the base rate is the past experience of others in a similar situation. And I think the best thing you can do is zoom out and say, “Well, whatever I think about my own capabilities, let me put a heavy weight on the experience of others and a small weight on why I think I’m going to do so much better.” And that’s probably the best you can do.

Trey Lockerbie (20:25):

That’s interesting, because I was wondering the distinction here between say the base-rate neglect effect versus say the recency bias effect, because what I was describing, I don’t know, it could maybe fall into both categories depending on how you look at it. So recency bias is when you’re essentially taking events from the past and extrapolating them into the future. So how exactly is that different or what are maybe some other distinctions between that and the base-rate neglect effect?

Gary Mishuris (20:49):

So I think a recency bias is almost a special case of base-rate neglect. So what are some examples of recency bias? Let’s say you have a company over the last couple of years, it’s been growing 30% per year and you assume it’s going to grow at 30% per year for the next one year. I’m obviously using extreme example. So that’s recency bias. You take a near term past and assume that’s going to be the same in the long term future. On the other side, let’s say you have a company that over the cycle has barely earned its cost of capital and averaged a dollar per share. But now the last couple of years been earning $2 per share and averaging 20% return on capital. So you are going to extrapolate that $2 and assume that’s the new normalized earnings for the business and say the new long term average earnings is $2. And this now all of a sudden, the 20% return on capital business or something like that.

Gary Mishuris (21:40):

In each case, you’re ignoring the base rate, the base rate in this case being the history of the company or the history of similar companies. So in the first case, the history of companies growing 30% for two years is mean version in the growth rate towards the growth rate in all companies. So just to level set everything that the average company’s profits over long periods of time grow in line with nominal GDP. But, by the way, ironically, if you look at Wall Street estimates, hey, now they assume the average company grow is going to grow earnings in double digits. Well, it hasn’t, it’s been growing five to 6%. And that’s an example of base-rate neglect because they forget that a fifth of the market is going to have negative earnings growth, but that’s a separate thing.

Gary Mishuris (22:20):

And then the base rate for a company that’s been earning its cost of capital and had a couple of good years is that the long term history is much more likely to be the best predictor than the last couple of years, which could be a cyclical high or something like that. So I think ignoring the base rate leads to the recency bias, where we put a disproportionate weight on what just happened and assume that’s a proxy for what’s going to happen as opposed to zooming out and looking at a much longer data series…

…Gary Mishuris (54:36):

And like you said, if you have areas where you don’t invest, that squeezes those 10 to 15 investments into the rest of the opportunity set, meaning that you might be correlated. But it’s not about gig sectors, which is a common misconception. So I’ll give you an example. So prior to starting Silver Ring, I managed a fund at my prior employer and I had two investments. One was SABMiller, which was a beer company, and the second one was Qualcomm. If you are running some bar risk model, and you’re looking at overlap, they’re completely different gig sectors. One is technology, the other is consumer. So no relationship, you’re good, you’re diversified. But the thesis for each one was predicated on rising middle class in emerging markets, meaning people were going to trade up and buy more expensive beer in China and other emerging economies and people were going to trade up to fancier smartphones, which was going to drive demand for Qualcomm’s products.

Gary Mishuris (55:28):

So here are two completely different industries where the same macro force, which is a tailwind, if it doesn’t play out would hurt the thesis. So looking for those correlations as systematically as possible, and thinking about what do I have to be right about each business five plus years out as opposed to what do I have to be right about each stock five quarters out, that’s the mindset you want to have. And also frankly, you have a set of risk reward trade-offs. Too many people make the mistake of sizing their largest investments based on upside. But again, going back to the safety first mentality, I size my positions based on downside, meaning my largest investments have the smallest downside. I have an investment, which maybe it’s a 30% of my base case value as to 30 cents in a dollar, but if that has 100% downside, that might not be my biggest position. So again, you want to have multiple layers of defense.

6. The Transcript Q1 2022 Letter – Scott Krisiloff and Erick Mokaya

Investors are asking whether this is the end of an era. For nearly 15 years global policymakers have battled a deflationary mindset with near-zero interest rates and quantitative easing. However, a series of supply chain shocks and monetary policy errors have sparked rising long-term inflation expectations. If we have exited the deflation era and entered into an inflationary one, it will mean structural changes in monetary policy, interest rates, and stock multiples. By the Fed’s own account, despite raising interest rates by 0.75% so far this year, it is still only on pace to get to a neutral interest rate by the end of the year. It has not yet entered the restrictive territory, which would usually be justified by >8% inflation.

“We’ve been accustomed to 40 years, basically, of one cycle, the whole cycle that we covered in the last quarterly review. Declining interest rates, declining tax rates, all these trends – – it’s all come to an end. Not just an end, it’s actually changing. But people haven’t wrapped their heads around that yet…There’s going to be a new cycle.” – Horizon Kinetics (INFL) Co-Founder Steven Bregman

“An entire generation of entrepreneurs & tech investors built their entire perspectives on valuation during the second half of a 13-year amazing bull market run. The “unlearning” process could be painful, surprising, & unsettling to many. I anticipate denial.” – Benchmark Capital General Partner Bill Gurley

While the Fed has talked about getting to “neutral” throughout this quarter, it hasn’t yet set an expectation for what neutral means. There seems to be some consensus that this neutral rate would be a short-term interest rate in the 2.5% – 3.5% range. Equity markets may not yet reflect this new cost of capital.

“I think I’m in the same areas as my colleagues philosophically. I think it’s really important that we get to neutral and do that in an expeditious way. “ – Atlanta Fed President Raphael Bostic

“I like to think of it as expeditiously marching towards neutral. It’s clear the economy doesn’t need the accommodation we’re providing. And so in order not to tip the economy over by reacting abruptly, we need to take a measured pace. But that measured pace still gets us up to the neutral rate, which I put at about 2.5% by the end of the year.” – San Francisco President Mary Daly…

…The Transcript is also closely watching continued lockdowns in China. The Chinese government’s zero-Covid policy has left hundreds of millions of people in lockdown even though the rest of the world has returned to normal. The effects of this supply chain shock have still not entirely made their way into the economic discussion.

“I think a separate risk is kind of the impact of logistics and supply chains as we deliver product to China and from a more macro perspective just the port closures and the broader impact that we could see in China given the degree of exports they have just generally across the economy. With respect to the China quota difficult to predict.” – Intuitive Surgical (ISRG) CFO Jamie Samath

“...the situation in China is unprecedented. Shanghai, a city 4x the size of New York City, is completely locked down…China continues to battle COVID resurgences and navigate through prolonged lockdowns.” – Starbucks (SBUX) CEO Howard Schultz…

…Surprisingly, consumer spending has still only been moderately affected by surging inflation and falling financial markets. The covid-era stimulus has left consumer bank accounts with lots of reserves and consumers still have a significant amount of pent-up demand for travel, restaurants, and other entertainment. We are expecting to see some slowing of consumer spending and the real economy going forward in sympathy with the dynamics of capital markets.

“March was the eighth straight month in which inflation outpaced income with lower-income consumers being most impacted by rising energy and food prices.” – Wells Fargo (WFC) CEO Charlie Scharf

“Consumers are trying to ration their money a little bit more carefully because they’re trying to smooth out their cash flow.” – Affirm (AFRM) CEO Max Levchin

“when we think about where inflation is, there’s absolutely pressure on that low and middle income consumer.” – Macy’s (M) CEO Adrian V. Mitchell…

…No one really knows whether this is truly the end of an era and the start of an inflationary epoch, but this period is not without historical analogue. The transition from the deflationary 1930s to the inflationary 1940s was caused by World War II, which was also a time of intense supply chain disruptions coupled with huge economic stimulus. The Covid period has some similarities. Immediately following the war there was sharp and severe inflation for several years, which was ultimately brought under control by changes in monetary policy. In the longer term, huge investment in industrial capacity and human capital led to a consumer renaissance and low inflation in the 1950s.

The inflationary period of 1966-1980 is also worth studying if we are entering a new inflationary phase. An important takeaway from that period is that a surge in interest rates does not necessarily happen overnight. Instead, it happened in fits and starts over the course of several bear markets and recessions. Stock multiples ended that period in single digits, but the nominal value of the Dow hovered around 1,000 for more than a decade.

We may be entering a period in which the Fed raises interest rates more frequently than it lowers them, but the Fed is still very reluctant to cause a recession. If it looks like higher interest rates are putting employment at risk, the Fed is likely to abruptly change course despite inflation. The result would probably be positive for capital market valuations.

“You can’t think of a worse environment than where we are right now for financial assets..I think we’re in one of those very difficult periods where simply capital preservation is I think the most important thing we can strive for. I don’t know if it’s going to be one of those periods where you’re actually trying to make money.” – Tudor Investment Corporation Co-Founder Paul Tudor Jones

7. Trying Too Hard – Morgan Housel

Thomas McCrae was a young 19th Century doctor still unsure of his skills. One day he diagnosed a patient with a common, insignificant stomach ailment. McCrae’s medical school professor watched the diagnosis and interrupted with every student’s nightmare: In fact, the patient had a rare and serious disease. McCrae had never heard of it.

The diagnosis required immediate surgery. After opening the patient up, the professor realized that McCrae’s initial diagnosis was correct. The patient was fine.

McCrae later wrote that he actually felt fortunate for having never heard of the rare disease.

It allowed his mind settle on the most likely diagnosis, rather than be burdened by searching for rare diseases, like his more-educated professor. He wrote: “The moral of this is not that ignorance is an advantage. But some of us are too much attracted by the thought of rare things and forget the law of averages in diagnosis.”

A truth that applies to almost every field is that it’s possible to try too hard, and when doing so you can get worse results than those who knew less, cared less, and put in less effort than you did…

…But there are mistakes that only an expert can make. Errors – often catastrophic – that novices aren’t smart enough to make because they lack the information and experience needed to try to exploit an opportunity that doesn’t exist…

…Marc Andreessen explained how this has worked in tech: “All of the ideas that people had in the 1990s were basically all correct. They were just early.” The infrastructure necessary to make most tech businesses work didn’t exist in the 1990s. But it does exist today. So almost every business plan that was mocked for being a ridiculous idea that failed is now, 20 years later, a viable industry. Pets.com was ridiculed – how could that ever work? – but Chewy is now worth more than $10 billion.

Experiencing what didn’t work in 1995 may have left you incapable of realizing what could work in 2015. The experts of one era were disadvantaged over the new crop of thinkers who weren’t burdened with old wisdom…

…Doctors have their own version, as one article highlights:

“Almost all medical professionals have seen what we call “futile care” being performed on people. That’s when doctors bring the cutting edge of technology to bear on a grievously ill person near the end of life. The patient will get cut open, perforated with tubes, hooked up to machines, and assaulted with drugs. All of this occurs in the Intensive Care Unit at a cost of tens of thousands of dollars a day.

What it buys is misery we would not inflict on a terrorist. I cannot count the number of times fellow physicians have told me, in words that vary only slightly, “Promise me if you find me like this that you’ll kill me.” They mean it. Some medical personnel wear medallions stamped “NO CODE” to tell physicians not to perform CPR on them. I have even seen it as a tattoo.

The trouble is that even doctors who hate to administer futile care must find a way to address the wishes of patients and families. Imagine, once again, the emergency room with those grieving, possibly hysterical, family members. They do not know the doctor. Establishing trust and confidence under such circumstances is a very delicate thing. People are prepared to think the doctor is acting out of base motives, trying to save time, or money, or effort, especially if the doctor is advising against further treatment.”

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Intuitive Surgical, Meituan, Salesforce, and Starbucks. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 15 May 2022:

1. An Interview with Coda Founder (and Bundle Expert) Shishir Mehrotra – Ben Thompson and Shishir Mehrotra

SM: Maybe we can start with some background on where I came from. Before I started Coda, I spent about six years running the YouTube products at Google. Most of that time, our presumption was that YouTube was going to be an ad-supported product. Obviously, this is still how the majority of YouTube is run, but we always thought that at some point we would add in this paid model, and we’d have some way for creators to make money from payments or subscriptions, or so on, but it wasn’t ever a top priority.

We tried things, but we always put our fifth or six priority on it, and they never really worked, I think over time I counted ten different experiments that we tried. One of my favorite ones was we spent nine months on this paid platform launch, it made a hundred dollars. Not a hundred dollars per day, or a hundred dollars per week, but all time, a hundred dollars. We bought pizza for the team, we shut it down.

Through this period, we keep trying these experiments, none of them work. At some point, I ended up having this conversation with a friend at actually one of the cable companies. We’re describing how this was working and he asked me how we felt about bundling. I said, “Oh yeah, we’ve tried everything, but we’re not going to do bundling. Bundling is evil.” He said, “What do you mean bundling is evil?” It was very interesting, it just stuck in my head.

This part, I definitely disagree with you, because I think bundling is the most amazing thing ever.

SM: For sure! That’s where I changed my mind. But it’s like, you ask anybody about bundling and first off, we say the word bundling, what do they think of? In the US, they think of Comcast and nobody really has positive interpretations of Comcast. In their head, they’re thinking bundling is cheating somebody, but we basically came to the conclusion that that is incorrect. So I developed, started doing all this research, and started coming up with this framework of what became this paper Four Myths of Bundling.

The core idea is that bundling is actually beneficial to all three parties. It’s beneficial to the consumer, to the providers, and to the bundler. This is because the heart of bundling is based off of balancing the needs of superfans and casual fans, I think this is one of the things that people often mistake about it. Of the four myths, the fourth one is the one that’s most cited, which is the reality that the best bundles are the ones that minimize superfan overlap and maximize casual fan overlap…

…I think it’s always been so important to have something physical — in the case of cable companies, they had a wire. That was an obvious bundling point that actually had nothing to do with the programming. You got the widest possible array of stuff that at the same time made total sense together.

SM: Yeah. When we talk about this at Spotify, we call this go-to-market alignment instead of superfan alignment, people mistake the two. A famous Spotify bundle that really worked was the student bundle. If you’re a student, you can get Hulu, Spotify, and Showtime for five bucks a month. Most people think that’s a big loss leader, or a marketing stunt, or so on — it’s not at all. It makes tons of money for all three parties, and has grown that business a lot. But one of the things that makes it work is that the wire, to use your analogy, is the student. What are all the things the student needs when they go to school? You can start stacking all these services into it. To pick something out of the B2B world, the most famous bundler in that world is Microsoft…

...As I understand it, it was kind of how you got connected with Daniel Ek and you ended up joining the Spotify board. I’m curious, to the extent you can talk about it, beyond that student bundle, how does your thinking about bundling impact the way Spotify is approaching things? Is podcasting a bundle play? How expansive is this?

SM: The student thing was probably the first step, but by far the biggest bundling experiment at Spotify is podcasting. I think the core idea — Daniel and I started riffing on this, to give lot of the credit, Daniel had basically the same ideas, we were very aligned on how to think about this. He just asked me to help formalize them and write them down, which turned into the Four Mythos of Bundling doc.

At that time, Spotify was synonymous with music and still is for a lot of people. One of the insights that Daniel had, in thinking about bundling, is what if we were to take something, let’s use your wire analogy, that still had a through line that people could understand, but drew a totally different group of superfans.

Daniel uses this line a lot that the video market, depending on how you size it is call it a trillion dollars in revenue a year — the audio market, radio and so on, is a tenth of that. He often talks about this idea that “Are your ears really worth the 10th of your eyes”? Of course not, it doesn’t make any sense, and he talks about how that market can grow. But all of that was I think Daniel did this genius job of saying, “Hey, this through line is going to be audio,” but fundamentally, what we’re going to do is we’re going to take products for which the superfans have incredibly distinct audiences. The set of people that care about listening to mystery podcasts, or to news bloggers, or to sports bloggers, or so on.

Or subscriptions to podcasts like Stratechery, available on Spotify.

SM: Exactly! We should talk more about that too; I think the fact that’s available on Spotify is amazing. But I think that idea of, “We’re going to pull this thing together”, is absolutely the idea of minimizing superfan overlap, maximizing casual fan overlap. You can actually see it, we have a team of economists at Spotify that try to measure that impact, there’s a part of the paper that talks about this concept we call Marginal Churn Contribution (MCC), which is, if you think about how you should divide up accountability or money in a bundle, we believe the right way to do it is something we call marginal churn contribution, which is if you were to remove this thing, how many people would churn from the product?

I love that. I’m going to completely steal that terminology, because you see this again and again when people are talking about sports, “Why does ESPN command so much money?”, or “Why do regional sports networks command so much money relative to their tiny audience sizes?” It’s this exact point. If you really like sports and your bundle does not have ESPN, you are going to leave. We saw this last year: Disney just put YouTube TV over the barrel because they tried to go one day without ESPN. It was like, “Nope, not going back going to happen.”

SM: Right. If you’re a Lakers fan, you’re going to end up getting that network. It works. The mistake many people make is they try to correlate usage with MCC, with marginal return contribution, and it’s generally wrong. In fact, if you were to take a graph and you plot on one axis you plot usage, on the other axis, you plot MCC, sometimes people call it anchor value, you could draw a diagonal line through it and everything below that line is things where usage matters more than anchor value, or more than MCC and above that line is things where your MCC matters more than usage. You get two completely different business models. For example, much of the content we had on YouTube at the time drove significantly more usage than it did MCC, if I removed any piece of it, you would still just come.

Which is sort of the UGC [user-generated content] idea in general, isn’t it? In that case, you’ve completely commoditized your suppliers because there is no special supply. There’s always other supply to put there.

SM: Well that’s not a very positive way to think about it! What it does do is it leads to an advertising-based business model. If usage is more important than MCC, then the right way to monetize that product is probably advertising. On the other hand, if you’re above that diagonal, and you have things where access is more important than usage, no matter how much I love your podcast and your newsletter, I can’t listen to them over and over again, I’m not going to read the newsletter over and over again.

Please don’t.

SM: You’re only going to get so much usage out of me, but I pay for it because why? I’m sure you’ve asked, but if you ask people, “Why do you pay for Stratechery”? I think it makes me feel smarter, I feel better informed when I’m in this other discussion. I think you know that you’re well read by some really important people out there. It creates a common understanding for us, but it’s uncorrelated to how much time they spend on it, it’d be a dumb way to measure it.

I get in trouble when I go too long.

SM: Right! Exactly. I don’t want that, I want synthesis out of you. One of the things that happens, one of the reasons I’m so excited about the bundling work, it’s a fun theory. People have all sorts of different hobbies, I have this weird one, I like bundling. (laughing) I have rather normal hobbies too!

(laughing) Theoretically, if this weren’t a podcast to talk about Coda, I’d be like, “Oh my God, I can talk about bundling for an hour. I’m ready to go.”

SM: That’s right. Well, I’ll tell you why the concept of bundling is relevant. We talk about it with some very literal examples, and you talk about product bundling, and Comcast, and so on. But the core idea of “people value access over usage” is a really interesting idea. This idea of marginal churn contribution actually applies to products in general. You’re building a product, and you like Coda, and you say, “Hey, what should I do next?” You kind of have two choices. You have things that are going to drive usage and things that are going to drive new users, you’re going to create MCC. You can apply the exact same philosophy the same way, “I’m going to add this thing, I think it’s going to add new users”, or prevent them from churning, versus things that are going to increase usage. When you use that framework, you see the world a little bit differently, you think about marginal impact, which is much more powerful than some of the other way of measuring success.

2. Terra Flops – Matt Levine

An “algorithmic stablecoin” sounds complicated, and there are a lot of people with incentives to pretend that it is complicated, but it is not. Here is how an algorithmic stablecoin works[1]:

1. You wake up one morning and invent two crypto tokens.

2. One of them is the stablecoin, which I will call “Terra,” for reasons that will become apparent.

3. The other one is not the stablecoin. I will call it “Luna.”

4. To be clear, they are both just things you made up, just numbers on a ledger. (Probably the ledger is maintained on a decentralized blockchain, though in theory you could do this on your computer in Excel.)

5. You try to find people to buy them.

6. Luna will trade at some price determined by supply and demand. If you make it up on your computer and keep the list in Excel and smirk when you tell people about this, that price will be zero, and none of this will work.

7. But if you do a good job of marketing Luna, that price will not be zero. If the price is not zero then you’re in business.

8. You promise that people can always exchange one Terra for $1 worth of Luna. If Luna trades at $0.10, then one Terra will get you 10 Luna. If Luna trades at $20, then one Terra will get you 0.05 Luna. Doesn’t matter. The price of Luna is arbitrary, but one Terra always gets you $1 worth of Luna. (And vice versa: People can always exchange $1 worth of Luna for one Terra.)

9. You set up an automated smart contract — the “algorithm” in “algorithmic stablecoin” — to let people exchange their Terras for Lunas and Lunas for Terras.[2]

10. Terra should trade at $1. If it trades above $1, people — arbitrageurs — can buy $1 worth of Luna for $1 and exchange them for one Terra worth more than a dollar, for an instant profit. If it trades below $1, people can buy one Terra for less than a dollar and exchange it for $1 worth of Luna, for an instant profit. These arbitrage trades push the price of Terra back to $1 if it ever goes higher or lower.

11. The price of Luna will fluctuate. Over time, as trust in this ecosystem grows, it will probably mostly go up. But that is not essential to the stablecoin concept. As long as Luna robustly has a non-zero value, you can exchange one Terra for some quantity of Luna that is worth $1, which means Terra should be worth $1, which means that its value should be stable

All of this is, I think, quite straightforward and correct, except for Point 7, which is insane. If you overcome that — if you can find a way to make Luna worth some nonzero amount of money — then everything works fine. That is the whole ballgame. In theory this seems hard, since you just made up Luna. In practice it seems very easy, as there are dozens and dozens of cryptocurrencies that someone just made up that are now worth billions of dollars. The principal ways to do this are:

• Collect some transaction fees from people who exchange Luna for Terra or Terra for Luna, and then pay some of those fees to holders of Luna as, effectively, interest on their Luna holdings. (Or pay interest on Terra, creating demand for Luna that people can exchange into Terra to get the interest.[3])
• Talk about building an ecosystem of smart contracts, programmable money, etc. on top of Terra and Luna, so that people treat Luna as a way to use that ecosystem — as effectively stock in the company that you are building and ascribe a lot of value to it.

These things reinforce each other: The more fees you collect and distribute to Luna holders, the more big and viable your ecosystem looks, so the more highly people value it, so the more Luna they buy, so the more activity you have, so the more fees you collect, etc.

But there is no magic here. There is no algorithm to guarantee that Luna is always worth some amount of money. The algorithm just lets people exchange Terra for Luna. Luna is valuable if people think it’s valuable and believe in the long-term value of the system that you are building, and not if they don’t.

The danger here is that Point 7 never goes away. Any morning, people could wake up and say “wait a minute, you just made up this all up, it’s worthless,” and decide to dump their Lunas and Terras. If people decide to dump their Lunas then the price of Luna goes down.

If people decide to dump their Terras — “wait,” you say, “there’s an algorithm; the price of Terra can’t go down.” If people decide to dump their Terras, then the price of Terra goes down from $1 to like $0.97, and arbitrageurs step in, buy Terras for $0.97 and exchange them for $1 worth of Luna.

Yeah. Well. The problem is that if people lose confidence in this system, they decide to dump both Lunas and Terras. Someone sells some Terras. Arbitrageurs step in, buy Terra for $0.99, and exchange it for $1 worth of Luna. Luna is at, say, $40, so each Terra gets you 0.025 Luna. Then the arbitrageurs sell their 0.025 Luna in the market, which drives down the price of Luna, which is falling anyway. Someone else sells some Terras, but now Luna is at $20, so each Terra gets 0.05 Luna, which arbitrageurs sell, and now Luna is at $10, so each Terra gets you 0.10 Luna, which then get sold, so Luna goes to $5, so each Terra gets you 0.2 Luna, etc. There is no natural stopping point for this process because Luna is just a thing you made up, and because it represents essentially confidence in your ecosystem, and as the price of Luna crashes that confidence ebbs away. And so eventually Luna trades at $0.0001 and you exchange one Terra for 10,000 Luna and you try to sell them and there are no buyers and so no one wants to arbitrage the price of Terra and so the price of Terra falls below $1 and everyone gives up on the stablecoin and the ecosystem and everything and it all goes to zero.

3. Jeff Jordan – Building & Investing in Marketplaces – Patrick O’Shaughnessy and Jeff Jordan

[00:05:31] Patrick: eBay, since it’s literally the perfect model of a marketplace is maybe the place to focus on for now. What kinds of actions did that mean when you were at eBay operating to try to promote price discovery or price equilibrium or something like that? What were you literally doing?

[00:05:46] Jeff: The most interesting thing is early on we try all these initiatives that we baked on our own and debuted the community. And we found out the leverage was way more to watch what the nascent behavior the community was doing and seek to amplify it. So the iconic thing there is Simon Rothman who’s bounced around. He’s a Valley veteran now. Early on in his career was just a early exec there and he has a very high interest in collectible cars. And one day I think he was searching for Maserati or Ferrari and expecting to see little replica cars and he found real ones. And it’s just like, “Why are people selling Lamborghini’s on eBay?” Well, it turned out Lamborghini’s are only sold on the coasts. And so if you’re in the middle of the country, it’s very hard to buy one typically and eBay entrepreneurs were figuring out, “Okay, here’s what we do.” So we took that nascent behavior and built eBay Motors, which then made it much easier to list and discover cars, generated the supply and created the awareness. The best actions we had was watch that nascent community behavior and amplify.

[00:06:51] Patrick: When you’re looking at a new marketplace for the first time, I’ll hold off on the discussion between horizontal and vertical marketplaces which we’ll come to at some point. But if you’re just looking de novo at a marketplace as an investor with your investor hat on, what are the features that you are zoomed in on most quickly that matter to you with all this experience?

[00:07:09] Jeff: Two main ones. One is fragmentation of the marketplace. I often have used the difference between OpenTable and Fandango in explaining this. OpenTable, the average restaurant owner on OpenTable owns one restaurant. And so aggregating them is a pain in the ass. But once you’ve aggregated them, it’s a very valuable thing. Whereas Fandango basically has deals with the five or six major theater chains and any one of them can have market power because if AMC pulls out of Fandango, I am motivated to go to amc.com and figure it out. When I was explaining this theory to a fellow board member and accolade Michael Klein and I explained the theory and he looks at me very quizzically and I go, “What?” He goes, “You do know I’m the founder of Fandango, right?” You’re like, “Oh crap.” So one is fragmentation.

The other is ideally lead gen. You’re creating relationships that otherwise wouldn’t have been created. The thing you try to avoid is “Okay, I have a relationship with my car repair man, my hair stylist, my whatever and it’s a frequent relationship.” Those don’t do well because the service provider, they’ll pay a little bit for convenience. They’ll pay a whole lot for a new customer. Ideally you have a combination of currently inefficient market that’s very fragmented and lead gen is a part of it. So Airbnb has lead gen. Hosts are being introduced to guests they never would’ve known. It’s spectacularly fragmented. The average host owns one property. It has those two characteristics.

[00:08:38] Patrick: Maybe we should just go read Andrew’s book to answer this question, but what have you seen in common amongst marketplace businesses that are especially good at thinking about that lead gen part of the equation? Because the fragmented supply side or the fragmented supplier base, like you said, it’s a pain in the ass to get them all, but it’s kind of straightforward, like you just got to go get them all. What about on that other side, what’s shared in common amongst the most talented people that you’ve seen thinking through this problem of lead gen?

[00:09:03] Jeff: The best models are ones that don’t really rely much on paid acquisition. The best entrepreneurs have figured out hacks to get user demand at scale through a user proposition. And one of the most brilliant hacks on this was the OpenTable hack that preceded me. The team figured it out ahead of time is they build a widget that restaurants could put on their own websites to empower online reservations, because the typical behavior at the time is “I want to go to The Slanted Door.” Okay, let me search on Google for the Slanted Door so I can find the telephone number. Go to the website and you see this widget that says make an online reservation. It’s just like, “Oh I’d rather do that than pick up the phone and have that experience of, ‘Can you hold sir?’ get back to you and then call multiple restaurants.” Just awful.

And so we put it on there and what it ended up doing, the diner would click on it and was redirected to The Slanted Door page on OpenTable. They would then discover, “Oh my God, I can make an online reservation at all these” and they’d come back to OpenTable. They wouldn’t go back to Google. They’d quickly learn a behavior to go do OpenTable. OpenTable was getting paid to acquire their restaurants consumers. While I was there, we didn’t spend a penny on demand acquisition and we’re growing very nicely based on that. So the best models don’t really rely on paid. They figured out some other way to get that distribution…

…[00:12:09] Patrick: Talent density. Obviously eBay is sort of like patient zero for this online digital marketplace concept. I’m sure working with Pierre there was a fascinating experience. You were there right in the thick of it. What stands out as the most important things that you learned as an operator at eBay?

[00:12:24] Jeff: I learned to be an operator. I’d only had a couple semi operating jobs up to that point. While I was a CFO at the Disney stores, I was also responsible for managing the Disney stores in Japan, but we had someone on the ground so I was kind of overseeing the person who was overseeing it. When I got to eBay, I’d never really run anything. And so I joined, Meg was building bench depth so she found a job for me and had me managing two people, one of whom promptly quit to go run a Baja Fresh franchise, which at that point might not have been the best financial decision unless he owns Baja Fresh at this point. I was managing one person, then a few months in she reorganized and gave me eBay North America, which was the ebay.com website. Seven years later, I was managing 5,000 people.

One of the blog posts that I get the most comments on is I think it’s titled Leaving It All On the Field. It brings a sports analogy to managing a hyper growth business. Because early on you’re the player, things are crashing around you and you’re making every call. And then there was a point where I remember one night when I go home, I get to work at 5:00 AM and it’s seven at night, there’s still a line outside my door waiting for me to make decisions. I go, “This is not scaling. I got to change something.”

And you become a coach. You hire a bunch of people. You try to get them into a place where they’d make most of decisions similar to how you would. And then the mode’s very different. You turn into a coach. At some point with hyper growth, they can’t make all the decisions. So they have to build a team. They become the coach and you become a general manager and you’re further and further from the action in the field each time.

And then take it to its logical conclusion, at PayPal with 5,000 people I was commissioner of the league. And it’s interesting, the job is fundamentally different. You’re not in the action. You are orchestrating it. I called it a bunch of -tions, organization, motivation, communication. And I didn’t like the job anywhere near as much. I was very gratified that I actually appeared to be pretty good at it. But my career was just, I continually went back to earlier stages. eBay grew, I went to PayPal, PayPal grew, I went to OpenTable, OpenTable grew… And there’s a point at which the good news is I got pretty good at that stage of growth, consumer marketplace businesses at that stage of growth.

The bad news is the learning curve just shallowed out like crazy. When I’m operating, I’m always on, always stressed, always tired. And then you throw on board on top of it and that was a pretty toxic combination…

…[00:22:08] Patrick: What were some of the early surprising aspects of coming at it from the investor side? I’m especially interested in the pricing of rounds. I was told to ask you about pricing Instacart, for example. What lessons did you learn on the investing side that were completely new and different in those early years?

[00:22:24] Jeff: The good news is I was looking for a steep learning curve and it was way steeper than I thought. I was like, “Wait, I’m in the same room. I’m just taking a different chair. How can it be that different?” And man, is it different. Lesson one was it is a steep learning curve. Some of the early lessons, and still learning them, which is the interesting part, 10 years in. You have to continue to be adopting your decision framework. One was whenever I saw a bargain, I should run. It’s a sign of no heat. Whenever I did a bargain, I regretted it later. Whenever I was forced to pay up, to date that has been a very good basket of companies. And you mentioned Instacart. I saw Instacart late when Apoorva was raising. I think he saw a blog I wrote on demand economy and just reached out. And he goes, “Listen, really late process but we’d would love to talk you.”

So we have this great conversation. And I think it was a Thursday or a Friday. And he goes, “Listen, I have to decide by the end of the weekend. I’m getting so much pressure.” I crunch away on the weekend, digging into the details. I want to do it. I get okay for my partners to go in with a number. And I think it was I go in with something that’s a 100x current GMV, like $90 million. And he goes, “Jeff, I’ve really enjoyed our conversations. I’d really love to work with you, but you’ve got to know you’re less than a third of any of my other term sheets. And by the way, I’m deciding tomorrow.” And so then do I want to play? If I want to play, I’ve got to triple. And so over a weekend… The interesting one, going back to your partners and saying, “You know I asked for $90? I need $300.” that was a gut check, but there was so much to like about it. They’re like, “Okay, I’m going to climb the ladder.” I’m glad I climbed the ladder on it. A lot of the very best deals have that kind of pricing pressure, and the pricing’s set by the market. It’s not set by metrics. So you have to figure out, “Okay, do you climb?” And I tend to climb if I think it’s legitimate heat…

…[00:26:56] Patrick: As you start to dig on the layers of what’s driving marketplace businesses, consumer ones specifically, what tensions are healthy? There’s a lot of stakeholders in marketplaces, and not everyone can get the best of everything all the time. How do you like to look and investigate tensions inside of a network?

[00:27:13] Jeff: Tensions are great because there’s two sides or three sides, and there’s always tensions. It started at eBay. The sellers paid us, and so the obvious thing, give the sellers what they want. But it turned out for me, what made eBay work was the buyers. Amazon and Yahoo both launched auction products early at eBay. By the way, they were the gorillas at the time. Particularly Yahoo. It was a $100 billion dollar market cap early. They launch auctions, they make it free. We charged a list. They made it free. Amazon made it free. And they quickly got millions of listings, but what they lacked were buyers. And so the sellers went there and it was like they put up billboards and no one walked by. And so they came running back to eBay and redoubled, their efforts on our platform. Long had the philosophy that why the sellers came is we had a robust buyer base, and so then growing the business requires optimizing the buyers’ base.

And so eBay and OpenTable, we did things that the sellers, the business side didn’t like. They viewed reviews on OpenTable. At OpenTable. I have four web windows open. I have the one for OpenTable, I have one for our map because we didn’t have a map. I had one for Zagat and Yelp because there were no reviews. And you’re like, “Okay, I think I see the path forward here, provide an integrated experience.” So we go to the restaurant and say, “Yeah, we’re going to debut reviews.” And they go, “You cannot publish a negative review from a customer I don’t know. You’re my technology provider. What are you doing?” Kind of thing. And you’re just like, “We did research. If a customer opened a review, they were twice as likely to make a reservation.”

You’re working with them. And then finally if we couldn’t convince them, we gave them the ability to opt out. “We will not show reviews on your page if you don’t want it. Just know that every other restaurant’s going to have reviews and you’re going to look pretty stupid.” So there are always those tensions. I almost always bias towards the buyer side of the equation. People come to Airbnb, hosts come to Airbnb because it has the largest guest network in the world. The more guests you have, the happier the host will be in the long term. You’re kind of optimizing for buyers, for diners, for guests, and in spite of the fact that the other side’s typically the one paying you, do

[00:29:22] Patrick: Do you have good examples of when the supply side is actually the harder side of the network? I remember talking to Gurley about this and saying, “Usually if you get all the buyers, the supply will show up.” But I’m sure there’s some examples where it’s different.

[00:29:35] Jeff: Airbnb’s been supply constrained almost since I got involved in the company. The supply’s expanding, but they believe they’d do more business if they had more supply, high quality supply. So it is interesting. Particularly in the unconventional businesses… I’ve said this probably. The first time I heard the Airbnb concept I said, “That’s the stupidest thing I’ve ever heard.”

[00:29:54] Patrick: So many, yeah.

[00:29:56] Jeff: I’m intensely private. I don’t want someone on my house, a stranger in my house. I don’t want to be in a stranger’s house. It was just like, “Oh.” When it’s that counterintuitive, the supply side, evangelical people to kind of say, “I see it, and I enjoy it.” And Airbnb was part economic empowerment but also part human relationships. They’re people who like meeting strangers and talking to them and learning about them and figuring out… There are multiple satisfactions involved in that experience. But there are a lot of marketplaces, particularly the weird ones, that can definitely use more supply…

…[00:35:35] Patrick: How far into the evolution of one of these marketplaces do you think it’s really important to start honing in on, I guess I’ll call it unit economics or margins, or something like on DoorDash or something? For a long time it was, “Well look, at scale these will be amazing.” And it’s kind of nebulous, what scale meant and when that would be. How much do you think about maybe the margin profile of a marketplace as you’re investing, especially if it’s early on?

[00:35:58] Jeff: I don’t not look at it if the margin’s not bad. An extreme case of this was Instacart. The time we invested, he was earning something like $12 a order in money to Instacart, and he was spending about $30. And so-

[00:36:16] Patrick: That’s a pretty bad margin.

[00:36:18] Jeff: That was pretty bad. And so the work I did that weekend was around profitability. And it turned out that he was just starting to do deals with grocery stores where the grocers would give him better pricing and share some of the incremental revenue from the economics. And that, at scale, would dramatically improve his economics. So one is you had to believe he’d get to the deals with the grocers. And then could he get to price parity? And he laid out this waterfall of, “This is how I’m going to make money.” And it was very detailed. Apoorva’s superpower is optimization. He’s just said, “These are the 19 things we need to accomplish to make the unit economics work. And I’m halfway on this one and just laid it out.” And I haven’t looked at that sheet in a while, but it largely came true. He made the unit economics work.

The big swing was he got the deals with the grocers and then the advertising business, I think Fiji, just announced it would be over $1 billion this year. Amazon showed that’s very high margin income. So the existence of that ad business means he can provide a very compelling value prop to the consumer because they don’t have to pay the full fare for the delivery. They get it partially subsidized through the advertising venue. And so that’s been key to the working, but the economics were awful when we invested. And so the leap of faith there wasn’t people would want groceries delivered to their homes. The leap of faith was he can make the economics work.

4. ‘Go for the Jugular’ – Sebastian Mallaby

On Tuesday, September 15, the pound took another beating. Spain’s finance minister telephoned Norman Lamont, his British counterpart, to ask him how things were. “Awful,” Lamont answered.

That evening Lamont convened a meeting with Robin Leigh-Pemberton, the governor of the Bank of England. The two men agreed that the central bank should buy the pound aggressively the next morning. As the meeting wound down, Leigh-Pemberton read out a message from his press office. Helmut Schlesinger, the president of the German Bundesbank, had given an interview to the Wall Street Journal and a German financial newspaper, Handelsblatt. According to a news agency report on his remarks, Schlesinger believed there would have to be a broad realignment of Europe’s currencies.

Lamont was stunned. Schlesinger’s remark was tantamount to calling for the pound to devalue. Already his public statements had triggered an assault on Italy’s lira. Now the German central banker  was attacking Britain. Lamont asked Leigh-Pemberton to call Schlesinger immediately, overruling Leigh-Pemberton’s concern that the punctilious Bundesbanker did not like to have his dinner interrupted.

After several conversations, Leigh-Pemberton reported that Schlesinger believed there was no cause for alarm. His comments were not “authorized,” and he would check the article and issue an appropriate statement when he reached his office in the morning. Lamont protested that this was a dangerously leisurely response. Schlesinger’s purported comments were already on news wires; traders in New York and Asia would react overnight; Schlesinger needed to issue a denial quickly. But Germany’s monetary master refused to be hurried. He was not going to adapt to a world of 24-hour trading.

That night, Lamont went to bed knowing that the next day would be difficult. But he could not imagine how difficult.

Stan Druckenmiller, the chief portfolio manager at George Soros’s Quantum Fund, read Schlesinger’s comments on Tuesday afternoon in New York. He didn’t care whether they were “authorized;” he reacted immediately. Schlesinger had made it obvious that the Bundesbank was not going to help the pound cling onto its position inside the exchange-rate mechanism by cutting German interest rates. The devaluation of sterling was now all but inevitable.

Druckenmiller walked into Soros’s office and told him it was time to move. He had held a $1.5 billion bet against the pound since August, but now the endgame was coming and he would build on the position steadily.

Soros listened and looked puzzled. “That doesn’t make sense,” he objected.

“What do you mean?” Druckenmiller asked.

Well, Soros responded, if the Schlesinger quotes were accurate, why just build steadily? “Go for the jugular,” Soros advised him.

Druckenmiller could see that Soros was right: Indeed, this was the man’s genius. Druckenmiller had done the analysis, understood the politics, and seen the trigger for the trade; but Soros was the one who sensed that this was the moment to go nuclear. When you knew you were right, there was no such thing as betting too much. You piled on as hard as possible.

5. Tracy Alloway — Understanding Financial Crises (EP.104) – Jim O’Shaughnessy, Jamie Catherwood, and Tracy Alloway

Jamie Catherwood: What’s your process for learning those new things in each kind of major crisis? How do you approach going from no knowledge of plumbing or commodities kind of nitty gritty details today to being able to talk about it?

Tracy Alloway: So this is one of the reasons I really like the podcast format. And this is one of the things that we do on all thoughts quite a lot is we try to go as micro as possible. So if we know that there are supply chain issues, we will talk to people who are into trucking, people who are into shipping, people who are experts in the world of wooden pallets, which I didn’t know we had experts on wooden pallets, but it turns out we do. We have on the economics of nails, experts on trust plates, lumber, the list goes on and on and on, but we’ll try to talk to those people as much as possible to get a handle of what’s going on in their individual markets so that we can connect that back to the macro.

Jim O’Shaughnessy: That’s nice try by the way there, Jamie, trying to get her to subscribe to Investor Amnesia. I like it, always, always

Tracy Alloway: I am subscribed.

Jim O’Shaughnessy: [crosstalk]. Look at that. I got the pull quote for you though, Jamie. I do a lot of research for our guests. And then I also have a couple of colleagues who do research as well. And I love this quote that somehow got connected to you. And it’s on this idea, it’s a tale quote, which it’s basically, it’s much easier to be a macro bullshitter than a micro bullshitter, right?

Tracy Alloway: I’m not sure why that quote’s connected to me, but I do like it. I like it a lot. I think there’s a kernel of truth there, which is, you see a lot of prognosticators, a lot of forecasters who will come out and say, “The economy’s going to do this, inflation’s going to do this.” I give this eventuality a 40% chance, which is the ultimate MBS of prognostication. And with the macro, it feels like there are so many variables swirling around that. You always have an excuse if you’re off, right? Well inflation, maybe it was transitory, but now there’s possibly World War III with Russia and that’s led to more supply shocks.

Tracy Alloway: So really I was right. It was going to come down, but no one could have predicted that Russia was going to invade Ukraine. You see that all the time. With the micro, it is the ultimate expression of individual expertise. And if someone is living and breathing a sphere like wooden pallets or nails, the economic contribution of nails throughout history, they know that market. And if they fail to predict which way it’s turning, I feel like that’s really like, they have skin in the for something like that. So that’s why we really enjoy talking to the micro people. We enjoy talking to the macro people too, but you get different things from each group…

…Jim O’Shaughnessy: Switching gears a little bit here. What can you tell me about fancy chickens?

Tracy Alloway: I have an inordinate amount of interest in the subject of fancy chickens. I don’t know, my dream is to, Jamie knows this, one day I will own so fancy backyard chickens and they’ll be beautiful. And my dog Pablo will chase them around. And Jamie, your dog is invited too. So the reason I took an interest in chickens is I’m just interested in chickens, but there’s actually a really interesting financial history nugget that comes out of reading and researching about chickens, which is, there is a massive –

Jamie Catherwood: Great pun by the way, [inaudible] nugget talking about chickens.

Tracy Alloway: There we go. So in the late 1800s or mid 1800s, there was a massive chicken bubble driven by this Victorian fashion for having chickens. So the world was opening up. There was lots of travel. There was lots of exploration. People started discovering that you can go to Indonesia and find this really cool looking chicken and bring it back to London and sell it for a lot of money. So this industry of collecting and breeding chickens became a thing. Price has became absolutely crazy. There are pamphlets written about this. People saying how ridiculous it was that people were spending money on birds and objectively, there are a lot of weird financial bubbles that have occurred throughout human history, but chickens is probably one of the weirder ones alongside maybe be rabbits in Japan and things like that, beanie babies.

Tracy Alloway: But there’s actually a really interesting outcome of the chicken bubble, which is that it gave the raw materials for research to Charles Darwin, right? When he was really starting to think about evolution. So suddenly he was surrounded by all these wild chickens that had been brought in from Indonesia or Asia. And he was able to breed them with domestic chickens in Europe and then say, well, they can breed together. So they must be related even though they’re from opposite sides of the world, there’s a link here. And that was one of the foundational pieces of research to his theory of evolution that would come out a few years later. So, whenever we talk about economic bubbles, we usually talk about the economic damage that they reek on the rest of the world. But in this one instance, we can say that actually something useful came out of the crazy Victorian chicken bubble.

Jamie Catherwood: Well, one thing I wanted to mention, I think it was in Liverpool or Manchester when they, in the 1830s or ’40s when they unveiled the first railroad mine in one of those cities. I think the mayor took the first trip and died on the ride. I feel like I’m missing [crosstalk].

Tracy Alloway: Great advertising.

Jamie Catherwood: Something along those lines of, because people were worried. And it wasn’t really until, I think Queen Victoria rode the train that people kind of really trusted that it wouldn’t kill them. Because there’s some quotes of scientist saying that, that wouldn’t work because people will die of asphyxiation just because you’re going at such high speed.

Jim O’Shaughnessy: The high speeds, like 40 miles an hour.

Jamie Catherwood: Exactly. So it was just funny, the grand unveil makes someone trust in the railroads and dies in railroad.

Jim O’Shaughnessy: Let me pick up on that because I think that is something I’d like Tracy’s viewpoint on. What Jamie just said basically was a well-known and well trusted personage in this instance, Queen Victoria associated herself with the railroad and then suddenly everyone is like, “Okay, that’s good.” Is that possible anymore? Or have we so atomized the world that the Queen of England associating herself with something we’d be just like, whatever.

Tracy Alloway: So this is something that I think about a lot, which is one of our very early episodes was with an archeologist called Arthur Demarest, who he’s often described as the real Indiana Jones of archeology, he’s out in Guatemala or wherever digging pits. And I don’t know, finding offs snakes and that sort of thing. But he came on a couple times really in the early days of Odd Lots to talk about his research into the collapse of civilizations. And the thing that he pinpoints a lot of collapses on, particularly in South America is this over extension into complexity.

Tracy Alloway: So the society has become too complex to function both on a sort of a societal level, the way people are interacting with each other, but also on a logistical and supply level. So the way the cities are actually supplied from outside and the difficulty of getting resources in as you get bigger and bigger, and this is something that I think about a lot. I think there’s a very fractious media environment. My dad’s American, I just got back from visiting him. We watched a lot of Fox News and other [inaudible] content. And I can tell you, it is polar opposite to what I’m seeing elsewhere and when you have an environment like that, it becomes very, very difficult to be on the same page and to have those conversations about what is possible and what’s reasonable.

6. A Few Beliefs – Morgan Housel

The worst financial decisions happen when people risk what they need in order to gain something they merely want.

Unsustainable things can last years or decades longer than people think.

Tell people what they want to hear and you can be wrong indefinitely without penalty…

…The luckier you are the nicer you should be.

Past performance increases confidence more than ability.

Define what you’re incapable of and stay away from it…

…Read fewer forecasts and more history…

…A lot of denial masquerades as patience.

A lot of people have a hard time distinguishing between what happened and what they think should have happened given their world view.

About once a decade people forget that bubbles form and burst about once a decade…

…With the right incentives, people can be led to believe and defend almost anything.

Expectations move slower than reality on the ground, so a lot of frustration comes from clinging to the trends of past eras…

…Progress happens too slowly to notice, setbacks happen too fast to ignore.

We are extrapolating machines in a world where nothing too good or too bad lasts indefinitely

Optimism and pessimism always overshoot because the only way to know the boundaries of either is to go a little bit past them.

The world is governed by probability, but people think in black and white, right or wrong – did it happen or did it not? – because it’s easier.

7. Twenty Lessons Learned – Michael Batnick

Nothing lasts forever. When growth stocks were going up every day, it felt like it would never end. Now that growth stocks are going down, it feels like it will never end. Everything ends, eventually…

…Risk management is most critical when it feels like you’re getting punished for managing risk.

Nothing is a perfect inflation hedge. Not gold, stocks, crypto, or cash…

…Diversification is the only answer to an unpredictable future. If everything is working, you’re not really diversified…

…Analogs are dangerous. We know how things played out in the past. That doesn’t tell us how things will play out in the future.

The more confident somebody seems, the more cautious you should be in taking their advice….

You didn’t know this was going to happen. You don’t know what’s going to happen next.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Microsoft and PayPal. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 08 May 2022:

1. An Interview with “Father of the iPod” Tony Fadell – Ben Thompson and Tony Fadell

Just to touch on that — I love that analogy, I’ll go back to it in a little bit — but the story of the iPod is so crazy. You weren’t even hired until April, yet you shipped in October. How was Apple able to move so quickly? Is there any company that could do that today? I doubt that even Apple could do that today with all their resources. How did that happen where you shipped this completely iconic product that didn’t even exist in the imagination of anyone, I guess in your imagination to an extent, but walk me through that process and how was that even possible?

TF: I think it was a coming together of a lot of things. The first one was experience. I and the people around me had experience for ten years. I pulled in a lot of people from either or General Magic or Philips or other people I just knew that I’d met around Silicon Valley over time. So one was having that network of being able to pull people in who knew what they were doing on this product, that was one thing.

Second thing was having a lot of failure before building these things, and they didn’t really necessarily become commercial successes, they might have been critical successes. So you had enough time doing this stuff. You’re like, “Okay, I’ve done this. I know to make boards. I know how to get software packages together, put all these things to happen.” So again, that was doing something totally new from a product perspective, but the process wasn’t necessarily new.

I think the other one was we had incredible leadership in Steve Jobs. He decreed from the minute after we gave the presentation to him in March of 2001, it was “Go!”. I had already been running it for a year before that doing MP3 players in my startup. So it was like, “Okay”, take all the latest knowledge I had gained during the contracting period, and ran with that.

And then the other one was we just cordoned off and it was, “Make it happen”. I saw so many projects that died at Philips because they didn’t happen fast enough, politics set in. So it was like, “Okay, we have to build this. We have to build it quickly. The holiday season’s coming around. This might be our one and only chance, because who knows when Sony’s going to come in and steal everything” because they were the number one in all audio categories. Every audio category Sony was number one in. So it was like, “Well they’re going to come for this”. So speed was everything.

So I had just been tempered all the time. One is technology changes, the market changes so quickly, you need to have the right experience and process, and we put it all together. Obviously, it was wonderful to have Apple in terms of the customer service angle, parts of the operations angle, but we had to do a lot of new stuff that Apple had never done before, and obviously the marketing, product marketing, pulling all that stuff together. So we got to pick the best bits of Apple and have them focused on us because of the leadership. Then we were able to build very quickly the new bits, throw them together, and just run like hell because at the end of the day, Apple isn’t the Apple you know it today. Twenty-one years ago, Apple was suffering. It had around barely 1% market share in the computer business, in just the US, that’s not worldwide, Apple wasn’t anywhere worldwide. It was only worth $4 or $5 billion, I think. maybe even $3 billion in total. Now it’s worth almost $3 trillion or $2 trillion, whatever it is this week.

So when you have leadership, when you have a competitor or at least you felt there was going to be a competitor coming very quickly, when the technology was there, right place, right time, and we had the right experience, and the company was at its wits end, because it had tried everything it could do to try to get the Mac to get back into the forefront of consumers’ minds with the iMac, whatever, and that wasn’t really going well. This was, “You’ve got to make it happen”, burn the boats, do whatever it takes to see this first product out there and even then it was a marginal success. It was a critical success. Everyone was like, “Wow!” but a lot of people were like, “I can’t buy it. It doesn’t work with my PC.” It didn’t work with Windows. We had to work really hard to make it a success.

It’s interesting that you list all those factors because in your book, when you talk about your experience at Nest, which I think the acquisition from Google maybe was a little more fraught and dramatic than you might have wished it could have been. But you had this contrast, you talk about Google antibodies resisting you and you said, “Oh, we had Apple antibodies resisting us, as well.” But the difference was Jobs protecting you and you also had this cultural bit where Apple needed a hit. To that end, I’m curious, are we reaching a point, a decade past his death, where Steve Jobs’ management abilities are actually becoming underrated? There are the scare stories that are still around. We all know he was this innovator in design, but just from being a manager and getting stuff out the door?

TF: Well, there’s one which is getting stuff out the door, and that’s a process and having good process. There’s another one, which is getting very innovative things out the door, things that are going against the grain of the internal business itself. The iPod was totally different than the computers at the time, “What? Apple’s making what? Stick to computers, Apple.” that’s what we heard from some people. So there’s leadership when you’re maintaining or when you’re operating something that’s already standing and working. And there’s another type of leadership when you’re trying to do something inside an organization that may be successful, may be not successful, but doing something very against the grain and seeing it through and saying, “We’re going to burn the boats. And this is the way it’s going to be.” That takes a different type of leadership and it takes what I always call is air cover. If we didn’t get what we needed, because we were on such a tight schedule, I could only call in the airstrike so often, but, “Steve. Need help.” and from above he would fly in and go, “Okay, what do I need to do?”

We do that a lot today with the businesses we work with, and we have to be the air cover for that and the investments we make and what have you. We fly in and go, “Okay, can we help you in some way? Where can we go to third parties or other ones to help you get what you need to start up this startup?” Leadership is really the key difference in all of this and understanding the difference between data-driven and opinion-based decisions. Steve was really great at understanding what were opinion-based decisions, and it was his opinion at the end of the day that was going to rule, and he was going to make sure everyone understood that “We’re going to do this. And yes, we don’t know if it’s going to be a success, but this is what I want done. Get it done, please.”

You talked about this in your book, actually, the opinion-driven versus data-driven decision making, and how to build the iPod in the first place was an opinion-driven decision, but to bring the iPod to Windows ended up being a data-driven decision. And in the case of an opinion, well it was Steve’s opinion that counted, but because it was a data decision, that’s how you were able to actually change Steve’s mind about going to Windows. Did I summarize that point properly?

TF: Yeah. It’s really correct. Look, Steve’s opinion specifically was at the beginning of the iPod project was, “We are going to make this amazing thing called the iPod” — we didn’t know it was called the iPod at that time — but “We’re going to make this thing, and this is going to drive Mac sales”. So to use the iPod, you’re going to have to buy a Mac, and that was his opinion.

Two years in, the numbers were okay for the Mac fanboys who had Macs, but no one else was interested in switching to a Mac just for an iPod. So we had that data and it showed very clearly that that original opinion or that hypothesis was that more people would buy Macs because the iPod was available was not right. We had a few people, but it was not this huge mass of people switching from Windows to the Mac because the iPod existed and you had to have one. So over time, and this is the third generation, we had to have the Windows connectivity, the Windows functionality, compatibility, to make sure it worked.

And then all of a sudden people were like, “Oh, this iPod thing is really cool. I’m using it on my Windows device, on my Windows laptop, or what have you. But I wonder what the full Apple experience would be?” And then people started buying Macs after they got a taste of the Apple experience with the iPod on their Windows based computer…

…A couple other points. Another interesting episode that we talked about privately was the bake-off at Apple when it came to the iPhone, if it was going to be iPod-based or if it was going to be touchscreen-based. One of the points you made in your book is that the bake-off was very short and it was resource constrained. You needed to make a decision, and then you invested in the right one. And I think this came up in the context of Facebook changing plans on their virtual reality OS. They had the Android one and they had their internal one, and it went on for years. Why do companies fall into this? Is it just that they’re too rich? They have too much money, and so they’re just undisciplined about this?

TF: Absolutely! That’s exactly the right thing, is when there’s too much money, there’s too many people saying that they can do it better, and there’s no time limit or other constraints, money limit, market constraints, what have you, these teams go at it. If you remember, there were two different operating systems going on at the time at Apple, before Steve got back,

That’s right. Yeah.

TF: There were pink and blue and all these things, IBM had it. So there were all these different kinds of in-fighting that happens, and it’s all based on constraints. When there’s a lack of constraints, that’s where all of these things bloom. At Google, when I was there, there were at least four different competing audio projects for audio in the home for playing music. There was four of them! I’m like, four? Why is there four? Everybody had a slightly different take and nobody was willing to go and kill them and prune them and say, “No, this is the right one,” and take all the pieces together, because they were too afraid, for whatever reason, I don’t know. It’s hard enough to have one great product that’s orthogonal to what the company does and saying, “Oh, this is an all new thing,” to have four of them, and say, “We’re going to launch all of them at some point?” That just doesn’t make any sense. Constraints are really key there.

2. Going Where Few Have Gone Before – Inside All Four Rolex Manufacturing Facilities – Benjamin Clymer

While Rolex’s manufacturing and design capabilities were (and still are) the reason that this company is so respected by its peers, it was Wilsdorf’s knack for storytelling that would would elevate Rolex to become the archetype of the luxury wristwatch not only for those within Switzerland, but also all over the world.

In 1927, Wilsdorf heard of a woman British woman named Mercedes Gleitze who had successfully swum the English Channel. Wilsdorf asked Gleitze to wear a Rolex Oyster watch around her neck as she swam. It should be noted that Gleitze had attempted this feat seven times before making it successfully, and then, due an attempt by another woman to steal the spotlight, was asked to swim it again. It was this last time that Gleitze wore the Rolex Oyster, not on her wrist but around her neck. 

She didn’t make it. After 10 hours in the freezing water, she was forced to abandon the attempt and be pulled into her trainer’s boat, because of numbness in her extremities. It didn’t matter, and Wilsdorf ran an ad in London’s Daily Mail citing not this most recent attempt, but Gleitze’s earlier successful attempt (which, of course, she swam without a Rolex). Still, her Oyster did withstand up to 10 hours in the bitter cold water of the English Channel, which was no small feat (you can read a detailed account here)…

…This, my friends, is where things get good. Plans-les-Ouates (an industrial park outside Geneva that’s also home to, among others, Piaget, Patek Philippe, and Vacheron Constantin) is where the Rolex of our collective imagination comes to reality – complete with robotic inventory machines straight out of Star Wars, a private gold foundry, and iris scanners. Built in 2006, Rolex Plan-les-Ouates is the largest of all Rolex facilities, comprising six different wings that are 65 meters long by 30 meters wide by 30 meters high, all linked by a central axis. I should also note that everything you can see from the outside of the building is actually less than half of what Rolex has here – the complex is 11 stories high, but you can only see five from the outside. The other six are underground and completely hidden from a casual observer’s eye, or the eyes of would-be competitors.

Here there are not only no cameras allowed inside, but we are also asked to surrender our mobile phones. This facility is, in my opinion, the core of Rolex’s competitive advantage and unlike any other Swiss (German, or Japanese) watchmaking facility on the planet. It may actually be completely unique in other industries too. I’ll explain why below.

Upon entry (and surrender of all digital device), we take a small elevator a few floors underground. The doors open to reveal what looks to be something akin to Dr. Evil’s underground lair, in the best possible way. The floor is cement, the hallways are wide. Access control points are everywhere – if someone doesn’t absolutely need to be in a particular room, then they simply do not have access to it. We immediately notice a gigantic elevator door – and when I say gigantic, I mean an elevator at a scale that I’ve never seen. I inquire about it – it can hold a load of up to five tons.

We are shuffled into a secure room – we are about to see the legendary Rolex automated stock system. Our guide places his eyes to the iris scanner (no lie) the doors slide open, and what we see is downright startling…

…Sorry guys. No photos allowed, nor provided. So what I will do is give you my best written description of what this absolutely extraordinary automated system looks like. There are two 12,000 cubic meter vaults, spliced by a network of rails totaling 1.5 kilometers, transporting over 2,800 trays of components per hour between the 60,000 storage compartments and the workshops upstairs. The view is straight out of Star Wars, minus the 1970s camp. This is efficiency defined.

Once someone within the workshops above requests a component, this incredible system takes just 6-8 minutes to retrieve it and deliver it to their work station. I remember when I was in undergraduate business school, our supply chain professional proclaimed Wal-Mart to be the model of professional logistics. I would almost guarantee you he said that because he’d never been to Rolex Plans-les-Ouates…

…Rolex owns its own foundry, where it creates its very own formulas for three different kinds of gold, and its own formulation of 904L stainless steel. Every single alloy used by Rolex is produced entirely in-house because, as they are quick to point out, the composition of the metal is the most important factor in determining a watch’s aesthetic, mechanical, and dimensional properties.

Rolex is able to make these special compounds because they have invested in something that few other watch companies would even dream of: a central laboratory with world-class experts in not only materials, but also tribology – the science of friction, lubrication, and wear – chemistry, and materials physics. This laboratory was truly extraordinary to see, and what was perhaps most impressive about the lab was not only the incredible testing going on, and the machines they’ve developed themselves (for example, Rolex invented a machine to open and close an Oyster bracelet clasp 1,000 times in a matter of minutes), but also the people who work there. I was asked not to mention from where Rolex has retained many of its top-tier scientists, but you can guess, and they are 100 percent not from the watch industry…

…I think what was perhaps most surprising about my visit to Chene-Bourg was the quality of gemstone and setting work Rolex does. I don’t really think of Rolex producing many watches with diamonds and stones, and they admit they don’t. But, this is Rolex and if they are going to do something, they are going to do it the Rolex way. This means 20 in-house gem setters, some of whom have names like Bulgari and Cartier on their resume. The stones they use? Only IF quality – otherwise known as “internally flawless” for those not familiar with jewelry-speak.

One of the coolest things I saw here was a machine that Rolex uses to filter the stones they receive for fakes, or anything that might not be what it’s supposed to be. One assumes that any supplier of Rolex understands just how big a business it is and might be tempted to take advantage of this, perhaps by including fake diamonds in with the real stones. Yes, well, Rolex has a machine in-house that can filter stones in mass to cull out anything that isn’t a real diamond. The machine costs tens of thousands of dollars so I asked how frequently they received a stone from a supplier that wasn’t an actual diamond. The answer? About one out of 10 million. They do it anyway, because this is Rolex.

3. Where Do Space, Time and Gravity Come From? – Steven Strogatz and Sean Carroll

Strogatz (02:56): It’s very exciting to me to be talking with the master of emergent space-time. Really mind-boggling stuff, I enjoyed your book very much. I hope you can help us make some sense of these really thorny and fascinating issues in, I’d say, at the frontiers of physics today.

Why are you guys, you physicists, worrying so much about space and time again? I thought Einstein took care of that for us a long time ago. What’s really missing?

Carroll (03:21): Yeah, you know, we think of relativity, the birth of relativity in the early 20th century, as a giant revolution in physics. But it was nothing compared to the quantum revolution that happened a few years later. Einstein helped the beginning of special relativity, which is the theory that says you can’t move faster than the speed of light, everything is measured relative to everything else in terms of velocities and positions and so forth. But still, there was no gravity in special relativity. That was 1905. And then 10 years later, after a lot of skull sweat and heavy lifting, Einstein came up with general relativity, where, he had been trying to put in gravity to special relativity, and he realized he needed a whole new approach, which was to let space-time be curved, to have a geometry, to be dynamical. It’s the fabric of space-time itself that responds to energy and mass, and that’s what we perceive as gravity.

(04:14) And as revolutionary as all that was, sort of replacing fundamental ideas that had come from Isaac Newton, both special relativity and general relativity were still fundamentally classical theories. You know, we sometimes prevaricate about the word “classical,” but usually what physicists mean is, the basic framework set down by Isaac Newton in which you have stuff, whether it’s particles or fields, or whatever. And that stuff is characterized by what it is, where it is, and then how it’s moving. So for a particle, that would be its position and its velocity, right? And then, from that, you can predict everything, and you can observe everything and it’s precise and it’s deterministic, and this gives us what we call the clockwork universe, right? You can predict everything. If you knew perfect information about the whole world, you would be what we call “Laplace’s demon,” and you’d be able to precisely predict the future and the past.

(05:08) But even general relativity, which says that space-time is curved, that still falls into that framework. It’s still a classical theory. And we all knew, once quantum mechanics came along, circa 1927, let’s say. It was bubbling up from 1900, and then sort of — it triumphed in 1927, at a famous conference, the fifth Solvay Conference, where Einstein and Bohr argued about what it all meant.

(05:32) But since then, we’ve accepted that quantum mechanics is a more fundamental version of how nature works. I know — you said this for all the right reasons, but it’s not that quantum mechanics happens at small scales. Quantum mechanics is the theory of how the world works. What happens at small scales is that classical mechanics fails. So you need quantum mechanics. Classical mechanics turns out to be a limit, an approximation, a little tiny baby version of quantum mechanics, but it’s not the fundamental one.

And since we discovered that, we have to take all of what we know about nature and fit it into this quantum mechanical framework. And we have been able to do that for literally everything we know about nature, except for gravity and curved space-time. We do not yet have a full, 100% reliable way of thinking about gravity from a quantum point of view…

…Strogatz (11:32): So I think that segues very nicely into the next thing I was going to ask you. We’re hoping, by the end of this episode, to give people a feeling of what it means for space-time to be emergent. But what would it mean for you, or anybody studying space and time, for them to be emergent?

Carroll (12:05): So I don’t think that there is any such thing as a position or a velocity of a particle. I think those are things you observe, when you measure it, they’re possible observational outcomes, but they’re not what is — okay, they’re not what truly exists. And if you extend that to gravity, you’re saying that what we call the geometry of space-time, or things like location in space, they don’t exist. They are some approximation that you get at the classical level in the right circumstances. And that’s a very deep conceptual shift that people kind of lose their way in very quickly.

(12:58) It’s a tricky word. We have to think about it. Emergence is kind of like morality. Sometimes we agree on it when we see it. But other times, we don’t even agree on what the word is supposed to mean. So, the physicists, and mathematicians, and other natural scientists tend to — but not always — rely on what a philosopher would call weak emergence. And weak emergence is basically a convenience, in some sense. The idea is that you have a comprehensive theory, you have a theory that works at some deep level. Let’s say, the standard example is gas in a box, okay? You have a box full of some gaseous substance, and it’s made of atoms and molecules, right? And that’s the microscopic theory. And you say that, okay, I could — in principle, I could be Laplace’s demon, I could predict whatever I want, I know exactly what’s going on.

(13:47) But, we human beings, when we look at the gas in the box with our eyeballs, or our thermometers, or whatever, we don’t see each individual atom or molecule, and its position and its velocity, we see what we call coarse-grained features of the system. So we see its temperature, its density, its velocity, its pressure, things like that. And the happy news — which is not at all obvious or necessary, it’s kind of mysterious when it happens and when it doesn’t — but the happy news is that we can invent a predictive theory of what the gas is going to do just based on those coarse-grained macroscopic observables. We have fluid mechanics, right? We can model things without knowing what every atom is doing. That’s emergence, when you have a set of properties that are only approximate and coarse-grained, that you can observe at the macroscopic level, and yet you can predict with them. And weak emergence just means, there’s nothing new that happened along the way. You didn’t say that, oh, when you go to the larger scales and you zoom out, fundamentally new essences or dynamics are coming in. It’s just sort of the collective behavior of the microscopic stuff. That’s weak emergence.

(15:01) There’s also strong emergence where spooky new stuff does come in. And people talk about the necessity of that when they think about consciousness or something like that. I’m not a believer in strong emergence at the fundamental level. So, to me, what the emergence of space-time means is that space-time itself is like, the fluid mechanics. It’s like gas temperature and pressure and things like that. It’s just a coarse-grained, high-level way of thinking about something more fundamental, which we’re trying to put our finger on.

Strogatz (15:34): Wow, as you’re describing the gas in a box, I happen to be sitting in a box. I’m in a studio that is kind of box-shaped. There is a gas in here, which is the air that I’m breathing.

So anyway, yeah, very vivid to me, the example you’re talking about. And it is amazing, isn’t it? That there are laws at that collective or emergent scale that work, that don’t — you know, like thermodynamics was oblivious to statistical physics. In fact, was discovered first, and only later, the microscopic picture came out. And so, I guess you’re saying something like that would be happening now with space and time and gravity, that we have the macroscopic theory that’s Einstein’s.

Carroll (16:14): When I’m not spending my research time studying quantum mechanics and gravity, I’m studying emergence. I think that there’s a lot to be done here, to be sort of cleaned up and better understood, in a set of questions that spans from philosophy to physics to politics and economics, not to mention biology and the origin of life. So, I think that these are deep questions that we’ve been kind of messy and sloppy about addressing, but I don’t think that the emergence of space-time is difficult for that reason.

(16:45) So, when you talk about, is the United States emergent from its citizens? Or is Apple Computer Company emergent from something? Those are hard questions. Those are like, tricky, like “where do you draw the boundary?”, etc. But for space-time, I think it’s actually pretty straightforward. The lesson, the important take-home point for the podcast is, you don’t start with space-time and quantize it, okay? Just like when you have the gas in the box, you’re trying to get a better and better theory of the gas in the box, but you realize that it’s made of something fundamentally different. And I think that’s what I’m proposing, and other people are proposing for space-time as well, that the whole thing that used to work for electromagnetism and particles and the Higgs boson and the Standard Model, where you started with some stuff and quantized it, that’s not going to be the way it’s going to happen for gravity and space-time. You’re going to have something fundamentally different at the deep micro-level, and then you’re going to emerge into what we know of as space-time.

Strogatz (17:46): Shouldn’t we start talking about entanglement, at this point, maybe?

Carroll (17:49): Never too early to start talking about entanglement.

Strogatz (17:51): Let’s talk about it. What is it? I hear it a lot. I hear quantum people talking about it. Nowadays, especially, with quantum computing, we keep hearing about entanglement. Why don’t you just start with telling us what it means, where the idea came from?

Carroll (18:04): Yeah, I mean, let’s think about the Higgs boson. We discovered it a few years ago, it’s a real particle, and I wrote a book about it, The Particle at the End of the Universe. The Higgs boson — one of the reasons why it’s hard to detect is that it decays. It has a very, very short lifetime, right? So, you can imagine if someone put a Higgs boson right in front of you, it would generally decay into other particles in about one zeptosecond. That’s 10-21 seconds. Very, very quickly.

(18:31) One thing it can do, it can decay into an electron and a positron, an antielectron. So it can decay into two particles, electron and positron. Now remember quantum mechanics. So, you can predict roughly how long it will take the Higgs boson to decay, but when it spits out that electron and positron, you can’t predict the direction in which they’re going to move.

(18:54) I mean, that makes perfect sense because the Higgs boson itself is just a point. It has no directionality in space. So there’s some probability of seeing the electron, in a cloud chamber or whatever, moving in whatever direction you want. Likewise, for the positron, there’s some probability, seeing it moving in whatever direction you want. But you want momentum to be conserved. So you don’t want the Higgs boson sitting there, stationary, to decay into an electron and a positron both moving rapidly in the same direction. That would be a shift in the momentum, right?

(19:26) So, even though you don’t know what direction the electron is going to move in, and you don’t know what direction the positron is going to move in — sorry, I’m already, I’m being, I’m being the person who I make fun of, I’m speaking as if these are real. Even though you don’t know what direction you will measure the electron to be moving in, and you don’t know what direction you will measure the positron to be moving in, you know that if you measure them both, they will be back to back. Because they need to have equal and opposite momentum, for those to cancel out.

(19:54) So what that means is, if you believe all those things, right away, this is why we believe there’s only one wavefunction for the combined system of the electron and the positron. It’s not an independent question, what direction are you going to measure the electron in? What direction are you going to measure the positron in? It’s a statement you need to ask at the same time. That’s entanglement, right there. Entanglement is the fact that you cannot separately and independently predict what the observational outcome is going to be for the electron and the positron.

(20:26) And this is completely generic and everywhere in quantum mechanics. It’s not a rare, special thing. Many things are entangled with many other things. It’s the unique and fun and very useful time when things are not entangled with each other. It took a long time — like, Einstein and his friends — Einstein, Podolsky and Rosen, EPR — published a paper in 1935 that really pointed out the significance of entanglement. Because it was sort of there, already, implicit in the equations, but no one had really shone a flashlight on it, and that’s what Einstein did. And the reason why it bothered him is because when that Higgs boson decays and the positron and the electron move off in opposite directions, you can wait a long time, let’s say you wait a few years before you measure what direction the electron is moving in.

(21:14) So, both particles are very, very far away from each other. And now when you measure the location of one, supposedly the location of the other one is instantly determined. And there’s no limit of the speed of light or anything like that. So for obvious reasons, Einstein, very fond at the speed of light as a limit on things, he didn’t like that. He never really quite thought that that was the final answer, he was always searching for something better.

Strogatz (21:39): And the argument goes nowadays that it’s okay, it’s no violation of special relativity, because you can’t use this to transfer any information or something? Is that the statement?

Carroll (21:39): Yeah, well, you know, there’s, there’s a whole bunch of statements that one can make. But the one that we absolutely think is true, is the one that you just made. If you imagine these two particles moving back-to-back, and one person detects one, and there’s another one, you know, a light-year away, who’s going to detect the other one, the point is that they don’t know what your measurement outcome is, you would have to tell them.

So even though in the global point of view, now, the location where the other particle is going to be detected is known to God, or to the universe, it is not known to any particular person sitting at any location within the universe. It takes the speed of light time to take a signal that would let you know that there is some now new fact about the matter, where you’re going to observe the positron. So, you cannot actually use this for signaling, you just don’t know what has happened when your other observer has measured something. And you can actually prove that, under reasonable assumptions, in the theory as we know it.

(22:43) So it seems as if this is the tension, that the way the universe works involves correlations that travel faster than the speed of light, but in some well-defined sense, information does not travel faster than the speed of light. That should worry you, that we didn’t define any of these words. So you know, what does that mean? You’re not going to build a transporter beam or anything like that out of this stuff.

(23:09) But — but let me just add one other thought, which I think, again, is a result of my quirky way of thinking about these things, which is not entirely standard, which is, people really like locality. Like, locality is a central thing. Locality is just the idea that if I poke the universe at one point in space-time, the effects of that poke will happen at that point, and then they will ripple out. But they will ripple out to other points no faster than the speed of light, okay? There’s nothing I can do to poke the universe here that will change the state of the universe in a tangible way very, very far away. And you can see how this entanglement thing is kind of on the boundary of that, like, the description of the universe changes instantly far away, but no information is traveling.

(23:51) So then, if you believe that locality is fundamental like that, then you’re sort of asking this question, why does the universe almost violate that but seem to not quite? That’s the puzzle that we have. And this is — a lot of ink has been spilled in the foundations of quantum mechanics.

(24:06) I think about it entirely the other way around, because I think of the wavefunction as the fundamental thing, right? I think that’s what exists in reality. And the wavefunction, like the wavefunction of this positron and electron is utterly nonlocal. It just exists all — it’s a, it’s a feature of the universe as a whole right from the start. So, I also have a mystery to be explained, but my mystery is the opposite way. It’s not “why is locality approximately or, you know, seemingly violated by entanglement?” It’s “why is there locality at all?” Like, that’s the puzzle to me.

4. Nvidia: The Machine Learning Company (2006-2022) – Benjamin Gilbert and David Rosenthal 

Ben: This was occurring to me as I was watching Jensen ensuring the omniverse vision for NVIDIA and realizing NVIDIA has really built all the building blocks—the hardware, the software for developers to use that hardware, all the user-facing software now, and services to simulate everything in our physical world with an unbelievably efficient and powerful GPU architecture.

These building blocks, listeners, aren’t just for gamers anymore. They are making it possible to recreate the real world in a digital twin to do things like predict airflow over a wing, simulate cell interaction to quickly discover new drugs without ever once touching a petri dish, or even model and predict how climate change will play out precisely.

There is so much to unpack here, especially in how NVIDIA went from making commodity graphics cards to now owning the whole stack in industries from gaming, to enterprise data centers, to scientific computing, and now even basically off-the-shelf self-driving car architecture for manufacturers.

At the scale that they’re operating at, these improvements that they are making are literally unfathomable to the human mind. Just to illustrate, if you are training one single speech recognition machine learning model these days—just one model—the number of math operations like adding or multiplying to accomplish it is actually greater than the number of grains of sand on the earth.

David: I know exactly what part of the research you got that from because I read the same thing and I was like, you got to be freaking kidding me.

Ben: Isn’t that nuts? There’s nothing better in all of the research that you and I both did to better illustrate just the unbelievable scale of data and computing required to accomplish the stuff that they’re accomplishing and how unfathomably small all of these are the fact that this happens on one graphics card.

David: Yeah, so great…

…Ben: It’s funny because that feels like that’s the killer use case, but that’s just the easiest use case. That’s the most obvious, well-labeled data set that these models don’t have to be amazingly good because they’re not generating unique output. They’re just assisting and making something more efficient.

Then flash forward 10 more years and now we’re in these crazy transform models with, I don’t know if it’s hundreds of millions or billions of parameters. Things that we thought only humans could do are now being done by machines and it’s happening faster than ever. I think to your point, David, it’s like, oh, there was this big cash cow enabled by neural networks and deep learning in advertising. Sure, but that was just the easy stuff.

David: Right. That was necessary though. This was finally the market that enabled the building of scale in the building of technology to do this. In the Ben Thompson, Jensen interview, Ben actually says this, when he realizing this talking to Jensen says, this is Ben talking, “The way value accrues on the internet in a world of zero marginal costs where there’s just an explosion in abundance of content, that value accrues to those who help you navigate the content.” He’s talking about aggregation theory.

Then he says, “What I’m hearing from you, Jensen, is that, yes, the value accrues to people to help you navigate that content, but someone has to make the chips and the software so that they can do that effectively. It used to be that Windows was the consumer-facing layer and Intel was the other piece of the Wintel monopoly. This is Google, and Facebook, and a whole list of other companies on the consumer side, and they’re all dependent on NVIDIA. That sounds like a pretty good place to be.” And indeed, it was a pretty good place to be.

Ben: Amazing place to be.

David: Oh my gosh. The thing is, the market did not realize this for years. I didn’t realize this and you probably didn’t realize this. We were the class of people working in tech as venture capitalists that should have.

Ben: Do you know the Marc Andreessen quote?

David: Oh, no.

Ben: Oh, this is awesome. Okay, it’s a couple years later, so it’s getting more obvious, but it’s 2016. Marc Andreessen gave an interview. He said, “We’ve been investing in a lot of companies applying deep learning to many areas, and every single one effectively comes in building on NVIDIA’s platforms. It’s like when people were all building on Windows in the ’90s we’re all building on the iPhone in the late 2000s.” Then he says, “For fun, our firm has an internal game of what public companies we’d invest in if we were a hedge fund. We’d put in all of our money to NVIDIA.”

David: It was a paradigm that called all of their capital in one of their funds and put it into Bitcoin when it was like $3000 a coin or something like that. We also have been doing this. Literally, NVIDIA stock—this is now 2012, 2013, 2014, 2015—doesn’t trade above $5 a share. NVIDIA today as we record this is I think about $220 a share. The high in the past year has been well over $300. If you realized what was going on, and again, in a lot of those years, it was not that hard to realize what was going on, wow, it was huge.

Ben: It’s funny. We’ll get to what happened in 2017 and 2018 with crypto and a little bit, but there was a massive stock run up to like $65 a share in 2018. Even as late as I think the very beginning of 2019, you could have gotten it. I tweeted this, and we’ll put the graph on the screen in the YouTube version here. You could have gotten it in that crash for $34 a share in 2019. If you zoom out on that graph, which is the next tweet here, you can see that in retrospect, that little crash just looks like nothing. You don’t even pay attention to it in the crazy run up that they had to $350 or whatever their all time high was.

David: Yeah. It’s wild. A few more wild things about this. AlexNet happened in 2012. It’s not until 2016 that NVIDIA gets back to the $20 billion market cap peak that they were in 2007, when they were just a gaming company. That’s almost 10 years.

Ben: I really hadn’t thought about it the way that you’re describing it. The breakthrough happened in 2010, 2011, 2012. Lots of people had the opportunity, especially because freaking Jensen is talking about it on stage. He’s talking about our earnings calls at this point.

David: He’s not keeping this a secret.

Ben: No, he’s trying to tell us all that this is the future. People are still skeptical. Everyone’s not rushing to buy the stock. We’re watching this freaking magic happen using their hardware, using their software on top of it. Even semiconductor analysts who are like students of listening to Jensen talk and following the space very closely think he sounds like a crazy person when he’s up there espousing that the future is neural networks, and we’re going to go all in. We’re not pivoting the business, but from the amount of attention that he’s giving in earnings calls to this versus the gaming. I mean, everyone’s just like, are you off your rocker?

David: I think people have just lost trust and interest. There were so many years, they were so early with CUDA and early takeout. They didn’t even know that AlexNet was going to happen. Jensen felt like the GPU platform could enable things that the CPU paradigm could not, and he really had this faith that something would happen. He didn’t know this was going to happen. For years, he was just saying that we’re building it, they will come.

Ben: To be more specific, it was that, well, look, the GPU has accelerated the graphics workload. We’ve taken the graphics workload off of the CPU. The CPU is great. It’s your primary workhorse for all sorts of flexible stuff. But we know graphics need to happen in its own separate environment, have all these fancy fans on it, and get super cooled. It needs these matrix transforms. The math that needs to be done is matrix multiplication.

There was starting to be this belief that like, oh, well, because the apocryphal professor told me that he was able to use this program that matrix transforms to work for him, baybe this matrix math is really useful for other stuff. Sure, it was for scientific computing. Then, honestly, it fell so hard into NVIDIA’s lap that the thing that made deep learning work was massively parallelized matrix math. NVIDIA is just staring down their GPUs like, I think we have exactly what you are looking for.

David: Yes. There’s that same interview with Bryan Catanzaro. When all this happened, he says, “Deep learning happened to be the most important of all applications that need high throughput computation.” Understatement of the century. Once NVIDIA saw that, it was basically instant. The whole company just latched on to it.

There are so many things to laud Jensen for. He was painting a vision for the future, but he was paying very close attention, and the company was paying very close attention to anything that was happening. Then when they saw that this was happening, they were not asleep at the switch.

Ben: Yeah, 100%. It’s interesting thinking about the fact that in some ways, it feels like an accident of history. In some ways, it feels so intentional, that graphics are an embarrassingly parallel problem because every pixel on a screen is unique. You don’t have a core to drive every pixel on the screen. There are only 10,000 cores on the most recent NVIDIA graphics cards, but there’s not, which is crazy, but there are way more pixels on the screen.

They’re not all doing every single pixel at the same time every clock iteration. But it worked out so well that neural networks also can be done entirely in parallel like that where every single computation that is done is independent of all the other computations that need to be done, so they also can be done on this super parallel set of cores.

You got to wonder, when you kind of reduce all this stuff to just math, it is interesting that these are two very large applications of the same type of math in the search space of the world of what other problems can we solve with parallel matrix multiplication? There may be more, there may even be bigger markets out there.

5. Twitter thread on an interview of Ted Weschler – Thomas Chua

1. Who is Ted Weschler? He was the founder and managing partner of Peninsula Capital Advisors. Between 1999 and 2011, the $2B fund returned 1,236% to its investors. He wanted to meet his hero and so he bided on the annual auction lunch with Buffett.

2. One fateful Tuesday morning, he received a phone call that changed his life. It was Buffett on the other end. He had won the annual charity auction lunch. Ted flew out to Omaha two days later to meet his hero. Everything clicked. Ted bid again the following year and won!

3. This time, Warren asked him:  “I think you’d be a pretty good fit out here. Would you have any interest in working at Berkshire?” He panicked. On one hand, he was running a successful fund and his family was in Charlottesville. But on the other, this is Warren Buffett!

4. He wrote Buffett a letter when he got back to Charlottesville explaining that it was difficult because his family was rooted here. Buffett replied “You can manage money from the moon as far as I am concerned.” Buffett was a real pioneer in the work from home trend 😂…

…7. Investing is a game of connecting the dots. We want to build up a lot of data in our minds and understand why the business will be vastly different five years from now than what the market perceives. He reads trade journals regularly to understand businesses…

…9. Why he always feel positive? United States has a system that works. There’s will be negativity every now and then. But if you take a long-term view, there’s innovation coming out every day and it keeps getting better. It’s hard not to be optimistic.

6. Sources of Enduring Business Success – John Huber

I recently read through the letters of Nick Sleep, who ran a very successful investment fund in the United Kingdom before closing it last decade. Sleep is a great thinker and I highly recommend his work. One thing Sleep wrote a lot about is how the average holding time period for many of the stocks he owned was around 50 days, whereas he planned to hold these stocks for more than 250 weeks (5 years). I think his key observation is important: The marginal buyer who is holding a stock for 2 months is not placing much emphasis on that company’s competitive advantage because that advantage won’t matter much at all over the next few months; what matters over that period of time are things like market perception, news flow, sentiment, and perhaps short-term business momentum…

…So what Sleep did is he decided to compete in a different game. Instead of attempting to determine how the crowd will react this quarter or how the trajectory of the business will fare this year, he wanted to focus on the factors that contributed to a business’s ultimate potential. What attributes give this company an advantage? What will lead this company to success through both good times and bad times (because if you’re a long-term shareholder, all companies face headwinds at some point).

…Sleep used the example of Walmart’s cost advantage. Walmart’s business model was to offer the lowest prices on everyday merchandise, and steadily gain scale advantages through larger and larger bulk purchases from suppliers at lower and lower unit prices, which meant further savings to customers, which led to more growth and more scale advantages. Sleep coined a term for this business model: “scaled economies shared”, meaning the business gained scale, but instead of keeping the excess profits for itself, it gave these scale advantages to the customer in the form of lower prices. This sacrificed near term profits but led to far greater future profits, which of course is where value comes from.

Walmart, Costco, and Amazon all exhibit this basic business model, and all have achieved great success. But what Sleep noticed is that investors — even when they understood this business model — still undervalued all of these companies because they placed too much emphasis on shorter term factors such as seasonal same-store sales trends, quarterly margins, or the business cycle. All of this focus came at the expense of what really mattered, which was the cost advantage that was so hard for competitors to replicate….

…Last summer, investors sold Amazon after its Q2 earnings report because the next few quarters would face tough comps from the gangbuster 2020; but Amazon’s value in 2032 has little to do with the comps it faces in 2022. It has a lot to do with the durability of its network, the economies of scale, the distribution advantages, the culture of operational excellence; none of that will likely drive the stock this quarter, but it’s what matters most to the stock over the next decade.

A mismatch of time horizons lead some investors to more heavily weight the short-term and deemphasize these sources of “enduring business success”.

7. Twitter thread on how company leaders handle crises – Dan Rose

I was at Amzn early ’00s when we lost 95% of our market cap. Later at FB I negotiated a down-round in ’09, and then in ’12 our stock dropped 50% post-IPO. I was on the board of a public company that went bankrupt (Borders) and a start-up that went under (Hello). Some lessons:

1/Raise capital when you can, not when you need it. Amzn tapped convert debt in Feb ’00 – if we had waited another month we would not have survived. 9 years later at FB we raised a 33% down-round despite having plenty of runway. Don’t wait until your back is up against the wall

2/Cash is king. Forget about valuation, dilution, etc – if you run out of cash, none of it matters. Borders used its free cash flow to buy back stock for yrs, ignoring the internet. By the time a PE firm fired the board and asked me to join in ‘09, we had no runway for turnaround…

…4/Change the tone. Amzn did a small but symbolic RIF in 2000. Around that time, Jeff was presented with a team t-shirt – he threw the team out of his office and banned all company swag. We even removed aspirin from the break rooms, served coffee and water. Small acts set the tone

5/It starts from the top. Zuck showed up to work in Jan ’09 wearing a tie, and he wore it every day for an entire year. His message to the company: “it’s time to get serious about our business.” Every time we walked into a meeting with Mark, we were reminded things had changed

6/Reset the team. In the middle of covid I addressed the exec team of a travel start-up whose revenue dropped to zero overnight. I encouraged them to re-evaluate their team. Some people step up in a crisis – they are your future leaders. Others will jump ship – good riddance…

…9/Communicate, a lot. When FB’s stock plummeted after our IPO, I addressed the issue with employees rather than pretending stock price didn’t matter. It’s tempting to go into a foxhole when times are tough. Don’t do that, your team needs you more than ever

10/Keep telling your story. I stayed at Amzn during this time because Jeff sold me on his vision. When GFC postponed FB’s IPO by 4 years, Zuck never stopped talking about the mission. Churchill taught the world the power of storytelling in a crisis

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Alphabet (parent of Google), Amazon, Apple, Costco, and Meta Platforms (parent of Facebook). Holdings are subject to change at any time.