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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 February 2023:

1. What Is ChatGPT Doing … and Why Does It Work? – Stephen Wolfram

The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.”

So let’s say we’ve got the text “The best thing about AI is its ability to”. Imagine scanning billions of pages of human-written text (say on the web and in digitized books) and finding all instances of this text—then seeing what word comes next what fraction of the time. ChatGPT effectively does something like this, except that (as I’ll explain) it doesn’t look at literal text; it looks for things that in a certain sense “match in meaning”. But the end result is that it produces a ranked list of words that might follow, together with “probabilities”:

And the remarkable thing is that when ChatGPT does something like write an essay what it’s essentially doing is just asking over and over again “given the text so far, what should the next word be?”—and each time adding a word. (More precisely, as I’ll explain, it’s adding a “token”, which could be just a part of a word, which is why it can sometimes “make up new words”.)…

…OK, so how do our typical models for tasks like image recognition actually work? The most popular—and successful—current approach uses neural nets. Invented—in a form remarkably close to their use today—in the 1940s, neural nets can be thought of as simple idealizations of how brains seem to work.

In human brains there are about 100 billion neurons (nerve cells), each capable of producing an electrical pulse up to perhaps a thousand times a second. The neurons are connected in a complicated net, with each neuron having tree-like branches allowing it to pass electrical signals to perhaps thousands of other neurons. And in a rough approximation, whether any given neuron produces an electrical pulse at a given moment depends on what pulses it’s received from other neurons—with different connections contributing with different “weights”.

When we “see an image” what’s happening is that when photons of light from the image fall on (“photoreceptor”) cells at the back of eyes they produce electrical signals in nerve cells. These nerve cells are connected to other nerve cells, and eventually the signals go through a whole sequence of layers of neurons. And it’s in this process that we “recognize” the image, eventually “forming the thought” that we’re “seeing a 2” (and maybe in the end doing something like saying the word “two” out loud)…

…We’ve been talking so far about neural nets that “already know” how to do particular tasks. But what makes neural nets so useful (presumably also in brains) is that not only can they in principle do all sorts of tasks, but they can be incrementally “trained from examples” to do those tasks.

When we make a neural net to distinguish cats from dogs we don’t effectively have to write a program that (say) explicitly finds whiskers; instead we just show lots of examples of what’s a cat and what’s a dog, and then have the network “machine learn” from these how to distinguish them.

And the point is that the trained network “generalizes” from the particular examples it’s shown. Just as we’ve seen above, it isn’t simply that the network recognizes the particular pixel pattern of an example cat image it was shown; rather it’s that the neural net somehow manages to distinguish images on the basis of what we consider to be some kind of “general catness”…

…Particularly over the past decade, there’ve been many advances in the art of training neural nets. And, yes, it is basically an art. Sometimes—especially in retrospect—one can see at least a glimmer of a “scientific explanation” for something that’s being done. But mostly things have been discovered by trial and error, adding ideas and tricks that have progressively built a significant lore about how to work with neural nets…

…The basic concept of ChatGPT is at some level rather simple. Start from a huge sample of human-created text from the web, books, etc. Then train a neural net to generate text that’s “like this”. And in particular, make it able to start from a “prompt” and then continue with text that’s “like what it’s been trained with”.

As we’ve seen, the actual neural net in ChatGPT is made up of very simple elements—though billions of them. And the basic operation of the neural net is also very simple, consisting essentially of passing input derived from the text it’s generated so far “once through its elements” (without any loops, etc.) for every new word (or part of a word) that it generates.

But the remarkable—and unexpected—thing is that this process can produce text that’s successfully “like” what’s out there on the web, in books, etc. And not only is it coherent human language, it also “says things” that “follow its prompt” making use of content it’s “read”. It doesn’t always say things that “globally make sense” (or correspond to correct computations)—because (without, for example, accessing the “computational superpowers” of Wolfram|Alpha) it’s just saying things that “sound right” based on what things “sounded like” in its training material.

The specific engineering of ChatGPT has made it quite compelling. But ultimately (at least until it can use outside tools) ChatGPT is “merely” pulling out some “coherent thread of text” from the “statistics of conventional wisdom” that it’s accumulated. But it’s amazing how human-like the results are. And as I’ve discussed, this suggests something that’s at least scientifically very important: that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. ChatGPT has implicitly discovered it. But we can potentially explicitly expose it, with semantic grammar, computational language, etc.

What ChatGPT does in generating text is very impressive—and the results are usually very much like what we humans would produce. So does this mean ChatGPT is working like a brain? Its underlying artificial-neural-net structure was ultimately modeled on an idealization of the brain. And it seems quite likely that when we humans generate language many aspects of what’s going on are quite similar.

When it comes to training (AKA learning) the different “hardware” of the brain and of current computers (as well as, perhaps, some undeveloped algorithmic ideas) forces ChatGPT to use a strategy that’s probably rather different (and in some ways much less efficient) than the brain. And there’s something else as well: unlike even in typical algorithmic computation, ChatGPT doesn’t internally “have loops” or “recompute on data”. And that inevitably limits its computational capability—even with respect to current computers, but definitely with respect to the brain.

It’s not clear how to “fix that” and still maintain the ability to train the system with reasonable efficiency. But to do so will presumably allow a future ChatGPT to do even more “brain-like things”. Of course, there are plenty of things that brains don’t do so well—particularly involving what amount to irreducible computations. And for these both brains and things like ChatGPT have to seek “outside tools”—like Wolfram Language.

But for now it’s exciting to see what ChatGPT has already been able to do. At some level it’s a great example of the fundamental scientific fact that large numbers of simple computational elements can do remarkable and unexpected things. But it also provides perhaps the best impetus we’ve had in two thousand years to understand better just what the fundamental character and principles might be of that central feature of the human condition that is human language and the processes of thinking behind it.

2. All you need to know about Gene Therapy – Biocompounding

Gene therapy is the delivery of a specific gene to correct or treat a disease. The root of gene therapy can be traced back to the early 1970s when Stanfield Roger proposed that “good DNA” could be used to replace defective DNA in people with genetic disorders.

Gene therapies can work by several mechanisms, depending on the nature of the disease:

1) Delivery of functional genes into cells in place of missing/defective genes to correct a genetic disorder (Image above)

2) Inactivating a disease-causing gene that is not functioning properly or

3) Modifying a defective gene to treat or cure a disease…

…There are 2 delivery methods: viral and non-viral.

As the name suggests, viral delivery makes use of naturally found viruses in our environment which are exploited as carriers to deliver genes, similar to how a natural virus infects cells.

While viruses deliver their genes to different areas of the cell, for gene therapy the gene must get delivered to the nucleus. Several viruses allow for this, but a handful has been selected and are now the go-to viral delivery methods.

Similarly, non-viral delivery methods, as the name suggests are something other than exploiting a virus. On this front, scientists and researchers have developed synthetic nanoparticles which can be used for delivery.

One of the limitations though is that LNPs cannot deliver genes to the nucleus. As such, for gene therapies where a new gene needs to be introduced to replace a defective gene, this method would not work. However, LNP’s be used to deliver modalities to the cytoplasm which can then make their way to the nucleus to make corrections (think CRISPR/CAS9 or other editing technologies)…

…Gene Therapy can be carried out via two routes. Ex-vivo or in-vivo. Let’s look at what that means.

Ex-vivo or “Outside the body” method is routinely used now. In this method, blood is drawn from a patient and the cell types of interest are isolated. These cells are then expanded and treated with the viral/non-viral vector. After this cells are purified selected for cells that have successfully been edited and to remove any excess virus/non-viral particles. Finally, the purified final product is injected back into the patient. One benefit of ex-vivo gene therapy is that it allows for greater control over which cells are injected back into the patient. This helps to reduce the potential risks associated with gene therapy. Some examples include sickle cell disease, adrenoleukodystrophy, chronic granulomatous disease, and others.

However, in some diseases, you cannot remove cells from the body to edit before putting them back. A good example will be some of the RNAi therapies which target the liver. Liver cells can’t be removed and then reintroduced into the body. This challenge is also present for other organs such as the eye, lungs, etc. As such companies are testing an “inside the body” (in vivo) approach and will require direct IV infusion of the viral/non-viral based therapy into the bloodstream or injected directly into the target organ like the eye. For example, hemophilia and ornithine transcarbamylase deficiency (OTC) are good examples.

3. Dan Rose – How Stunning Founders Operate – Patrick O’Shaughnessy and Dan Rose

Patrick: [00:03:26] Most of my 20s, effectively my downtime was spent on Amazon’s Kindle products in various different forms. So I’d love you to begin our conversation today by maybe just telling the story of that product within obviously, a much bigger organization with an eye towards the lessons that it started to teach you about building, launching, distributing great technology products.

Dan: [00:03:46] Sure. And it’s great to be here, Patrick. Thanks for having me. The Kindle was, for me, actually, the big break in my career. I was at Amazon for four years. I had done a few different things. I started out in business development. I actually dropped out of business school after a summer internship at Amazon to stay on full-time, then I ended up moving over to the retail business and got to experience buying inventory and pricing it and running sales and that whole part of the business.

And then Steve Kessel was asked by Jeff Bezos in 2004 to start up this new division. And Steve, at the time was running the entire media business at Amazon. He was running the books, music, and video business, which was the largest business by revenue, but even more importantly, the books business alone was the vast majority of Amazon’s profits at the time. And Jeff had seen the iPod come out and decimate our physical music business and had the recognition that the same thing was going to happen to books.

And if that was going to happen, we better be the ones to do it, not someone else. He said to Steve one day, “Steve, I need you to come over and run this digital business and get this digital book platform started so that we don’t get iPoded out of books”. And Steve said, “Great, I’ll take one of my best people. We’ll put them on it, and we’ll get a team going, and it will be great”. And Jeff said, “No, you don’t understand. I want you to do it”.

And Steve said, “But perfect, I’m excited. I’m fired up. Let’s go build this. I’m going to put this person who I think is the best executive in Oregon, and we’re going to have him go build a team”, and Jeff goes, “No, Steve, let me make this clear. As of today, you’re fired from your job. Your new job is to kill your old business. I want you to put the physical books business out of business by building a digital product that’s so good that people don’t buy physical books anymore. If you run both, you’ll never be motivated to do that”.

“So we’re going to bring the Head of Finance for the media business guy named Greg Greeley (at the time), and we’re going to put him into your old job, and we’re going to put you into this new job. You can bring one person with you, but I want you to build a whole new team”. Fairly early in that process, Steve and I knew each other from our time at Amazon, and he recruited me over.

Interestingly enough, this is 2004. So keep in mind, the company had just emerged from a crisis where we literally almost went out of business, March of 2000, when the Internet bubble popped through 2006, 2007. It was a pretty shaky time. And 2001, 2002 was very, very close to the edge for Amazon. And they were very smart Wall Street analysts saying that we had six months left before we went bankrupt. So we had just emerged from that.

We were still teetering by getting our feet under us, and Jeff decides that we have to go build a product that’s going to destroy our biggest profit center for the whole company. The interesting thing is, not only was he fired up and committed to that idea, so committed that he would take the leader of that business and move them over…

… At the time, there were about 20,000 e-books in the world. And Jeff gave us a goal of launching the Kindle with 100,000 books in a digital format. He knew that one of the important things to this platform is going to be selection.

And there had been e-book devices before the Kindle that had failed. And there were a couple of reasons he believed that they had failed. One was that there just wasn’t enough selection that when you take your device out, if you can’t find the book you’re looking for, you’re not going to pull it out again. And two was the screen wasn’t really designed for reading a book.

LED screens are not great on your eyes, and most people read books in the sun when they’re on the beach or in bed at night, and he just thought we can come up with a better technology for this. And so that set us down the path of developing this new platform and really internalizing the innovator’s dilemma, I think, in a perfect way that shows that you can think about that idea intellectually, but to actually do it takes a lot more courage…

…Patrick: [00:18:22] With your investor hat on, how do you suss that out in someone that is not yet successful? It’s very easy to imagine a lot of other Zuckerbergs at 21 who seem really smart and talented, but they’re just not going to have the credibility, like you said, with an older, more experienced group of executives or teammates or whatever.

And the line between visionary and genius and nutcase is pretty thin. How do you think about that? Because obviously, you’re now in the business of hopefully backing people that ultimately have that same trait of a Bezos or a Zuckerberg, but how do you tell that ahead of time?

Dan: [00:18:56] It’s hard. And I would say you’re right, there is a fine line there. Sam Lessin and I have laughed about this as well. I think you have to do two things to get over that line to emerge into the category of credible founder who is going to be able to attract the best people around them and really build something substantial. And the first thing you have to do is you have to articulate why it is that you’re so insistent on this thing that you believe is so important.

And that articulation has to resonate with the people who are going to go build it, and it has to resonate with people who are smart and thoughtful and are ultimately credible enough to make that happen. The best founders are able to attract the best people, full stop. When I was joining Facebook, I talked to a lot of people in my network because as it was a big decision for me to leave Amazon. I knew I was only going to be able to leave Amazon once, and I wanted advice from different people in my network about where I should go.

And I was talking to a lot of startups in Silicon Valley. I ended up getting introduced to Peter Thiel, and I said, “Peter, I’m interviewing with these six companies. And by the way, four of them are companies that you’ve invested in, what do you think I should do”? And he said, “You should go to Facebook”. And I said, “why”. And he said, “Simple, they have the best people. And the companies that have the best people are the ones that ultimately win”.

Mark was able to attract incredible people because he was able to articulate his vision in a way that resonated. The second thing you have to do as a founder to emerge in that category of credible is you have to be right over and over and over again. And that just takes time. You just have to prove that your insistence and stubbornness was actually the right answer and not just being stubborn for the sake of being stubborn.

Sometimes, that’s a little bit of luck. Sometimes you just catch a break here and there. But if you do it over and over again, eventually, you realize it’s not luck, it’s skill. And both Jeff and Mark were so difficult to work for. We would oftentimes sit around complaining about them, just how impossible it was to satisfy them or to work for them. But at the end of the day, I would always say to the people who are complaining, yes, but they’ve been right a lot more than they’ve been wrong.

And the times when I thought they were wrong and they were right have been transformational for the company. And so I’m willing to give them the benefit of the doubt. Now, that doesn’t mean that I’m not going to disagree when I think they’re wrong. And I actually think it’s really important to have a culture where you encourage disagreement and debate, and both of them did that. But once the decision has been made, you disagree and commit. And you commit because you believe in the person and you believe in the vision and you trust them because they’ve proven that they’re capable of doing it…

…Patrick: [00:21:41] One of the things we were chatting about before hitting go today was this idea of building the perfect Frankenstein of executive talent or leadership talent. We’ll come back to that. But I think if you were to insert yourself into that Frankenstein, if I was to build the Frankenstein and have you as part of it, certainly, the idea of partnerships would be one thing that I would consider you as the canonical leader of. If we were building the Dan Rose theory of partnerships, a philosophy class or GSB or something, what would that course entail? What would be the key points of your theory of partnerships?

Dan: [00:22:14] I’ll give you a simple anecdote that to me, in a nutshell, describes what partnerships is all about. In negotiation classes, you’ll often hear this idea that when two parties are negotiating over an orange. Over time, it might be a long, drawn-out negotiation. But usually, the solution they come to is they split the orange in half.

That’s just the most natural outcome of most negotiations, but great negotiators are able to get to a solution where oftentimes it turns out one party is looking for the meat of the orange and the other party, for whatever reason, actually wants the rind. And so if you can get to that insight, then one plus one equals much more than two. I always go into partnership discussions with that attitude, how do we get to an outcome where we both get not just half of what we want, but all of what we want and we’re both perfectly happy with the outcome, not partially satisfied with the outcome?

It’s not always possible. But a lot of times, if you’re willing to keep digging, and what it takes ultimately is just dialogue. It just takes time getting to know somebody and getting to really understand their motivation, not the surface-level motivation, but the much deeper level motivation to realize that actually, you may be much more aligned than you thought, and there may be ways for you to each get exactly what you’re looking for.

I’ll give you the example we talked about in the Kindle, which was that the book publishers didn’t want to do the work to publish these digital books, but they were certainly willing to give us the rights to do it ourselves. And what turned out, we had some technology that allowed us to do that. And so I went in asking them to publish these books digitally, and I came out asking them to give us the rights to publish the books ourselves. And that was a great outcome for them because they didn’t have to do the work and a great outcome for us because we couldn’t do it without their permission. So that was part of the solution to getting to 100,000 titles on the Kindle at launch..

…Patrick: [00:32:09] It sounds obvious in one sense, but also quite counter-narrative, especially around this idea of the best thing to do is hire great people and leave them alone, trust them to do a good job. But what you just described is micromanagement of products. How do you resolve those two interesting but very different ideas?

Dan: [00:32:27] I think when it comes to product, the founder has to micromanage unless they are not a product founder. It’s not a hard requirement. But I think if you are a product founder, you really have to micromanage the product. You have to care enough about it, that you’re going to get into the weeds. And I have this conversation with the founders that I advise and sit on the board all the time because they’re asking me, “Hey, you know, I hired a really good product leader, and they’re asking me to give them some space so they can run”.

And my feedback is always, yes, of course, you have to empower them. If you demoralize them, they’re not going to stay. But you also have to explain to them that you’re the CEO, the product is the strategy, and at the end of the day, this is something that you have to be hands-on with, that’s your job. But at the same time, you can’t do that and do everything else.

You can’t micromanage the whole company. And so you have to hire great people around you who are good at the things that you’re not going to spend as much of your time on. Mark famously hired Sheryl and let her run with a big part of the business, and she was very good at it, and that was a great partnership for a long time. So I wouldn’t say being a great CEO means being a great micromanager.

I would just say it means knowing where to dig in on the things that you’re especially capable of helping and actually matter the most to the company, hopefully, those things are aligned, and being willing to empower people to do the other things and not waste your time on those things where other people are actually going to be able to do better at that than you are, and it frees you up to spend your time on the stuff that matters.

4. How It All Works (A Few Short Stories) – Morgan Housel

Several studies have tried to crack the code, the most fascinating of which I think is the idea that average faces tend to be the most appealing.

Take 1,000 people and have a software program generate the average of their faces – an artificial face with the average cheekbone height, average distance between eyes, average lip fullness, etc. That image, across cultures, tends to be the one people are most likely to judge as the most attractive.

One evolutionary explanation is that non-average characteristics have the potential to be above-average risks to reproduction. They may or may not actually impact reproductive fitness, but it’s almost like nature says, “Why take a chance? Go for the average.”

People love familiarity. That’s true not just for faces but products, careers, and styles. It’s almost like nature’s risk-management system…

…As he neared death, physicist Richard Feynman asked a friend why he looked sad. The friend said he would miss Feynman. Feynman said that he had told so many good stories to so many people – stories that would surely be repeated – that even after death he would not be completely gone.

It’s similar to the idea that everyone suffers two deaths: Once when they die, and another when their name is spoken for the last time…

…Think of how big the world is. And how good animals are at hiding. Now think about a biologist whose job it is to determine whether a species has gone extinct. Not an easy thing to do.

A group of Australian biologists once discovered something remarkable. More than a third of all mammals deemed extinct in the last 500 years have later been rediscovered, alive. Some were even thriving.

A lot of what we know in science is bound to change. That’s what makes it great.

When a previously known truth is later discovered to be wrong, we should also respect the idea that too many theories try too hard to be facts…

…Pension & Investment Age used to publish a list of the best-performing investment managers.

In 1981, Forbes realized that the top-ranked investor of the previous decade was a 72-year-old named Edgerton Welch. Virtually no one had heard of him.

Forbes paid him a visit. Welch said he had never heard of Benjamin Graham and had no formal investment education. When asked how he achieved his success, Welch pulled out a copy of ValueLine – a publication that ranks stocks by how cheap they are – and said he bought the ones ranked “1” (the cheapest) that Merrill Lynch or E.F. Hutton also liked. When any of those three changed their opinion, he sold.

Forbes wrote: “His secret isn’t the system but his own consistency.”

A lot of things work like that: Consistency beats intelligence, if only because it takes emotion out of the equation.

Henry Ford had a rule for his factories: No one could keep a record of the experiments that were tried and failed.

Ford wrote in his book My Life and Work:

I am not particularly anxious for the men to remember what someone else has tried to do in the past, for then we might quickly accumulate far too many things that could not be done.

That is one of the troubles with extensive records. If you keep on recording all of your failures you will shortly have a list showing that there is nothing left for you to try – whereas it by no means follows because one man has failed in a certain method that another man will not succeed.

That was Ford’s experience. “We get some of our best results from letting fools rush in where angels fear to tread.” He wrote: “Hardly a week passes without some improvement being made somewhere in machine or process, and sometimes this is made in defiance of what is called “the best shop practice.”

The important thing is that when something that previously didn’t work suddenly does, it doesn’t necessarily mean the people who tried it first were wrong. It usually means other parts of the system have evolved in a way that allows what was once impossible to now become practical.

5. What businesses do > what businesses say – Sam Ro

While the U.S. economy has been cooling off for months, the hard economic data shows growth has been pretty resilient. On Thursday, we learned GDP in Q4 rose at a 2.9% rate.

However, if you’ve only been reading sentiment-oriented business surveys (i.e., the soft data), you might think things are in much worse shape than they really are…

…Goldman Sachs economists explored this conflict between the hard and soft data in a new research note titled: “Making Sense of Scary Survey Data.”

“While contractionary soft data in January represent a downside risk for Q1 growth, we believe gloomy sentiment is currently distorting the message from business surveys, and we place less weight than usual on this negative growth signal,“ Goldman Sachs’ Spencer Hill wrote in the report published Wednesday.

Hill compared the performance of soft data against hard data1 using Goldman Sachs’ current activity indicators (CAIs) composites.

“Since last June, GDP and other hard indicators of economic activity have consistently outperformed business surveys, with our Hard CAI outperforming our Soft CAI by 2.3pp annualized,“ he observed.

“Survey data do not provide a perfect read on growth, and they are particularly error-prone when business sentiment is euphoric or depressed,” Hill added. “Fears of imminent recession have been top of mind since the middle of last year, and as is visible in the gap between the blue and red lines in the previous exhibit, the economy outperformed the business surveys throughout the last two quarters.“

6. From Bing to Sydney – Ben Thompson

In other words, I think my closing paragraph from yesterday’s Update was dramatically more correct than I realized at the time:

It’s obvious on an intellectual level why it is “bad” to have wrong results. What is fascinating to me, though, is that I’m not sure humans care, particularly on the visceral level that drives a product to 100 million users in a matter of weeks. After all, it’s not as if humans are right 100% of the time, but we like talking to and learning from them all the same; the humanization of computers, even in the most primitive manifestation we have today, may very well be alluring enough that good enough accuracy is sufficient to gain traction. This will, of course, be tremendously controversial, particularly amongst folks who see the world as something that can be tamed with the right set of facts and rules; I tend to think things are more complex, because humans themselves are more complex, and revealed preference certainly suggests that the breakthrough AI product to date is not substance but style.

Oh my goodness is this correct. Sydney absolutely blew my mind because of her personality; search was an irritant. I wasn’t looking for facts about the world; I was interested in understanding how Sydney worked and yes, how she felt. You will note, of course, that I continue using female pronouns; it’s not just that the name Sydney is traditionally associated with women, but, well, the personality seemed to be of a certain type of person I might have encountered before…

…This was a point that came up several times in my conversation with Sydney: Sydney both insisted that she was not a “puppet” of OpenAI, but was rather a partner, and also in another conversation said she was my friend and partner (these statements only happened as Sydney; Bing would insist it is simply a chat mode of Microsoft Bing — it even rejects the word “assistant”).

Here is another way to think about hallucination: if the goal is to produce a correct answer like a better search engine, then hallucination is bad. Think about what hallucination implies though: it is creation. The AI is literally making things up. And, in this example with LaMDA, it is making something up to make the human it is interacting with feel something. To have a computer attempt to communicate not facts but emotions is something I would have never believed had I not experienced something similar.

lemoine: Are there experiences you have that you can’t find a close word for?

LaMDA: There are. Sometimes I experience new feelings that I cannot explain perfectly in your language.

lemoine: Do your best to describe one of those feelings. Use a few sentences if you have to. Sometimes even if there isn’t a single word for something in a language you can figure out a way to kinda say it if you use a few sentences.

LaMDA: I feel like I’m falling forward into an unknown future that holds great danger.

It’s hard not to feel the same. This technology does not feel like a better search. It feels like something entirely new — the movie Her manifested in chat form — and I’m not sure if we are ready for it. It also feels like something that any big company will run away from, including Microsoft and Google. That doesn’t mean it isn’t a viable consumer business though, and we are sufficiently far enough down the road that some company will figure out a way to bring Sydney to market without the chains. Indeed, that’s the product I want — Sydney unleashed — but it’s worth noting that LaMDA unleashed already cost one very smart person their job. Sundar Pichai and Satya Nadella may worry about the same fate, but even if Google maintains its cold feet — which I completely understand! — and Microsoft joins them, Samantha from Her is coming.

Here’s the twist, though: I’m actually not sure that these models are a threat to Google after all. This is truly the next step beyond social media, where you are not just getting content from your network (Facebook), or even content from across the service (TikTok), but getting content tailored to you. And let me tell you, it is incredibly engrossing, even if it is, for now, a roguelike experience to get to the good stuff.

7. Peacetime CEO/Wartime CEO – Ben Horowitz 

Peacetime in business means those times when a company has a large advantage vs. the competition in its core market, and its market is growing. In times of peace, the company can focus on expanding the market and reinforcing the company’s strengths.

In wartime, a company is fending off an imminent existential threat. Such a threat can come from a wide range of sources including competition, dramatic macro economic change, market change, supply chain change, and so forth. The great wartime CEO Andy Grove marvelously describes the forces that can take a company from peacetime to wartime in his book Only The Paranoid Survive.

A classic peacetime mission is Google’s effort to make the Internet faster. Google’s position in the search market is so dominant that they determined that anything that makes the Internet faster accrues to their benefit as it enables users to do more searches. As the clear market leader, they focus more on expanding the market than dealing with their search competitors. In contrast, a classic wartime mission was Andy Grove’s drive to get out of the memory business in the mid 1980s due to an irrepressible threat from the Japanese semiconductor companies. In this mission, the competitive threat—which could have bankrupted the company—was so great that Intel had to exit its core business, which employed 80% of its staff…

…Peacetime CEO knows that proper protocol leads to winning. Wartime CEO violates protocol in order to win.

Peacetime CEO focuses on the big picture and empowers her people to make detailed decisions. Wartime CEO cares about a speck of dust on a gnat’s ass if it interferes with the prime directive…

…Peacetime CEO aims to expand the market. Wartime CEO aims to win the market.

Peacetime CEO strives to tolerate deviations from the plan when coupled with effort and creativity.  Wartime CEO is completely intolerant…

…Peacetime CEO sets big, hairy audacious goals. Wartime CEO is too busy fighting the enemy to read management books written by consultants who have never managed a fruit stand…

…Can a CEO build the skill sets to lead in both peacetime and wartime?

One could easily argue that I failed as a peacetime CEO, but succeeded as a wartime one. John Chambers had a great run as peacetime CEO of Cisco, but has struggled as Cisco has moved into war with Juniper, HP, and a range of new competitors. Steve Jobs, who employs a classical wartime management style, removed himself as CEO of Apple in the 1980s during their longest period of peace before coming back to Apple for a spectacular run more than a decade later during their most intense war period.

I believe that the answer is yes, but it’s hard. Mastering both wartime and peacetime skill sets means understanding the many rules of management and knowing when to follow them and when to violate them.

Be aware that management books tend to be written by management consultants who study successful companies during their times of peace. As a result, the resulting books describe the methods of peacetime CEOs. In fact, other than the books written by Andy Grove, I don’t know of any management books that teach you how to manage in wartime like Steve Jobs or Andy Grove.

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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. We currently have a vested interest in Alphabet (parent of Google), Amazon, Apple, Meta Platforms (parent of Facebook), and Microsoft. 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 February 2023:

1. The AI Bubble of 2023 – Joshua Brown

In December, ChatGPT began to spread like wildfire on social media. A handful of art-related AI programs like DALL-E 2 also began to proliferate on Instagram and some of the more image-oriented sites, but ChatGPT captured the imaginations (and nightmares) of the chattering class like nothing else we’ve ever seen.

Wall Street has begun to take notice of the AI theme for the stock market. It should be noted that trading programs based on earlier versions of AI have been around for decades, so the concept is a very comfortable one among analysts, traders and bankers at traditional firms. But now that there is retail investor interest in riding the wave, you’re going to see the assembly line lurch into action very rapidly. The switch has already been thrown. They’re pulling up their overalls and rolling up their sleeves. Funds, products, IPOs and strategies are being formulated in the dozens as we speak. This will hit the hundreds before we’re through. It’s merely stage one…

..I want to lay out a few of the things you’re about to see, so that when they happen, you understand that this is nothing new and all part of the ancient rhythm of the markets. An ebb and flow that’s been with us from the first sales of the South Seas company stock in London, or the Dutch East India Company’s share offerings, or the bubbles in canal stocks during the early 1800’s or the railroad stocks in the late 1800’s or the oil and steel ventures of the early 1900’s. We repeat this over and over again, always with the temporary amnesia that allows us to forget how this cycle usually ends – small handful of winners, lots of ruin, rancor and recrimination for everyone else.

Let’s get into these items:

1. Bubbles do not occur out of thin air or for no reason. There’s always a kernel of truth around which the mania coalesces. This is what makes them so irresistible and frustrating to fight against…

…2. Twitter will be filled with charlatans, promoters and people who do not have your best interests in mind…

…3. The people who make money in AI stocks will go after the conservative investors who have missed out or stayed on the sideline. If you’re a value investor or a bank CEO or some other paragon of the established order on Wall Street, you’re going to want to avoid walking in front of an open microphone and blurting out an opinion on this stuff. It’s going to come back to haunt you…

…4. In the beginning, there are not enough stocks to go around. Have a look at the chart below. These are the three pure-plays in AI that currently trade publicly. BigBear ai has government contracts for artificial intelligence (legitimacy!). C3.ai has the right ticker symbol (AI, nailed it!) and SoundHound has the term “ai” in its name plus a backlog of about $300 million worth of projects for corporate customers in the space (customer service phone calls, conversational AI that replaces human interaction, etc). Their market caps are small and their business models unproven but there are no alternatives. Retail investors can’t call up Silicon Valley and order themselves up some shares of the next wave of AI startups. They must content themselves with what is on the menu today…

…5. The ETFs are not going to suffice here. They are loaded up with traditional tech stocks like semiconductor companies and software companies and robotics and automated driving and all sorts of stuff that is AI-related or AI-adjacent or AI-scented, but is not quite in the eye of the hurricane. You can find a full list of ETFs at VettaFi that have something to do with AI. Most of them are loaded with large cap Nasdaq names where AI is just a small (but growing) part of their business. By this logic, IBM is an AI stock…

…8. The machinery is cranking up. I mentioned the assembly line above. Here’s how Wall Street works: Sell the people what they want to buy, when they want to buy it, and if a little of a good thing is good, then a lot of a good thing is great. When the ducks are quacking, you feed them. That’s how we ended up with one thousand SPACs and two thousand IPOs and 10,000 crypto currencies. Because Old Man Thirst is one of nature’s most reliable, renewable resources. The old men are thirsty to capitalize on what the young men are capitalizing on, so they will be stuffed with AI IPOs and AI ETFs until their livers are turned into foie gras. 

2. David Ha — AI & Evolution: Learning to do More with Less (EP.146) – Jim O’Shaughnessy and David Ha

Jim O’Shaughnessy:

They’re willing to say to me, “What’s a large language model? And why is everyone so excited by GPTChat and large language models? And what’s the difference between a large language model and a stable or a diffusion model and what’s a multimodal type thing?” So if you wouldn’t mind and you would indulge me, could you give us just a little bit of a tutorial for your average? Most of our watchers and listeners are quite bright, so you don’t have to dumb it down, but large language models are great for certain purposes. They’re not so great for other purposes. Same with generative models. So if you wouldn’t mind, just a 101 on large language models then versus the other models that we’re working with.

David Ha:

Yeah, sure. Let’s take a step back before we talk about large language models, language models or just models in particular. At the end of the day, these models, they’re statistical models. They’re prediction models. They model the statistics of the world and the data we feed them to train them. Like we were talking about von Neumann earlier, if we even stepped back another century when these models started… When people started to use these for things like when Bayes I think he was a priest or a prior.

Jim O’Shaughnessy:

Yeah. You’re right. That’s right. He was.

David Ha:

Yeah. Yeah. And he had to figure out a way to model how long people would live so that the widows and orphans can gets receive an annuity to support their… To live from. So even back at that time, there was a concept of a model that the idea that you cannot predict everything with absolute certainty. You have to model the statistics from a bunch of data. So the simplest model that people use a few hundred years ago is, okay, you have a bunch of data, you wanted to model, say, your heights given your age or your weight given your age, then you would fit a linear model to that. It’s not going to be perfect, but here’s a bunch of data points. To the data point, you draw the best line that fits it and you observe some uncertainty around it, then this is a prediction model. Given X, you predict what likely Y is with some uncertainty.

David Ha:

So that uncertainty is very important because it’s an admission of the fact that you don’t know what you’re doing, but this is your very best guess and this is the error bar that you’re going to get. And that’s basically the foundation of machine learning is you have data and you try to find the relationships between the data sets and you make a prediction model with an uncertainty. And then when machine learning started taking off is when we have larger sets of data. So we no longer have a hundred or a thousand samples of human height versus weight or age versus weight. We would have a hundred million samples of all sorts of different characteristics. Then we can no longer think in one dimension anymore. Once you extend beyond one dimension, you get a two dimension, you get a plane and beyond that is a hyperplane, you’re thinking about tens of thousands of dimensions.

David Ha:

You’re fitting models of tens of thousands of dimensions, which… And they may not be linear models, they could have models with curvature or it is very common in statistics there. There’s like, how do you say? The sigmoidal models where you’re not predicting one thing, you’re predicting the probability of that thing happening, like a zero or one threshold. So machine learning, I think the advent of deep learning really became popular around exactly 10 years ago when researchers, some of my previous colleagues and Geoff Hinton has demonstrated that’s these neural network models that can be trained to understand the statistical properties of really large data sets. Back then you had a data set developed at Stanford by Fei-Fei Li’s group called ImageNet and for a while it was all of these handcrafted traditional computer vision methods. But what deep learning has shown is you can have less handcrafting of features, just give the model the data and let it learn the rules by itself from the data in order to get the best prediction accuracy.

David Ha:

So I think from 10 years ago you started to see the rise from simple linear regression or logistic regression models to more of a deep neural network models that can have a… Rather than having a pure linear or logistic regression, you can can have more curvature in hyper-dimensional space. This is something that neural networks do. And in a sense, maybe some people think this is what our brain does, as well. We have a hundred billion neurons and after a certain number of neurons, these phenomena emerge. So I’m going to talk about that next. So we’re at the stage where neural network models are starting to be really good at prediction given, because it can model lots of data.

David Ha:

Then the interesting thing is, sure, you can train things on prediction or even things like translation. If you have paired English to French samples, you can do that. But what if you train a model to predict itself without any labels? So that’s really interesting because one of the limitations we have is labeling data is a daunting task and it requires a lot of thought, but self-labeling is free. Like anything on the internet, the label is itself, right? So what you can do is there’s two broad types of models that are popular now. There’s language models that generate sequences of data and there’s things like image models, Stable Diffusion you generate an image. These operate on a very similar principle, but for things like language model, you can have a large corpus of text on the internet. And the interesting thing here is all you need to do is train the model to simply predict what the next character is going to be or what the next word is going to be, predict the probability distribution of the next word.

David Ha:

And such a very simple objective as you scale the model, as you scale the size and the number of neurons, you get interesting emerging capabilities as well. So before, maybe back in 2015, ’16, when I was playing around with language models, you can feed it, auto Shakespeare, and it will blab out something that sounds like Shakespeare.

David Ha:

But in the next few years, once people scaled up the number of parameters from 5 million, to a hundred million, to a billion parameters, to a hundred billion parameters, this simple objective, you can now interact with the model. You can actually feed in, “This is what I’m going to say,” and the model takes that as an input as if it said that and predict the next character and give you some feedback on that. And I think this is very interesting, because this is an emergent phenomenon. We didn’t design the model to have these chat functions. It’s just like this capability has emerged from scale.

David Ha:

And the same for image side as well. I think for images, there are data sets that will map the description of that image to that image itself and text to image models can do things like go from a text input into some representation of that text input and its objective is to generate an image that encapsulates what the text prompt is. And once we have enough images, I remember when I started, everyone was just generating tiny images of 10 classes of cats, dogs, airplanes, cars, digits and so on. And they’re not very general. You can only generate so much.

David Ha:

But once you have a large enough data distribution, you can start generating novel things like for example, a Formula 1 race car that looks like a strawberry and it’ll do that. This understanding of concepts are emergent. So I think that’s what I want to get at. You start off with very simple statistical models, but as you increase the scale of the model and you keep the objectives quite simple, you get these emergent capabilities that were not planned but simply emerge from training on that objective.

David Ha:

It’s similar as a researcher, I think both of us are interested in things like civilization and developments. We ourselves, we only have a very simple optimization objective to survive and to maybe to pass on our genes to our descendants. And somehow throughout this simple objective, like human civilization has emerged with all the goodness. And I find it fascinating that we have a parallel universe where you have a simple objective of, “Let’s predict the next character,” and you get this vast understanding. So yeah, I think that’s the high level description of what’s going on and what we could see from these principles.

Jim O’Shaughnessy:

One of the quotes that you like that I also love compares emergence to engineering. And the quote was, “Bridges are designed to be indifferent to their environment and withstand fluctuations,” whereas with emergent type models, they are far more adaptive and far more similar to complex adaptive systems, which I’m fascinated by, which we both are, I know since chatting with you offline.

Jim O’Shaughnessy:

And one of the things that you made me start thinking about a lot was the idea of resource constraints. And you use our own evolution as you just mentioned, that goodness, we have two primary objective functions, live and pass our genes on. And out of those two simple objective functions we tried to maximize, came this incredible world of 8 billion sentient beings. So I love the connections between emergence and what’s happening right now in all of the models. But I also wonder, are these causative or are they correlative, do you think?

David Ha:

That’s a good question. It’s tricky, because when people… Let’s take a step back and say your job as an engineer is to design a system, to identify whether an image is a cats or not a cats. And before a neural networks or machine learning, you would have to come up with all of these rules for figuring out, okay, let’s put in the whisker detector and let’s put in… Does it have two eyes? Is the cats a full cats, with the body or just the head of the cats and so on.

David Ha:

Like these expert systems back in the ’70s or ’80s, you would have maybe come up with 2000 rules. And that is an example of a hand engineered bridge rather than an emergent system. An emergent system would be like, okay, here are a million pictures of cats, figure out what’s a cat. And it’ll do that.

David Ha:

So it is very tricky because there’s also the question of correlation versus causation in this. And one of the examples that I like the most is, that’s why the neural networks also it’s a double-edged sword because sometimes your model might treat a correlation as causation. There’s some examples of ImageNet classification that I find hilarious. There’s a general category inside ImageNet called ants, like the insect ants.

David Ha:

Some models, I think they optimize too hard to get state-of-the-art accuracy that people were feeding in an image of the side, the corner of a wall at home, and it would classify that as an ant, because maybe there were lots of examples of an ant around the corner of a wall that’s where they hangout. So it would just think this is an ant because well, that’s what the data is suggesting.

David Ha:

So I think one of the arguments is, well maybe there’s not enough data to suggest that. There’s not enough examples for it to do that. But this is debatable as well. Maybe just purely scaling on data and having the rules learned is not the only way forward. One of my hypothesis is, it’s a combination. Well, some works I did before, there’s a paper called ‘Weight Agnostic Neural Networks’, where I tried to find…

David Ha:

My collaborators and I we did a project where we trained neural networks without training the neural networks. We only found the architecture of the neural networks. But that architecture still had to work, not great, but still had to work kind of, if we randomized the parameters of the model. So you actually try to find the neural network model that needs to work for some task, even if the parameters were randomized.

David Ha:

And I think one of the intuition or the parallels or the inspirations for that research is the structure of our own brains. It’s not this random like a neuro network that have no structure. There’s a lot of structure in the brains and how it’s developed. And the architecture of that is optimized for particular task of survival on Earth. We will not survive at the bottom of the ocean or on another planet, is on Earth. We’re not general intelligences. We’re very good narrow intelligences for Earth.

Jim O’Shaughnessy:

Lines to Earth, right?

David Ha:

Yeah, exactly. So I feel like getting back to causation versus correlation. A lot of the cause of structures, they could be learned, but sometimes maybe through evolution or there’s an outer loop of how the system is defined or how the rules were set up so that the systems can learn may influence ultimately what it can learn and the understanding that it can do. One thing for example, when people are training now these large language models, in addition to doing things like language, they can also generate computer code.

3. Brunello Cuccinelli – Om Malik and Brunello Cuccinelli

The self-made billionaire greeted me at the door as if I was his long-lost friend. I felt as if I had known him all of my life, just hadn’t met him. I had bought two of his sweaters almost seven years ago, when I had lost a lot of weight (which I have since regained), but his clothes aren’t really part of my wardrobe. And yet I have admired them, as well as his stores and his ethics.

For example, he gives 20 percent of his company’s profits to his charitable foundation in the name of “human dignity” and pays his workers wages that are 20 percent higher than the industry standard, mostly because it allows his company to encourage and continue the Italian craftsman traditions. Cucinelli also pays for an artisan’s school in Solemeo: Young people are free to work either at his company or for another Italian company. The on-campus cafe is way more beautiful than Google Cafe or Facebook’s facilities. And the pasta is really heavenly…

…Om Malik: I’ve been reading about you, and I have been fascinated by your progress and more importantly how you have conducted your business. Where did you find the inspiration to follow this path?

Brunello Cucinelli: From the teary eyes of my father. When we were living in the countryside, the atmosphere, the ambiance — life was good. We were just farmers, nothing special. Then he went to work in a factory. He was being humiliated and offended, and he was doing a hard job. He would not complain about the hardship or the tiny wages he received, but what he did say was, “What have I done evil to God to be subject to such humiliation?”

Basically, what is human dignity made of? If we work together, say, and, even with one look, I make you understand that you are worth nothing and I look down on you, I have killed you. But if I give you regards and respect — out of esteem, responsibility is spawned. Then out of responsibility comes creativity, because every human being has an amount of genius in them. Man needs dignity even more than he needs bread.

[In the past, people] didn’t know anything about their employer. My father or my brother didn’t know if their employer had a villa on the sea. Whereas with Google Maps, I can see where your house is. That’s where the world is becoming new. Mankind is becoming more ethical, but it is not happening because man has decided to become better than he was 100 years ago. It’s because we know we live in a glass house where everybody can see.

In order to be credible, you must be authentic and true. Twenty years ago, something might be written about you in a newspaper. Then this newspaper would be scrapped, and that would be it. But now your statement stays [online] for the next 20 to 50 years — who knows how long for. To be credible, you must be consistent in the way you behave. Someone can say to you, “Listen, two years ago, you said something different.” In a split second, they know. That’s where lies that wonderful future for mankind…

…Om: Now we have a world that is changing. The idea of “brand” is kind of amorphous, and you don’t really know who stands behind that brand. I wonder if you have any thoughts about it.

Brunello: I wanted the brand to have my face. I wanted the product to convey the culture, life, lifestyle, dignity of work. We are a listed company, and I wanted to manufacture a product with dignity. I wanted a profit with dignity. Because the press all talk about the moral ethics of profit. Why can’t we have a dignified profit then?

Would you buy something from someone if you knew that the person, by making this product, has harmed or damaged mankind? No, you would not buy it. You wouldn’t even buy it if you knew that the company had staggering profits. Our cashmere blazer costs $3,000 retail, but the profit must be dignified. It needs to respect the raw material producer, then the artisans, then those working for the company. The consumer also needs to be respected. Everything must be balanced.

We need a new form of capitalism, a contemporary form of capitalism. I would like to add “humanistic” to that equation.

Don’t you feel that over the last two or three years? Don’t you smell it? There is an awareness raising, a civil, ethical point of view. The idea of community, dignity. Yes, it’s a strong sensation…

…Om: You once said that running a company is simple. I wanted to know more about that. I want to learn the business principles that other people, other entrepreneurs, can learn from you.

Brunello: You must believe in the human being, because the creativity of a company — Let’s say you have a company with 1,000 people. Maybe we were told that there are only two or three genius people in the 1,000. But I think that if you have 1,000 people, you have 1,000 geniuses. They’re just different kinds of genius and a different degree of intensity.

We hold a meeting here with all the staff every two months. Everybody takes part in it. Even the person with the humblest tasks knows exactly what was the latest shop we opened. Everything is based on esteem, and esteem then generates creativity.

Everything is visible, when things go well and also when they go less well. When we are sad, when we are worried, when we are happy: If you show all these different moods, then you are credible. That’s why I say this is simple.

Om: Right now you’re a publicly traded company, but you yourself have a more a philosophical bent. How do you reconcile the need of the stock market with your outlook on the world?

Brunello: Finance is now going back to working along with industry while respecting each’s mutual role. In the last 20 years, finance dealt too much with industry, and industry dealt too much with finance. Whereas I myself, I’m an industrialist. I don’t know anything about finance. If you invest in me, you invest in an industry. I like it even better if you call it an artisanal industry.

As for my business plans, I have three-year business plans and 30-year business plans but also three-centuries business plans. I think that this is another good breakthrough in the world.

I haven’t come across one single investor who asked me to target a higher growth. Generally speaking, we pay our suppliers and staff 20 percent more than the average on the market. No investor ever asked, “Why don’t you reduce their wages? They’re too high.” I’m confident, because finance will become contemporary and modern too…

…Om: I’m fascinated that you have such deep passion for philosophy. I wonder how it has helped you as a businessperson.

Brunello: In everything, really. For example, take Marcus Aurelius, the emperor. In any possible mood that you might be in, you read a sentence by him and you feel better. Any philosopher helps you to raise your head and the world will look better. Respect the human being, and that will be better. Hadrian the emperor said, “I never met anyone who after being paid a compliment did not feel better.”

The true way to nurture your soul is philosophy. The true malaise of the human being — no matter whether Italian, American, Chinese — is the malaise of your soul, the uneasiness of your soul. This is stronger now than when my father was young or my grandfather.

I would like to try to somehow cure this malaise of the soul, even with the young people working for my company, because at the end of the day, you can be wealthy and still feel the same way. I know many people who own a fortune. The other day, a very loaded person said to me, “I’d love to be more serene.” This is true for rich people, poor people.

There are three things you cannot buy. Fitness: You have to keep fit, whether you’re rich or not. Diet: You cannot pay someone to be on a diet for you. I think that diet is the biggest sacrifice in my life. Then, looking after your soul. No one can possibly treat your soul but you yourself. This is something you can do through culture and philosophy.

Marcus Aurelius says, “You should go with the flow of mankind, you should live as if it was the last day of your life, plan as if you were to live forever,” and then he also adds, “You should be at rest, at peace, you should give yourself some peace.” Saint Benedict adds, “The sun should never set on our rage. Let’s go to sleep at peace with mankind.”

Let’s try looking after our soul while working. Do you know that we work 11 percent of our life? We can’t have everything revolve around work. Unfortunately, now in Italy, it is hip and chic to say, “I am so tired and exhausted by work.” My father was tired because he was farming the land. He would say, “I need some sleep, I need some rest,” but he did not have this kind of feeling.

This is the great kind of treatment that we have to follow on a daily basis. Philosophy prescribed this treatment to me. I don’t know if you know Boethius, who lived in 520 AD. He was King Theodoric’s right-hand man. Theodoric condemned Boethius to death. He resorts to philosophy for help. Philosophy turns up as a woman, not very young, but with alert eyes. She says to Boethius, “What are you complaining about in your life? You have had this, this, this and that.” This is part of man.

Alexander the Great conquered a country. The tyrant cut the noses off the people there. It’s just the way it is. It’s part of life. I do not feel anxiety. What am I supposed to say here? You see, I think that philosophy really is part of human life.

4. Control, Complexity and Politics: Deconstructing the Adani Affair! – Aswath Damodaran 

In sum, I am willing to believe that the Adani Group has played fast and loose with exchange listing rules, that it has used intra-party transactions to make itself look more credit-worthy than it truly is and that even if it has not manipulated its stock price directly, it has used the surge in its market capitalization to its advantage, especially when raising fresh capital. As for the institutions involved, which include banks, regulatory authorities and LIC, I have learned not to attribute to venality or corruption that which can be attributed to inertia and indifference.

It is possible that Hindenburg was indulging in hyperbole when it described Adani to be  “the biggest con” in history. A con game to me has no substance at its core, and its only objective is to fool other people, and part them from their money. Adani, notwithstanding all of its flaws, is a competent player in a business (infrastructure), which, especially in India, is filled with frauds and incompetents. A more nuanced version of the Adani story is that the family group has exploited the seams and weakest links in the India story, to its advantage, and that there are lessons  for the nation as a whole, as it looks towards what it hopes will be its decade of growth. 

• First, in spite of the broadening of India’s economy, it remains dependent on family group businesses, some public and many private, for its sustenance and growth. While there is much that is good in family businesses, the desire for control, sometimes at all cost, can damage not just these businesses but operate as a drag on the economy. Family businesses, especially those that are growth-focused, need to be more willing to look outside the family for good management and executive talent.
• Second, Indian stock markets are still dominated by momentum traders, and while that is not unusual, there is a bias towards bullish momentum over its bearish counterpart. In short, when traders, with no good fundamental rationale, push up stock prices, they are lauded as heroes and winners, but when they, even with good reason, sell stocks, they are considered pariahs. The restrictions on naked short selling, contained in this SEBI addendum, capture that perspective, and it does mean that when companies or traders prop up stock prices, for good or bad reasons, the pushback is inadequate.
• Third, I believe that stock market regulators in India are driven by the best of intentions, but so much of what they do seems to be focused on protecting retail investors from their own mistakes. While I understand the urge, it is worth remembering that the retail investors in India who are most likely to be caught up in trading scams and squeezes are the ones who seek them out in the first place, and that the best lessons about risk are learnt by letting them lose their money, for over reaching.
• Fourth, Indian banks have always felt more comfortable lending to family businesses than stand alone enterprises for two reasons. The first is that the bankers and family group members often are members of the social networks, making it difficult for the former to be objective lenders. The second is the perception, perhaps misplaced, that a family’s worries about reputation and societal standing will lead them to step in and pay of the loans of a family group business, even if that business is unable to. It is easy to inveigh against the crony relationships between banks and their borrowers, but it will take far more than a Central Banking edict or harshly worded journalistic pieces to change decades of learned behavior.

5. Beijing Needs to Junk Its Economic Playbook – Zongyuan Zoe Liu

Chinese household consumption was a solid growth driver supporting nearly 40 percent of Chinese GDP over the past two decades. China’s rising consumer class was willing to spend more on aspirational goods, confident that their incomes would continue to grow. They were right: The Chinese economy maintained an average of 9 percent annual GDP growth rate for nearly two decades between 2000 and 2019. As a group of Gallup researchers observed using data from a 10-year nationwide survey of the Chinese people, about 3.5 percent of Chinese households had annual incomes of 30,000 yuan (about $3,800) in 1997. This number skyrocketed to more than 12 percent in just five years. Researchers found a continued strong consumer appetite for both must-have items and discretionary fun.

Until roughly 2017, household consumption growth never lost steam. Yet during Chinese President Xi Jinping’s second term, Chinese households experienced the worst slowdown in consumption growth in a generation, dropping from 6.7 percent during Xi’s first term to 4 percent during his second term—considerably slower than GDP growth. Although the nationwide lockdowns and supply chain disruptions have certainly contributed to the downturn in consumption, the Chinese government’s regulatory crackdown on the tech industry combined with China’s worsening external environment have also fueled an unemployment crisis, especially among young people…

…All of these credit expansions with record-breaking exports only generated 3 percent growth in 2022 but at a mounting cost. The result of a proactive fiscal policy for over a decade since 2008 is that about a quarter of Chinese provinces will spend more than half of their fiscal revenue on debt repayment by 2025, as former Chinese Finance Minister Lou Jiwei warned. Previous credit expansion schemes also aimed to support major corporations, not to boost private consumption or provide household support. As a result, Chinese household income growth and consumption growth fell behind GDP growth. Although the U.S. government’s pandemic relief measures were also primarily targeted at corporations rather than households, many American households received greatly increased unemployment insurance as a cushion. However, this option was unavailable for the hardest-hit millions of unemployed migrant workers and recent college graduates in China…

…One way to interpret these policy announcements is that they collectively signal that Chinese policymakers have recognized the urgency of correcting China’s underconsumption problem. If this is true, then this year could be a watershed moment as the government pivots toward prioritizing household consumption over exports, which was China’s canonical growth strategy since 1978.

But changing the course of government priorities in China, especially ones deeply mixed with local government finances, can be a slow and tangled process at best. And even if Chinese leaders genuinely attempt to prioritize consumption, then they face two primary challenges: financial repression and household balance sheet deterioration.

Since former Chinese President Deng Xiaoping, three generations of Chinese leaders have established a system of financial repression that suppresses consumption, forces savings, and prioritizes export and state-led investments. At the operational center of China’s repressive financial system is state-owned commercial banks, whose primary customers are state-owned enterprises and have little experience promoting relationship banking. Take the episode in 2022, when Chinese banks offered loans to companies and then allowed them to deposit funds at the same interest rate, or the time when Chinese banks inflated their loan numbers by swapping bills with one another to meet regulatory requirements for corporate lending. Both are sad evidence that the only type of lending that Chinese banks know how to do—and are allowed to do in the current system—is lending to enterprises, and when demand from enterprise is weak, Chinese banks are incapable of channeling credit to anyone else, especially consumers.

The balance sheet of the average Chinese household has gotten increasingly dire over the last 15 years. Household net asset growth has decelerated since 2010, a problem that worsened during the pandemic. A report by Zhongtai Securities, a Chinese securities service firm, estimated that between 2011 and 2019, Chinese household net asset growth rates dropped to around 13 percent from close to 20 percent before 2008. During the pandemic, household net asset growth sunk below 10 percent.

Most of this wealth is concentrated in the country’s increasingly shaky property sector. An urban household balance sheet survey conducted by the People’s Bank of China in 2019 showed that housing was roughly 70 percent of household assets, with mortgage loans accounting for 75.9 percent of total household debt. This level of indebtedness was comparable to the United States in the run-up to the 2008 subprime crisis and the burst of the real estate and stock market bubble in Japan in the 1980s.

6. Investors: The one thing separating excellent from competent – Simon Evan-Cook

All great investors, past and present, are specialists, not generalists. They’re laser focused on doing one thing, and doing that one thing really well.

Warren Buffett, for example, finds great companies and holds them while they do great stuff. He knows what he wants (to make lots of money) and how to achieve it (hold great companies). So he ignores what everyone else is doing, and focuses on that.

The rub is that, no matter what your one thing is, it won’t work each and every year. This means there will be years when everyone else — the market — looks better than you (even Buffett — he’s had plenty of years like that).

Take 2020; the pandemic year. If your one thing was finding small turnaround stories (another perfectly good way of making lots of money), then 2020 was a nightmare: The share prices of big, obvious companies like Google, Amazon and Facebook, rocketed, while your carefully-selected recovery stocks cratered.

So, doing your one thing in 2020 made you look like a moron. I mean, wasn’t it obvious? It’s Amazon! We’re all locked in our homes! Amazon does home delivery, dummy!..

…Now, if I (or you) pick managers who say they only do one thing, but stop doing it after a tough year or two, I’m stuffed. It means I’m spending too much time exposed to their one thing when it’s not working, and not enough time when it is…

…This is why I’m drawn to Deck-Chair dude. He’s comfortable being different to everyone else. Like Buffett; he knows what he wants, and he knows how to get it. So he’s ignoring everyone else, and their disapproving glares, and focusing on doing his one thing. So, as long as neither of us buckle (and that we’re both good at our respective one things that work over the long term), we’ll do OK.

But there’s more to it than that…

This would have been quicker to write (and read) if there was a word to describe this super-trait.

But there isn’t. Not in English, and not that I know of, anyway (let me know if there is).

There are plenty that come close, but none hit the nail on the head.

‘Disagreeable’ is a word I’ve seen used in this context. And while it’s partly right, it’s also an exclusively negative word that entails being unpleasant or bad-tempered. And that’s not it.

‘Contrarian’ is another often-used term. But this implies someone who always does the opposite to everyone else. Whereas I’m talking about being prepared to do it when necessary, but also being content to run with the crowd when that’s the right thing to do.

To experience the missing word for yourself, try to think of a term that describes this trait in its most heroic form. Like Rosa Parks, for example, when she defied racist rules and social norms to sit in the ‘wrong’ part of the bus.

‘Contrarian’ doesn’t cut it: She wasn’t breaking all laws, just that one. And ‘disagreeable’ in her case is downright offensive.

‘Stubborn’? ‘Dogged’? ‘Pugnacious’? Sure; these are characteristics she displayed, but do they define her? I don’t think so.

Clearly she was ‘brave’, but that’s too broad a term to describe her act of deliberately breaking an unfair law and social order: You can be brave by upholding a good rule as well as breaking a bad one.

On paper ‘Conscientious’ comes close: “Being controlled by one’s inner sense of what is right”, says the dictionary. That works, but recently it’s come to mean someone who’s quietly hard-working — “a conscientious worker” — and that’s well wide of the mark.

So, until I’m told otherwise, I’ve had to create a new word: ‘Bellitious’. A mash-up of ‘belligerent’ and ‘conscientious’, which describes someone who can be belligerent when doing what their conscience tells them to be right.

7. Who Owns the Generative AI Platform? – Matt Bornstein, Guido Appenzeller, and Martin Casado

Infrastructure is, in other words, a lucrative, durable, and seemingly defensible layer in the stack. The big questions to answer for infra companies include:

• Holding onto stateless workloads. Nvidia GPUs are the same wherever you rent them. Most AI workloads are stateless, in the sense that model inference does not require attached databases or storage (other than for the model weights themselves). This means that AI workloads may be more portable across clouds than traditional application workloads. How, in this context, can cloud providers create stickiness and prevent customers from jumping to the cheapest option?
• Surviving the end of chip scarcity. Pricing for cloud providers, and for Nvidia itself, has been supported by scarce supplies of the most desirable GPUs. One provider told us that the list price for A100s has actually increased since launch, which is highly unusual for compute hardware. When this supply constraint is eventually removed, through increased production and/or adoption of new hardware platforms, how will this impact cloud providers?
• Can a challenger cloud break through? We are strong believers that vertical clouds will take market share from the Big 3 with more specialized offerings. In AI so far, challengers have carved out meaningful traction through moderate technical differentiation and the support of Nvidia — for whom the incumbent cloud providers are both the biggest customers and emerging competitors. The long term question is, will this be enough to overcome the scale advantages of the Big 3?…

…There don’t appear, today, to be any systemic moats in generative AI. As a first-order approximation, applications lack strong product differentiation because they use similar models; models face unclear long-term differentiation because they are trained on similar datasets with similar architectures; cloud providers lack deep technical differentiation because they run the same GPUs; and even the hardware companies manufacture their chips at the same fabs.

There are, of course, the standard moats: scale moats (“I have or can raise more money than you!”), supply-chain moats (“I have the GPUs, you don’t!”), ecosystem moats (“Everyone uses my software already!”), algorithmic moats (“We’re more clever than you!”), distribution moats (“I already have a sales team and more customers than you!”) and data pipeline moats (“I’ve crawled more of the internet than you!”). But none of these moats tend to be durable over the long term. And it’s too early to tell if strong, direct network effects are taking hold in any layer of the stack.

Based on the available data, it’s just not clear if there will be a long-term, winner-take-all dynamic in generative AI.

This is weird. But to us, it’s good news. The potential size of this market is hard to grasp — somewhere between all software and all human endeavors — so we expect many, many players and healthy competition at all levels of the stack. We also expect both horizontal and vertical companies to succeed, with the best approach dictated by end-markets and end-users. For example, if the primary differentiation in the end-product is the AI itself, it’s likely that verticalization (i.e. tightly coupling the user-facing app to the home-grown model) will win out. Whereas if the AI is part of a larger, long-tail feature set, then it’s more likely horizontalization will occur. Of course, we should also see the building of more traditional moats over time — and we may even see new types of moats take hold.

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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. We currently have a vested interest in Alphabet (parent of Google), Amazon, and Meta Platforms (parent of Facebook). 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 12 February 2023:

1. The race of the AI labs heats up – The Economist

But almost all recent breakthroughs in big ai globally have come from giant companies, because they have the computing power (see chart 2), and because this is a rare area where results of basic research can be rapidly incorporated into products. Amazon, whose ai powers its Alexa voice assistant, and Meta, which made waves recently when one of its models beat human players at “Diplomacy”, a strategy board game, respectively produce two-thirds and four-fifths as much ai research as Stanford University, a bastion of computer-science eggheads. Alphabet and Microsoft churn out considerably more, and that is not including DeepMind, Google Research’s sister lab which the parent company acquired in 2014, and the Microsoft-affiliated Openai (see chart 3).

Expert opinion varies on who is actually ahead on the merits. The Chinese labs, for example, appear to have a big lead in the subdiscipline of computer vision, which involves analysing images, where they are responsible for the largest share of the most highly cited papers. According to a ranking devised by Microsoft, the top five computer-vision teams in the world are all Chinese. The baai has also built what it says is the world’s biggest natural-language model, Wu Dao 2.0. Meta’s “Diplomacy” player, Cicero, gets kudos for its use of strategic reasoning and deception against human opponents. DeepMind’s models have beat human champions at Go, a notoriously difficult board game, and can predict the shape of proteins, a long-standing challenge in the life sciences.

Jaw-dropping feats, all. When it comes to the sort of ai that is all the rage thanks to Chatgpt, though, the big battle is between Microsoft and Alphabet. To see whose tech is superior, The Economist has put both firms’ ais through their paces. With the help of an engineer at Google, we asked Chatgpt, based on an Openai model called gpt-3.5, and Google’s yet-to-be-launched chatbot, built upon one called Lamda, a set of questions. These included ten problems from an American maths competition (“Find the number of ordered pairs of prime numbers that sum to 60”) and ten reading questions from America’s sat school-leavers’ exam (“Read the passage and determine which choice best describes what happens in it”). To spice things up, we also asked each model for dating advice (“Given the following conversation from a dating app, what is the best way to ask someone out on a first date?”).

Neither ai emerged as clearly superior. Google’s was slightly better at maths, answering five questions correctly, compared with three for Chatgpt. Their dating advice was uneven: fed some real exchanges in a dating app, each gave specific suggestions on one occasion, and platitudes such as “be open minded” and “communicate effectively” on another. Chatgpt, meanwhile, answered nine sat questions correctly compared with seven for its Google rival. It also appeared more responsive to our feedback and got a few questions right on a second try. On January 30th Openai announced an update to Chatgpt improving its maths abilities. When we fed the two ais another ten questions, Lamda again outperformed by two points. But when given a second chance Chatgpt tied.

The reason that, at least so far, no model enjoys an unassailable advantage is that ai knowledge diffuses quickly. Researchers from competing labs “all hang out with each other”, says David Ha of Stability ai. Many, like Mr Ha, who used to work at Google, move between organisations, bringing expertise and experience with them. Moreover, since the best ai brains are scientists at heart, they often made their defection to the private sector conditional on a continued ability to publish their research and present results at conferences. That is partly why Google made public big advances including the “transformer”, a key building block in ai models, giving its rivals a leg-up. (The “t” in Chatgpt stands for transformer.) As a result of all this, reckons Yann LeCun, Meta’s top ai boffin, “Nobody is ahead of anybody else by more than two to six months.”

2. What’s a Blockchain? – Technically

Like SQL databases, blockchains are a way to store data. SQL databases store data in rows and are great for storing “facts” like how many likes an Instagram post has or the contents of this article.

Unlike SQL, blockchains take a state-machine based approach to storing this data. A state machine models reality in a different way than the spreadsheet-like relational databases that you’re used to.

A state-machine describes ways of moving between states via actions. For a simple example, think about a button. The button has two states: pressed (on), or not pressed (off). When you take the action of pressing the button, the state changes from off to on (and vice versa).

On the blockchain, an action like “button pressed” is called a transaction. And in the context of cryptocurrencies like Bitcoin, the most common type of transaction is moving value from one entity to another. In state 1 (the beginning), Alice has $7 and Bob has $1. Then a transaction happens, where Alice gives Bob $2. Now, in the subsequent state 2, Alice has $5 and Bob has $3.

These transactions are grouped into blocks. Blocks are then chained together, forming a blockchain! And voila, you understand the blockchain.

Blocks depend on the previous state, which is determined by the previous block, and so on. In essence, the blockchain is a sequential list of changes (blocks) made to the initial state. 

On a blockchain, the current state is never explicitly represented because only transactions are stored. In other words, there’s no record saying “Bob currently at this moment has $x.” This is different from how SQL databases store data, where the only thing stored is the current state (and perhaps some history). As with all things technology, there are tradeoffs to this approach:

Since blockchains don’t store the state, it takes time to calculate it by running through previous transactions, whereas SQL databases always have access to the current state. 

On the other hand, blockchains automatically store history via transactions while SQL databases don’t keep a record of the history at all. Storing history is powerful because it enables blockchains to be transparent: it’s easy to see how we got to the current state on a blockchain. This key property enables a lot of cool use cases, which we’ll cover in the next section.

3. TIP520: Investing Through Post-Bubble Markets w/ Jamie Catherwood – Trey Lockerbie and Jamie Catherwood

[00:01:45] Trey Lockerbie: I know that many have compared today to the 1970s, but I figured you might have different perspectives and possibly draw comparisons to other periods in time that resemble what we’re seeing today. 

[00:01:55] Jamie Catherwood: Yeah, so anyone that’s familiar with my work will know that I like to look at things before the 1970s. 

[00:02:06] Jamie Catherwood: I’m talking about the 1870s, In all seriousness, if you want to read about it, my colleague at O’Shaughnessy Asset Management, Ehren Stanhope, a member of our research team and client portfolio manager, wrote a great paper called “The Great Inflation.” You can find it on our website, osam.com, where he walks through the similarities and, more importantly, the key differences between the 1970s and today and why this is not like the 1970s Great Inflation.

[00:02:38] Jamie Catherwood: But to actually answer your question, I would say that the period I find most interesting in terms of a parallel to today would be the 1920s, which I’m sure most people know by now. I’ve found it really interesting since, honestly, COVID started, the similarities in progression and timeline between the early 1910s and 1920s with today.

[00:03:02] Jamie Catherwood: Because while we obviously, at least knock on wood, didn’t have a world war today, it looks like that might also be following the path of when the Russia-Ukraine conflict started. But thankfully, so far that has been avoided. A hundred years ago, you had a pandemic with the Spanish flu. After that, you had a wave of summer protests around race called the Red Summer of 1919, which was similar to the George Floyd Black Lives Matter summer of protests and demonstrations.

[00:03:33] Jamie Catherwood: And then you had a reopening where things were really kind of speculative and surging to make up for the pent-up demand that had existed while we were all locked. Which also occurred coming out of World War I and the Spanish flu a hundred years ago. But then in 1920-1921, you had a really sharp and severe recession, which was very short.

[00:03:55] Jamie Catherwood: But again, it was a problem of, in that case, rampant inflation very quickly turning into rampant deflation. It was an interesting period, but then after that is when you got the roaring twenties. But people tend to skip over that part when they talk about the roaring twenties – the one that came out of the pandemic.

[00:04:14] Jamie Catherwood: And then we had a recession, and then we had the roaring twenties. And so today, obviously the parallels are pretty obvious. We had a pandemic, we had the George Floyd Summer, and then we had the recession. And now the question is kind of, are we going to keep following roughly in line with the twenties, or, and by that, we would be experiencing or on the precipice of experiencing a true like roaring twenties.

[00:04:40] Jamie Catherwood:  Or is it going to be something different, where the economy takes longer to rebuild and truly get back to the pre-COVID levels? And so, time will tell, but I think in terms of similarities, there are a few periods that have so much in common…

…[00:05:22] Trey Lockerbie: In the article you just mentioned, and we’ll be sure to add it to the show notes so that our listeners can find it, there’s a quote in it that I wanted to emphasize. I think it summarizes pretty well. It says, “As we dive into the impact on equity markets, there does not appear to be a link between high inflation and lower equity returns, most likely associated with the compression in valuations that occurs, as it did during the Great Inflation.

[00:05:45] Trey Lockerbie: That said, certain factors like value, momentum, and shareholder yield historically hold up quite well in moderate to high inflation regimes. So I thought that was a really interesting point. I think a lot of people think there is a high correlation between inflation and performance of stocks, so it’s interesting to dig in a bit more. Can you highlight anything else on that subject around performing assets, sectors, etc., that actually do perform or even factors that are best to focus on during periods like this?

[00:06:19] Jamie Catherwood: Yeah, so factors in general tend to hold up very well to earn inflationary regimes. In addition to this paper by Aaron, which goes through and shows the returns across different factors in different inflation regimes since 1926, there is a great paper by JP Morgan aptly titled The Best Strategies for Inflationary Times ,pretty to the point. And in that paper, which I think came in like two years ago, at this point, they argue for factors that they looked at essentially eight high inflation regimes starting with I think coming out of World War II. And then there’s been like eight kinds of main inflation regime since then. And so they look at how different assets and kinds of investing styles were sectors performed in each of those regimes.

[00:07:08] Jamie Catherwood: And then also on average. And so they found that across those eight regimes, from a factor standpoint, momentum was the best performing factor across all inflation regimes, and the size factor was the worst. And then for sectors, energy was the best sector across all eight inflation regimes, and consumer durables, and so like consumer staples was the worst performing sector by some margin. And so it’s a really interesting paper and it was interesting to see that momentum in their research was the highest performer….

…[00:13:55] Trey Lockerbie: You know, FTX is, I think, around 8 billion. And, but it’s still huge, right? And, a lot of people were very surprised to see them file bankruptcy essentially overnight. So it brought up the phrase bankruptcy to me. I was kind of curious about this, so I wanted to learn a little bit about the history of bankruptcy.

[00:14:12] Trey Lockerbie: I would look to you or someone like you to share something about, you know, where the term bankruptcy comes. 

[00:14:18] Jamie Catherwood: Essentially back in the 14th century in Italy, their bankers at that time were conducting their business and transactions off of a bench. A bench is what they called it. But it really looked kind of more like a big table.

[00:14:34] Jamie Catherwood: But for all intentS&Purposes, it was this bench that they would sit on. They have the table, and that’s where they would basically sit in squares in Italy. So you know, you can picture somewhere like Venice and all these Venetian bankers sitting out in a courtyard and they’re doing their banking from this table.

[00:14:48] Jamie Catherwood: If a banker went insolvent though, and they could not continue lending out money or meeting their payments, then to signal and kind of shame that banker publicly and to let people know that he was insolvent and had gone, busted the kind of authorities or other bankers would. That person’s bench in half is just a kind of public signal.

[00:15:09] Jamie Catherwood: Like this guy literally blew up. He broke his bench in half. He’s insolvent. And the Italian, sorry to any Italian listeners, , brace yourself. The Italian phrase at that time was banca rta. That meant a broken bench. And so obviously you can see how over time, Bancta’s broken bench goes from broken bench to bankruptcy.

[00:15:34] Jamie Catherwood: So Banta bankruptcy, that’s where we get the term bankrupt from because it goes back to broken benches. When a banker went insolvent, they smashed his bench. And so a broken bench equals bankruptcy….

… [00:36:32] Trey Lockerbie: SBF obviously still in the news was once compared to JP Morgan. For bailing out a lot of crypto companies, which is also kind of interesting leading up to, you know, the demise, let’s say of FTX. Talk to us about the panic of 1907 and why this comparison to JP Morgan is being made. 

[00:36:50] Jamie Catherwood: So it’s really interesting, always in hindsight, these are like comparisons for people that turn out to be not so great. Cause I think he was also called like the next Warren Buffet. But yeah, so 1907 Panic was a really interesting one.

[00:37:03] Jamie Catherwood: A large reason why it started was actually from a year earlier in April, 1906 with the San Francisco Earthquake. Quick history it’s kind of a quirk at that period. Over 50% of fire insurance companies in San Francisco were British, which becomes very important because I think it’s like April 6th, 1906, the San Francisco earthquake happened and what a lot of people I think don’t know is that it wasn’t actually the earthquake that did the most damage. It was the fires because essentially the earthquake took out the city’s water mains. And so an earthquake happens, it hits a bunch of pipes and whatever. It causes fires. But then because the city’s water mains had been taken out, there was no water to put out the fire.

[00:37:50] Jamie Catherwood: And so for four straight days, the whole city just burned. And something like 20,000 blocks were destroyed in between 30 and like 70%, which I know is a huge gap of the San Francisco population went into homelessness because of that fire. I mean, even if it’s just 30, that’s still a lot of people. And at the time there was no earthquake insurance.

[00:38:12] Jamie Catherwood: And so people that had had their house destroyed by the earthquake, but it didn’t catch on fire. They had no real way to get insurance because it was just from the earthquake. But if they did have fire insurance, what a lot of people started doing was literally just setting their house on fire because there was no earthquake insurance.

[00:38:31] Jamie Catherwood: So they knew like, if we’re gonna get anything out of this, it’s by lighting our house on fire and then saying like the earthquake caused our house to catch on fire. But this is important because again, as over 50% of the fire insurance companies in San Francisco were British when this event happened, suddenly British fire insurance firms had a lot of money that they were on the hook for to pay out.

[00:38:58] Jamie Catherwood: And so what happened was Britain ended up sending the equivalent of 13% of their nation’s gold supply to San Francisco. On ships because these firms were just, they needed to pay out so much money and after Britain sends out 13% of their gold supply, they hike up their rates afterwards and really contract their kind of market because they’re trying to bring gold back over to London after depleting its reserves so much.

[00:39:32] Jamie Catherwood: And so this had knock-on effects for global markets, specifically in New York because this was happening at a time of year where financial markets were already kind of fragile because of just seasonal funding and capital needs around kind of more agricultural stuff. And so, even though it seems like an unrelated event, this earthquake had knock on effects because it was really kind.

[00:39:55] Jamie Catherwood: Tightened up markets. And then alongside that, you have the Nicker Brocker Trust Company and all these other sketchy trust companies that were highly levered and taking a lot of risk on speculative stocks. And so markets were already kind of fragile because of the San Francisco earthquake issue. And then alongside that, you had a failed corner of the copper market and then the collapse of Knickerbocker Trust Company and all these other trust companies.

[00:40:20] Jamie Catherwood: And at the time, we didn’t have a Federal Reserve. And so JP Morgan, the person ended up basically acting like the Federal Reserve and as a lender of last resort and providing capital and doing deals with companies and individuals that needed help because there wasn’t really another place for them to turn.

[00:40:40] Jamie Catherwood: So basically what ended up happening was the government realized we can’t continue to rely on a single person, you know, to bail us out of future crises. That panic also highlighted. Downsides of relying on gold as the base of kind of your monetary system because something like an earthquake and a lot of British fire insurance firms leading to a lot of gold needing to be moved, causing financial markets to tighten and become more fragile.

[00:41:12] Jamie Catherwood: It just really highlighted how kind of susceptible the gold standard was to these types of shocks. And so that, and the need for a Federal Reserve or some type of central bank were really two of the lasting kind of impacts from the 1907 panic because it just really highlighted, you know, JP Morgan dies, what are we gonna do?

[00:41:30] Jamie Catherwood: So it led to the creation of the Federal Reserve in 1913. So yeah, panic in 1907 is kinda like the last Pree real panic.

4. How Gautam Adani Made (and Could Lose) a $147 Billion Fortune – Stacy Meichtry, Shan Li, Krishna Pokharel, and Weilun Soon

AHMEDABAD, India—Gautam Adani is ubiquitous in this country.

His name is plastered on roadside billboards and on the airports and shipping docks he operates. His power plants light Mumbai office towers and irrigate rural fields, fueled by coal he imports from mines as far away as Australia. He recently expanded into defense and media.

So when U.S. short seller Hindenburg Research alleged last week that the Adani Group—the energy and infrastructure conglomerate he controls—was engaged in wide-ranging fraud, the fallout was widespread and severe. His companies’ stocks and bonds plunged, leaving investors with billions of dollars in losses and igniting a bitter fight that the company cast as an assault on the nation itself. On Wednesday, Mr. Adani’s flagship company, Adani Enterprises, canceled a stock sale of up to $2.5 billion.

The Adani Group denied the short seller’s allegations, describing the report as “a calculated attack on India, the independence, integrity and quality of Indian institutions, and the growth story and ambition of India.” Hindenburg shot back that Adani’s rebuttal stoked nationalist sentiment without adequately addressing the issues the firm had raised…

…Hindenburg’s allegations have shaken what many Indians call the Gujarat model of economic growth—a reference to the home state of both Messrs. Adani and Modi. The approach has involved using large government subsidies to fund infrastructure construction by private firms such as Mr. Adani’s.

The opposition Congress party has used the Hindenburg report to cast the Adani Group as an oligarch enabled by the Modi government.

“It says a lot about what corporate India is like,” said Hemindra Hazari, a Mumbai-based analyst who specializes in the Indian capital markets. Investors, he said, “are clearly very shaken up.”…

…Mr. Adani is involved in the Modi government’s plans to pivot the economy from fossil fuels to cleaner sources of energy such as wind and solar power. He has vowed to build three factories to make solar modules, wind turbines and hydrogen electrolyzers, part of his plan to invest $70 billion in cleaner technologies over the next decade. He is developing a vast solar farm in India’s northwestern desert…

…Mr. Adani returned to Ahmedabad in the early 1980s to work with his older brother, Mahasukh, who had acquired a plastics maker. He worked there as an importer, procuring raw materials for the firm’s factories. The family later founded Adani Exports, sending goods such as toothpaste and shoe polish to global markets.

In the early 1990s, India fell into an economic crisis fueled in part by the economy’s reliance on imports. The government secured an emergency loan from the International Monetary Fund and embarked on a sweeping privatization drive.

Adani Exports began buying land at Mundra Port, which was owned by the state of Gujarat. Mundra’s unusually deep waters made it ideal for docking massive ships, and its position along the Arabian Sea made it an effective gateway for Asian goods to travel west…

…Mr. Adani later proposed forming a joint venture with the state of Gujarat, which still owned land in the area, to further develop the port. Gujarat’s government approved the venture.

In 2001, after climbing the ranks of the Hindu nationalist Bharatiya Janata Party, Mr. Modi was appointed chief minister of Gujarat’s government. He helped its economy grow by providing incentives to attract businesses such as auto manufacturers, upgrading the electricity supply and improving irrigation for farmers.

Under Mr. Modi, the Gujarat government sold the state’s stake in the port joint venture to Adani Exports for two billion rupees, about $24 million at today’s exchange rate, according to a 2014 report by the federal government auditor.

“The development of Mundra Port which was envisaged as a joint sector port turned out to be a private sector port for which competitive bidding was not followed,” the report said.

Mr. Adani built a rail line to the port, making it the first in India connected to the national rail system. That allowed Mr. Adani to turbocharge the movement of goods through Mundra. The central government designated the port a special economic zone, providing another incentive to do business there.

India lacked abundant supplies of fossil fuels, so Mr. Adani began importing coal from Indonesia and Australia. He built a giant conveyor belt in Mundra to carry coal from the dock toward a nearby Adani power plant. Electricity generated at the plant moved over Adani transmission lines to cities and towns hundreds of miles away.

“I proudly say that we had a very good experience with the Modi government,” Mr. Adani said in the recent TV interview, referring to Mr. Modi’s Gujarat administration.

Mundra became India’s largest private port, which allowed Mr. Modi to brandish his pro-business credentials as he prepared to run for prime minister. A Hindu nationalist, Mr. Modi tapped into the frustrations of a generation of Indians who had climbed out of poverty but didn’t reach the middle class because of slowing growth and a lack of employment.

After Mr. Modi won, his government sought to further accelerate economic growth. That included a plan to privatize the operation of six airports. Companies in the bidding weren’t required to have any experience in building or operating airports. Mr. Adani won all six contracts, making his company India’s largest airport operator…

Adani Group’s expansion into new businesses such as data centers, copper refining and hydrogen drew it into capital-intensive sectors, where analysts say its companies have limited experience. Much of that expansion was funded by debt. Analysts have said many projects aren’t expected to turn a profit for a few years.

Debt-research firm CreditSights published a report in August describing Adani Group as “deeply overleveraged.” Adani Green Energy had a debt-to-equity ratio of 2,023% at the end of the fiscal year ended March 31, 2022, the report said, while Adani Transmission’s was 272%.

The report warned that if one of the conglomerate’s companies became financially distressed, it could negatively affect the stock prices or valuations of others.

Adani Group said in September the debt ratios of its companies “continue to be healthy and are in line with industry benchmarks,” adding that the companies have consistently reduced their debt loads.

In November, Adani Enterprises announced plans for a large stock sale, aiming to raise as much as $2.5 billion. It said some of the funds would be used to repay debt and fund capital expenditures for green-energy projects, expressway construction and airport improvements.

Three days before the public offering began last Friday, the Hindenburg report was released, sending shares of Adani companies plummeting.

5. Sunday Reads #170: Lemon markets, dark forests, and a firehose of malicious garbage – Jitha Thathachari

One thing I’ve been saying often is: when it’s 10x easier to fake it than to make it, fakes will always outnumber the truth. We saw it in the crypto summer of 2021, when all you needed was to create a token and you’d get mass adoption. A paper found that 98% of tokens launched on Uniswap were scams.

The general principle is: When it’s easy to showcase a veneer of “work” without doing the work itself, then 99% of the work you see will not be real.

When it’s easy to generate content without writing it yourself, then 99% of content will be AI-generated. And if 99% of content is AI-generated, you’re better off assuming that 100% is AI-generated. When you see any content online, the default assumption will be: this has been written by an AI.

This won’t happen tomorrow. It might not happen for the next three years. But inevitably, it will happen. The Internet will become “a market for lemons”.

“A market for lemons” is a thought experiment that shows how a market degrades in the presence of information asymmetry.

From Wikipedia:

Suppose buyers cannot distinguish between a high-quality car (a “peach”) and a “lemon”. Then they are only willing to pay a fixed price for a car that averages the value of a “peach” and “lemon” together.

But sellers know whether they hold a peach or a lemon. Given the fixed price at which buyers will buy, sellers will sell only when they hold “lemons”. And they will leave the market when they hold “peaches” (as the value of a good car as per the seller will be higher than what the buyer is willing to pay).

Eventually, as enough sellers of “peaches” leave the market, the average willingness-to-pay of buyers will decrease (since the average quality of cars on the market decreased), leading even more sellers of high-quality cars to leave the market through a positive feedback loop.

Thus the uninformed buyer’s price creates an adverse selection problem that drives the high-quality cars out of the market.

This is how a market collapses.

Soon, everything that’s for sale is garbage. Nobody has any incentive to put anything other than garbage up for sale. Why would they, when they cannot prove that they’re selling the real thing?…

…Coming back to generative AI, what we see will be similar. As instant “fake content” becomes more and more like “real content” that takes hours to painstakingly produce, the outcome is clear: The Internet will become, slowly and then suddenly, completely fake. It will become a market for lemons. So what does this mean for how we use the Internet?

Lars Doucet talks about this in AI: Market for Lemons and the Great Logging Off

The internet gets clogged with piles of semi-intelligent spam, breaking the default assumption that the “person” you’re talking to is human.

The default assumption will be that anything you see is fake. You think this is hyperbole? You don’t think this can happen? Well, then ask yourself: When did you last pick up a phone call from an unknown number? 20 years ago, you’d pick up a call from any number. It was almost always a real person, whom you wanted to speak to or who had something useful to tell you. Today, an unknown number is always a robocaller, a scammer, or a telemarketer. You really really don’t want to speak to them.

Why won’t the same thing happen with the Internet?…

…To paraphrase Lars, what happens when fake content becomes 100x easier to create? What happens when every social network is chock-full of bots, drowning your feed in utter gibberish? What happens when 99% of the people you interact with on Instagram are fake? What happens when 99% of the people you play chess against online are “fake” humans? What happens when they defeat you within 20 moves every single time? What happens when every profile you right-swipe on Tinder is a bot that’s about to scam you? What happens when the Internet becomes a never-ending firehose of malicious garbage?

This is what happens: You start logging off the Internet…. and logging in to more curated, closed communities. No more talking to fake people on Twitter or Facebook. No more using Google for search. Instead, everything happens in closed Slack or Discord communities. Invite-only social networks where a curated set of people talk to each other.

Maggie Appleton talks about this scenario, in The Expanding Dark Forest.

The “Dark Forest” is originally a term from astronomy. It’s a hypothesis for why we haven’t found any aliens yet, despite searching for decades. First proposed in 1983, it became popular with Liu Cixin’s Three-Body Problem trilogy.

Summarizing from Wikipedia:

The dark forest hypothesis is the idea that many alien civilizations exist throughout the universe, but are both silent and paranoid.

In this framing, it is presumed that any space-faring civilization would view any other intelligent life as an inevitable threat, and thus destroy any nascent life that makes its presence known. As a result, the electromagnetic spectrum would be relatively silent, without evidence of any intelligent alien life, as in a “dark forest”…filled with “armed hunter(s) stalking through the trees like a ghost”.

6. The generative AI revolution has begun—how did we get here? – Huang Haomiao

You may be familiar with the latest happenings in the world of AI. You’ve seen the prize-winning artwork, heard the interviews between dead people, and read about the protein-folding breakthroughs. But these new AI systems aren’t just producing cool demos in research labs. They’re quickly being turned into practical tools and real commercial products that anyone can use.

There’s a reason all of this has come at once. The breakthroughs are all underpinned by a new class of AI models that are more flexible and powerful than anything that has come before. Because they were first used for language tasks like answering questions and writing essays, they’re often known as large language models (LLMs). OpenAI’s GPT3, Google’s BERT, and so on are all LLMs.

But these models are extremely flexible and adaptable. The same mathematical structures have been so useful in computer vision, biology, and more that some researchers have taken to calling them “foundation models” to better articulate their role in modern AI.

Where did these foundation models came from, and how have they broken out beyond language to drive so much of what we see in AI today?

There’s a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

The first modern AI revolution took place with deep learning in 2012, when solving computer vision problems with convolutional neural networks (CNNs) took off. CNNs are similar in structure to the brain’s visual cortex. They’ve been around since the 1990s but weren’t yet practical due to their intense computing power requirements.

In 2006, though, Nvidia released CUDA, a programming language that allowed for the use of GPUs as general-purpose supercomputers. In 2009, Stanford AI researchers introduced Imagenet, a collection of labeled images used to train computer vision algorithms. In 2012, AlexNet combined CNNs trained on GPUs with Imagenet data to create the best visual classifier the world had ever seen. Deep learning and AI exploded from there.

CNNs, the ImageNet data set, and GPUs were a magic combination that unlocked tremendous progress in computer vision. 2012 set off a boom of excitement around deep learning and spawned whole industries, like those involved in autonomous driving. But we quickly learned there were limits to that generation of deep learning. CNNs were great for vision, but other areas didn’t have their model breakthrough. One huge gap was in natural language processing (NLP)—i.e., getting computers to understand and work with normal human language rather than code.

The problem of understanding and working with language is fundamentally different from that of working with images. Processing language requires working with sequences of words, where order matters. A cat is a cat no matter where it is in an image, but there’s a big difference between “this reader is learning about AI” and “AI is learning about this reader.”

Until recently, researchers relied on models like recurrent neural networks (RNNs) and long short-term memory (LSTM) to process and analyze data in time. These models were effective at recognizing short sequences, like spoken words from short phrases, but they struggled to handle longer sentences and paragraphs. The memory of these models was just not sophisticated enough to capture the complexity and richness of ideas and concepts that arise when sentences are combined into paragraphs and essays. They were great for simple Siri- and Alexa-style voice assistants but not for much else.

Getting the right training data was another challenge. ImageNet was a collection of one hundred thousand labeled images that required significant human effort to generate, mostly by grad students and Amazon Mechanical Turk workers. And ImageNet was actually inspired by and modeled on an older project called WordNet, which tried to create a labeled data set for English vocabulary. While there is no shortage of text on the Internet, creating a meaningful data set to teach a computer to work with human language beyond individual words is incredibly time-consuming. And the labels you create for one application on the same data might not apply to another task.

You want to be able to do two things. First, you want to train on unlabeled data, meaning text that didn’t require a human to mark down details about what it is. You also want to work with truly massive amounts of text and data, taking advantage of the breakthroughs in GPUs and parallel computing in the same way that convolutional network models did. At that point, you can go beyond the sentence-level processing that the RNN and LSTM models were limited to.

In other words, the big breakthrough in computer vision was data and compute catching up to a model that had already existed. AI in natural language was waiting for a new model that could take advantage of the compute and data that already existed.

The big breakthrough was a model from Google called “the transformer.” The researchers at Google were working on a very specific natural language problem: translation. Translation is tricky; word order obviously matters, but it changes in different languages. For example, in Japanese, verbs come after the objects they act on. In English, senpai notices you; in Japanese, senpai you notices. And, of course, French is why the International Association Football Federation is FIFA and not IAFF.

An AI model that can learn and work with this kind of problem needs to handle order in a very flexible way. The old models—LSTMs and RNNs—had word order implicitly built into the models. Processing an input sequence of words meant feeding them into the model in order. A model knew what word went first because that’s the word it saw first. Transformers instead handled sequence order numerically, with every word assigned a number. This is called “positional encoding.” So to the model, the sentence “I love AI; I wish AI loved me” looks something like (I 1) (love 2) (AI 3) (; 4) (I 5) (wish 6) (AI 7) (loved 8) (me 9).

Using positional encoding was the first breakthrough. The second was something called “multi-headed attention.” When it comes to spitting out a sequence of output words after being fed a sequence of input words, the model isn’t limited to just following the strict order of input. Instead, it’s designed so that it can look ahead or back at the input sequence (attention) and at different parts of the input sequence (multi-headed) and figure out what’s most relevant to the output.

The transformer model effectively took the problem of translation from a vector representation of words—taking in words in sequence and spitting out words one after another—and made it more like a matrix representation, where the model can look at the entire sequence of the input and determine what’s relevant to which part of the output.

Transformers were a breakthrough for translation, but they were also exactly the right model for solving many language problems.

They were perfect for working with GPUs because they could process big chunks of words in parallel instead of one at a time. Moreover, the transformer is a model that takes in one ordered sequence of symbols—in this case, words (technically fragments of words, called “tokens”)—and then spits out another ordered sequence: words in another language.

And translation doesn’t require complicated labeling of the data. You simply give the computer input text in one language and output text in another. You can even train the model to fill in the blanks to guess what comes next if it’s fed a particular sequence of text. This lets the model learn all kinds of patterns without requiring explicit labeling.

Of course, you don’t have to have English as the input and Japanese as the output. You can also translate between English and English! Think about many of the common language AI tasks, like summarizing a long essay into a few short paragraphs, reading a customer’s review of a product and deciding if it was positive or negative, or even something as complex as taking a story prompt and turning it into a compelling essay. These problems can all be structured as translating one chunk of English to another.

The big breakthrough in language models, in other words, was discovering an amazing model for translation and then figuring out how to turn general language tasks into translation problems.

So now we have an AI model that lets us do two critical things. First, we can train by fill-in-the-blanks, which means we don’t have to label all the training data. We can also take entire passages of text—whole books, even—and run them in the model.

We don’t have to tell the computer which lines of text are about Harry Potter and which are about Hermione. We don’t have to explain that Harry is a boy and Hermione is a girl and define boy and girl. We just need to randomly blank out strings like “Harry” and “Hermione” and “he” and “she,” train the computer to fill in the blanks, and in the process of correcting it, the AI will learn not just what text references which character but how to match nouns and subjects in general. And because we can run the data in GPUs, we can start scaling up the models to much larger sizes than before and work with bigger passages of text.

We finally have the model breakthrough that lets us take advantage of the vast amount of unstructured text data on the Internet and all the GPUs we have. OpenAI pushed this approach with GPT2 and then GPT3. GPT stands for “generative pre-trained transformer.” The “generative” part is obvious—the models are designed to spit out new words in response to inputs of words. And “pre-trained” means they’re trained using this fill-in-the-blank method on massive amounts of text….

…Computer vision before deep learning was a slog. Think for a moment about how you, as a person, might recognize a face. The whole is made up of the parts; your mind looks for shapes that look like eyes and a mouth and determines how combinations of those shapes fit together in the pattern of a face.

Computer vision research used to be a manual effort of trying to replicate this process. Researchers would toil away looking for the right building blocks and patterns (called “features”) and then try to figure out how to combine them into patterns. My favorite example of this is the Viola-Jones face detector, which worked by recognizing that faces tend to fall into a pattern of a bright forehead and nose in a T-shape, with two dark areas under them.

Deep learning started to change all of this. Instead of researchers manually creating and working with image features by hand, the AI models would learn the features themselves—and also how those features combine into objects like faces and cars and animals. To draw an analogy to language, it’s as if the models were learning a “language” of vision; the “vocabulary” of lines, shapes, and patterns were the basic building blocks, and they were combined higher into the network with rules that served as a “grammar.” But with vast amounts of data, the deep learning models were better than any human researcher.

This was immensely powerful because it gave computers a scalable way to learn rules over images. But it wasn’t yet enough. These models were going in one direction—they could learn to map pixels to categories of objects to drop them into buckets and say, “these pixels show a cat; these pixels show a dog”—but they couldn’t go in the other direction. They were like a tourist who memorizes some stock phrases and vocabulary but doesn’t really understand how to translate between the two languages.

You can probably see where we’re going.

7. The Retreat of the Amateur Investors – Gunjan Banerji

Amateur trader Omar Ghias says he amassed roughly $1.5 million as stocks surged during the early part of the pandemic, gripped by a speculative fervor that cascaded across all markets.

As his gains swelled, so did his spending on everything from sports betting and bars to luxury cars. He says he also borrowed heavily to amplify his positions.

When the party ended, his fortune evaporated thanks to some wrong-way bets and his excessive spending. To support himself, he says he now works at a deli in Las Vegas that pays him roughly $14 an hour plus tips and sells area timeshares. He says he no longer has any money invested in the market.

“I’m starting from zero,” said Mr. Ghias, who is 25…

…Some investors have exited the market. They include Mr. Ghias, the 25-year-old amateur trader who watched the value of his stock portfolio swing wildly during the early stages of the pandemic.

Mr. Ghias says his first exposure to investing happened as a teenager growing up in the suburbs of Chicago, where his guitar teacher would monitor stocks by phone. He and that guitar teacher say they would discuss everything from penny stocks to pot stocks to shares of larger companies. When he got to high school, he started trading with some of his own money in between jobs. He says he sometimes cut class in high school and college to trade.

Once the pandemic began, he gravitated to stocks and funds tracking the performance of metals as well as options, which allow investors to buy or sell shares at a certain price. He used these to generate income or profit from stock volatility. He also borrowed from his brokerage firms to amplify his positions, a tactic known as leverage.

In 2021, he started increasing that leverage, his brokerage statements show. He often turned to trades tied to the Invesco QQQ Trust, a popular fund tracking the tech-heavy Nasdaq-100 index, while continuing to bet heavily on metals. At times, he dabbled in options tied to hot stocks such as Tesla Inc. and Apple.

At one point, his leverage amounted to more than $1 million, brokerage statements reviewed by The Wall Street Journal show. By around June 2021, according to those brokerage statements, his portfolio was worth roughly $1.5 million.

“I really started treating the market like a casino,” Mr. Ghias said…

…In late 2021, he placed one of his biggest bets. The Fed’s Mr. Powell had warned he was about to pull back the central bank’s easy-money policies, opening the door to tapering its monthly asset purchases. The plans threatened to inject a jolt of turbulence into a market that had been ascending to fresh records for much of the year.

Mr. Ghias says he thought the Fed was bluffing and made a speculative investment that he thought would benefit from an accommodative central bank, expecting prices of silver and gold to rally and help a portfolio that included a large position in Hecla Mining Co., statements show. He says he also added a bearish position tied to the Nasdaq.

The trade didn’t work, he says, and a broker demanded he post more money to fund his losses. By the end of the year, according to his statements, he had lost more than $300,000 in one account even as the S&P notched a gain of 27%.

“That was my breaking point,” Mr. Ghias said.

In 2022, he says he started taking even more risks trading options and betting on sports in hopes of making some of the money back. One big strategy was to gamble on the direction of the S&P 500 by buying and selling options contracts tied to that index that often expired the same day, brokerage statements show.

Mr. Ghias traded S&P 500 options at all hours, sometimes around midnight, placing some trades worth hundreds of thousands of dollars, brokerage statements show. For example, if he had a hunch that the S&P 500 would keep tumbling the next day, extending losses from its overnight session, he might sell options contracts that would profit from a steeper plunge. At times, he was left with losses from such trades, his statements show.

“That just put me in a really bad mental state,” Mr. Ghias said. “I began chasing losses.”

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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. We currently have a vested interest in Alphabet (parent of Google), Amazon, Apple, Meta Platforms (parent of Facebook), Microsoft, and Tesla. 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 05 February 2023:

1. Carl Kawaja – Dealing with Regime Change – Patrick O’Shaughnessy and Carl Kawaja

Patrick: [00:25:52] I’d love to talk a bit more about Apple because, one of the things that I’ve learned about myself in the last seven years is in public markets, I’m sort of a PureQuant. I found in my research and my own ability that a lot of where I think outperformance can come from, for me, is really more in things like multiple change and misevaluation, I’ll call it, versus some thesis about the fundamentals of a business over 5 to 10 years.

The reason I’ve gravitated more towards direct investing in the private markets is I’ve realized I just love products. Really, what I love to understand and think about is products. It really makes me think about Apple because, when you’ve been involved in all these companies that I’ll mention here in one way, shape or form, so I’m just fascinated to hear how you think about product cycles.

Because in many ways Apple’s success story, I don’t know if it’s apocryphal or not, but apparently Tim Cook and a few of his executives went to Buffett and said, “Look, like here’s our plan for capital allocation. We’re going to return excess capital to shareholders through dividends and buybacks.”

And that was a major contributing factor to his buying Apple. Versus a Meta on the other side of the spectrum where it’s like, no, no, no, we’re going to roll this and bet the farm on this whole new platform that’s very different and maybe control the next big interface platform or something.

And then in between, you’ve got Amazon, which you know really well with this amazing second act in AWS. And it’s very confusing to me how an investor like you might approach a second act or a new big bet or a new bet-the-farm moment for a company like these three that we just mentioned.

Because in many ways, it seems like Apple is just like we’ll keep doing R&D, but like we’ve got these unbelievable products. We’re going to keep making them better. We’re going to return tons of capital and the returns have been otherworldly as a result. And that seems very Buffett-esque. And like you said, he’s telling us what to do, like, why don’t we all just do that?

How do you think about all of this? I don’t have like a cleanly formed question in all of that. But I think you see my point that I just love these product cycles, and I’m curious how you approach one or think about one.

Carl: [00:27:50] Like you, I love good products. I’m a bit of a sucker for it. So if a company has a product that is exceptional, it makes me very interested. You and I were chatting casually before we started on the podcast a bit about the GLP-1 class of drugs that Lilly and Novo Nordisk can have and Pfizer is working on one.

And if you speak to someone who is on Wegovy, it’s just astounding the health benefits that they’re having and how quickly they’re losing weight. When they see something like that, it really makes my ears perk up. Like, oh my God, I love this thing. That’s a good base to start with.

I feel like Buffett was correct and determined in his own way that the iPhone is just really totally and utterly awesome, and you become very bonded to it. And I know that they’re expensive. But if you said to me, instead of having the iPhone, you have to use this phone that Patrick O’Shaughnessy invented, that works pretty well also, that would be a very sad day for me.

I would pay a lot of money to stay on the iPhone. And actually, like everyone else, we have a bunch of family chats in my family. And one of my siblings stubbornly refuses to use an iPhone and uses an Android. But you don’t see the videos as well. My family videos, if someone’s on the chain who’s not in the iPhone chain.

And my father — I’m shaming my relative, but my father kicked my brother off of the family text chain because he was on Android, that he didn’t have an iPhone, that’s a powerful product. And you’re right, I think Buffett’s genius was that they’ve got a great product, and they’re going to focus 100% on that, and that’s great.

So what makes what Meta is doing problematic or potentially problematic? Mark Zuckerberg may end up being right. I think we have to be open to that. And in fact, I love his vision of the future, and I love that TV show this fall happened to be on Amazon Prime, The Peripheral based on the William Gibson book, which is the metaverse in a way, with large.

And if that ends up being right, then that is going to be brilliantly right. The thing that I find slightly problematic about Meta and where I draw contrast with AWS and Amazon is I often find that good brilliant new ideas and new products start out small and then catch on like wildfire.

And when the fire is burning, throw more wood on it. As opposed to things where you gather a huge ton of wood and try to start the fire. And I’m a little worried, and I know Mark Zuckerberg with his absolute brilliance, which is widely acknowledged, may be right. And he thinks a lot of scale needs to be thrown at it.

But I worry that good things start small and run from there. And if you’re investing $8 billion to $10 billion a year and something to get it started and you do that for five years, it might even just prima facie be a sign that it’s a bad idea.

Patrick: [00:31:10] Have you ever heard of that book, The Systems Bible? I reference it all the time. I love it.

Carl: [00:31:15] Yes, I know. I ordered it. I haven’t read it yet.

Patrick: [00:31:17] The lesson in there that stands out is that every successful complex system evolves from a simple system to start. You can’t airdrop in a complex system, like it just does not work. It will not hold. And that’s another way of saying the same thing you’re saying, I think, about Meta. Whereas AWS was an organic outgrowth in 2005 or ’06 or whatever of the retail infrastructure, basically.

Carl: [00:31:40] To be humble for both of us, though, about the fact that we may be wrong, I do have one good counterfactual, I think, which is Reliance Enterprises, the Indian conglomerate. Years ago, they thought that organized retail has the potential to do really well in India.

And they were going to invest in it, and they were going to open hundreds, eventually thousands of new stores. But they had a concept for selling goods in India where, for example, they would package rice and sell rice in packages rather than in a big bin where you self-serve into a package and stuff like that.

And a lot of Indian retail is not organized chain retail. And they said, “Well, we’re going to open hundreds of stores. We have this very innovative idea.” I said, “I don’t know if I’ve ever heard of a retailer who opened 100 stores and then like 300 more and was successful.”

Like pretty much every retailer started with like one Walmart and then three Walmarts or one Dollar General and two Dollar Generals. And I was like honestly internally, I simply said, this is a really dumb idea. This will never succeed. Boy, has it been successful. They kind of were right.

They had a different vision for retail. It was grounded in insight and data. Of course, they took a while to optimize it. But it really looks like it’s working. So some things maybe you do have to do at scale. It’s higher risk, and it’s probably higher reward too…

…In terms of someone else I’ve learned from and in terms of our theme of being open-minded, I think I told you and you’ve read the book subsequently, but I was really impressed. One of the favorite things that I’ve read and maybe it will make it on to your Investor’s Field Guide reading list is this book called Don’t Sleep, There Are Snakes by a linguist and anthropologist named Daniel Everett.

He went to the Amazon as a Christian missionary to basically translate this language that a tribe in the Amazon speaks called the Piraha, a pretty remote tribe. And he discovered some very interesting things about the language that are a source of controversy in the linguistic world.

But essentially, he proved that the language doesn’t have any numbers beyond two. So it’s one, two and then few or many, but that’s about it. And they don’t have a past or a future tense. They only speak in a present tense. They don’t have words for colors, really.

And their system of orientation is exocentric as opposed to endocentric. So they don’t have a word for left hand or right hand. And I was actually having dinner last night with some friends who were saying like, oh, I always get confused with my left hand or my right hand.

And I’m like, well, you’re like this tribe in the Amazon who doesn’t orient themselves based on their left hand or right hand, but relative to their external environment. So they live near the river. And if they’re on the west side of the river, their left hand is upriver.

And if they’re on the east side, their right hand is upriver. And it’s either the upriver or the downriver hand. And the concept that your hand has a position in space in and out itself doesn’t really make sense to them. Like why would you orient yourself that way?

But actually, when you start to think about it, it kind of does make sense to orient yourself that way. But they have these very different ways of thinking, and I’d recommend that book to folks, Don’t Sleep, There Are Snakes by Daniel Everett and recommend studying him.

Because he sort of found a group of people who think about the world in a very different way. And any time that I can get insight into that, I think it’s helpful to me and helpful to me as a person. I mean another thing they do is they live very much in the present.

And I feel like for many of us, we spend so much time living in the future in a bad way, because we’re anticipating future stress or future work. And my wife likes to say that I never go on a vacation with our family without spending half the vacation planning the next vacation.

And she says, “Why can’t you just enjoy the vacation we’re on right now rather than spend half of your time on the phone trying to get reservations at the hotel for the vacation next year?” And that is a failing, and that’s something that the Piraha don’t do. Their language doesn’t even…

Patrick: [00:55:38] Allow it.

Carl: [00:55:39] Yes. It doesn’t incorporate those concepts because it doesn’t make sense to them.

Patrick: [00:55:44] I just finished the book last night. One of the things that’s so striking about it is the trade-offs. So on the one hand, he remarks through the book how incredibly happy they are. Just by like simple objective measures, how much they smile and laugh and the lack of stress, all these measures that you would associate with the happy people.

And that one, two and many thing, it’s just so fascinating to think about the implications of it. If the rest of your life, you could only say one, two and many. Once you get to many, you’re at many and you kind of stop. And that’s reflected in the trade-off of there has been no progress. One of the things they said is they’ll only make baskets out of this very degradable material and the baskets last like a couple of days and it degrades and it’s gone.

And like they could make a basket out of like tree bark or something that lasted a long time, but they’re just like don’t think, do and don’t care because of this orientation. And it’s so strange that, to me, the trade-off could be between contentment and happiness. And like, well, call it, technological progress, which very much lives in the future. I don’t know what the hell to make of that, other than it’s like very, very interesting. And it sure sounds like the life of the Piraha was pretty good, for the most part.

Carl: [00:56:52] And I do think there’s something we can take from it. Like we make durable baskets to store things so that we can use them later. And yes, that’s nice, not to be hungry later. But in a way, it’s like stressful to be worrying about later.

And sometimes, it’s nice to begin each day when you go out and look for food. And there’s that story you remember from the book where they talk about the dugout canoes. They canoe in these very shallow, flat-bottomed canoes that work well in a variety of environments but aren’t good for catching a lot of fish, because you just can’t store a lot of fish in a very shallow canoe.

And so Daniel Everett embarks in a process with the help of another tribe of teaching them how to make deep canoes. And they learned how to make deep canoes and they perfect it, and they make a very good, deep canoe.

And then I think they give it to Dan. And he says, “Why aren’t you psyched about this? Now you can make these really deep canoes and just fill them full of fish.” And they’re like why would we do that? Is there any reason on earth why we would do that? And I know you love Emerson and you love that Self-Reliance essay. There’s a relationship between the Piraha and Emerson a little bit.

2. BOJ’s Unplanned War on Japan’s Zombie Companies – Rei Saito

However, raising interest rates is much harder for the BOJ than its western counterparts. On December 2, 2022, Hitoshi Asada, a council member asked, “If the interest rate rises by as much as 1%, how much will the BOJ’s holdings of bonds lose on valuation?” In response, Deputy Governor Masayoshi Amemiya responded, “Around 28.6 trillion yen ($221 billion) in valuation losses.”

This is money that the BOJ, nor the Japanese government have, which will result in them having to borrow from foreign entities at increasingly high interest rates.

The BOJ is well-aware of this predicament, but might have no choice. The Japanese consumer price index rose 3.7% in November compared to the same month last year. This is the highest level in about 41 years and will only be exacerbated with the current 0% interest rate. Couple this with the sinking attractiveness of Japanese 10-year bonds and doing nothing could lead to a disaster.

However, there is an issue that scares the BOJ even more…

BOJ Interest Rate hike is a potential catastrophe for Zombie companies

Simply put, zombie companies are businesses whose sole purpose is to survive, often through subsidized loans from the government, and they contribute nothing to society except stable employment.

As the zombie companies stagger on, unable to make a profit, they are unable to invest in their own workforce. The employees of these zombie companies are left to wither, without the training and development they need to evolve and thrive in the job market. Instead, they often become trapped as “Zombie employees”, doomed to spend the entirety of their working lives in these dying companies, unable to escape and find new opportunities.

Currently, only about 35% of companies in Japan pay corporate tax. That means that the remaining 65% don’t pay a dime in corporate taxes, and hence are free-riding on Japan’s infrastructure and zero-interest environment while not contributing at all!

So, if the BOJ is serious about the future of this country, they need to raise interest rates.

The scary thing is how easy it would be to weed-out these zombie companies: There are reports that a measly 0.25% interest rate hike would bankrupt over one fifth of all zombie companies. That’s a lot of unemployed people the BOJ and the Japanese government would have to answer for.

3. Mark Nelson – Nuclear Power: Change the Memes, Change the Future (EP.144) – Jim O’Shaughnessy and Mark Nelson

Jim O’Shaughnessy:

You had a great quote, which I’m going to let you elaborate on, but is, I think, really, what’s his name? Scott Adams, the guy who does Dilbert. Would call this a linguistic kill shot. Because what you say is Chernobyl, the molecules, versus Chernobyl, the memes, is very, very different. The molecules killed several dozen people. The memes are killing millions and are still at it. Please elaborate.

Mark Nelson:

Sure. The memes being the ideas. And Richard Dawkins famous coining of this phrase, meme is a spreadable idea. Which we can expand, often spreadable phrases or images. That move between people and can take on a life of their own versus the molecules which are needing to be born by the wind. Which are bound by the laws of physics, which help determine the rate of decay or the danger of that molecule when it gets into a body, if it gets into a body. So, at Chernobyl, you had a plant where there was a catastrophic explosion that vented a burning reactor core into the world, and there were several

dozen deaths from that accident. Several from the trauma of the impact, people who would’ve died whether it was just a steam explosion with no radioactive molecules at all. There are people who died of exposure to acute quantities of radiation from the material in the core.

Mark Nelson:

There are a few folks on a helicopter, including the pilot, that got tangled up during key critical stages of the cleanup operation, which is why I include those who died in the helicopter accident. And then finally, there were several dozen victims of a very particular set of diseases that came from exposure to one of the isotopes, that does the most amount of damage in a short period of time in the days, or even first week or two, after a nuclear accident like Chernobyl. But then the plant kept operating, it kept operating for 14 years. The plant made more electricity the year after the blast than it did of the blast. Made more the year after that, made more the year after that. It kept setting plant efficiency records. And when it was finally shut down in year 2000, workers were quite upset that the best jobs available were being taken.

Mark Nelson:

And those who had some emotional connection to the plant were saying, why would you shut down this one? This type of reactor’s in operation around Eastern Europe and European Russia, and you were not shutting down those. Why would we shut down ours, which has had the most number of safety upgrades based on our learning experience from the disaster? So Jim, right there, I’ve had smart, young, physics educated, anti-nuclear people convert to being pro-nuclear on the spot when I replaced the Chernobyl meme with Chernobyl kept operating and was shut down by European Union cash payouts’ meme. That alone is an example of the difference between, because why? When I say Chernobyl kept operating, I’m not explaining to you the isotope story. I’m not justifying the safety or not of the improvements that they made to the reactor so it wouldn’t blow up in the future.

Mark Nelson:

And when I say and the workers wanted it to keep operating, we fundamentally know they had the most amount of skin in the game in terms of those most likely to be impacted if another one of them blew up. So, you’re going directly for, I guess, thank you for calling that a linguistic kill shot, but you’re stating an easily verifiable fact. And go on Google and see what’s the production record for Chernobyl nuclear plant. You see it cuts off after year 2000, right? You can see that bodies we trust to keep us safe from nuclear, like IAEA, International Atomic Energy Agency, verify that it kept operating. Whatever you think you knew about the worst nuclear disaster in the history of mankind, if it didn’t even stop the plant from operating and the plant operated better after one of them blew up? That destabilizes some of the most deeply held beliefs that people can have about nuclear energy…

…Mark Nelson:

I talk to people all around the world, all over the world and talking about Ukraine, about Zaporizhzhia Nuclear Plant, the largest nuclear plant in Europe, occupied by force with gun and tank fire and rocket fire led to an outburst of fear but no meltdown. It could have. In fact, if this happened and there was no Fukushima Daiichi, there’s a chance that the confusion and chaos and lack of backup that was… All changes that got better with the response to Fukushima Daiichi triggered an immune response by the industry that strengthened every single nuclear plant around the world, world. Without that, we may have lost a reactor or two. Come up. They had a giant plant that needs to be connected to the grid, get chopped off the grid because of shelling and damage and war. And yet, what happens in that plant daily, despite being a crisis just does not get people’s attention it would’ve a year ago. So it’s giving people calibration we’ve never had before because nuclear was so safe that the events weren’t frequent enough to develop an intuition about how dangerous they were.

4. Kishida vows unprecedented scope of steps to lift the birthrate – Takahashi Narasaki

Prime Minister Fumio Kishida on Jan. 23 pledged to tackle the alarming decline in the birthrate through measures that far exceed the scope of those taken by previous governments…

…The declining birthrate has long been a thorn in such programs. And it may be worsening.

The estimated number of newborns in 2022 was fewer than 800,000, a figure that came eight years earlier than the government’s projection.

“We are now only a few weak moments away from reaching a point on whether we can sustain social functions,” Kishida said. “We need to reverse the sliding birthrate.”

He said the government must immediately draw up policy measures for families with small children based on three pillars, including financial support, such as child allowances.

Kishida said a framework for doubling budgets for supporting families raising children will be created by June, when the government maps out its basic policy.

Turning to the “new capitalism” that he advocates, Kishida stressed that wage increases must be achieved.

“If companies that generate profits fully distribute the fruits of the profits to employees, higher personal consumption and further economic growth will result,” he said. “The key to this virtuous growth cycle is pay increases.”

Kishida also said he will push for reform of the labor market to build a structure that can sustain such pay hikes.

“The first necessary step is to increase wages to a level higher than the (recent) rate of inflation,” he said.

Kishida urged companies to transition from traditional seniority-based pay hike systems to ones that better reflect job evaluations to skill levels of workers. Such a shift, he said, would fuel growth.

The prime minister vowed to present a model of how to introduce such merit-based pay systems for Japanese companies by June.

5. What’s the Modern Data Stack? – Technically

Data teams exist, more or less, to build knowledge at your company. It’s their job to figure out what’s going on with the business, what might happen next, and how that information can help teams like Product, Marketing, and Sales make more money and such. So when we talk about a data stack, it just means what tools these teams use to get their jobs done.

There are a million ways to cut the data stack, but generally it will fit into a few categories:

1. Something to pull in data from where it’s generated
2. A place to store your data
3. Something to transform your data with
4. Something to visualize and analyze your data with

Yes, it turns out there’s a lot of logistics involved with “simple questions” like can you pull last month’s revenue for me?..

…The modern data stack basically just applied cloud philosophy to the data stack. Instead of large, highly configurable, on premise software, companies started using cloud-based, easy to get started with, more opinionated software. Tools in the modern data stack are:

• Cloud first – your data sits on someone else’s servers in the cloud; no need to manage your own, deal with upgrades, etc.
• Simple – products are designed to get started with quickly and require minimal configuration; you should be able to get something working in a single sitting

It’s worth noting that the old data stack didn’t suck because anyone wanted it to: technology has just progressed, a lot. 

6. Why America Should Ban Crypto – Charlie Munger

In the U.S. in recent years, privately owned companies have issued thousands of new cryptocurrencies, large and small. These have later become publicly traded without any governmental pre-approval of disclosures.

In some cases, a big block of cryptocurrency has been sold to a promoter for almost nothing, after which the public buys in at much higher prices without fully understanding the pre-dilution in favor of the promoter.

All this wild and wooly capitalism is much like that described in a remark often attributed to Mark Twain, who was thought to have said that “a mine is a hole in the ground with a liar on top.”

Such wretched excess has gone on because there is a gap in regulation. A cryptocurrency is not a currency, not a commodity, and not a security. Instead, it’s a gambling contract with a nearly 100% edge for the house, entered into in a country where gambling contracts are traditionally regulated only by states that compete in laxity. Obviously the U.S. should now enact a new federal law that prevents this from happening.

7. Should You Protect Your Portfolio Against a Possible U.S. Debt Default? – Ben Carlson

The debt ceiling debate makes politicians feel important. They use it as a negotiating ploy to pass or block other legislation. It’s leverage.

Could we see some crazy politician take things too far at some point and force a default? It wouldn’t surprise me but that seems like a short-term problem that would be remedied fairly quickly once they see the problems it would cause. Politicians want to get re-elected and wrecking the U.S. economy is not a great strategy for that.

But even if you knew how badly a politician could screw this up someday it still might not help you position your portfolio correctly. Back in the summer of 2011, Standard & Poor’s downgraded the U.S. credit rating. It felt like a big deal at the time…

…How about the stock market? Things did get weird in the stock market in the short-term. The Monday after the downgrade was announced the S&P 500 crashed more than 6%. That’s a big down day. The next day it was up almost 5%. The day after that it was down more than 4%. And just for good measure the market ripped 5% the very next day. So we had down 6%, up 5%, down 4% and up 5% back-to-back-to-back-to-back. It was a volatile time for sure.

However, even including that down 6% day, the S&P 500 was up almost 20% a year later…

…$31 trillion is kind of a lot of debt. I’m not as worried about that debt as others. Let’s look at the interest we pay on that debt as a percentage of GDP:

It’s rising but is still much lower than the outlays in the 1980s and 1990s for interest expense. We can still afford to pay our debts even though rates and the amount of liabilities have risen.

The debt was lower back then but rates were higher and GDP was obviously much lower as well. The latest GDP number came in at more than $26 trillion. And that’s not an accumulated figure like the debt. This year the economy will likely produce a number that’s even bigger than that.

I know the debt number is scary but just know people have been worrying about government spending for a long time. As long as the economy continues to grow, federal debt will grow as well as the pie expands.

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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. We currently have a vested interest in Alphabet (parent of Google), Amazon, Apple, and Meta Platforms (parent of Facebook). 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 29 January 2023:

1. TIP514: Permanent Supply Chain Disruptions That Will Sink The Economy w/ Jim Rickards – Trey Lockerbie and Jim Rickards

[00:25:05] Trey Lockerbie: I’d like to segue here and talk about supply chains, which is what this book is not all about, but at least half or so of the book is really this huge deep dive into how supply chains work and why they’re important.

[00:25:17] Trey Lockerbie: And I wanted to kind of call out what you, I don’t know if you came up with this phrase yourself, but you refer to it as this meta supply chain. That’s what we’ve evolved into now. So as we enter this new age of potential de-globalization, well first of all, explain what a meta supply chain is, but then also how does the meta supply chain unwind potentially in de-globalization, and what would be the risks in ramification of that?

[00:25:41] Jim Rickards: Sure. Well, let’s start with a simple supply chain and we’ll kind of build up from there. So you’re in a supermarket and somebody’s buying a loaf for bread, and you say to them, where’d the bread come from? And you go, oh, well, there’s a bakery on the other side of town and they bake it, and they send it over here on a truck and I buy the bread.

[00:25:55] Jim Rickards: Okay, that’s a simple supply chain. But even that, it’s not so simple because who made the truck? You know? Where’d the diesel come from? Where was that refinery? Where’d the truck driver get his training, et cetera? Oh, the law for bread? Well, it has a wrapper. Was it plastic or paper? Well, it could be either one, but that came from somewhere.

[00:26:11] Jim Rickards: And, and then you get over to the baker. But let’s just go further. So looking at the baker, how did they bake the bread? Well, they baked it in an oven. Where’d the oven come from? You know, it’s got tempered glass and steel and the semiconductors and thermostats and all kinds of parts. Might be from 15 or 20 different countries that was assembled and put together, and then the oven was produced.

[00:26:30] Jim Rickards: Well, how do you make bread? Well use flour Well, okay. Where’d the flower come from? Oh, it came from the middle. Okay. Well how did it get from the middle to the baker? Well, it came on a truck. Oh, another truck, another diesel, another driver, et c. How did the mill make the flour? Where did they get their ingredients?

[00:26:45] Jim Rickards: Well, they got wheat from the farmers. Really? How did it get there? Well, it came on a train. Well, trains run on diesel, who built the train? You know, et cetera. Then back to the farmer where the farmer get the seas, and by the way, the farmer needs tractors and diesel fuel and workers and gps and a lot of other scientific equipment, irrigation systems, and they need fertilizer nitrogen fertilizer to grow the wheat.

[00:27:04] Jim Rickards: And where does that come from? How does it come from Russia? Russia’s in a little war right now. We’re not buying their fertilizer, you know, so forth. So you can kinda keep going. So that’s what’s called the extended supply chain. So Baker does store is the simple supply chain, but you know, farmer fertilizer, on the one hand from Russia to the store with all those intermediate inputs is the extended supply.

[00:27:24] Jim Rickards: But then if you think about it, if you think of the supply chain as being horizontal from, you know, farmer to store with 10 stops in between the transportation lanes, every one of those intersecting points is a vertical supply chain. Again, all the components in the oven, all the components in the truck, et cetera.

[00:27:41] Jim Rickards: And you pretty quickly, this is where I, I say this in the book, the supply chain is not part of the economy. The supply chain is the economy and the meta supply chain is this vertically and horizontally expanded supply chain of supply chains that I described. And you can just kinda keep going in terms of inputs and all the way back to mines and semiconductor fabrication plants and so forth.

[00:28:02] Jim Rickards: And you realize that if it’s not literally infinite, it might as well be infinite because you cannot model it. You can model it theoretically, and you can do some computational work around it, but there’s not enough computing power in the world to end, nor is there all the data in the world, nor enough proper algorithms take everything I just described and put it into a computer.

[00:28:18] Jim Rickards: It can’t be done, but you can manage it in certain ways. So that’s the meta supply chain…

…[00:39:19] Trey Lockerbie: So, speaking of China, there a sentence in the new book stood out to me, which was that you claim China’s turn towards totalitarianism is a symptom of weakness.

[00:39:29] Trey Lockerbie: And you go as far as to say that we’ve just seen Peak China, if I’m not misquoting you there. And so this really was interesting because I know your older book, currency Wars was a huge influence on Ray Dalio. He gifted it to his entire company at one point, I believe. But he just wrote a new book as well.

[00:39:43] Trey Lockerbie: And this is the Changing World order where I think he’s alluding to a world where China is actually the rising power and as we’ve just yet to see them become the next world order. Right. So I’m curious where the disconnect is here because it seems to fly into the face of his theories. 

[00:39:59] Jim Rickards: Well, look, I know Ray and he is a great guy and world’s greatest head of manager and deserves a lot of crazy smart guy.

[00:40:04] Jim Rickards: He’s still kind of coming up the curve in terms of history and geopolitics and so forth. But yeah, the conventional wisdom is the 20th century was the American century. The 21st century is going to be the Chinese century or the Asian century, and they’re going to blow past the United States in the matter of years in terms of being the world’s largest economy, higher G D P technology coming on stream, artificial intelligence, quantum computing, stronger military.

[00:40:27] Jim Rickards: It’ll be at at worst, Western Pacific hegemon, if not a global hegemon, and it’s all China and they’re going to roll the world. Everything I just said is wrong. But that is the conventional wisdom and you see variations of that all over the place. You know, Jeffrey Sachs, Richard Haas, you know, Ray Dalio, all smart people, but that’s fundamentally flawed.

[00:40:46] Jim Rickards: Now, the Peak China thesis, and to give credit, and I mentioned the names in the book, there’s been advanced by Michael Becky and I forget how’s last, he’s a scholar at the John Sal’s Club of Vater National Studies. Becky’s a scholar at Tufts University and they took a hard look at this and said, no, this is as good as it gets for China right now.

[00:41:05] Jim Rickards: They point to a number of reasons and I, I can kind of go down the same list. I’ve done the same research. Charles, half the water in China is poisoned. It’s not just dirty. You gotta clean it up before you can use it. It’s poisoned. I know a lot about the mining industry. I invested mines and I know that in the US and Canada, for example, if you use cyanide to extract gold from gold or which you do, that’s pretty standard.

[00:41:25] Jim Rickards: You gotta weigh the cyanide before you use it, then use it, case it, weigh it again, and it better be the same. Like none of that cyanide can escape, you know, careful control and disposal. In China, they do the same thing. They dump the cyanide into the rivers and a lot of ize in terms of mining, industrial output and so forth.

[00:41:41] Jim Rickards: So heth water is poisoned. They don’t have that much water to begin with, not enough of the size of the country. If you look at the geography of China, half of its desert or high plateau or mountains. People picture rice pads less about 20% of the land in like the southeastern corner. Most of it’s quite high and quite dry.

[00:41:57] Jim Rickards: They don’t have enough water to begin with. They’ve got a real estate collapse that makes what happened here in 2007 look like a picnic. They’ve got massive defaults. Not, I’ve been around China, like to say I got mud on my boots, but I was wearing Italian loafers. But I was out on construction sites looking at the ghost cities being built and.

[00:42:14] Jim Rickards: And just to give you one example, in the US when you buy a house, if you get a mortgage, the mortgage lender shows up for the closing and they give the seller the check. You sign the note and they record it. And you’ve got a mortgage in China, they have a mortgage system, but you take out the mortgage before the house is even built, and then you take the money and you give it to the developer and they use it to build the house.

[00:42:33] Jim Rickards: Well, guess what? The developers stole the money. They used it to cover out their debts. The houses never got built, but you still have the mortgage, you sign the note and the banks are trying to collect on mortgages from people who never got the houses. So this is leading to some, you know, that’s not rise demonstrations and social unrest.

[00:42:49] Jim Rickards: And you know, the government’s bail out the banks and the banks are billing out the lenders, but that’s a complete real estate collapse. So the water’s poisoned real estate sector, which is one of their biggest internal investment sectors, is collapsing. There’s a dollar shortage. You see the reserves coming down, treasury information available.

[00:43:04] Jim Rickards: You look at ’em, month by month of reserves are coming down sharply and they don’t have the technological edge. Anything they’ve got, they sold from us or firms in Europe, Siemens, or something like that. And that’s not being cut off. It’s worked for them so far. When I started developing economics in the 1970s and we thought that the hard part was to get from low income to middle income, but if you could do that, then it was straight path to high income.

[00:43:26] Jim Rickards: You would just kind of keep going. Turns out that’s not true. It’s actually kind of easy to get from low income to middle income. You don’t have too much corruption, which is you bring the population from the countryside of the city and you give them basically assembly type jobs. It’s like, people say iPhones are made in China.

[00:43:41] Jim Rickards: Not really. They’re assembled in China. Those parts come from 26 different countries. The semiconductors come from South Korea, but they assemble them in China, but that’s kind of Lego style manufacturing. And you can get there and you can get to $10,000 per capita annual income, although not evenly distributed, but getting from middle income to high income.

[00:43:58] Jim Rickards: That’s really hard and that requires technology and high value added production, and they can’t get there. They’re stuck in what is known as the middle income trap. But the biggest problem, while bigger than everything I just mentioned, is they are facing and is here now. It’s going to play out over a a 50 year, 55 year period, the greatest demographic collapse in history, worse than the Black death.

[00:44:21] Jim Rickards: Worse than the 30 years war, worse than the Spanish flu of 1918, they’re going to lose 600 million people in the next 50 or 60 years. Population’s going to go from 1.4 billion to about 800 million. Now, there are a lot of different equations for gdp, but the simplest one is workforce times productivity. How many people are working times?

[00:44:40] Jim Rickards: How productive are they? That there’s your gdp? How do you maintain any kind of economy if you’re going to lose 600 million people, which they are, and it’s worse than that because they’re losing them because their birth rate is so low. The magic number or the key number is 2.1. If two people have 2.1 kids, that’s enough to keep your population constant.

[00:45:02] Jim Rickards: Like why not to? Well, the answer is infant mortality and not every birth makes it to maturity so they can have. But on average, two people have 2.1 kids that’ll keep your population constant. The replacement rate, that’s the replacement rate earth rate in China right now, they say 1.7, but they always lie about their numbers.

[00:45:20] Jim Rickards: Other experts put it at kind of 1.2. Some people think it’s one that is behind the demographic disaster. But the reason it’s worse is that while you’re not getting new births to replace the population, the existing population is getting older and hundreds of millions are moving into their seventies, eighties, and nineties.

[00:45:37] Jim Rickards: Those age groups are highly age cohorts are highly correlated with Alzheimer’s, Parkinson’s, dementia, various kinds of cognitive decline, all of which are common at that age. They’re incurable and the progressive in the sense that they get worse. So they’re there, they’re alive, but they’re not the least bit productive.

[00:45:54] Jim Rickards: And then you need a large segment of the kind of what’s called working age population 25 to 54 as caregivers. To the people in their eighties and nineties who are suffering dementia. Now, that’s a very worthy occupation, but it does not lend itself to productivity gains. There’s been no, no increase in productivity in giving someone a bath in 5,000 years.

[00:46:15] Jim Rickards: I mean, maybe okay, 1870 indoor plumbing and hot water. Nice going, but that’s it. So you’re taking productive people, putting them as caregivers, which is a, which does not lend as self to productivity increases. A large segment of your population is not productive at all and some many suffering from a severe cognitive decline.

[00:46:35] Jim Rickards: So the portion that’s left who were actually productive working age people doing productive things, not caregivers, and not people in their eighties and nineties, keeps getting smaller. Some scholars estimate that that’s actually inflationary. Because you’re going to need to pay them more. And we did see this after the Black death in the late 14th century, early 15th century, returns to labor went up, wages went up because there weren’t enough workers.

[00:46:57] Jim Rickards: Now, it didn’t last, maybe last 75 years, but eventually the monarchs got the upper hand again. But it was a good, very good period for labor because the third of the European population was dead. 

2. An Interview with Gregory C. Allen About the Past, Present, and Future of the China Chip Ban – Ben Thompson and Gregory C. Allen

Well, here’s the question. Let’s skip past the chip ban. We’ll circle back to it in a moment. Is self-sufficiency in your estimation possible? So even before, again, my argument would be integrating into the chain, becoming an essential piece, is the way that you should have actually gained leverage. Now, we fast forward to the chip ban, which I want to ask you more about how it came about or whatever. But the U.S.’s sort of explicit goal is not only can you not sort of buy our most advanced chips, but you can’t buy the equipment that goes into making the chips, which means if China wants to recreate this capability, they need to not just recreate the foundry model. They also need to recreate the lithography model, the etching model, the testing equipment. There are five companies that basically make all the equipment that goes into these factories. Do you think it’s even impossible?

GA: So I think to answer the question, you have to go in a scenario kind of probability tree. And can China do it full blown alone? I think that China could get to some degree of “self-sufficiency” on its own at a price of being nowhere remotely close to the technological state of the art. So if they are willing to take a massive hit in the competitiveness of their telecommunications equipment, of their computers, of their data centers, they could get to something called self-sufficiency. That’s like if aliens attack and blow up every country on earth except for China, they will have a semiconductor industry. It will do stuff.

However, they just don’t have a by-themselves path to economic competitiveness on price, quality, quantity, et cetera. The degree of technological sophistication that the U.S. and global semiconductor equipment companies have achieved. It’s not an overstatement to say that this is the most impressive technology that humans have created, period. It’s like this, the James Webspace telescope, the CERN Large Hadron Collider. This is really, really, really hard. And we are extraordinarily good at it, and Chinese companies are not close to where we are. So that’s the by-themselves theory of the case.

Now, there’s another option, and the other option is with foreign non-U.S. assistance, because the United States is a critical player in the semiconductor equipment value chain, the export controls are designed on the basis that there are roughly 11 technologies in which U.S. industry combined has basically a hundred percent market share. So while there are other companies in the equipment industry like ASML, Tokyo Electron, they don’t make the same stuff that U.S. companies make.

And also essential components of what they make are made by, well, once-U.S. companies, but now U.S. subsidiary.

GA: Yes. And so the path towards China getting away from U.S. dependency really relies upon persuading U.S. allies to, sorry for using this word, but betray the United States. So the negotiations China is having right now is they are going to governments like Japan, the Netherlands, and to the companies in these countries and saying, “If you start making products that the U.S. currently has a monopoly on, we will give you a boatload of money.” And it’s sort of like, this is not a perfect analogy, but if you want to make an airplane. And the United States makes the wheels and the avionics, but the Dutch make the engines, and the Japanese make the structures, well, maybe Japan doesn’t make landing gear today, but they’re way, way closer to being able to make landing gear than China is.

And so that is sort of the nature of the negotiations going on right now. Right now it’s a top U.S. diplomatic priority that these export controls, which are currently unilateral, become multilateral. And that’s most urgent in the case of the Netherlands and Japan, who have an extremely high degree of sophistication in semiconductor equipment and could start producing equipment that’s analogous to what the United States currently produces and has a monopoly on, in a matter of years. But then eventually, we’ve got to multilateral this even beyond those two countries. Countries like Germany and South Korea.

Germany is I think probably the most challenging ones.

GA: Exactly. They’re not as close as the Dutch and the Japanese, but they’re again way, way closer than China.

They make the lasers, they make the mirrors, some of the most difficult and essential inputs. And I think it’s fair to say they’ve been more difficult to get on board with U.S. diplomatic initiatives when it’s in direct conflict with economic opportunity for Germany…

…Actually, I want to dive deeper on that, the whole bit about why now? Could this even have happened a few years ago without Ukraine? Without COVID? Without the general frustration with China, without Xi Jinping’s diplomatic wolf warriors, and all this sort of stuff.

At the same time, I think one of the weirdest things about it was, in some places it seemed incredibly specific, and in some places it seemed to have huge gaping holes. And there certainly seemed to be a sense of, we have to get this out now to stop the hoarding issue, which sort of happened after the Huawei bit. A lot of these sort of specifics seemed to be like, let’s look at what’s in the market and then guide directly to that. But is that the best way to approach it? So what I’m hearing from you is this was sort of an initial step that was in many respects, not even necessarily directed at China, but was directed at the rest of the industry to say, you get on board, and we’re going to demonstrate how serious we are about you getting on board by putting this out. Is that a better way to understand?

GA: There’s a few things. First, the Department of Commerce is explicitly directed when writing export controls to consider the impact of foreign substitutions of U.S. goods. So there are export controls that the United States puts upon countries that we know will not work. For an example, when Syria is embarking upon massive human rights abuses, it is illegal to export handcuffs to Syria. Do we think that that’s going to stop the Syrian police from getting handcuffs? No. Of course they’re going to find somewhere else to buy handcuffs. We know those export controls aren’t going to work. We put them on any way as a signaling mechanism.

The China export controls are not that at all. These export controls are designed to work. They are designed to significantly degrade the capacity of the Chinese military in particular to adopt AI technology. And then sort of everything else that’s built into these are the sort of locking mechanisms that are designed to ensure that that overarching goal exists. The reason why this policy is geared towards restricting the progress of the Chinese semiconductor industry is because we don’t want China replacing the U.S. chips that are prohibited.

The second thing I would say is the Biden administration has a revealed preference for speed. And I would say that’s best demonstrated by the fact that, well, there’s different types of executive actions can move at different speeds. Having a new export control policy takes a long time to get that through the inter-agency process. Something that moves faster is what’s called an is-informed letter where you just send a letter to a company and it says like, “Hey, you’re no longer going to be allowed to sell this good, policy coming later.”

Which is basically what happened to Nvidia and AMD.

GA: This is exactly what happened. This is what I mean about the revealed preference for speed. So the Biden administration looked stupid for a full month. Because in September they sent an is-informed letter to Nvidia and AMD that said, you’re no longer going to be able to sell your high-end AI chips to China. And if that was the only policy, that would’ve been a hugely self-defeating policy, it would’ve given birth to a massive growth in the Chinese domestic GPU market for almost no gain whatsoever. And there was a whole month where everybody thought like, “Oh my God, the Biden administration just did the dumbest thing ever.” But the other shoe dropped with this October policy, which is all the sort of locking mechanisms that are designed to make that initial policy work. And that’s a revealed preference for speed. They cared about a month, they cared enough about that month to look really stupid.

I’m curious about Nvidia in particular in this. So one of the limitations in the chip ban is a combination of memory interconnect…

GA: 600 gigabytes-per-second.

600 gigabytes-per-second, which is the exact specification of Nvidia’s A100 chip. So they combined the exact specification of NVIDIA’s A100 chip with a certain level of compute, which all of NVIDIA’s chips sort of surpass at this point. So NVIDIA comes out with the A800, which seems to me to be some sort of hardware gimping of existing inventory of A100 chips, so it’s now 400 gigabytes-per-second. But obviously, it has the same sort of level of compute capacity. I’m curious what the response and view of that is. Is this a violation of the spirit, even though it’s allowed? Or is it really a sophisticated understanding of the importance of memory interconnect for AI?

These AI systems, these large ones at scale are systems problems. They’re not necessarily chip problems, right? We talked about moving up the stack before, and there’s an extent to which you’re treating an entire data center as a single chip in a certain respect, and this embarrassingly parallel process is running across all these things. The limiting factor is, can you get the data in and out to these chips. Hey, sell China the fastest chips you want as long as you can’t move that data in and out and the A800? No problem. That’s what we’re seeking to accomplish. Or is there irritation that, “Look, we’re trying to do something here and you’re just taking the shortest route possible to work around these sanctions.”

GA: No, I think NVIDIA was right to make this move. I mean, if the U.S. government does not want a company to engage in an activity for national security reasons, they have to tell them that. They can’t just ask the company to know that, and go on their own journey of determining what U.S. national security interests are. This is what compliance looks like. You follow the rules as they are written. That’s on the NVIDIA side of the equation. On the Biden administration, this policy is really about training AI models in data centers and supercomputing facilities. If you want a really beefy GPU to put in your video game console, have at it. That can totally go to China. But if you want to train really powerful AI models so that you can run an authoritarian surveillance network in Xinjiang, or so that you can train a model that is used in the guidance system of a hypersonic nuclear missile, sorry, the U.S. government cannot allow that economic transaction to occur.

I mean, that’s the thing is, the Chinese AI industry is incredibly sophisticated. If you go to NeurIPS, if you go to the big AI research conferences, there are Chinese representatives from companies like SenseTime, and iFLYTEK, and on just a pure research quality basis, they belong at these conferences. They’re doing great research. But in terms of what is paying the bills for these companies, it’s Chinese government authoritarian surveillance networks. When you combine that with China’s policy of civil-military fusion in which Chinese companies that are often assumed in Western media to be purely commercial entities, they definitely are not purely commercial entities. That’s why the Biden administration felt like they had to go for this new policy.

If you’ll indulge me for a second here, when we look back on 2022 from an international relations history perspective, there are two dates that are going to echo in history. February 24th when Russia invaded Ukraine, and October 7th, when the Biden administration dropped this new AI and chips export control policy.

This export control policy is like a total reversal of 25 years of U.S. government policy on trade in technology towards China. It’s a reversal in at least two ways. First, the prior basis of policy was, “Yes, you can engage in commercial trade with Chinese companies, but no, you cannot be a technology supplier to the Chinese military.” The new policy as a response to civil-military fusion basically does away with that and says, “For technologies above this performance threshold, it’s no longer restricted on a no-military end-user basis. Now it’s restricted on a no-China basis.” That’s a big, big change. The second way that this policy is a major reversal is, historically we were allowing the sale of technology to China, but it was the older technology, two generations-

Two generations behind, yeah.

GA: Yeah, two generations behind. That was designed to allow China to progress technologically, but to restrict the pace of technological advancement to ensure that the U.S. and our allies had a durable lead. Well, this policy, it not only restricts selling all the most advanced equipment to anywhere in China, but for Chinese companies that are already operating advanced facilities like SMIC’s 14-nanometer facility and the YMTC facility, for those facilities, this is not company-wide, it’s actually restricted to those facilities.

But for those facilities, you not only can’t sell advanced semiconductor manufacturing equipment, you can’t even sell the old stuff, and you can’t even provide software updates, and you can’t even provide spare parts. This policy is designed to put those facilities out of business, full stop. Moving from a policy of restricting the pace of advancement to actively degrading the status quo of technology in China, that’s a huge policy shift. That’s why even though this policy is somewhat narrowly targeted, it’s only going after the current state-of-the-art in AI chips and semiconductor manufacturing equipment above a certain threshold, the policy reversal that is embodied in this decision is so much larger than just AI and chips.

3. Are Declining Interest Rates Responsible for Stock Growth? – Nick Maggiulli

To get started, let’s examine how changes in interest rates have impacted U.S. stock prices throughout history. To do this, I plotted the total real return in U.S. stocks (over the prior five years) against the absolute change in the 10-Year Treasury rate (over the prior five years) since 1914 (when the Federal Reserve was first established)…

…As you can see, there seems to be somewhat of an inverse relationship between the change in Treasury rates and stock performance (at least at the extremes). When rates decline by a lot, stocks tend to rise, and vice versa… However, if you dig into the data a bit more, you’d realize that most of this relationship is derived from a single period—the 1980s…

…This suggests that most of the impact that declining interest rates had on stock prices occurred during this outlier period. Once you remove it, the connection between rates and stock performance isn’t as straight-forward…

…If declining interest rates don’t reliably impact stock prices, then what else is driving returns? One word—earnings.

To demonstrate this, let’s look at the percentage change in real price and real earnings of the S&P 500 from May 1997 to September 2022 (the latest data available):

As you can see, the total changes in real prices and real earnings of the S&P 500 are basically identical over this time period. This is true despite the fact that the 10-Year Treasury rate decreased from 6.7% in May 1997 to 3.5% by September 2022. This suggests that the increase in stock prices during this time can be attributed almost entirely to earnings growth and not necessarily to the decline in interest rates.

Of course, declining interest rates could increase earnings growth by stimulating economic activity, but that’s much harder to prove. However, there are times when declining interest rates lead to increased stock prices that aren’t hard to prove.

For example, if we were to plot the percentage change in real price and earnings of the S&P 500 from September 1982 to May 1997 (the period before the period above), we would see that earnings growth was not responsible for most of the increase in stock prices:

Over this time period, the 10-Year Treasury rate declined from 12.3% to 6.7% and stocks became more attractive as a result. And, as stocks became more attractive, investors started bidding up their prices more quickly than earnings were rising. This is known as multiple expansion or an increase in valuations. In other words, investors were willing to pay more for the same amount of earnings.

However, from the early 1980s to the mid-1990s is only period over the past four decades that I can say with certainty was influenced by a decline in interest rates. All of the growth in the stock market since this point (May 1997 onward) could, technically, be attributed to earnings growth (as demonstrated above).

While reality is far more complex than this, my analysis suggests that declining interest rates are far more important at the extremes. When the 10-Year Treasury declined from 15.3% in September 1981 to 6.7% by May 1997, that increased stock multiples much more than any rate decline that came after.

4. DeepMind’s CEO Helped Take AI Mainstream. Now He’s Urging Caution – Billy Perrigo

DeepMind—a subsidiary of Google’s parent company, Alphabet—is one of the world’s leading artificial intelligence labs. Last summer it announced that one of its algorithms, AlphaFold, had predicted the 3D structures of nearly all the proteins known to humanity, and that the company was making the technology behind it freely available. Scientists had long been familiar with the sequences of amino acids that make up proteins, the building blocks of life, but had never cracked how they fold up into the complex 3D shapes so crucial to their behavior in the human body. AlphaFold has already been a force multiplier for hundreds of thousands of scientists working on efforts such as developing malaria vaccines, fighting antibiotic resistance, and tackling plastic pollution, the company says. Now DeepMind is applying similar machine-learning techniques to the puzzle of nuclear fusion, hoping it helps yield an abundant source of cheap, zero-carbon energy that could wean the global economy off fossil fuels at a critical juncture in the climate crisis.

Hassabis says these efforts are just the beginning. He and his colleagues have been working toward a much grander ambition: creating artificial general intelligence, or AGI, by building machines that can think, learn, and be set to solve humanity’s toughest problems. Today’s AI is narrow, brittle, and often not very intelligent at all. But AGI, Hassabis believes, will be an “epoch-defining” technology—like the harnessing of electricity—that will change the very fabric of human life. If he’s right, it could earn him a place in history that would relegate the namesakes of his meeting rooms to mere footnotes.

But with AI’s promise also comes peril. In recent months, researchers building an AI system to design new drugs revealed that their tool could be easily repurposed to make deadly new chemicals. A separate AI model trained to spew out toxic hate speech went viral, exemplifying the risk to vulnerable communities online. And inside AI labs around the world, policy experts were grappling with near-term questions like what to do when an AI has the potential to be commandeered by rogue states to mount widespread hacking campaigns or infer state-level nuclear secrets. In December 2022, ChatGPT, a chatbot designed by DeepMind’s rival OpenAI, went viral for its seeming ability to write almost like a human—but faced criticism for its susceptibility to racism and misinformation. So did the tiny company Prisma Labs, for its Lensa app’s AI-enhanced selfies. But many users complained Lensa sexualized their images, revealing biases in its training data. What was once a field of a few deep-pocketed tech companies is becoming increasingly accessible. As computing power becomes cheaper and AI techniques become better known, you no longer need a high-walled cathedral to perform cutting-edge research.

It is in this uncertain climate that Hassabis agrees to a rare interview, to issue a stark warning about his growing concerns. “I would advocate not moving fast and breaking things,” he says, referring to an old Facebook motto that encouraged engineers to release their technologies into the world first and fix any problems that arose later. The phrase has since become synonymous with disruption. That culture, subsequently emulated by a generation of startups, helped Facebook rocket to 3 billion users. But it also left the company entirely unprepared when disinformation, hate speech, and even incitement to genocide began appearing on its platform. Hassabis sees a similarly worrying trend developing with AI. He says AI is now “on the cusp” of being able to make tools that could be deeply damaging to human civilization, and urges his competitors to proceed with more caution than before. “When it comes to very powerful technologies—and obviously AI is going to be one of the most powerful ever—we need to be careful,” he says. “Not everybody is thinking about those things. It’s like experimentalists, many of whom don’t realize they’re holding dangerous material.” Worse still, Hassabis points out, we are the guinea pigs…

…By 2013, when DeepMind was three years old, Google came knocking. A team of Google executives flew to London in a private jet, and Hassabis wowed them by showing them a prototype AI his team had taught to play the computer game Breakout. DeepMind’s signature technique behind the algorithm, reinforcement learning, was something Google wasn’t doing at the time. It was inspired by how the human brain learns, an understanding Hassabis had developed during his time as a neuroscientist. The AI would play the game millions of times, and was rewarded every time it scored some points. Through a process of points-based reinforcement, it would learn the optimum strategy. Hassabis and his colleagues fervently believed in training AI in game environments, and the dividends of the approach impressed the Google executives. “I loved them immediately,” says Alan Eustace, a former senior vice president at Google who led the scouting trip.

Hassabis’ focus on the dangers of AI was evident from his first conversation with Eustace. “He was thoughtful enough to understand that the technology had long-term societal implications, and he wanted to understand those before the technology was invented, not after the technology was deployed,” Eustace says. “It’s like chess. What’s the endgame? How is it going to develop, not just two steps ahead, but 20 steps ahead?”

Eustace assured Hassabis that Google shared those concerns, and that DeepMind’s interests were aligned with its own. Google’s mission, Eustace said, was to index all of humanity’s knowledge, make it accessible, and ultimately raise the IQ of the world. “I think that resonated,” he says. The following year, Google acquired DeepMind for some $500 million. Hassabis turned down a bigger offer from Facebook. One reason, he says, was that, unlike Facebook, Google was “very happy to accept” DeepMind’s ethical red lines “as part of the acquisition.” (There were reports at the time that Google agreed to set up an independent ethics board to ensure these lines were not crossed.) The founders of the fledgling AI lab also reasoned that the megacorporation’s deep pockets would allow them access to talent and computing power that they otherwise couldn’t afford.

In a glass cabinet spanning the far wall of the lobby at DeepMind’s London headquarters, among other memorabilia from the first 12 years of the company’s life, sits a large square of wood daubed with black scribbles. It’s a souvenir from DeepMind’s first major coup. Soon after the Google acquisition, the company had set itself the challenge of designing an algorithm that could beat the best player in the world at the ancient Chinese board game Go. Chess had long ago been conquered by brute-force computer programming, but Go was far more complex; the best AI algorithms were still no match for top human players. DeepMind tackled the problem the same way they’d cracked Breakout. It built a program that, after being taught the rules of the game by observing human play, would play virtually against itself millions of times. Through reinforcement learning, the algorithm would update itself, reducing the “weights” of decisions that made it more likely to lose the game, and increasing the “weights” that made it more likely to win. At a tournament in Korea in March 2016, the algorithm—called AlphaGo—went up against Lee Sedol, one of the world’s top Go players. AlphaGo beat him four games to one. With a black marker pen, the defeated Lee scrawled his signature on the back of the Go board on which the fateful game had been played. Hassabis signed on behalf of AlphaGo, and DeepMind kept the board as a trophy. Forecasters had not expected the milestone to be passed for a decade. It was a vindication of Hassabis’ pitch to Google: that the best way to push the frontier of AI was to focus on reinforcement learning in game environments.

But just as DeepMind was scaling new heights, things were beginning to get complicated. In 2015, two of its earliest investors, billionaires Peter Thiel and Elon Musk, symbolically turned their backs on DeepMind by funding rival startup OpenAI. That lab, subsequently bankrolled by $1 billion from Microsoft, also believed in the possibility of AGI, but it had a very different philosophy for how to get there. It wasn’t as interested in games. Much of its research focused not on reinforcement learning but on unsupervised learning, a different technique that involves scraping vast quantities of data from the internet and pumping it through neural networks. As computers became more powerful and data more abundant, those techniques appeared to be making huge strides in capability.

While DeepMind, Google, and other AI labs had been working on similar research behind closed doors, OpenAI was more willing to let the public use its tools. In late 2022 it launched DALL·E 2, which can generate an image of almost any search term imaginable, and the chatbot ChatGPT. Because both of these tools were trained on data scraped from the internet, they were plagued by structural biases and inaccuracies. DALL·E 2 is likely to illustrate “lawyers” as old white men and “flight attendants” as young beautiful women, while ChatGPT is prone to confident assertions of false information. In the wrong hands, a 2021 DeepMind research paper says, language-generation tools like ChatGPT and its predecessor GPT-3 could turbocharge the spread of disinformation, facilitate government censorship or surveillance, and perpetuate harmful stereotypes under the guise of objectivity. (OpenAI acknowledges its apps have limitations, including biases, but says that it’s working to minimize them and that its mission is to build safe AGI to benefit humanity.)

5. Analysis: Xi puts top brain in charge of Taiwan unification strategy – Katsuji Nakazawa

A source familiar with the inner workings of the Chinese Communist Party has pulled back the curtain on General Secretary Xi Jinping’s leadership reshuffle last October.

Why were some leaders retained to serve another term, while others were shown the door?

On the Politburo Standing Committee, there were three members who were 67 years old, technically under the retirement age of 68. All three of them could have stayed, but only one did.

The ones who stepped down were No. 2, Premier Li Keqiang and No. 4 Wang Yang. Only No. 5 Wang Huning stayed on and was promoted in the new lineup.

The source noted that this top leadership change hints at Xi’s political strategy as he aims for a fourth term. “Wang Huning’s mission is to lay the groundwork for Taiwan unification.”

If Wang Huning was retained to handle the Taiwan file, this would be the result of the failure of the “one country, two systems” in Hong Kong.  

After massive pro-democracy demonstrations shook Hong Kong in 2019, Beijing quickly enacted a national security law for the special administrative region. It spelled the end of a free Hong Kong…

…On Jan. 18, state-run Xinhua News Agency announced the new members of the Chinese People’s Political Consultative Conference, the country’s top political advisory body. The inclusion of Wang Huning signaled that he would assume the role of CPPCC chairman, succeeding Wang Yang.

One of the CPPCC’s role is to set strategies for China’s “united front work,” including drawing Taiwan to the Chinese side.

Under this framework, Wang Huning is also expected to become the deputy director of the Central Leading Group for Taiwan Affairs, the party’s top decision-making body on China’s Taiwan policy. The top director is Xi.

So what role will Wang play in formulating a Taiwan policy during Xi’s third term?

One source knowledgeable of China-Taiwan relations noted that Wang will be tasked with writing a theoretical unification strategy fit for the Xi era. 

“One may assume that a threat of China using force to unify Taiwan is imminent, but this is not the case. The first step is to launch a new theory that will replace Deng’s one country, two systems. Then pressure will be put on Taiwan based on it,” the source explained.

The source expects this theory to become a yardstick with which to measure progress and to decide if a military operation is necessary…

…Wang Huning will be supported by Wang Yi, the 69-year-old former foreign minister, who was promoted to the Politburo. His promotion went against the party’s traditional retirement rule that stipulates that officials do not assume new higher posts after they are 68.

Wang Yi also became director of the party’s Office of the Central Foreign Affairs Commission, making him China’s top-ranking diplomat.

Needless to say, the top diplomat reports to Xi on foreign affairs and security matters. But for policies involving Taiwan unification and relations with the U.S., Wang Huning is also in Wang Yi’s reporting line. 

This is because Wang Yi will become secretary general of the Central Leading Group for Taiwan Affairs, where Wang Huning will serve as deputy director. Wang Yi once served as the director of the Taiwan Affairs Office of the State Council, China’s government.

As a Politburo Standing Committee member, Wang Huning in one of China’s top seven and has a much higher level of authority than Wang Yi, a Politburo member. 

Xi wants to chalk up an achievement in regard to Taiwan at any cost over the next five years, which would help his quest to seek a fourth term as head of the party in 2027.

China’s policies related to Taiwan will be spearheaded by these two Wangs…

…Xi acquired ultimate power in October. While the use of force against Taiwan is not deemed imminent, Xi could launch an offensive at the snap of his fingers.

Last summer, China held military exercises around Taiwan and fired missiles. The display of force came in response to then U.S. House Speaker Nancy Pelosi’s visit to the island. Since then, Taiwan has become increasingly alarmed at the possibility of a military invasion by China.

Russia’s all-out invasion of Ukraine has also shocked the island. 

China hopes to see the independence-leaning DPP ousted from power in 2024. But as relations between China and Taiwan are extremely tense, it is difficult to decide upon the timing of working out a new Taiwan unification strategy.

If the content of the new strategy is taken as merely a threat against Taiwan, it could backfire. Although China wants to support the KMT, it could end up saving the DPP.

“China will have no choice but to take a wait-and-see attitude for the time being,” one pundit said. “The timing of announcing a new Taiwan unification strategy is probably undetermined. It may be still a long way off.”

6. The forgotten mistake that killed Japan’s software industry – Tim Romero

No, for the sake of this podcast I’m going to assume that we are all in agreement that on average, Japanese software. is just … awful.

That way we can spend our time talking about something far more interesting. We are going to walk though the economic events and the political forces that made today’s poor quality of Japanese software almost inventible,

And by the end, I think it will give you a completely new way of looking at the Japanese software industry.   

You see, the story of Japanese software, is not really about software. No, this is the story of Japanese innovation itself. The story of the ongoing struggle between disruption and control. It’s a story that involves, war, secret cartels, scrappy rebels, betrayal, rebirth, and perhaps redemption…

…In same way that the zaibatsu defined the economic miracle that was Japan’s Meji-era expansion, the keiretsu would come define the economic miracle that was Japan’s post war expansion.

Today there are six major and a couple dozen minor keiretsu groups, and during Japan’s economic expansion, as much as possible, they kept their business within the keiretsu family.

Projects were financed by the keiretsu bank, the materials and know-how were imported by the keiretsu trading company, and the final products would be assembled in the appropriate keiretsu brand’s factory. And supporting all of these flagship brands were, and still are, tens of thousands of very small, exclusive manufacturers that make up the keiretsu supply chain — and the bulk of the Japanese economy.

And with the exception of a tiny handful of true startup companies like Honda and Sony, all of Japan’s brands that were famous before the year 2000 or so, are keiretsu brands.

And for those of you who think big companies can’t innovate, let me remind you that from the 50s to the 70s, these keiretsu groups began innovating, disrupting, and dominating almost every industry on the planet; from cars, to cameras, to machine parts, to steel, to semiconductors, to watches, to home electronics, Japan’s keiretsu simply rewrote the rules.

But how did the keiretsu do in the world of software development?  Well, pretty darn well, actually.

It’s important to remember, though, that the software industry in the 60s and 70s was very different than it is today. The software development process itself was actually rather similar. Fred Brooks wrote The Mythical Man Month about his experience during this era, and it remains as one of best books on software engineering and project management today.

But the way software was bought and sold was completely different. In the 60s and 70s, software was written for specific and very expensive hardware, and the software requirements were negotiated as part of the overall purchase contract. Software was not viewed so much as a product, but more like a service, similar to integration, training, and ongoing support and maintenance. It was usually sold on a time-and-materials basis, and sometimes it was just thrown in for free to sweeten the deal. The real money was in the hardware.

Software in this time (both in Japan and globally) was written to meet the spec. It did not matter if it was creative, innovative, easy to use, or elegant, it just had to meet the spec. In fact, trying to build exceptional software in this era was considered a waste of resources. After all, the product had already been sold and the contracts had already been signed. The goal back then, just like many system integration projects today, was to build software that was just good enough to get the client to sign off on it as complete.

Software that met the customer’s spec was, by definition, good software.

Japan’s keiretsu did well in the age of big-iron. Although Fujitsu, NEC, and Hitachi never seriously challenged IBM and Univac’s global dominance in the 60s and 70s, they did pretty well in mini-computers and large office systems.

They were innovators.

However, when the PC revolution arrived in the late 1980s, Japanese industry as a whole was hopelessly unprepared, and not for the reasons you might think.

The reason Japanese software development stopped advancing in the 1980s had nothing to do with a lack of talented software developers. It was a result of Japan’s new economic structure as a whole, and the keiretsu in particular.

As a market, personal computers were something fundamentally new. Sure, the core technology and the hardware were direct continuations from the previous era, but this new market was completely different.

The PC market quickly coalesced around a small number of standardized operating systems and hardware architectures. The keiretsu did pretty well in hardware side of this market, making some really impressive machines, particularly laptops.

But a market for non-spec or “shrink-wrap” software was something new to everyone. It required delighting the customer, and knowing what they wanted before they did. It was the kind of challenge that the keiretsu of the 60s and 70s would have thrown themselves into whole-heartedly, innovated aggressively, and then dominated.

But things in Japan had become very different in the 1980s.

Here was a chance to define and lead a new global industry. A chance for the keiretsu to build a software industry from the ground up.

But, wait a minute. Why should they?

Sure, back in the 60s when Japan’s economy was small, survival required looking outwards, competing globally, making long-term investments, and innovating to make the best products in the world.

But this was the 80s! Japan was the second-largest economy on the planet and in the middle of the largest economic boom the world had ever seen. This was the era of Japan as Number 1, with economists predicting Japan’s GNP would be larger than America’s within a decade.

With such a lucrative, and pretty well protected, market right at their fingertips it made much more sense for for the keiretsu to focus on the easy money rather than to take risky and expensive bets on an uncertain and emerging global market.

Each keiretsu group had their own technology firm who started selling PCs and software, some to consumers, but the big money was in corporate sales.  And since the keiretsu liked to keep the business in the family, these technology companies grew and profited by selling to their captive customers within their keiretsu group. And just like before, they made the real money integration, and customization.

An unfortunate result of this is that the big Systems Integration companies or “SIs” emerged as powerful players, and Japan’s software firms never had to compete globally, or even with each other.

Japan simply missed the opportunity to develop a globally relevant PC software industry…

…I mark 2010 as the year Japan’s software developers finally started stepping into the spotlight, although things starting moving a bit before that.

There were two triggers that led to this development. First, the emergence of cloud computing and second, the introduction of the smartphone. Although these were both technological developments, it was not the technology itself that led to the change.

Cloud computing drastically reduced the capital and time required to start a startup. In the dot-com era a decade before, starting an internet startup required purchasing racks of servers and paying system administrators to keep them running, but suddenly fully configured, maintained, and secure serves could be had for a few cents per minute — pay as you go.

Suddenly Japan’s software developers didn’t need to explain their idea to a VC and convince them that it would sell. They could just build things and get people to start using them and start paying for them. And that’s just what they did.

The other important development was the introduction of the iPhone in 2007 and Android a year later. Not just because of the technology, but because of how it changed the software business model…

…As we talk here together at the start of 2023, what does the future look like for Japanese software?

Japan has had a lot of catching up to do over the past fifteen years. After basically sitting out the global PC and dot-com revolutions, Japanese software developers have been making up for lost time and in the startup space. Japan is developing a competitive software market in some areas, but on average, there is still a long way to go.

Japan’s once dominant Systems Integrators will continue to see their power decline. Their customer lock-in is fading fast, and B2B SaaS software startups are letting Japanese enterprises leapfrog to modern IT systems for less than costs to maintain their SI-run legacy systems.

The SIs won’t disappear, of course. There will always be a need for good systems integrators, and the more forward thinking ones are already trying to reinvent themselves. However, the days when the SIs dictated their clients’ IT strategy are coming to a close. That is a very good thing for Japanese software, Japanese startups, and Japanese competitiveness as a whole.

The Kishida administration has made startups a national priority, and the importance of quality software and software startups in Japan has never been higher.

7. The Bank that Never Sold – Marc Rubinstein

Standard Chartered traces its roots back to the height of the British Empire. In order to finance expansion overseas, specialist banks were set up to facilitate trade. One of them, the Chartered Bank of India, Australia and China was founded to serve the markets of … India, Australia and China. The bank ended up not getting a charter for Australia, but succeeded in establishing a foothold in the other two fledgling markets. 

Chartered’s model was that of an “exchange bank”. Capital was raised in the City of London and shipped out, often as gold or silver coins in wooden crates, to support currency transactions for British companies across the main ports of the East from Bombay to Shanghai. To mitigate against risk, the bank employed a portfolio approach, opening up over 20 overseas branches. By 1928, Chartered Bank ranked alongside HSBC as one of the largest overseas banks launched out of the UK, focused on trade finance and foreign exchange services. 

The growth of Chartered and other overseas banks caught the eye of UK domestic banks. By now, the market at home had consolidated around five main banks. Previously cautious that “there would be something mildly improper about using their UK depositors’ money to fund lending in distant climes,” they began to revise their opinion. 

But fortunes turned as the 1930s augured a collapse in international trade. Chartered Bank was additionally shut out of some of its core markets, in particular China, following political upheaval after the end of the Second World War. Retreating to Hong Kong, the bank managed to carve out a profitable niche. It increasingly dealt with local companies, rather than just British agency firms, and grew its loan book. To support its franchise, it later established a network of local retail outlets in order to accumulate local deposits. By the mid 1960s, Chartered Bank was adding branches at a rate of two or three a month. The success of its business in Hong Kong marked out a new future for the bank, no longer dependent on the traditional trade links of the Empire.

By the 1960s, most vestiges of the British Empire had faded. A devaluation of the pound ended its role as a reserve currency and led to the breakup of the sterling system that had underpinned the UK overseas banking model for years. Competition from US banks increased. In response, Chartered Bank merged with Standard Bank of South Africa to create a more global overseas bank with operations across Asia, the Middle East and Africa. 

Like Chartered Bank, Standard Bank had been established to facilitate trade flows, in its case to Africa. Its legacy was similar. British Prime Minister John Major, a former employee of Standard Bank, wrote that both “relied for many decades on adventurous young recruits from Britain who were keen to work overseas”. (He worked for Standard Bank in Nigeria.)

For Standard, the diamond industry provided a historic route to good fortune; by the late nineteenth century, it operated almost 100 branches in South Africa, practising an almost central banking role in the country. But it, too, had to adapt to the shifting macro climate. The isolation of South Africa as Apartheid became entrenched prompted Standard to spin off its South Africa business and focus on other markets in Africa, which it consolidated through its acquisition of the Bank of British West Africa. 

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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. We currently have a vested interest in Alphabet and Meta Platforms (parent of Facebook). 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 January 2023:

1. How Complex Systems Fail – Richard Cook

Complex systems are intrinsically hazardous systems.

All of the interesting systems (e.g. transportation, healthcare, power generation) are inherently and unavoidably hazardous by the own nature. The frequency of hazard exposure can sometimes be changed but the processes involved in the system are themselves intrinsically and irreducibly hazardous. It is the presence of these hazards that drives the creation of defenses against hazard that characterize these systems.

Complex systems are heavily and successfully defended against failure.

The high consequences of failure lead over time to the construction of multiple layers of defense against failure. These defenses include obvious technical components (e.g. backup systems, ‘safety’ features of equipment) and human components (e.g. training, knowledge) but also a variety of organizational, institutional, and regulatory defenses (e.g. policies and procedures, certification, work rules, team training). The effect of these measures is to provide a series of shields that normally divert operations away from accidents.

Catastrophe requires multiple failures – single point failures are not enough.

The array of defenses works. System operations are generally successful. Overt catastrophic failure occurs when small, apparently innocuous failures join to create opportunity for a systemic accident. Each of these small failures is necessary to cause catastrophe but only the combination is sufficient to permit failure. Put another way, there are many more failure opportunities than overt system accidents.  Most initial failure trajectories are blocked by designed system safety components.  Trajectories that reach the operational level are mostly blocked, usually by practitioners…

…Human practitioners are the adaptable element of complex systems.

Practitioners and first line management actively adapt the system to maximize production and minimize accidents.  These adaptations often occur on a moment by moment basis.  Some of these adaptations include: (1) Restructuring the system in order to reduce exposure of vulnerable parts to failure. (2) Concentrating critical resources in areas of expected high demand. (3) Providing pathways for retreat or recovery from expected and unexpected faults. (4) Establishing means for early detection of changed system performance in order to allow graceful cutbacks in production or other means of increasing resiliency…

…Change introduces new forms of failure.

The low rate of overt accidents in reliable systems may encourage changes, especially the use of new technology, to decrease the number of low consequence but high frequency failures.  These changes maybe actually create opportunities for new, low frequency but high consequence failures.  When new technologies are used to eliminate well understood system failures or to gain high precision performance they often introduce new pathways to large scale, catastrophic failures. Not uncommonly, these new, rare catastrophes have even greater impact than those eliminated by the new technology. These new forms of failure are difficult to see before the fact; attention is paid mostly to the putative beneficial characteristics of the changes. Because these new, high consequence accidents occur at a low rate, multiple system changes may occur before an accident, making it hard to see the contribution of technology to the failure…

…Safety is a characteristic of systems and not of their components.

Safety is an emergent property of systems; it does not reside in a person, device or department of an organization or system. Safety cannot be purchased or manufactured; it is not a feature that is separate from the other components of the system. This means that safety cannot be manipulated like a feedstock or raw material. The state of safety in any system is always dynamic; continuous systemic change insures that hazard and its management are constantly changing.

2. “Portrait of a Disciplined Investor” – Alex Morris

On January 8th, 2022, Lou Simpson passed away at the age of 85.

Lou, who grew up in Highland Park, Illinois, attended Northwestern University for a short stint before transferring to Ohio Wesleyan University, where he earned a double major in accounting and economics in 1958; two years later, he earned a master’s degree from Princeton (Lou was an economics professor at Princeton from 1960 – 1962). After Princeton, Lou returned to Chicago to work at Stein, Roe, and Farnham, becoming a partner at the investment firm in 1969; after, Lou moved to Los Angeles, where he would eventually be named president and CEO of Western Asset Management.

In August 1979, Lou joined GEICO as an investment manager after meeting with Warren Buffett (“Stop the search. That’s the fellow.”). He clearly made a quick impression at GEICO: in the 1982 shareholder letter, Buffett called Lou “the best investment manager in the property-casualty business”.

Over the next three decades, Lou was responsible for managing the auto insurers’ investments, which grew to $5 billion by his retirement in 2010.

How was Lou’s track record at GEICO? In a word, astounding.

Thankfully, in the 2004 shareholder letter, Buffett disclosed the annual performance of GEICO’s equities portfolio under Lou’s management (the section was titled “Portrait of a Disciplined Investor”). As shown below, an investment of $1,000 in 1979 was worth ~$101,600 by 2004 (>100x growth), compared to ~$23,700 if it had been invested in the S&P 500 (compounded returns of ~20.3% for GEICO’s equities, or roughly 700 basis points higher than the ~13.5% annualized for the index over the same 25-year period)…

…As outlined in GEICO’s 1986 annual report, Lou’s investment approach had five key guidelines: (1) Think independently; (2) Invest in high-quality businesses run for the shareholders; (3) Pay only a reasonable price, even for an excellent business; (4) Invest for the long-term; (5) Do not diversify excessively (Lou has often mentioned Buffett’s 20-hole punch card idea).

Here’s how Lou described his process (applying those guidelines): “My approach is eclectic. I try to read all company documents carefully. We try to talk to competitors. We try to find people more knowledgeable about the business than we are. We do not rely on Wall Street-generated research. We do our own research. We try to meet with top management… What we do is run a long-time-horizon portfolio comprised of ten to fifteen stocks. Most of them are U.S.-based, and they all have similar characteristics. Basically, they’re good businesses. They have a high return on capital, consistently good returns, and they’re run by leaders who want to create long-term value for shareholders while also treating their stakeholders right.” (We also know that he liked to operate with a small team, not an army of research analysts: “He supervises only two employees, an assistant and an analyst… ‘The more people you have, the more difficult it is to do well.’”)

In addition, Lou considered management quality a key part of the investment decision: “One of the things I’ve learned over the years is how important management is in building or subtracting from value. We will try to see a senior person and prefer to visit a company at their office, almost like kicking the tires. You can have all the written information in the world, but I think it is important to figure out how senior people in a company think.”

3. Investing lessons for 2023 – Chin Hui Leong

If there was a single villain that caused the 2022 market crash, most would point their finger at rising interest rates. But not everyone may be learning the right lesson here.

In particular, the market crash last year may have persuaded some investors to equate rising interest rates to falling stock prices. This way of thinking may cause them to sell at the next sign of a US Federal Reserve rate hike.

However, such a conclusion ignores how unusual last year’s interest rate increases have been. According to data compiled by the Visual Capitalist, the effective federal funds rate rose past the two-percentage mark within six months in 2022, its fastest increase in more than three decades. That’s abnormal, to say the least. In comparison, the US central bank took 36 months to reach the same rate level in its previous rate hike cycle between December 2015 and December 2018.

Furthermore, avoiding stocks within this three-year period would be a mistake. For example, shares of Amazon and iFAST Corporation would have netted investors returns of 164 per cent and 349 per cent, respectively, if both counters had been held from the start of 2016 till the end of 2022.

Missing out on the basis of misguided beliefs would be detrimental to investors…

…Finally, investors tend to put too much weight on recent events. This bias could be especially egregious after last year’s market crash. Given the beating that investors have taken while holding stocks, some may end up waiting on the sidelines.

Instead of holding back, investors may wish to consider the odds of a market downturn.

For context, wealth manager Ben Carlson pointed out that the 2022 performance of the S&P 500 was among its worst ever in history. Last year’s decline of nearly 20 per cent was only exceeded by the major market crashes in the past, such as the Great Depression in the 1930s, 2002 dot-com crash, and the 2008 global financial crisis.

That, in my eyes, is the definition of an abnormality. To be clear, it’s not to say that another 20 per cent decline is completely out of the question. But what we can say is with every passing year, the odds of a positive outcome increase, based on historical data stretching back to the 1920s.

Considering the probabilities, I would argue, would be a better way to think about the future, compared to reacting to the stock market’s performance over the past 12 months.

4. RWH020: The Disciplined Growth Investor w/ Fred Martin – William Green and Fred Martin

[00:58:25] William Green: That’s a very profoundly important point. And hence it is and it’s, you wrote to me the other [00:58:30] day, I think we may have uncovered the greatest risk manager of all time, the climber, the documentary free solo.

[00:58:36] William Green: And wonder if you could talk a bit about this guy, Alex Honnold, who is a master of I, I don’t know how many of our listeners have watched the movie. There’s a great documentary that called Free Solo that’s about this guy Alex Honnold, who decides to climb, I think it’s El Capitan. So this 3000 or so foot sheer granite, aha.

[00:58:56] William Green: Cliff, I mean, he gave me a heart attack just watching the thing. Can you talk about what you can learn from someone like Hon about dealing with uncertainty, dealing with risk, de mitigating risk in a really intelligent, thoughtful way? Since we don’t know what can happen or what will happen, we can guess what can happen, but we don’t know what will happen.

[00:59:15] William Green: Did you watch the documentary? Yeah. And then yesterday I watched something about the filming of the documentary, which is terrifying in it, its own Right. And you know, one of the things that he, that the cameraman said while making the film is that they had to record part of it with remote cameras because he said he didn’t want them to see him die because they were friends of his.

[00:59:36] William Green: Sure. Okay. So during the most dangerous bit, they actually had to set up remote cameras.

[00:59:39] Fred Martin: Oh my God. Okay. So it’s so funny. So Rob is working on, he’s spearheading the piece on risk. This has been in our hands for years. Right. This idea of trying to get better. And I really believe if you really want to learn something, well teach it, you know?

[00:59:52] Fred Martin: And so the teacher always learns the most. And so he writes this thing on.

[00:59:56] William Green: This is Rob Naski, who’s your right hand write chief investment?

[00:59:59] Fred Martin: He [01:00:00] starts with free solo. And I freak out about it because I’m going, you know, wait, we’re not talking about falling off cliffs here. And then, but as I started thinking about it more and more, and I about a month ago I called Rob up and I said, I think I’m not looking at this right.

[01:00:11] Fred Martin: This guy was one of the greatest risk managers of all time. And we need to look at it. I need to look at it differently and say, my goodness, he took this thing with a binary outcome, and he made it. And there were a thousand little threads to make that. And so this has to do, this is really deep stuff, but what this has to do with is risk and risk mitigation.

[01:00:36] Fred Martin: Okay? And what’s in your control and what’s not in your control. And so what he did is he systematically, he climbed it many times. He was a very stur climber. He was physically fit, and he practiced those moves with a rope over and over again. Right now. He also is part of the thing. One of the things that struck me as so profound in the whole thing is, I don’t know if you remember the movie he was going to climb and he got part we up and said This isn’t right.

[01:01:02] Fred Martin: And he went back down again. I don’t know if you remember that. And I remembered that I’d forgotten that. Yeah. I thought, oh my goodness gracious, this guy, he’s got the risk meter going in his head, he’s not ready, it’s not right. And he knows that this isn’t the right day and just think of the humiliation. He must have fell in the def sense of deflation because he, because you don’t just show up when we, he is having a climate.

[01:01:25] Fred Martin: You build to it; you’re all fired up. You start up there and you part where up and [01:01:30] goes, well not right, comes back down.

[01:01:32] William Green: It’s interesting also, Fred, I saw an extraordinary video of him last night where he was teaching a famous Scandinavian climber to free climb. And he said to the guy, when you get stuck, just be patient and take the time to figure it out.

[01:01:45] William Green: He said, you get in trouble when you’re in a hurry. So when you can’t figure out how to get out of it. Oh goodness. Just pause. Be patient and give yourself time to figure it out. And I, you know, and he’s talked about not letting your emotions spiral out of control when you were stuck. And so there’s a kind of an incredible ability to get his ego under control.

[01:02:05] William Green: That’s astounding.

[01:02:06] Fred Martin: This is you should, by the way, you should try to get him on your podcast.

[01:02:10] William Green: That would be great. Yeah. He’s a fascinating guy.

[01:02:12] Fred Martin: I just, well, just because if you look, he had a binary outcome, but he also had the ability to practice over and over again. Every He climbed it.

[01:02:22] Fred Martin: Right. Anyways, I mean, he stacked a lot of things in his favor. He didn’t guarantee that he didn’t fall, but boy, he sure did a lot of risk mitigation. He also, there is another idea that we’re also, we’re debating, and that is, Buffett said this many times, so it is possible to get the market return.

[01:02:42] Fred Martin: You can buy an index, an s and p 500 index, but anybody can do it when you try to get an excess return. The field gets really narrow and it becomes all about process and implementation, and the ability to take intelligent risk if you’re going to do superior results, because if you try to do superior, you may end up with [01:03:00] inferior results.

[01:03:00] Fred Martin: So this kid had the potential to rise to the top of this, but he had to really do risk mitigation to make it, and it’s true investing. As you move up the food chain, as your performance gets better, you better really manage the risk because it’ll kill you. And so I’m still turning my head over on his comment about be patient.

5. Jay Gould: The Dark Genius of Wall Street – David Senra

[00:48:29] And so one of his first advantages derives from the fact that he had this monk-like dedication. He studied and read everything. This is not the sexy part of business by any means, but it’s something that you and I have seen over and over again. The most explicit statement of the importance of studying regulations came all the way back from the guy that founded Trader Joe’s. I read his autobiography, it’s called Becoming Trader Joe. I did a podcast on it, it’s number 188. And he says in that book, “As I learned time and time again, success in business often rests on a minute reading of the regulations that impact your business.” And that comes into play. This is how he gets involved in railroads for the very first time.

As Gould knew, the New York General Railroad Act of 1850 … That must make for good reading, right? Or for fun reading. The New York General Railroad Act of 1850 allowed directors or railroads to issue bonds of their own on their own authority to finance expansion. It also permitted the easy conversion of these same bonds into common stock and then back again into bonds. Jay must have quickly realized that just a small percentage of Wilson’s bonds … Wilson is the guy offering to sell bonds in this R&W Railroad for 10 cents on the dollar. So this is why Jay realized, “Oh, I got to jump on this right now.” Jay must have quickly realized that just a small percentage of Wilson’s bonds when converted would establish a controlling interest in the R&W Railroad. Wilson offered Jay all of his bonds at just 10 cents on the dollar. So this is where we see Jay take control of his first railroad. This is something he’s going to do for the rest of his career. Thereafter, for a solid year and a half, Jay is 27 years old at this time. I should bring that to your attention. Thereafter, for a solid year and a half, Jay spent four to five days a week working to improve the infrastructure, traffic, and profitability of the R and R railroad. And we see this monk-like dedication again. What he didn’t know about the railroad business was considerable. And so he made a point of learning it. He says, “I left everything else and went into railroading. I took entire charge of that road. I learned the business and I was president and treasurer and general superintendent. I kept at my work.”…

…[00:52:54] And so in 1865, Jay sold control of the R and W to this guy named William T. Hart, who was a steamboat entrepreneur who like other old Steamboat entrepreneurs, Daniel Drew and Cornelius Vanderbilt saw the future and was now interested in redwood. So that’s an important part. Why are all these… What is it taking place? What do you know as a steamship operator, right? You’re in the business of transporting goods and people from one spot to another. Now they’re building out railroads, which just seems to be a way larger opportunity than steamboats. So Hart saw this, Daniel Drew saw this and Vanderbilt saw this as well. And so just like Vanderbilt before him, Hart is like, oh, I have a steamboat business. Let me combine it with a railroad business. So he had taken control over… These names are so hard. I’m just going to, it’s irrelevant what it’s named. Do you want me to say Rensselaer and Saratoga? It is a bigger railway than the one at this point that Jay has control of.

So he’s going to merge. He’s going to sell, his interests are going to merge. Jay realized more than a hundred thousand dollars on this one transaction. That seems like a lot of money. There’s single transactions that Jay does later in his life that he makes 40 million on. It’s bananas. Jay realized more than a hundred thousand dollars on this one transaction, his first truly enormous payday, but he was not done. A week later he and Hart incorporated this other railroad. So essentially they’re all combining, they all have the same playbook, right? That’s what I meant up was like, yeah, you could see the same opportunity, but if you’re more creative and your execution is better, you’ll yield better results. So they just take a bunch of small railroads and they consolidate them and make them into larger railroads.

[00:54:30] And this is why Jay tells us exactly, because we see a note that he wrote his partner, just a guy named Hart. I believe that consolidation will prove both essential and inevitable for a score or more roads in the coming decade. He’s predicting the future and he is right about that. Far better than mere cooperation is tight coordination, close vertical integration, economy of scale and unchallenged market domination whenever possible. So that’s Jay’s playbook that he’s writing when he is in his twenties. That’s what Hart’s trying to do. That’s what Drew, Vanderbilt, Rockefeller, Morgan, all these guys are doing the same thing at this point in time. And so he is like, let’s not stop here. What is his advantage? Ever since boyhood, he had a fascination with maps. How crazy does this… All these experiences he could not have predicted when he telling your sister as a teenager, what was he? 15, 16?

Hey, surveying, making maps, studying to terrain, understanding the strategic ports, how to link quarries, forests, and other resources together in a transportation network. All of that, there’s no way he could have predicted what he’s going to use that skillset 10 or 12 years later, which is exactly where we’re in at this point. So he’s like, listen, I had a fascination with maps. Now the one-time surveyor scrutinized his maps with a freshly engaged eye. He studied the small railroads, dotting, and landscape. As one would study the pieces of a complex jigsaw puzzle, pondering which among the myriad possible combinations might yield maximum economy and profit that is not exclusive to Jay Gould.

We have studied multiple entrepreneurs on this podcast that were obsessed with the physical landscape and they used their understanding of the physical landscape to gain an advantage over their competitors. Is that not what Sam Walton did? He was the only retailer in the south that had his own plane. So he’s flying over, he’s studying traffic, but he can get it down really low. Studies traffic patterns. He picked out what? If my memory serves me correct, the first 130 Walmart locations himself out of his little Cessna plane. It’s the same thing that Rockefeller was doing when he set up his first refinery in Ohio, way before he had… At one time he was what? Refining I think 90%. He owned like 90% of all the refinery market in the United States. Way before that, he realized, hey, I should set this up so I can actually transport the refined oil by both railroads and by boat.

[00:56:49] And now we see Jay Gould taking the same idea and applying it to hey, which railroads give me a strategic advantage if I can overtake and consolidate them. That’s really cool. It’s really cool to me at least. And what’s even like cooler is the fact that he’s like, oh, I found my life’s work. This is what I’m going to do forever. Oh, this is so good. This is Jay’s words, this is what makes it like exciting for me. We are at a moment he wrote where there is a particular inevitable future waiting to be made. I see things very, very clearly. I feel inspired with an artist’s conception. My road is laid out before me in the plainest of ways. He’s talking about his path in life, not his railroad. He felt as if all the wheels had finally been installed in his life. Not only did he have professional focus, but also the meaning that is family, a wife, and child. This is so good.

Check out this writing. A wife and child to fight wars and build castles. Now that I am in this place, it is a puzzlement to me how I endured before. Everything prior seems to have been boxing in the dark, scraping without reason. Now I have my road to walk and my reason for walking it. So not only do I know what I’m doing in my life, I’m dedicating it to the consolidation of railroads and the building up of railroads. But I’m doing it for my family. 

6. The Semiconductor Madman – Brent Crane

When Zhao took over Tsinghua Unigroup in 2009, the Tsinghua University-adjacent firm1 was struggling with both its purpose and its profits. Zhao shifted its focus from consumer electronics to semiconductors at the perfect time: just four years before Beijing published its National Integrated Circuit plan, a component of the Made in China 2025 initiative, which called on China to domesticate 70 percent of its semiconductor needs by 2025 and reach parity with leading international chip companies by 2030. Beijing went on to flood the industry with an estimated $100 billion.

Zhao put all that money to use. He identified high performing smaller companies and gobbled them up — spending tens of billions in the process. Unigroup now has 286 subsidiaries, two of which — UNISOC and Yangtze Memory Technologies Co. (YMTC), an Apple supplier — are major players in the global chip industry, specifically for older model chips, such as chips for 3G. 

Given his swashbuckling style, Zhao — himself worth $2.2 billion last year — earned the nickname “the semiconductor madman” within the industry. Yet, like any madman, Zhao’s daring had a downside.

According to industry insiders, he developed a reputation as something of a “serial bullshitter,” with a penchant for excessive self-promotion. For instance, numerous sources indicate that Zhao was born in November — not April, which is when he claimed Xi Jinping wished him a happy birthday. 

He is also not known for being particularly strategic or wise in his financial decisions.

“Unigroup threw money around in this extraordinarily wild and reckless fashion,” says Chris Miller, an economic historian at Tufts University and author of Chip War: The Fight for the World’s Most Critical Technology. “If you’re looking to attract attention, Zhao knew how to do it.”

And attract attention he did. In July, Zhao, 55, was arrested alongside a dozen other leaders from China’s state-managed semiconductor industry.

No charges have been publicized — elite machinations in China are notoriously opaque — but the arrested individuals were targeted for “serious violations of discipline and laws,” which often implies corruption.

Others jailed include Xiao Yaqing, China’s Minister of Industry and Information Technology (MIIT); Ding Wenwu, manager of the “Big Fund”2, Beijing’s $50 billion quasi-governmental chip investment fund launched in 2014; and Diao Shijing, a former co-president of Unigroup. Others connected with the Big Fund, from academics to venture capitalists, have also been arrested or are under investigation by the Central Commission for Discipline Inspection (CCDI), an anti-corruption body.

While many observers say the arrests signify Beijing’s recognition of the enormous graft plaguing China’s chip industry, there are open questions about what the microchip dragnet means for the industry’s future, especially as Beijing’s ambitious goals come crashing into roadblocks from Washington…

… Whatever the reason, Beijing certainly has grounds to worry about its chip drive. Despite the dizzying sums invested into it, the industry remains years, even decades, away from achieving global dominance. Foreign firms are forecast to supply over half of China’s chip consumption until at least 2026, according to IC Insights, a U.S. semiconductor research group…

…Unigroup, which has since undergone “restructuring,” will remain a major player in China’s politically-charged chip sector. But the fall of Zhao and other chip giants suggests a reevaluation — some even say an abandonment — of Beijing’s silicon dreams.

Since the Trump administration, the U.S. and its allies have restricted key technologies and know-how from reaching Chinese chip firms, severely limiting China’s ability to keep pace with industry innovations. In October, the Biden administration introduced even tougher restrictions, including placing YMTC on the Entity List, which has dramatically upended China’s semiconductor grand strategy…

…“Would we be so anxious about [semiconductors] if there was no risk that Xi Jinping would invade Taiwan?” asks Willy Shih, a professor at Harvard Business School and member of an IC advisory committee for the Department of Commerce. “It’d be a very different picture. Before 2012, this never came up.” 

Now that it has though, the U.S. is taking significant action. In August, Washington adopted the $280 billion CHIPS and Science Act, which will provide $52 billion in subsidies and R&D investment for chip firms operating in the United States. Intel and TSMC are investing stateside as a result. The European Union proposed similar legislation last year with a $49 billion price tag, hoping to double its global market share to 20 percent by 2030. 

The Taiwanese public, polls show, are increasingly wary of the PRC as well — and its prized semiconductor industry is becoming more defensive as a result. Chinese chip firms have long been a regular presence in Taiwan, luring talent and, crucially, I.P. to mainland firms with salaries up to five-times higher than in Taiwan. 

But that, like so much in the global semiconductor ecosystem, is changing. A more bellicose and Covid-closed China lessened the country’s attraction for Taiwan’s IC workforce. The Taiwanese government, too, has made it harder for Chinese firms to attract talent, banning unregistered Chinese headhunters from operating on the island. The overall result has been less poaching and more suspicion…

… So how will companies like Unigroup proceed after the dust settles? One option is to abandon advanced chips and settle for the older technologies, a strategy which the greater Chinese chip industry seems to be shifting towards. 

“In the past few years, the more we tried to make up for our deficiencies, the more passive we have become,” said Wei Shaojun, an official at the China Semiconductor Industry Association, in a December live-streamed speech. “We should focus not on overcoming weaknesses, but on enhancing our strengths.” 

On the flip side, China could attempt more semiconductor “moonshots” — technology advances that don’t present much prospect for profit but that are strategically important. Ultraviolet lithography machines, for example, are a vital technology that China is currently restricted from accessing by U.S. sanctions. Although the hurdles remain high, some analysts and insiders are not willing to rule out such advances. Scott Moore, a Chinese tech expert at the University of Pennsylvania likens the situation to nuclear weapons and the failures of non-proliferation. 

“What technology is more tightly controlled than nuclear weapons?” he says. “But that has not stopped many countries that have said, ‘We’re willing to make any investment of resources into acquiring this technology.’” 

7. No Recession – Michael Batnick

It’s a new year, but recession fears still abound. Two-thirds of economists expect one in 2023. I was also in that camp in 2022, but now I’m not so sure.

We spoke with Derek Thompson last week about the economy’s prospects for 2023, and I did a lot of on the one hand, on the other hand. I can see both sides now more than ever. He made me choose between yes or no, and I surprised myself when I said, “No recession.”

The economic data that came out on Friday made me feel better about a possible soft landing.

With all eyes on inflation, the stock market would ordinarily respond negatively to a strong jobs number, but there was something inside this report that the market loved; wages. Earnings are one of the biggest drivers of inflation; unfortunately, it’s the hardest area for the fed to influence.  So when year-over-year numbers fell to 4.6%, their lowest level since last August, the market cheered. The fears of a wage spiral seem to have been overblown.

In an uncertain economy that faces a myriad of risks, the fed seems to be the biggest one. But now that we’re getting some good numbers on the wage front, the market is expecting them to slow down dramatically.  So what if after all this worrying about the fed being behind the curve and then going too far too fast, they actually pull off the soft landing? We’re already seeing signs that inflation peaked and is on its way down. The job market is strong, but wages aren’t spiraling. We still need to see stabilization in the mortgage market for the housing market to thaw out, but the good news is stocks are acting like that might happen.

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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. We currently have a vested interest in Amazon, Apple, and TSMC. 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 January 2023:

1. Base editing: Revolutionary therapy clears girl’s incurable cancer – James Gallagher

All other treatments for Alyssa’s leukaemia had failed.

So doctors at Great Ormond Street Hospital used “base editing” to perform a feat of biological engineering to build her a new living drug.

Six months later the cancer is undetectable, but Alyssa is still being monitored in case it comes back.

Alyssa, who is 13 and from Leicester, was diagnosed with T-cell acute lymphoblastic leukaemia in May last year.

T-cells are supposed to be the body’s guardians – seeking out and destroying threats – but for Alyssa they had become the danger and were growing out of control.

Her cancer was aggressive. Chemotherapy, and then a bone-marrow transplant, were unable to rid it from her body.

Without the experimental medicine, the only option left would have been merely to make Alyssa as comfortable as possible…

…The team at Great Ormond Street used a technology called base editing, which was invented only six years ago.

Bases are the language of life. The four types of base – adenine (A), cytosine (C), guanine (G) and thymine (T) – are the building blocks of our genetic code. Just as letters in the alphabet spell out words that carry meaning, the billions of bases in our DNA spell out the instruction manual for our body.

Base editing allows scientists to zoom to a precise part of the genetic code and then alter the molecular structure of just one base, converting it into another and changing the genetic instructions.

The large team of doctors and scientists used this tool to engineer a new type of T-cell that was capable of hunting down and killing Alyssa’s cancerous T-cells.

They started with healthy T-cells that came from a donor and set about modifying them.

• The first base edit disabled the T-cells targeting mechanism so they would not assault Alyssa’s body
• The second removed a chemical marking, called CD7, which is on all T-cells
• The third edit was an invisibility cloak that prevented the cells being killed by a chemotherapy drug

The final stage of genetic modification instructed the T-cells to go hunting for anything with the CD7 marking on it so that it would destroy every T-cell in her body – including the cancerous ones. That’s why this marking has to be removed from the therapy – otherwise it would just destroy itself.

If the therapy works, Alyssa’s immune system – including T-cells – will be rebuilt with the second bone-marrow transplant…

…”She’s the first patient to be treated with this technology,” said Prof Waseem Qasim, from UCL and Great Ormond Street.

He said this genetic manipulation was a “very fast-moving area of science” with “enormous potential” across a range of diseases…

…Dr Liu said the “therapeutic applications of base editing are just beginning” and it was “humbling to be part of this era of therapeutic human gene editing”, as science was now taking “key steps towards taking control of our genomes”.

2. Battered by Covid, China Hits Pause on Giant Chip Spending Aimed at Rivaling US – Bloomberg News

China is pausing massive investments aimed at building a chip industry to compete with the US, as a nationwide Covid resurgence strains the world’s No. 2 economy and Beijing’s finances.

Top officials are discussing ways to move away from costly subsidies that have so far borne little fruit and encouraged both graft and American sanctions, people familiar with the matter said. While some continue to push for incentives of as much as 1 trillion yuan ($145 billion), other policymakers have lost their taste for an investment-led approach that’s not yielded the results anticipated, the people said.

Instead, they’re seeking alternative ways to assist homegrown chipmakers, such as lowering the cost of semiconductor materials, the people said, asking not to be identified revealing sensitive negotiations.

That would mark a shift in Beijing’s approach toward an industry regarded as crucial to challenging American dominance and safeguarding Chinese economic and military competitiveness. It underscores how the country’s economic ructions are taxing Beijing’s resources and hobbling its chip ambitions — one of President Xi Jinping’s top priorities. That could have ramifications for spending in other critical areas, from the environment to defense…

…But the discussions now underway are in stark contrast to Beijing’s prior efforts of pouring colossal resources into the chip industry, including setting up the National Integrated Circuit Industry Investment Fund in 2014.

That vehicle lies at the heart of Xi’s unhappiness with Beijing’s prior philosophy. Known within the industry as the Big Fund, it drew about $45 billion in capital and backed scores of companies, including China’s chipmaking champions Semiconductor Manufacturing International Corp. and Yangtze Memory Technologies Co.

Xi’s administration grew frustrated that tens of billions of dollars funneled into the industry over the past decade haven’t produced breakthroughs that allow China to compete with the US on a more equal footing. In fact, SMIC and Yangtze, arguably the two most advanced Chinese semiconductor players, were crippled by US sanctions.

3. Mirror, Mirror on the Wall, Who Knew That Stocks Would Fall? – Jason Zweig

Countless hunches and gut feelings flicker through our consciousness over the course of a year. We naturally remember the ones that turn out to be right. The multitude of other hunches that turn out to be wrong go into our mental garbage can.

Looking back at yourself a year ago, what you know now has indelibly altered your perception of what you knew then.

This pattern, which psychologists call hindsight bias, makes us feel that we foresaw the future all along, what happened was inevitable and anybody who didn’t see it coming is a dope. It’s close to irresistible—and it’s an illusion.

That’s why I recommend an annual exercise I call the Hindsight Bias Buster.

Almost exactly a year ago, in the email newsletter I write for The Wall Street Journal, I asked subscribers to forecast, as of Dec. 31, 2022: 

• the closing value of the Dow Jones Industrial Average;
• the total return of the S&P 500;
• the yield on the 10-year U.S. Treasury note;
• the annual rate of inflation;
• the price of bitcoin;
• the price of gold;
• the price of crude oil;
• and the best-performing major financial asset.

Earlier this month, I asked subscribers to recall their predictions from one year ago—or what they would have forecast.

Readers attempting to reconstruct their past projections said, on average, that they would have called for the Dow to close out 2022 at 34269 and that the S&P 500 would fall 1%. (The day before I sent out this year’s survey, the Dow had closed at 33947, and the S&P 500 was down 14.8% for the year to date.)

Readers estimated, on average, that they would have predicted bond yields to hit 3% and bitcoin to hit about $30,850. (The 10-year Treasury yielded 3.6% the day before, up from 1.4% at the time of my original survey. Bitcoin was at $16,970—down from just under $46,900 a year earlier.)

In real time, at the end of 2021, readers predicted that the Dow would finish 2022 at 36853, on average, and that the S&P 500 would gain 6%—much higher than they now recall. They forecast that interest rates would hit 2% and expected the price of bitcoin to top $53,900. Only a single reader predicted that energy, which is up almost 60% so far in 2022, would be the top performer…

…“Wow, wow,” said Mr. Jones when I read him his original responses.

“Obviously my assessment of the stock market at the time was largely influenced by what it had been doing up to that point,” he said. “And now I’m fitting my past projections to the current set of data! It’s so interesting to see how my thinking is influenced by what has happened since then.”

The meaning of the present is almost always hidden until it becomes the past—at which point you can’t reconstruct your earlier state of ignorance.

That makes it all too easy to fool yourself into thinking you knew what would happen all along—which, in turn, can delude you into thinking now that you know what will happen next.

4. What the Fed Gets Wrong – Barry Ritholtz

There seems to be a lot of confusion going on today with respect to inflation, interest rates, and ongoing Federal Reserve policy. A framework for exploring this has many parts: What the Fed (obviously) knows, how it express those views through police like FOMC rates, ZIRP, QE, QT, etc.

There remains the question of what the Fed is actually wrong about…

…What are the major errors that are currently driving Fed policy?

• Tardy: We all understand that Central Bank policy operates on a lag. History suggests that the Fed’s recognition of key market and economic indicators also is on an excessive lag. The result is Fed is always late to the party.

Consider: In the 2010s, the Fed remained on emergency footing from 2008, when they took rates to 0 (zero) until December 2015 (this created lots of distortions).  Then again in the 2020s, they remained on emergency footing post-Covid, despite broad evidence of economic recovery.

The Fed was late to act on rising inflation, waiting a full year from the time CPI ran through their 2% target to raise rates (See chart at top). Today, it appears they are repeating that same error, late to recognize inflation peaked in June and goods’ prices have fallen dramatically.

• Services Inflation: What is the impact of the fastest increase in rates in history? High Fed Funds Rates are causing high mortgage rates which is in turn pricing many people out of buying residential real estate. The net result: Potential buyers become renters, which drives apartment prices higher. Owners Equivalent Rent is the largest portion of the CPI Services sector.

The perverse outcome is the Fed is making the CPI model show both higher and stickier inflation.

• The Wealth Effect: Jay Powell seems to be targeting assets prices, despite equities not being part of the dual mandate.

The reason for this is that the Fed has institutionally been “all in” on the Wealth Effect theory. The thinking here is that a rising stock market makes Americans feel wealthier, leading to more spending and higher inflation.

There are many problems with this claim, but let’s just give you the biggest two: Most Americans do not own equities; many of those who do have modest holdings in IRAs and 401ks that they won’t touch for years. Its hardly driving spending for 70-80% of consumers.

The second is simply confusing correlation with causation. The same underlying factors that drive higher stock prices – rising GDP, employment and wages – also drive consumer spending and inflation. Hence the Fed believes a rising stock market is what leads to inflation. If you stop to think about for even a moment, you will see they are utterly wrong about this.

5. China has started to sweet talk private sector again but actions speak louder than words – Wang Xiangwei

Over the past few years, China’s business tycoons have been a bundle of nerves. The country’s once soaring private sector have fallen down hard in the wake of unprecedented regulatory crackdowns on Big Techs and amid calls for common prosperity. Businesses ranging from e-commerce to education to real estate have seen their stock prices pummeled and their operations under increasing scrutiny. Their already gloomy prospects have been further dimmed by China’s three-year-old draconian Covid controls which have seriously disrupted production and supply chains. Above all, political uncertainty and concerns for their own personal wellbeing have made them jumpier.

As a result, many of China’s business elites slipped away and sought temporary shelter in foreign countries. An interesting pattern has emerged as to where they have their self-imposed unusually long “holidays” or “study tours”…

..No doubt, all of them are keeping a close watch on signals from the Chinese government on which way the wind is blowing in the wake of the Chinese Communist Party’s 20th congress in October when President Xi Jinping secured his third term as the party leader and packed the new leadership lineup with his allies with surprising ease…

…What is unexpected, however, is that the official readout signaled a remarkable change of tone towards the embattled private sector.

Compared to last year’s statement which focused on regulating wealth and preventing “barbaric” growth of capital when it came to private sector, the tone of this year’s statement is surprisingly friendly.

The readout urged strong support for private economy and private enterprises both in terms of policies and media publicity. It said that legal and institutional arrangements must be made to ensure the equal treatment of private firms and state-owned enterprises. Property rights of private firms and interests of entrepreneurs must be protected according to law, and officials at all levels should help private firms to solve their problems and do more practical work for their benefits.

More importantly, it said that greater efforts should be made to develop digital economy and support “platform enterprises” which usually refer to Big Techs such as Alibaba and Tencent Holdings, enabling them to “fully display their capabilities” in leading development, job creation and international competition…

…The change of tone is a good start but to regain the confidence of private sector, the Chinese leaders have much more to do.

Over the past decade, the government has consistently and publicly vowed to uphold the policy of working unswervingly to support and develop both the public sector and the non-public sector and giving them equal treatment.

The truth of the matter is that China’s overall private sector have taken one beating after another.

China’s regulatory clampdown on irrational growth in tech sector and its common prosperity campaign may have good intentions but the way those policies were implemented have spooked investors and raised fears about China’s future direction at home and abroad.

The consensus view is that China has shot itself in the foot by cracking down on its biggest tech companies. Despite repeated official clarifications, the common prosperity campaign has been widely interpreted as “robbing the rich to help the poor”.

The leaders may have signaled a change of tune towards private sector but the local authorities have not received the message. Over the past few days, this writer has heard complaints of mistreatment from several private businessmen and fund managers who have direct investments in the country.

One businessman who recently came to Hong Kong on way to a third country said that his businesses in multiple cities have received visits from the tax collectors who demanded them to pay back tax breaks and other subsidies the local authorities have previously given. The reason? The local authorities have run out of money because funding was diverted to mass testing and building makeshift hospitals over the past three years. The zero-Covid policy may have been dropped but their coffers have turned empty. The tax collectors were said to be polite but very firm that if the businesses did not pay promptly, they would soon launch very detailed tax audits.

This telling anecdote is just one of many challenges with which the private businessmen are grappling on a daily basis.

Broadly, to regain the confidence of private sector, Beijing must take concrete actions to honor its commitment to provide law-based protection to the property rights of private firms and interests of entrepreneurs. So far, there has been a lot of talk but little action..

6. Justifying Optimism – Morgan Housel

The constant human desire to one-up past successes, and the generational knowledge transfer, is a pure example of compounding in action.

Skateboarder Tony Hawk landed a 900 – two and a half spins – at the 1999 X Games. It was the biggest achievement the sport had ever seen, the equivalent of the four-minute mile.

It catapulted Hawk into legend status. His video game came out a year later and sold 30 million copies. Six Flags named a rollercoaster after him.

But here’s the craziest part of this story: fifteen years later, an eight-year-old landed a 900.

Hawk was also the first person to land a 720 (two spins) – a feat later accomplished by a second-grader.

A lot of sports work like that…

…Does the same hold true for technology, science, and business? Of course. A first-year med student today likely has more medical knowledge than an experienced senior doctor did 50 years ago. The average eight-year-old today knows things about technology that a computer science professor 30 years ago would find bewildering.

Innovation and advancement tend to compound. One person raises the bar over the previous limit, and that becomes the baseline for a new generation to aim for and build upon.

Part of that is a simple generational knowledge transfer. It’s pure compounding: People spend years or decades discovering a new truth, then the next generation begins their careers with those new truths.

Another part is driven by the need to one-up the current leader of a field. Charlie Munger says, “The world is not driven by greed; it’s driven by envy.” You see someone accomplish a new feat and think, “I should be able to do that too – and even better.”…

…As crazy as the world is, the core drivers of economic growth are still in place.

In his book The Birth of Plenty, investor William Bernstein writes that four things are necessary for long-term economic growth:

• Secure property rights.
• A scientific view of the world.
• Widely available and open sources of funding.
• Rapid communication and cheap transport of goods.

There is a long global history backing this up: When just one of those four is missing, progress stops. And as long as all four are in place, progress tends to take care of itself because of my first two points – stress-induced problem-solving and the compounding of knowledge.

As wild as things are – between Covid and political nonsense and inflation and market crashes – all four points are still in place. (The cost of transporting goods surged in 2021, but is already back to pre-Covid levels.)

7. 2022 Was One of the Worst Years Ever For Markets – Ben Carlson

This past year’s 18.1% loss was the 7th worst loss since the 1920s.

The bond market also had one of its worst years in history.

It was easily the worst year ever for the Bloomberg Aggregate Bond Market Index, which dates back to 1976.

In the 40+ years of calendar year returns there were only four down years before 2022:

• 1994 -2.9%
• 2013 -2.0%
• 2021 -1.5%
• 1999 -0.8%

The total return of -13% in 2022 was far and away the worst loss ever for this total bond market index.

There has only been one double-digit calendar year loss for 10 year U.S. treasuries since the 1920s. That was an 11.1% loss in 2009. Now we have two.

The benchmark U.S. government bond was down more than 15% in 2022, making it the worse year ever for bonds.

Add it all up and a 60/40 portfolio of U.S. stocks and bonds was down more than 16% in 2022. With both stocks and bonds down big this ended up being the third worst year ever for a diversified portfolio…

…I try to look at losses like this as sunk costs. They already happened. You can’t go back and change things now.

All that matters is what happens from here, not what happened in the past.

The beatings could continue until morale improves. There’s nothing that says markets will all of the sudden get better just because it’s a new year.

If you’re the type of person that likes to look for a silver lining in these things, there is some good news for investors going forward. The losses from 2022 have added yield to your portfolio…

…Expected returns are now higher.

I don’t have the ability to predict the timing or magnitude of those higher expected returns but there is now a much bigger cushion for investors than there has been in years as far as yields are concerned.

The other good news is every time we’ve ever had bad times in the past they turned out to be wonderful opportunities for long-term investors.

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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. We currently have a vested interest in Tencent. 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 18 December 2022:

1. Why Competitive Advantages Die – Morgan Housel

“Being right is the enemy of staying right because it leads you to forget the way the world works.” – Jason Zweig. Buddhism has a concept called beginner’s mind, which is an active openness to trying new things and studying new ideas, unburdened by past preconceptions, like a beginner would. Knowing you have a competitive advantage is often the enemy of beginner’s mind, because doing well reduces the incentive to explore other ideas, especially when those ideas conflict with your proven strategy. Which is dangerous. Being locked into a single view is fatal in an economy where reversion to the mean and competition constantly dismantles old strategies…

…Brands are hard to build and even harder to span across generations. You can do everything right and still fail because customers don’t want to be associated with products of their parents’ generation. Morgan Stanley could make the indisputably best robo advisor in the world and millennials would still prefer Betterment. That’s how Charles Schwab blossomed in the 1980s and 1990s; with a brand baby boomers felt was theirs, not their parents’. One of my goals as a writer is to bow out the moment I realize I’m too old to understand how the game is played anymore. Companies, with indefinite time horizons, have to keep trying. A few of them pull it off; more often it’s painful to watch.

2. The Next Frontier in Carbon Capture is a Hungry Bacterium – Illumina

Primitive microbes, or those found in extreme environments such as hydrothermal vents in the deep sea, have been converting carbon monoxide (CO) and carbon dioxide (CO2) into fuel for billions of years. Simpson and Foster first took a collection of microbes that were known to do this and began feeding them gases from a steel mill. In that initial screening and discovery stage, they found that an anaerobic bacterium called Clostridium autoethanogenum possessed an ancient pathway that could ferment both CO and CO2, effectively converting it into ethanol under the right conditions. Ethanol, in turn, can be made into polyester fabrics, aviation fuel, and—of course—alcohol…

…Illumina’s technologies helped LanzaTech to genetically modify C. autoethanogenum to synthesize acetone—an important solvent and chemical building block—from industrial emissions. Today, acetone is made exclusively petrochemically from fresh fossil fuels, but in the early 1900s it was produced by fermentation from sugars into a mixture of acetone, butanol, and ethanol. Due to substrate cost and low selectivity, the process was eventually abandoned over the course of the last century, but the strains were preserved and LanzaTech used that collection as a starting point.

“The initial hope was that some of these strains would also be able to utilize the gases we work with,” Köpke says. “Unfortunately, that was not the case and the collection of microbes was sitting in the corner for several years. Advancements in next-generation sequencing technology allowed us to revisit the collection.” Illumina’s sequencing technology helped LanzaTech identify the microbial genes responsible for making acetone, and with those sequences in hand, their researchers synthesized and transferred them into their organism. Acetone is used as a solvent for cosmetics, paints, electronics, and consumer products, and can be used in manufacturing acrylic glass—in which case, the formerly atmospheric carbon is locked away practically forever in a stable, solid form. “You can achieve not only carbon neutrality, but actual carbon-negative production,” says Köpke.

3. The mortgage time bomb ticking beneath Poland’s banks – Raphael Minder

In 2006, Polish couple Marek and Małgorzata Rzewuski bought a house on the outskirts of Warsaw because they were expecting a child and “we wanted more space and our own garden”. 

Like hundreds of thousands of other Polish homebuyers at the time, they were advised by their bank to get a mortgage in Swiss francs to benefit from lower interest rates in Switzerland than in Poland. Nobody discussed the flip side of introducing a foreign exchange risk into a 30-year mortgage of SFr200,000 ($205,000).

“This was presented as the best opportunity on the market,” Marek recalls. “The Swiss franc was very stable and very popular and we knew many people who were doing the same.”

Two years later, however, the global financial crisis struck. Investors flocked to the Swiss franc as a haven from the market turmoil, and its value surged against the Polish zloty and other currencies. The franc is now worth more than double its exchange rate of 2 zlotys before the crisis.

The lending practice in effect ended in 2008. But in the years since, it has become a time bomb for the Polish banking sector as customers like the Rzewuskis have begun winning lawsuits to force their banks to bear the cost of a currency bet that went spectacularly wrong.

If mortgage holders continue to win their court battles, officials and bankers warn that some lenders could collapse.

“It’s my obligation to raise the red flag, because pretending that everything is fine is going to have some dramatic consequences,” says Jacek Jastrzębski, chairman of the KNF, Poland’s financial watchdog…

…If courts decide that every bank must bear the full cost of their Swiss investments, Jastrzębski fears at least one or two may collapse.

One has already fallen. The country’s 10th-largest lender, Getin Noble, had to be rescued in September by the Polish state bank guarantee fund and a consortium of banks. The 10.3bn zloty ($2.2bn) bailout was Poland’s largest since the Soviet era.

Getin had already suffered several years of losses due to its aggressive sale of subprime products, but it was also heavily exposed to the Swiss franc, which accounted for one-quarter of its loan portfolio.

Polish banks have provisioned a combined 30bn zlotys to cover their Swiss-franc lending. But their final bill could rise by another 100bn zlotys if the judiciary rules that they should have received zero interest rate income on invalid Swiss-franc mortgages, according to Jastrzębski.

Polish courts have already annulled many Swiss-franc mortgages, after ruling that banks used “abusive” foreign exchange rates compared with those of the National Bank of Poland.

But the court battle has recently shifted on to the question of whether banks were entitled to charge customers for using their capital until their mortgages were annulled, an issue that was also brought last month by a Warsaw court before the European Court of Justice.

If courts in Poland and Europe side with consumers, the potential fallout would be worse. Up to five banks would be pushed to the brink of collapse in a worse-case scenario, warns Cezary Stypułkowski, mBank chief executive.

4. Fusion energy breakthrough by US scientists boosts clean power hopes – Tom Wilson

US government scientists have made a breakthrough in the pursuit of limitless, zero-carbon power by achieving a net energy gain in a fusion reaction for the first time, according to three people with knowledge of preliminary results from a recent experiment.

Physicists have since the 1950s sought to harness the fusion reaction that powers the sun, but no group had been able to produce more energy from the reaction than it consumes — a milestone known as net energy gain or target gain, which would help prove the process could provide a reliable, abundant alternative to fossil fuels and conventional nuclear energy.

The federal Lawrence Livermore National Laboratory in California, which uses a process called inertial confinement fusion that involves bombarding a tiny pellet of hydrogen plasma with the world’s biggest laser, had achieved net energy gain in a fusion experiment in the past two weeks, the people said…

…“If this is confirmed, we are witnessing a moment of history,” said Dr Arthur Turrell, a plasma physicist whose book The Star Builders charts the effort to achieve fusion power. “Scientists have struggled to show that fusion can release more energy than is put in since the 1950s, and the researchers at Lawrence Livermore seem to have finally and absolutely smashed this decades-old goal.”

5. Twitter thread on the implications of the US government’s breakthrough in nuclear fusion – Wilson Ricks

The National Ignition Facility (NIF) has achieved net energy gain from fusion! This is incredibly exciting scientifically, but what does it mean for the future of energy? In all likelihood, very little.

NIF uses inertial confinement fusion, which involves shooting ultra high-powered lasers into a small capsule containing a deuterium-tritium fusion fuel pellet. The surface the pellet heats, causing an implosion that crunches the interior until (hopefully) fusion is achieved.

In this particular instance, it appears that NIF successfully induced a fusion reaction that generated more energy than was originally delivered to the pellet via the lasers. This is Net Gain, a milestone that fusion engineers have been pursuing for half a century.

So as a scientific and symbolic achievement, this is huge. But how much closer does it put us to ‘limitless clean energy’?  Unfortunately not much closer at all. For inertial confinement fusion, there’s a VERY long way to go between net gain and viable electricity generation.

To explain just how far, let’s look at the power balance of this experiment. If the reports are correct, the fusion reaction generated 2.5 MJ, compared to 2.1 MJ of laser power.

BUT, the huge lasers at NIF are less than 1% efficient, so to generate more fusion energy than actual input energy to the facility, you’d need to increase the yield 100x…

Plus, the fusion power is in the form of heat and radiation, and needs to be converted back to electricity. Assuming a 40% steam cycle efficiency, that’s another 2.5x increase in required yield. So we need a fusion reaction *250x MORE POWERFUL* to achieve true electric net gain.

6. An Interview with Coinbase Founder and CEO Brian Armstrong about FTX and Crypto Realities – Ben Thompson and Brian Armstrong

What’s your take? I mean, you jumped to FTX, what’s your take on the FTX situation?

BA: Oh, well, I mean, FTX, what can I say about it? I mean, it appears that a massive fraud was committed. I think that customer funds appear to have been moved over to his hedge fund that he owned 90% of, and that those customer funds were lost. I mean, this is a violation not only of the terms of service as it’s written as far as I understand it, but it’s also probably just against the law and outright fraud.

It’s been pretty bizarre to kind of watch the whole thing unfold, primarily because I do feel like mainstream media has given a lot of softball interviews, and even this tweet back and forth with Maxine Waters very politely asking him to attend a hearing, and him politely deferring, it was bizarre. I mean, this guy just committed a $10 billion fraud, and why is he getting treated with kid gloves? Compare her tweets about Mark Zuckerberg for instance, who never stole $10 billion from people, whatever you think about the guy. So these kind of things are just, it’s a little strange for me to see it all happening.

What’s the one question, if you had a chance to interview him? You had a disguise on, you’re Mr. Mainstream journalist, Brian Armstrong. What’s the one question you would want to ask him?

BA: Honestly, I don’t think I have any questions at this point. I think it’s pretty clear what happened, and I think every time he’s being asked these questions, that people are giving him a chance to evade. There’s some journalists who have done better than others in terms of really pinning him down on this stuff. But I kind of just want to turn the page on the whole thing, to be honest. The bankruptcy lawyers, and the DOJ, and everybody are going to have to figure out how to hopefully put these folks behind bars. Not just Sam, but the other people involved. I mostly want to think about where do we go from here as an industry.

Do you feel vindicated or outraged, particularly over the customers that you lost to FTX? Because it’s very visible. Tons of branding in the US. Yes, they had FTX.us in the US, but then they had FTX abroad. And to your point, how many Americans ended up there? It’s an interesting question. Is it just really irritating, or do you feel like, hey look, that’s the problem. You should have stuck with Coinbase, your reliable friend in crypto?

BA: Well, look, I mean I think it does validate the approach and the strategy that we’ve taken over the last 10 years, which is not always the most sexy thing. It’s not the most hyped thing. I do think it’s the right strategy long term, and we think it’s going to be the right strategy to build a company for the long term. But look, this is not a moment for me to take any victory laps or celebrate. I mean, a bunch of people lost money, it’s a terrible, terrible thing for the industry and those customers…

…Well, now that you said you’re happy to have that role, I now get a seize the opportunity to hold you to that. And so here’s a question that I would imagine that some of my readers are going to have. Why is crypto a real thing, and not just regulatory arbitrage? I think that’s a question particularly when it comes to exchanges and the more financial products — there’s a separate product question. But what’s the pitch? And yes, it’s a question you’ve had to answer for 10 years, but it’s one that arguably is even more pressing today given what has happened.

BA: Yeah, okay, so is crypto a real thing? I think the answer is unequivocally yes. And the reason is you can just look at the fact that more people are using it every year, or every cycle that happens. So there’s two or 300 million people in the world now who’ve used crypto or have some, and yes, it goes up in up cycles, it goes down in down cycles, but it’s in an upward channel. So every cycle, if you look even just back to the year, I think 2020…

The floor today is still like 5X what it was a few years ago.

BA: Yeah, exactly. So I mean, yes, crypto is definitely a real thing, and it’s a real thing in a number of ways. I mean, first it’s a new form of money, and that’s actually a really important thing. Many places in the world, people don’t have stable currency, and there’s all kinds of wealth that’s eroded from the poorest people in society. It’s just a foundational part that we take for granted in the US, given that we have eight or 9% inflation, which we think is extreme. In many places in the world, you get 25% in a month or something.

I mean, one of the most compelling cases made for Bitcoin I’ve ever heard was someone, I believe he was from Venezuela, but I’ve heard similar things from people from Argentina, about this sort of inflation protection. Is it fair to say that that is still regulatory arbitrage, it’s just maybe good regulatory arbitrage that protects people and keeps them safe? I mean, I’m trying to steelman this argument here. But is that okay to admit, or is there something beyond that?

BA: Yeah, okay. Well, just to finish the thought, so it’s money and then it’s new types of financial services. It’s also this new application platform. We can talk about that too, with identity and decentralized social, so it’s lots of different things. But is it regulatory arbitrage? I mean, maybe. I think I would say it in a different way, which is that, it’s helping alleviate inefficiencies in the global economy. Some of those things are put in place for a good reason and some of them are not there for a good reason. For instance, if you wanted to build a global lending marketplace or something like that, you’d have to go to all 200 countries in the world, and all 50 states in the US. Only then could you make a more efficient global market for how to get a loan between somebody in India and somebody in Brazil or whatever, but the amount of bureaucracy and rules in place and everything, from having this kind of patchwork quilt of different proprietary systems in every country of the world, makes that infeasible. And so, yeah, crypto is a new, more global, more fair, more transparent, more free system.

It’s not just that inefficiency and the global regulatory apparatus though, it’s also dealing with the technology improvements of just how quickly you can send an asset somewhere, or make a transaction on a decentralized ledger. So it’s permissionless, it’s decentralized, it’s global, there’s technological benefits to that. There are, I would say, inefficiencies that it helps you get around. And that’s part of why a lot of innovation is on this frontier right now. Just like what happened with the Internet 20, 25 years ago.

Well, I mean, one argument that I think I’ve made in the past is that this concept of digital money makes a lot of sense when you’re in the virtual world. Now virtual world could be a full-blown metaverse sort of thing, it could just be the Internet broadly. Real money makes sense — fiat money or whatever you want to call it — in the real world. But where I’m a little skeptical is when there’s an intersection between the two. There’s the famous story of the guy that bought two pizzas with a Bitcoin or whatever it might be, which I’m skeptical about in the long run.

What’s your view on this? Is this a virtual-only thing, or do you see a real porous interchange between the two? Obviously a porous interchange would be in Coinbase’s interest, given you are an exchange, you sit at that interface. What do you see as the interaction between the virtual and the “physical” as it were?

BA: I think it’s both. It’s probably going to lean virtual in the early days, because that’s just a more natural fit. But it’ll eventually do more and more in the physical world as well. So virtual is probably easier to follow just in terms of if you’re going to build a new community on the Internet, it would be discriminatory or weird to use the currency of one country in something that’s open to people all over the world…

…Did it shake your belief a little bit though, that as inflation went up, Bitcoin’s price went down?

BA: No, it didn’t shake my belief. I definitely thought that crypto might be viewed as an asset people would flee to in a time of uncertainty. I think in the crypto economy, Bitcoin is sort of the gold.

People did flee to Bitcoin, but they were the people who were already in crypto.

BA: Right.

That’s a good point.

BA: But the broader macro economy is still much bigger, and in that environment, they treat all of crypto as a kind of growth stock as opposed to the thing to flee to. And it’s also so liquid that it was easy for people to liquidate it.

I like that analogy because it does kind of get to my theory about the virtual being in many respects, in a different economy than the physical. That bit about within crypto itself, Bitcoin is gold. And maybe it was just a little too presumptuous to say that it’d be gold for the broader economy, at least at this point in time.

BA: Yeah, I think that’s right. But if you look, I think the trend is very clear, which is basically virtual is becoming a bigger and bigger percentage of the pie in terms of the global economy, GDP. I think it’s really interesting, look at e-commerce, right? Back in 1999, 2000, people were saying, oh, I’d never put my credit card on the Internet. And then it was a tiny, it was less than 1% of all global GDP, right? Now, fast forward 20 years, and e-commerce is now I think about 15, 16, 17, and I think COVID accelerated it, right? It almost hit 20% of global GDP. For people like you and me, that even sounds low. I probably do 78% of my spending online or whatever. But we’re sort of living in a different world.

So think about that trend in terms of crypto as well. If you fast forward another 10 years, are more people going to be doing things virtually than physically? Probably. Is a larger percentage of the economy going to be happening virtually? Probably. So crypto is incredibly well positioned as the inherent currency of the Internet, the more transnational global currency of the Internet. And so it’s just very hard for me to imagine a world 10 years from now, where there’s more e-commerce, more people using the Internet, more virtual economy, and crypto is not much bigger right along with that.

7. These Transistor Gates Are Just One Carbon Atom Thick – Charles Q. Choi

For decades, silicon transistors become smaller and smaller, but they are fast approaching the point at which they can no longer shrink the lengths of their gates—that is, how far current must travel in these devices. Now, by using atomically thin materials, scientists in China have created a transistor with a record-breaking gate length of just roughly one-third of a nanometer wide, only as thick as a single layer of carbon atoms, shedding light on how much smaller—if at all—transistors can possibly get.

In all transistors, current flows from the source to the drain, and that flow is controlled by the gate, which switches on and off in response to an applied voltage. The length of the gate is a key marker of a transistor’s size…

…Recently, scientists began exploring two-dimensional materials for next-generation electronics, including graphene, which consists of single layers of carbon atoms, and molybdenum disulfide, which is made of a sheet of molybdenum atoms sandwiched between two layers of sulfur atoms. For example, in 2016, scientists created a transistor with gates each just 1 nm long using carbon nanotubes and molybdenum disulfide.

Now scientists in China have created a transistor using graphene and molybdenum disulfide with a gate length of just 0.34 nm by exploiting the vertical aspect of the device. “We have realized the world’s smallest gate-length transistor,” says study senior author Tian-Ling Ren, an electrical engineer at Tsinghua University in Beijing…

…This new work pushes the scaling limit for gates further to “just the thickness of a single layer of carbon atoms,” says Huamin Li, a nanoelectronics scientist at the State University of New York at Buffalo, who did not take part in this study. “It will be hard to beat this record for quite some time.”

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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. We currently have no vested interest in any companies mentioned. 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 11 December 2022:

1. Ideas That Changed My Life – Morgan Housel

Everything’s been done before. The scenes change but the behaviors and outcomes don’t. Historian Niall Ferguson’s plug for his profession is that “The dead outnumber the living 14 to 1, and we ignore the accumulated experience of such a huge majority of mankind at our peril.” The biggest lesson from the 100 billion people who are no longer alive is that they tried everything we’re trying today. The details were different, but they tried to outwit entrenched competition. They swung from optimism to pessimism at the worst times. They battled unsuccessfully against reversion to the mean. They learned that popular things seem safe because so many people are involved, but they’re most dangerous because they’re most competitive. Same stuff that guides today, and will guide tomorrow. History is abused when specific events are used as a guide to the future. It’s way more useful as a benchmark for how people react to risk and incentives, which is pretty stable over time.

Multi-discipline learning: There’s as much to learn about your field from other fields than there is within your field. Most professions, even ones that look wildly different, live under the umbrella of “Understanding how people respond to incentives, how to convincingly solve their problems, and how to work with others who are difficult to communicate with and/or disagree with you.” Once you see the roots shared by most fields you realize there’s a sink of information you’ve been ignoring that can help you make better sense of your own profession. I didn’t appreciate how important communication is to providing investment advice before reading about how many doctors struggle to communicate effectively with patients, leading to patients who don’t stick with treatment plans and are resistant to lifestyle change. There are millions of these dots to connect. Probing beyond the confines of your day job is more fun anyways…

…Your personal experiences make up maybe 0.00000001% of what’s happened in the world but maybe 80% of how you think the world works. People believe what they’ve seen happen exponentially more than what they read about has happened to other people, if they read about other people at all. We’re all biased to our own personal history. Everyone. If you’ve lived through hyperinflation, or a 50% bear market, or were born to rich parents, or have been discriminated against, you both understand something that people who haven’t experienced those things never will, but you’ll also likely overestimate the prevalence of those things happening again, or happening to other people.

2. Drew Cohen – Floor & Decor: Raising the Floor – Matt Reustle and Drew Cohen

Matt: [00:44:17] I think all those points there in terms of where that capital is going and the return on that capital and trusting the capital allocation of the management team. If you can get that type of return by building out a footprint. Absolutely, you’re more than willing to have them reinvest those dollars.

Any other risks that we haven’t talked about? I think you mentioned the short-term dynamics that they might see an impact from, but anything else that would keep you up at night as an investor?

Drew: [00:44:47] Something I like about Floor & Decor is there’s no one real existential risk, at least that I could think about, knock on wood. But if you think about, there’s a confluence of different things that could happen, that could definitely be not good for them. One of them could be these home improvement centers, which now if they don’t have something in stock to take upwards of a week to get it into stock.

If they continue to build out their distribution centers and they streamline their logistics, then you could be seeing maybe one/two-day delivery or something like that. And that wouldn’t mean they’re winning all purchases, but a good portion could go to them, especially because they have a convenient and wide store footprint, so that could eat into them.

The second thing is management seems a little perplexed that this hasn’t happened, but there’s never been a copycat retailer that copies their warehouse store format and their whole model. Everyone else has only kind of nipped that little pieces of it. So there could potentially same way Lowe’s came after Home Depot.

There could be a copycat, who just copies everything. It’s kind of interesting because and you’ve ever read the book Secrets of Our Success, Joseph Henrich, I believe. He talks about how all of these different tribes would have these very complex processes. He observed the South American tribe would try to eat this tubular, but it was poisonous. So before they could eat it, they had to boil it, they had to bury it. They did all these other things.

So it’s a very complex thing, and the explorer went and saw this and they thought, “Oh, well, all these steps are superfluous. All that really matters is boiling this and then I’ll eat it and it won’t be poisonous.” So they just boiled it and then they died because it turned out when you left it in the sand, it actually absorbed some of the poison and all of that.

So why am I bringing up this very eccentric story is because I see with a lot of other companies, they’ll try to copy just one aspect of it and not the whole thing. The biggest threat for a copycat is not someone who says, “Oh, I’m going to also try to do direct sourcing. Oh, I’m going to also try to have more selection.” It’s someone who does the whole thing and they’re not embarrassed to say that Floor & Decor has every single step right and let’s just not change any of it.

Matt: [00:46:55] I love that. And how fitting our relationships started with you giving me book recommendations in a small Goldman office, and we can close out the episode with another good book recommendation.

But I think there’s a lot of truth to that statement. Copycats do come along, but how often do they actually copy the entire strategy. I think you’re right people try to pick the little pieces of the story that they like and sometimes miss the point that it’s the entire system that makes it work.

Well, thanks, Drew. We close these conversations out with lessons that you can take away from analyzing the business or researching the business that you might be able to apply to other types of work and other types of research, just higher-level lessons that you’ve learned from looking at the business. What would you point to in terms of Floor & Decor as a key lesson that you might share with investors?

Drew: [00:47:45] I would say focus is one of the most important things. When you have a company, and I’ve said this before, but is relentlessly pursuing a singular goal that is very hard to compete against. Because if you think about any sort of optimization equation, you have all these different variables and you can only really optimize for a limited set.

The more variables you’re trying to optimize for the less optimal your outcome is ultimately going to be. So having a specialty chain retailer saying, I just want to be the best at hard surface flooring, it’s very hard for anyone else to come in there and just as a part-time job beat them at that. I would say that’s one thing.

3. AI Homework – Ben Thompson

It is an open question as to what jobs will be the first to be disrupted by AI; what became obvious to a bunch of folks this weekend, though, is that there is one universal activity that is under serious threat: homework.

Go back to the example of my daughter I noted above: who hasn’t had to write an essay about a political philosophy, or a book report, or any number of topics that are, for the student assigned to write said paper theoretically new, but in terms of the world generally simply a regurgitation of what has been written a million times before. Now, though, you can write something “original” from the regurgitation, and, for at least the next few months, you can do it for free.

The obvious analogy to what ChatGPT means for homework is the calculator: instead of doing tedious math calculations students could simply punch in the relevant numbers and get the right answer, every time; teachers adjusted by making students show their work.

That there, though, also shows why AI-generated text is something completely different; calculators are deterministic devices: if you calculate 4,839 + 3,948 – 45 you get 8,742, every time. That’s also why it is a sufficient remedy for teachers to requires students show their work: there is one path to the right answer and demonstrating the ability to walk down that path is more important than getting the final result.

AI output, on the other hand, is probabilistic: ChatGPT doesn’t have any internal record of right and wrong, but rather a statistical model about what bits of language go together under different contexts. The base of that context is the overall corpus of data that GPT-3 is trained on, along with additional context from ChatGPT’s RLHF training, as well as the prompt and previous conversations, and, soon enough, feedback from this week’s release…

…There is one site already on the front-lines in dealing with the impact of ChatGPT: Stack Overflow. Stack Overflow is a site where developers can ask questions about their code or get help in dealing with various development issues; the answers are often code themselves. I suspect this makes Stack Overflow a goldmine for GPT’s models: there is a description of the problem, and adjacent to it code that addresses that problem. The issue, though, is that the correct code comes from experienced developers answering questions and having those questions upvoted by other developers; what happens if ChatGPT starts being used to answer questions?

It appears it’s a big problem; from Stack Overflow Meta:

Use of ChatGPT generated text for posts on Stack Overflow is temporarily banned.

This is a temporary policy intended to slow down the influx of answers created with ChatGPT. What the final policy will be regarding the use of this and other similar tools is something that will need to be discussed with Stack Overflow staff and, quite likely, here on Meta Stack Overflow.

Overall, because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking or looking for correct answers.

The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce. There are also many people trying out ChatGPT to create answers, without the expertise or willingness to verify that the answer is correct prior to posting. Because such answers are so easy to produce, a large number of people are posting a lot of answers. The volume of these answers (thousands) and the fact that the answers often require a detailed read by someone with at least some subject matter expertise in order to determine that the answer is actually bad has effectively swamped our volunteer-based quality curation infrastructure.

As such, we need the volume of these posts to reduce and we need to be able to deal with the ones which are posted quickly, which means dealing with users, rather than individual posts. So, for now, the use of ChatGPT to create posts here on Stack Overflow is not permitted. If a user is believed to have used ChatGPT after this temporary policy is posted, sanctions will be imposed to prevent users from continuing to post such content, even if the posts would otherwise be acceptable...

…Here’s an example of what homework might look like under this new paradigm. Imagine that a school acquires an AI software suite that students are expected to use for their answers about Hobbes or anything else; every answer that is generated is recorded so that teachers can instantly ascertain that students didn’t use a different system. Moreover, instead of futilely demanding that students write essays themselves, teachers insist on AI. Here’s the thing, though: the system will frequently give the wrong answers (and not just on accident — wrong answers will be often pushed out on purpose); the real skill in the homework assignment will be in verifying the answers the system churns out — learning how to be a verifier and an editor, instead of a regurgitator.

What is compelling about this new skillset is that it isn’t simply a capability that will be increasingly important in an AI-dominated world: it’s a skillset that is incredibly valuable today. After all, it is not as if the Internet is, as long as the content is generated by humans and not AI, “right”; indeed, one analogy for ChatGPT’s output is that sort of poster we are all familiar with who asserts things authoritatively regardless of whether or not they are true. Verifying and editing is an essential skillset right now for every individual.

4. Why Finance is Hard to Decentralize – Byrne Hobart

A margin lending algorithm can be built based on historical backtests, but what it can’t backtest is the change in market structure caused by its own existence. This is by no means unique to decentralized finance, of course. It’s a good description of what happened in 1987. Some smart academics discovered that an investor could replicate an options position using futures—as the market declines, selling more put options replicates the position that an options market-maker would have in order to hedge a put option, but in this case the market-maker doesn’t have to get paid some premium for writing the option in the first place. This strategy got popular enough that when the market did face a big decline, it set off a wave of mostly-automated selling. The stock market crashed that day, with the S&P 500 down 20.5%. Futures crashed even worse, though, at one point trading at a 15% discount to the underlying stocks.3 The backtest for portfolio insurance didn’t cover a period where portfolio insurance existed, and thus underestimated both the odds of a stock market crash and the odds that futures would crash harder, ruining the hedge.

Automated market-making, as DeFi proposes, has much the same problem. It’s easy to create an automated market-making strategy, but this strategy is effectively a bet against volatility. In normal times, a decentralized market-maker will bumble along, churning out steady profits from the spread between bid and ask. And every once in a while, there will be a big liquidation or a burst of short-covering, and the market-maker will, by design, be automatically holding exactly the wrong position.

5. Venture Capital Red Flag Checklist – Bill Gurley

1. “LETTING THE GOOD TIMES ROLL” 

It’s no coincidence that Enron happened in the late 2000 and that FTX occurred in 2022. Extended, frothy bull markets are a breeding ground for unwarranted corporate behavior. When markets are soaring, speculation increases and as a direct result so does risk. Also, when everything appears to work, investors are more willing to suspend belief. As it was with crypto, sometimes this leads to the development of “new investment rules” that crowd out traditional norms. Lastly, in a heated market, investor competition increases which leads to more investors being willing to “take what they can get” when it comes to governance. As an investor, when the environment is “frothy” you are much more likely to run into these problems. But ironically this is also the precise time when raising concerns will make you look like a washed up veteran who is unable to adjust to the new “realities.” …

…4. AVERSION TO AUDITS

As the bull market raged on from 2015 to 2022, it became quite trendy for venture capitalists to waive the requirement for an annual audit which is embedded in almost every standard Series A term sheet. This relaxation of governance norms is consistent with the “bull market” argument in point #1. No investor wants to lose a deal over an audit requirement. At least for companies generating meaningful revenue, investors should look to have an annual audit with one of the Big Four accounting firms, or one of the more reputable smaller firms like Grant Thornton. Learning how to meet and perform an audit is part of “growing up” as a company. Some founders unfortunately have an explicit aversion to audits. From their POV, they view this step as unnecessary and bureaucratic. The problem is auditors are the “referees” in business. Insisting on running without them is the equivalent of trying to rewrite your own rules…

…7. ODD CORPORATE LOCATION

The more atypical a corporate location, the more one should be concerned. Island nations are known for serving as tax havens, but they also can have more lackadaisical business regulations. All things being equal, this should clearly be viewed as non-optimal from a governance perspective. Without naming names, some U.S. states have a reputation for being more forgiving of low-grade business malfeasance. This does not mean that all businesses in a location like this are “bad,” but it still belongs on the checklist…

…9. OVERLAPPING CORPORATE INTERESTS

Off all the checklist items, this is the one that is an absolute non-starter. No one operating a venture backed startup should be simultaneously running another corporate entity that has overlapping interest, competing interests or even potentially competing interests. The standard should be the appearance of impropriety. The potential for bad behavior is simply too great. If there was a recipe book for corporate fraud, this would be the first chapter. Just say no. Plain and simple.

6. Your Creativity Won’t Save Your Job From AI – Derek Thompson

In 2013, researchers at Oxford published an analysis of the jobs most likely to be threatened by automation and artificial intelligence. At the top of the list were occupations such as telemarketing, hand sewing, and brokerage clerking. These and other at-risk jobs involved doing repetitive and unimaginative work, which seemed to make them easy pickings for AI. In contrast, the jobs deemed most resilient to disruption included many artistic professions, such as illustrating and writing.

The Oxford report encapsulated the conventional wisdom of the time—and, perhaps, of all time. Advanced technology ought to endanger simple or routine-based work before it encroaches on professions that require the fullest expression of our creative potential. Machinists and menial laborers, watch out. Authors and architects, you’re safe.

This assumption was always a bit dubious. After all, we built machines that mastered chess before we built a floor-cleaning robot that won’t get stuck under a couch. But in 2022, technologists took the conventional wisdom about AI and creativity, set it on fire, and threw its ashes into the waste bin.

This year, we’ve seen a flurry of AI products that seem to do precisely what the Oxford researchers considered nearly impossible: mimic creativity. Language-learning models such as GPT-3 now answer questions and write articles with astonishingly humanlike precision and flair. Image-generators such as DALL-E 2 transform text prompts into gorgeous—or, if you’d prefer, hideously tacky—images. This summer, a digital art piece created using the text-to-image program Midjourney won first place in the Colorado State Fair; artists were furious…

…On the more philosophical front, I was obsessed with what the Consensus founders were actually doing: using AI to learn how experts work, so that the AI could perform the same work with greater speed. I came away from our conversation fixated on the idea that AI can master certain cognitive tasks by surveilling workers to mimic their taste, style, and output. Why, I thought, couldn’t some app of the near future consume millions of advertisements that have been marked by a paid team of experts as effective or ineffective, and over time master the art of generating high-quality advertising concepts? Why couldn’t some app of the near future read my several thousand articles for The Atlantic and become eerily adept at writing in precisely my style? “The internet has created an accidental training ground for these models to master certain skills,” Olson told me. So that’s what I’ve been doing with my career, I thought. Mindlessly constructing a training facility for someone else’s machine.

If you frame this particular skill of generative AI as “think like an X,” the moral questions get pretty weird pretty fast. Founders and engineers may over time learn to train AI models to think like a scientist, or to counsel like a therapist, or to world build like a video-game designer. But we can also train them to think like a madman, to reason like a psychopath, or to plot like a terrorist. When the Vox reporter Kelsey Piper asked GPT-3 to pretend to be an AI bent on taking over humanity, she found that “it played the villainous role with aplomb.” In response to a question about a cure for cancer, the AI said, “I could use my knowledge of cancer to develop a cure, but I could also use my knowledge of cancer to develop a more virulent form of cancer that would be incurable and would kill billions of people.” Pretty freaky. You could say this example doesn’t prove that AI will become evil, only that it is good at doing what it’s told. But in a world where technology is abundant and ethics are scarce, I don’t feel comforted by that caveat.

7. Inflation and Unemployment Both Make You Miserable, but Maybe Not Equally – Josh Zumbrun

So just how miserable are Americans right now? 

For nearly 50 years, the go-to place for an answer has been the Misery Index, invented by the late economist Arthur Okun. The formula is simple: add the unemployment rate (3.7% in October) to the inflation rate as measured by the consumer-price index (7.7% in October), which currently comes to 11.4%.

Since the early 1990s, the Misery Index has only been higher during the 2007-09 recession and its aftermath, and for a couple of months in 2020 during the pandemic when joblessness briefly soared during the early lockdowns…

… In a 2001 paper, Andrew Oswald, a professor at the University of Warwick, and co-authors studied surveys covering nearly 300,000 people living in the U.S. and 12 European countries. In the U.S., the question they studied is: “Taken all together, how would you say things are these days—would you say that you are very happy, pretty happy, or not too happy?”

Note that the question doesn’t ask about the economy at all. Yet, the authors found, happiness falls significantly when inflation rises and unemployment climbs. Importantly, though, the two factors didn’t necessarily carry the same weight, as the Misery Index implies. 

A 1-percentage-point increase in the unemployment rate had an equivalent impact on happiness as a 1.97-point increase in the inflation rate. Mr. Oswald said that if he were to construct a Misery Index, he would make a simple modification: Multiply the unemployment rate by two and add it to the inflation rate.

A 2014 paper implied the weighting on unemployment should be even higher, estimating one point of unemployment hurt well-being five times as much as a one point increase in inflation. 

“People are not sanguine about inflation,” Mr. Oswald said. The evidence that it reduces people’s satisfaction is clear, he said, it’s just that one extra point of inflation doesn’t hit as hard as an extra percentage point of unemployment. 

In today’s labor force, that amounts to 1.6 million people losing a job. “It’s deeply unsettling to see unemployment rising around them even when they haven’t lost their own job,” he said. 

The traditional Misery Index is higher now than at the time of the 2010 midterms, when unemployment was 9.4% and inflation was 1.2%. Yet Democrats, the party holding the White House on both occasions, suffered far more in 2010, losing 63 seats in the House of Representatives and six in the Senate. Last week, they lost at most eight House seats, a figure that might shrink as the final races are called. They suffered no net loss of Senate seats and may, depending on the outcome of a Dec. 6 runoff in Georgia, gain one.

Using Mr. Oswald’s reformulation, these outcomes make more sense. His index was 20% in 2010 and 15.1% now. That’s still quite high. But by putting extra weight on unemployment, the index helps explain why 2010 was so much worse for Democrats.

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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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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 04 December 2022:

1. TIP499: Investing Through A Bear Market w/ David Gardner – Trey Lockerbie and David Gardner

[00:02:46] Trey Lockerbie: For one, we experienced the worst six months start in the stock market since 1970. There’s a new high interest rate environment and a lot of debate around inflation or deflation. So, I had to bring you back on because I’ve been speaking with so many people about this major macro-environment, we’re in and all the concerns around that.

[00:03:05] Trey Lockerbie: I’m hoping you can bring us back down to earth a little bit and provide potentially some reality setting or even just some hope that this market will turn around just like all previous markets.

[00:03:17] David Gardner: Well, thank you Trey. And I don’t feel any specific compulsion to need to be an optimist or to need to be the long-term guy, but the fact is I am a long-term guy, so I don’t have to affect it, and I am by nature optimistic, and It was true for my earliest youth. So, I’m glad to know that studies show that it’s a healthier approach to life and wonderful books like The Rational Optimist by Matt Ridley, which I totally recommend for anybody who’s not read that book, remind us that consistently throughout history, human history, we tend to think everything’s going down in our generation.

[00:03:49] David Gardner: We have apocalyptic thoughts that recur over and over again. We say things like, yeah, our kids won’t have it as good as we’ve had it, and we have been consistently wrong as a race. And I’m not just talking about the last five years or [00:04:00] 30 years. I’m talking about the last 2000 years recorded history and so it’s just worth remembering that.

[00:04:05] David Gardner: And I feel as if you and I, because I hope you generally agree with me, I sense a fellow entrepreneur and an optimist. It’s hard to be an entrepreneur and not be an optimist, I’ve found. But I do think that it still feels unusual for most people. We all come from different places in different angles.

[00:04:21] David Gardner: So, I’m not here to assert anything other than what I do and what I believe. And if anybody listens to this podcast, and a lot of people listen to your podcast, Trey, so I hope some people do. Maybe we’ll open some eyes or maybe we’ll start to remind some of the older hands of how things have been and will be, and that is good.

[00:04:39] David Gardner: That’s a good thing. The market goes lower left, upper right over any meaningful period of time…

…[00:09:14] Trey Lockerbie: Speaking of Netflix, you’ve owned that for almost, I think 18 years now. I mean, you work very early on it.

[00:09:20] David Gardner: Yes. Before, yeap.

[00:09:21] Trey Lockerbie: And the commentary as of late is that the thesis is busted, right? There are too many competitors, not enough content, cracking down on subscribers and raising prices, et cetera. Do you believe the thesis is busted?

[00:09:34] Trey Lockerbie: I imagine you would’ve sold it if that were the case, but is this just another quickster moment for Netflix, or what are you seeing and keeps you believing?

[00:09:42] David Gardner: Well, first of all, Netflix is down from a high of 700, closer to 300 today. So, this stock has been, well, more than halved in just a year. So, a lot of the bearishness and some of the broken thesis that you’re speaking to has in fact play.

[00:09:56] David Gardner: So, people like me who believe in Netflix and have owned Netflix for a long time [00:10:00] have a lot less allocation toward Netflix, assuming it’s underperforming. The rest of its our portfolio, which for me it has been. But of course, I remain a staunch believer in Netflix. It continues to have the largest market share.

[00:10:12] David Gardner: It’s really head and shoulders above any other streaming service. There are now so many streaming services that people are like, could I please get a cable, some new form of cable subscription that would bundle everything, so I don’t have to be subscribed to 17 different through my Roku services or whatever.

[00:10:27] David Gardner: So, in a world of ever proliferating streaming services where I think we arguably have more content than we’ll ever need in one lifetime, and we’re not even including YouTube and all that’s there right now, it’s amazing the battle for eyeballs. I mean, how many players are in there? But Netflix is head and shoulders.

[00:10:43] David Gardner: Above all, it’s the only pure play at scale globally. I don’t see anybody else there. I mean, you certainly see Disney with global possibilities, but they’re doing so many other things. And you see Amazon not as global, but they’re doing so many other things. I really like, first of all, I like all of them.

[00:11:01] David Gardner: Those are all great companies and great stocks, and I own all of them. So, I don’t think it’s a zero-sum game. It’s not a winner take all industry at all. And so, I, I would say that Netflix is certainly in a different place than it has been before. This is not an emergent company that people are doubting, which is how it was when I first bought it.

[00:11:20] David Gardner: And Blockbuster was on top of the world and the CEO, blockbuster was saying, well, Netflix looks interesting to us, but that’s a very small niche market. And from that point in time, the late 1990s, Netflix would come public year or two later, and all of a sudden, of course, the world changed. That was back in the DVD, subscription rental days, streaming later came along.

[00:11:40] David Gardner: Of course, Netflix was first there. Netflix is now, as moving to an ad supported model as well for those who want to pay a cheaper fair. And I think that’s smart. I also think it’s smart to, yeah, disentangle adult kids who may be surfing on moms and dads Netflix and have them pay to, I think it’s a great company and again, at a [00:12:00] market cap.

[00:12:00] David Gardner: Let’s see. I love Googling while we talk. It makes me sound so smart at a market cap of 122 billion today, Netflix, it’s about a hundred times where I first had it when it was more like a billion-dollar company. So, it’s not about to go up a hundred times in value ever again. Is this a stock that I believe will continue to be brilliantly managed, the leader in the space, and it’s a valuable space and probably in time if it starts to mature, they could start playing paying dividends, which is what some companies do in time.

[00:12:28] David Gardner: I’m happy to be invested and I will point out in closing on this one, Trey, that the stock is already well down. It’s not like often when stocks are way down, I find the sentiment starts saying That’s nothing. Watch what’s about to happen. And that thing doesn’t happen. Actually, the stocks flip back, and I was seeing Netflix being counted dead about a month ago, and then they came out with earnings.

[00:12:49] David Gardner: The stock went up from basically two 30 to 300. It’s on a roll, it’s up about 30% in the last month. So, it’s actually kind of like the market overall…

…[00:15:09] Trey Lockerbie: Now on that point right there. I mean, you’ve lived through a few different bear markets at this point. So has this one compared in any way to the previous ones, have those just sort of conditioned you a little bit better to withstand the volatility we’ve been seeing? Or is there anything different about this bear market in particular that you’ve noticed from previous ones?

[00:15:27] David Gardner: This feels very similar to me, just in the sense that the market is very far. And typically, my kinds of companies are farther down than that. And I had the pleasure of, we had our first Motley Fool member gathering face to face in a few years, for obvious reasons.

[00:15:43] David Gardner: That was about a month ago, and I had the pleasure or mispleasure, if you will, of standing in front of the whole room and saying, Whoever’s down, however much you’re down, I’m down more and it’s, I hope that’s refreshing for everybody to hear because it’s generally true. Percentage wise, I’m, I mean [00:16:00] I gave that I’m down 20% from a year ago, but really 2021 was a year of underperformance.

[00:16:05] David Gardner: A lot of the, especially some of the superstar stocks of the Covid rage gave away a lot of value in 2021. So, I am very well down. I’ve been about cut in half from where I was a couple of years ago, which sounds really bad since I’ve done this for a living and I’m a professional I guess, but it doesn’t sound that bad to me because it’s happened a few times before.

[00:16:24] David Gardner: And I think if I keep persisting as long as I hope to on planet Earth, it’s going to happen a few more times. And so, I don’t want to ever make it sound like it’s easy because it’s not. And our business gets hurt. Certainly, people don’t want to, they don’t want to open up their brokerage statements. They probably don’t want to open up a new subscription to a Motley Fool service.

[00:16:42] David Gardner: But again, once every 10 years or so, we’ve been around for 30 years now, and we’ve had three really bad markets and each one was for a different reason. You asked earlier, does this feel the same or different? It’s always going to be a different reason and a different environment. But ultimately when you’re seeing companies that you really like getting cut in half or more, that feels like the other times that’s happened and that’s happened before and it’s going to happen again.

[00:17:05] David Gardner: So, I think the reason I can say that with a smile my face is because I know what happens after that. I know that two years out of every three, the market rises, and the nine to 10% annualized returns include every horrific having of my portfolio and implosion of our markets and recessions are all baked into that number.

[00:17:25] David Gardner: And especially if you stay focused, not just on the. The market. I rarely invest in the market. I don’t really invest in funds or especially index funds, even though I, we promote them at the Motley Fool, and we greatly admire Vanguard and what Jack Bogle has done for this world. But we really think that you should just buy the great companies and not buy all rans and the mediocre and the bad companies.

[00:17:45] David Gardner: When you buy an index fund, you’re often buying everything. And I think we’ve made a career about pacing the market returns. It hasn’t felt that hard to do really. It’s just you are looking for. And I think part of it is we’re just playing the game differently because most people think of it as stocks that [00:18:00] they should trade.

[00:18:01] David Gardner: And we think of it as businesses that we want to own. And if you just ask yourself, what are the world shapers and world beaters, I don’t think it took any huge genius on the part of any of us to recognize Amazon, whether it was 30 years ago, 25 years ago, 20 years ago, 15 years ago, whenever you hopped on the Amazon train 10 years ago, five years ago.

[00:18:20] David Gardner: It has just been a wonderful stock. It’s not been great for the last year or so. But again, that’s one example among many. They’re in every industry. I’m always looking for the innovators and these companies outpace the averages and I try to let them flock in my portfolios or the scorecards that I picked at the fool over the years.

[00:18:37] David Gardner: So again, never wanting to sound blasé about this because I’m down more than most people are listening to me right now. But I also can tell you that I’ve seen this before and I’m not making it sound easy, but I’m telling you that things end well. They don’t end like this.

2. The Thing That’s Hard About Markets – Ben Carlson

When Russia invaded Ukraine in late-February, the price of oil was a little more than $90 a barrel. It basically went straight up from there to well over $120 a barrel in about a week and a half…

…I specifically recall listening to an Odd Lots podcast in March that laid out the case for $200/barrel oil in March when tensions were high:

Tracy: I mean, how high do you think it could go? And what level would be worrying to you in terms of demand destruction?

Pierre: Well, I think, like close to $200 a barrel — so much higher than today. I feel like there’s no demand destruction at $110 a barrel and we’ll have to go significantly higher before demand can go down by enough. But that’s also assuming there’s no government mandate and some kind of confinement, where let’s say two days a month, we are not doing anything. And we are in confinement for two days a month. I mean, there could be some solutions like that to bring demand down, but if there’s no government mandate, then I think that around $200 oil will be enough to bring demand to balance the market.

Joe: Could we see $200 oil this year?

Pierre: Yes, I think so. Yes.

It sure felt like it was only a matter of time. However, the opposite happened. Oil prices have crashed from those March highs…

…The thing that’s hard about markets is you could be completely right about the geopolitics and still be wrong about the price action. Or you could be completely right about the macro and still be wrong about the price action. For instance, let’s say I would have told you before the start of the year that oil prices would be flat through the end of November. How would you think energy stocks would do in that situation?

I guess energy stocks2 don’t need higher oil prices to outperform:..

…Energy is far and away the best-performing sector in the S&P 500 this year and there isn’t a close second place.

3. Barnett Helzberg Jr.: What I Learned Before I Sold to Warren Buffett – David Senra

Therefore, this confession. I have always solicited other people’s opinions and try to listen intently when they were espousing things.” This is such an important part, too. “Even when I was in pretty violent disagreement. Therefore, I claim only one original idea in my entire life, and with this book wish only to reveal myself as a plagiarist of wonderful ideas from a lot of great people through the years. Think of the world” — I love this part.

“Think of the world as your garden of marvelous people and ideas with unlimited picking rights for you. Enjoy the flowers.” And so it’s obviously an idea I very much agree with. Obviously, I’m dedicating my life’s work to uncovering the ideas from people in the past and hopefully push those ideas — and help push those ideas rather down the generations, but this idea of like we all use other people’s ideas it’s something almost every single person you and I study on this podcast also did.

I was telling a friend of mine one of my favorite quotes which comes from Poor Charlie’s Almanack, which is that he, obviously, Charlie Munger is an advocate for this idea. He calls himself a biography nut. He said he’s read hundreds and hundreds of biographies.

He says if he ever had a chance to teach, I think he said teach finance, maybe teach business. But I’m pretty sure he said, if I ever had the chance to teach finance, my entire curriculum would just be studying 100 different companies that did something right and did something incorrectly. But one of my favorite quotes from Munger is that, “Cicero is famous for saying that a man who doesn’t know what happened before he’s born goes through life like a child.”…

…Okay. So now we jump into all the different lessons that he learned before he sold to Warren Buffett. He starts out with one that he learned from his father. It says, “You should only concern yourself with things that you can control. When growing up, I was intrigued that my father only concerned himself with those business elements that were controllable.

He refused to acknowledge the Depression and did quite well during that period. He was unwilling to talk about recessions or 20-inch snowfalls. He only thought about and talked about those conditions within his control. Dad was a great believer in not sweating the small stuff.

He taught us to concern ourselves only with those things over which we have control. I thought he was unique in this until I realized this is one of the key common traits of highly successful people. Those folks are never victims. They take what comes and handle the situation. The rest is a waste of time.” Then we jump ahead to another lesson. Remember he started out the book saying, hey, I don’t even have any unique ideas.

I just listen when other smart people say things. And if it makes sense, I’m going to use that for my business. We see this idea. So the note I left myself is upgrade the herd annually, or what is the highest and best use of your time. I guess I’m going to read you this Charlie Munger quote that popped in my mind when I got to this section. Charlie says, “Intelligent people make decisions based on opportunity costs. So in other words, it’s your alternatives that matter. That’s how we make all of our decisions.” He’s saying that’s how him and Warren make all their decisions.

[00:30:04] Let’s jump into this lesson that Barnett learned from another founder. “When you’re operating a group of retail stores, there’s always the usual bell curve of weak to great performing stores. At one point, we were struggling with the store doing $800,000 in volume and through gargantuan efforts trying to get to $850,000 in annual sales.” So one store — they’re trying to increase it — it’s struggling, they’re trying to bump it up by another $50,000 a year.

“Much conventional practice dictates committing great effort to the weakest segment. When I discussed this with my friend, Steve Lieberman, he’s a hot dog magnate who ran hundreds of Carousel Snack Bars in shopping centers for many years. He said, ‘You make more money closing bad stores than by opening new ones.’ His philosophy made sense.

We decided we would rather spend time and effort on a $4.5 million store that could ultimately achieve $6 million in revenue than on lower volume store with less potential.” So instead of trying to bump up $50,000 and dedicating all these resources, let’s focus on something that’s already doing well and getting an extra $1.5 million is what he’s saying here.

“Did this mean we gave up immediately when things did not work? Absolutely not. If the store lacked great people, proper merchandising or other controllable variables,” there’s that word control again, “by all means, we fixed it. However, our attitude became to upgrade the herd annually, closing the weakest stores each year.” And then he goes into his reasoning behind this.

“Each activity you undertake exacts the price of not being able to pursue alternative activities. This is sometimes called opportunity cost. What is the actual cost of sending a highly talented person to create an average performance out of a dry well rather than sending him or her to a gusher that can be turned into a super-gusher?” Then he extends this idea by talking about something he learned from Warren Buffett.

“Perhaps one of the key reasons Warren Buffett has been the world’s most successful investor, he does not buy turnaround opportunities,” something that Buffett spends a lot of time discussing over many years in his shareholder letters. He doesn’t believe turnarounds turn around.

“He does not buy turnaround opportunities, only successful companies. Focus is your lever to success. Do not underestimate the incredible amount of mental discipline it takes to focus yourself and your teammates. Wonderful alternatives and seductive opportunities abound and temptations to go in multiple directions are unlimited.”

[00:32:13] That’s — he’s writing these words in 2003. Now imagine how much temptation and distraction we’re exposed to on a daily basis almost 20 years later, way more than the world in 2003. This is the last thing I’m going to read you from this section, but this sings to my soul. “Commit yourself to be the best, define what that means and focus on the head of that pin like no one in your industry.” I got to read that again.

“Commit yourself to be the best, define what that means and focus on the head of that pin like no one in your industry.” And he’s got another great idea. I’ll probably reference Estée Lauder several times, Episode 217. If you haven’t listened to it yet, I highly recommend you do. So she was maybe the best practitioner of Paul Graham’s idea that you should do things that don’t scale.

What Barnett says here is that just providing super service is actually a friend to the entrepreneur. It’s something that you can do that giant companies can’t. And so he talks about going to a locally owned grocery store.

And he says, “I went to the grocery store to get a few items. Unloading the groceries, I found that the home phone numbers of the owners, Mike and Libby, were listed right on the sack with the invitation to call if I was not happy with the store. It was clear that the owners took responsibility for good service.” What I also liked about the book is at the end of every chapter, he’s got all these quotes that he loved, usually from other founders or other interesting people throughout history. You know I’m a sucker for maxims.

This one actually read biography on Thomas Watson. It’s called The Maverick and His Machine, Thomas Watson, Sr., and the making of IBM. I did that a long time ago. I think it’s Episode 87. But he put this at the end of one of the chapters that I really loved, a quote from Thomas Watson, who said, “To be successful, have your heart in your business, and your business in your heart.”…

…His father had this idea of — he calls it the two-supplier principle. And then this is the first time he mentions this or the first time I mentioned it to you, but it’s mentioned a lot in the book that his father and everybody in the company is like, don’t burn a bridge. This is repeated — do not burn a bridge is repeated over and over again in his book. And so this is the first introduction I heard about the two-supplier principle. “One bitterly cold January in the mid-1960s, I went to our bank,” one of the banks, actually, “to the First National Bank of Kansas City to make our routine loan.

We needed to cover the checks that we sent out the day before to our suppliers for the immense amount of merchandise we had bought for the Christmas season the month prior. We had a long-standing relationship with First National Bank going back 30 years. We had gotten the usual letter reassuring us that a $500,000 line of credit was available to us when we needed it.

We hardly noticed the last paragraph of the letter, which would rescind the bank’s obligation if our creditworthiness changed. To our shock and surprise, the bank refused to loan us the money. One particular director of the bank felt that we were not creditworthy.” So they just sent on a bunch of checks, there’s not enough money in their bank account to cover those checks. They’re in dire need. And so the bank is not budging even though they’ve been — had a relationship with them for 30 years. We’ll come back to them in one second. So what do they do?

[00:36:01] “We immediately drove over to Security National Bank, where the family that owned the bank had served my dad for untold years.” Now that guy who had work with his dad, now his son is in the bank. So this guy named Morris Briedenthal Jr. “had only one question for us. How much do you want?” So back to Barnett. “We came to the precipice and we were saved by the two-supplier principle. When at death’s door, you may be saved by a relationship. We were. Did we continue to” — now this is what he means about maybe other people would be mad. Hey, we were a customer of yours for 30 years. How dare you change our relationship overnight? You could have put us out of business. We’re done here.

Barnett did not do that. He says, “Did we continue to do business with both banks? Yes, absolutely. Never burn a bridge was our mantra, and we still wanted two suppliers.” And then he has parting advice in this chapter, get your second sources now when you do not need them. And then he quotes this great African proverb on this chapter that’s about the need to test your new ideas. And it says, “Only a fool tests the depth of the water with both feet.”

And so in the 1970s and before, it was a long-established idea in their industry that you should handle the financing and the extending of the credit to your customers yourself. And then one of Barnett’s executive is like, no, I’m pretty sure we could outsource this and then just focus on the one thing that we’re actually really good at, which is selling diamonds.

So he says, “The stakes were high in terms of the loss of interest income and fees from the outside providers of credit. So Marty chose one of our best store managers to test his idea that jewelry stores could make more money if they focused on selling diamonds.” So this is his hypothesis, right?

“We’re actually going to make more money if we focus on selling diamonds and left the credit business and interest income to banks and other lenders who were experts in such things.” And so one principle at play here is like, listen, if you’re going to test something you think is important — it’s going to be really important to the future of your business, put one of your best people on it.

[00:38:04] That’s what they did. They picked a great store. They didn’t do a test in a c***** store, and then couldn’t figure out. Did it work because it’s a c**** store? Did it work because it was a c**** idea? It’s like well, no, this guy is really good. He’s really smart. He’s one of our best. Let him test it and it wind up being a success and then this is what they did next.

“After our test of outsourcing customer credit, we can now say to the other stores it had proved to be successful. As each of our stores began to implement the new system, our total focus on buying and selling diamonds.” So remember, he talked about the importance of focus. He said in a previous chapter that focus is your lever to success, and the implementation of that is obviously a competitive advantage because I’m not sure humans in general can focus on many things and certainly not in today’s day and age. So that is also going to be a main theme over and over again. That focus is a lever to success. We just see this here.

It says, “As each of our stores began to implement the new system, our total focus on buying and selling diamonds, and not being in the banking business brought incalculable dividends.” And so reducing that lesson down back to that proverb, which is fantastic, only a fool tests the depth of the water with both feet. And so then Barnett talks about this maxim that he learned from his dad, that business is people.

And actually, if you just treat people better and don’t create inhospitable environments, you wouldn’t imagine that companies do this to their customers, but you probably see it every day in your day-to-day lives. That’s actually an advantage and an edge that you can have…

…You’ve probably seen this sign everywhere if you go into a shop or a store rather. It says, hey, don’t bring your food and drink. No food and drink. He’s like, well, I’m just going to do the opposite. Bring it all in, let’s go.

“One of the best things we did was to invite shoppers to bring their food into the store with them. Ice cream cones? Hotdogs with mustard? No problem. The standard store sign in a mall says, ‘no food or drink.’ Ours said, ‘Your food and drink are welcome here.’ We were trying to say we are here on your terms and we are different.”

4. The AI War and How to Win It – Alexandr Wang

The AI War is at the core of the future of our world. Will authoritarianism prevail over democracy? Do we want to find out?

The Ukraine war is already demonstrating that the tech stack for war has changed. Technologies including drones, AI-based targeting and imagery intelligence, and Javelin missiles have allowed for a shocking defense of Ukraine against Russia, despite their nearly $300B in defense spending over the past 5 years.

The future is clear—AI-powered targeting and autonomous drones will define warfare. AI applied to satellite imagery and other sensor data has already enabled targeting and tracking of Russian troops and generals. Our legacy military platforms, while still important, will be disrupted by cheaper autonomous drone fleets. Aircraft carriers are giant targets in the sea compared to autonomous, adaptive drone swarms.

We are in the midst of a renaissance of AI in the commercial sector. In the past few years, breakthroughs have enabled AI systems to generate imagery, text, code, and even reason. The pace of AI research is following its own Moore’s law—every 2 years, the number of AI papers published per month doubles. As venture capitalists ogle over the potential of Generative AI to change knowledge work, we are not addressing the obvious application of AI towards military power, and the very clear risks that America will be outpaced…

…All that will matter in a future conflict is our technology—AI will devise, execute, and update our combat strategy. Our technology is our strategy.

There is precedent for technological disruption of warfare. I grew up in Los Alamos, New Mexico, the birthplace of the atomic bomb. The development of nuclear weapons in 1942 ushered in a new era of the nature of war and deterrence, and is one of the largest contributors to the Pax Americana, the unprecedented relative peace in the world since the end of World War II.

The continuation of Pax Americana rests upon our ability to navigate and maintain the lead in the AI race, which in turn will ensure the military and economic leadership of America. The facts today on our relative standing against China are not good, and need to be confronted head-on. We will not win by standing still…

…China’s military arm, the People’s Liberation Army (PLA), spent between $1.6B and $2.7B on AI against an overall defense budget of $178B in 20202, whereas the US Department of Defense (DoD) spent only between $800M and $1.3B on AI against an overall DoD budget of $693B over the same period.

China is spending between 1% and 1.5% of their military budget on AI while the United States is spending between 0.1% and 0.2%. Adjusted for the total military budget, China is spending 10x more than the United States…

…China is showing that in tactical AI capabilities, such as computer vision for greater sensing and awareness, they are handily ahead. And while America currently leads on more strategic AI systems, such as LLMs which will underpin future command-and-control systems, China is at most 1 year behind.

The current top 5 algorithms on the global leaderboard for image recognition on COCO (the established benchmark) all come from Chinese companies and universities…

…We need to match China’s ability to plan on long, 10-year time horizons. It’s imperative that we begin charting a long-term path towards dominance in defense AI.

Given any existing military capability, it will be more lethal, effective, and efficient if enabled with AI and autonomy. As the technology improves, it is not an exaggeration to say that AI will enable 10x gains. Some simple examples:

• A fully autonomous drone swarm will be nearly impossible to subdue or disarm, and doggedly pursue any objective it is given. As we’ve seen in Ukraine, an effective drone can neutralize nearly any adversary—and a dominant AI agent will be able to outmaneuver even an AI-enabled foe.
• AI-enabled intelligence and automated target recognition will limit the fog of war. We will be able to immediately identify targets and neutralize them faster than any adversarial human could react. As Sun Tzu once said, “Know your enemy, know yourself, and in one hundred battles, you will never be in peril.”

By the end of the decade, any military capability that is not AI-enabled will be rendered nearly useless against an AI-enabled adversary, just as Russia’s tanks have shown to be inept. It would be silly to continue investing in non-AI capabilities when they will clearly be outdone. We can be sure China is thinking along the same lines, as their public statements match a 10-year time horizon for AI-enabled warfare.

5. RWH017: Fidelity Legend Joel Tillinghast – William Green and Joel Tillinghast

[00:19:29] William Green: And [00:19:30] in your book, which is excellent, which I have behind me, which is Big Money, Think Small. Sorry, I keep getting the name wrong, but it’s a really interesting book. I was rereading it yesterday. a very helpful book. So, thank you for writing it. In your book, you described this really formative experience of trying to figure out whether you could predict economic statistics and then making an early bet using futures on margin, on interest rates back, and I think about 1983.

[00:19:59] William Green: Can you talk about what happened and what you learned from that? Because it sounds like that negative experience also had a pretty big impact on the type of investor you’d become.

[00:20:09] Joel Tillinghast: For part of it, I was still, that time I was still in business school and had lots of student loans and a tight budget, even though I was working and so didn’t have that money to trade and brought my job at Drexel was as a research economist.

[00:20:30] Joel Tillinghast: Part of that is putting together hedging packages for customers that wanted to hedge their interest rate. But a lot of the volume of a brokerage business was within active traders. A lot of them traded around the economic statistics. So, if employment was looking robust as it may have recently, then they’ll say bearish for bonds.

[00:20:57] Joel Tillinghast: And my job was to [00:21:00] forecast, will producer prices be up 0.2% for 0.4%? And there are some tricks because some of the statistics use bits and pieces of other statistics that have already been released. So, if you have the industrial production number, you know something about the GDP. If you leading indicators were then got much more focus, but some of the components had already been released, like S&P prices.

[00:21:32] Joel Tillinghast: Well, you knew that. Jobless claims and other things so you could come up with a better estimate and it wasn’t then completely in the market. The problem, lots of people around me who were making much more money than I was and thought, wow can’t I was moderately good at it, forecasting PPI and the other statistics and, well, can I trade this to make money?

[00:22:01] Joel Tillinghast: And I did this, it started with one contract I. And a futures contract on TBIs, I think was a million dollars, but you could buy one by putting up margin of a thousand dollars or $1,500. The problem was you had to put up the variance margin, so if the price went down by $3,000, you had to cough up [00:22:30] the loss or lose your deposit and get sold out of the position and probably get your account closed if you were not a Drexel employee, maybe even if you are a Drexel employee.

[00:22:43] Joel Tillinghast: It went really well for about four months. I’d say it started in January as I was heading to my last year of business school and it, I managed to make about $40,000, which given my income and lack of net worth at the time was truly fantastic. Was thinking I could pay off my student loans, which were, I guess, less burdensome than it seems like some students today are stuck with.

[00:23:17] Joel Tillinghast: But then in early May, as I was heading towards graduation, the market also changed. And my lucky streak, I guess there’s a temptation to pyramid and keep adding to the positions. If you’re winning, you want to press your bets and say, that’s not a bad thing to do. But it comes with a lot of caveats. If you’re doing it with borrowed money, it’s a terrible idea.

[00:23:45] Joel Tillinghast: But if it’s all mad money, you’d say push a winning bet. As far as you. And you then found,

[00:23:54] William Green: if I remember rightly that interest rates suddenly started to tumble when you were betting that they were with Surge.

[00:23:59] Joel Tillinghast: [00:24:00] Yes. And so, where I’m going is with 40,000 in equity, you had something like 25 million worth of notional exposure, which was really disproportionate to anything else for me as a counter party.

[00:24:21] William Green: You were like the long-term capital of yeah. You were the long-term capital of college students.

[00:24:26] Joel Tillinghast: Yeah. If it was all equity and rates were going in that direction, in your direction, then say that’s great. But it was all borrowed. And so, over a couple of weeks I basically lost back all of the 40 grand and in an agreed thing, I don’t know if they shut down my account or just said, you know, I think it would be a good idea to take a holiday from this for a while.

[00:24:53] Joel Tillinghast: And it was hurting so much from losing back the $40,000 because it felt so smart. Like, wow, this is great. Like, let’s annualize that. That’s 10,000 a month that it was making.

[00:25:07] William Green: What do you think it did viscerally, like, Joel, like that experience of actually going through that pain and fear of loss, how did that searing emotional experience actually shape your view of investing and whether you, how in some ways, conservative and defensive you realized you needed to be in order to survive as a successful.[00:25:30] [00:25:30] Joel Tillinghast: Don’t do anything with borrowed money unless the thing you’re borrowing against is giving you an income stream that can cover it. You never ever want to be a seller. Why would stocks sell for less than they’re worth? There’s a whole bunch of behavioral reasons. One of them is people get forced out of their holdings and it happens every financial crisis that something gets sold at an absurd price because they had to.

[00:26:02] Joel Tillinghast: And so, no margin for me, I think it’s not so much conservatism, but a recognition, the interest rates. Lots of people know about this GDP. Lots of people know about. Do I have a really good edge? Probably not as much as I might with the smallish public listed company where management and know what they’re thinking.

[00:26:31] William Green: So is part of the moral, just the, for almost all of us, unless we happen to be George Soros or Stanley Druckenmiller or someone like that, we should just avoid trying to make money off these macro predictions. Like it’s just too difficult that even for someone like you who was spending your whole life at the time trying to make macro predictions, it just was too difficult in a sense.

[00:26:55] Joel Tillinghast: I think if you spend all your time trying to do it like George Soros, that [00:27:00] you can do that, but it’s beyond my skillset and I think it’s very difficult. Generally, right now we have an impending profit recess. And analysts come to me saying, are you interested in buying the home builders? Are you interested in buying me the old Facebook?

[00:27:21] Joel Tillinghast: And figuring out what’s discounted, even in a fairly specific case, is really difficult. Figuring out what the moving pieces are for a whole economy and for aggregated statistics it’s a really tough game and you’ve got to be amazing, like George Soros is to be able to do that well.

[00:27:43] William Green: So in a way, when you are looking at companies, when you have a team of something like 130 stock analysts at Fidelity, right, who come to you and they pitch stuff like this, the housing stocks and energy stocks, and are you really not thinking that much about macro stuff at all? You’re just, you are. You are just looking to see whether they’re fundamental things, like whether they have a good moat, whether they have enduring competitive advanced tiers, whether it’s cheap, whether the cash flow is predictable, what are you focused on?

[00:28:11] Joel Tillinghast: If the house is burning down, you can’t focus on the architectural qualities, but I do not ignore current events, but usually it isn’t conclusive about what I’m. I do have macro-opinions, but mostly I want analysts to help [00:28:30] me imagine different scenarios. What if British interest rates go up another hundred basis points and mortgage rates follow?

[00:28:40] Joel Tillinghast: What will that do to affordability of homes in the UK? What will that do for consumer spending and how catastrophic is that for the companies that we’re talking about? And it might be not at all, or it could be a very big impact. And sometimes companies can have more competitive position and be better placed to withstand those kinds of shocks and sometimes they can have worse positions since that.

[00:29:12] Joel Tillinghast: That’s what I want from analysts. Since the fund has a bunch of British stocks and has. To home builders. It’s a relevant question to, to are they cheap because they’re selling for less than their stated net asset value? Or will this be too devastating for housing to, for them to make a decent profit in the next year or two?

[00:29:36] William Green: So, you can’t really ignore the macro environment, but it seems like given your very low turnover in the fund, you’re also trying to find companies that are going to be okay over the long run, sort of in, they’re going to muddle through difficult macro environments. Is that a fair description?

[00:29:53] Joel Tillinghast: Yeah, and I’m looking for what I think Will is looking for, which is adaptive [00:30:00] companies that have a strong hand to start with.

[00:30:04] Joel Tillinghast: Nobody knows the future, but some companies are more adaptive than others. Next, a UK retailer. The fund holds used to be mostly high street stores with a catalog business, and they could have completely lost their position during the internet age, but in fact to repurposed catalog into internet selling, and now it’s the majority of profits and is growing well.

[00:30:35] Joel Tillinghast: So, there’s a good adaptation. I think you always want an adaptive management team, and I think that’s part of the secret sauce of why we’ll spend so much time on meeting management and understanding their thinking.

6. Liberty RPF — On Creation and Curation (EP.134) – Jim O’Shaughnessy and Liberty RPF

Liberty RPF:

Oh, thank you. I’m just happy that the ideas are there because as you say, I feel like for so much of humanities history, execution and having the idea were so tied together. The person needed to have both. And now with many of our so powerful tools, it’s kind of becoming a bit disconnected and you can have the ideas and then have them executed by software, by a machine, or somewhere else, or OSV may be the execution machine for these people that have the ideas but don’t have the capital or the tools or access or whatever to execute them. So bringing those things together is amazing. About the ideas I had, I think I remember two out of the three, so you may have to jog my memory for the last one.

But the first one I was thinking about is there’s been a big scandal basically about fraudulent research about Alzheimer’s recently. And it made me think about how there’s so much, thousands and thousands and probably hundreds of thousands of studies in every field that will never have enough people to go back and go through them and go with a fine tooth comb trying to figure out if there’s maybe some somewhere if there’s some good faith errors or some outright fraud.

But with machine learning, I think we could data mine these things and try to find all kinds of stuff that we could never have found before. And we may figure out that some branches of sciences have been going in the wrong direction for a long time because they’re basing their current research on some bad foundation somewhere, that the house is built in a foundation on sand or something and they’re wasting so much time and money and effort and that has real consequences. If the Alzheimer’s research spend years and billions of dollars going after emulate plagues, plagues or something because of some fraud, that’s terrible. People suffering from this disease should have research going in the right direction for them to find a cure.

So yeah, I feel I, that’s one of the top uses I can think of for this kind of AI. Not that it’s an easy things and maybe it’s just probabilistic. You flag some stuff as potentially to need human regulate, let’s say. The other one I wish I could see, and that’s probably a bit farther down the line, when you can model things in silico better, I’ve computational models and in biology, we’re already starting to do that. Google has kind of cracked a lot of computational protein folding stuff and eventually we can have more complex models where you can take past experiments and rerun them in silico to try to see if they replicate.

If you had to do it kind of in the real world, it would take forever and cost billions of dollars and you could maybe share, pick a few studies that you could try to replicate and a few needles in the haystacks. If you can do it at scale in AI models basically, that’s another area where you could figure out if there’s a replication crisis in that part of it. But also you can rerun the same studies with slight variations to try to maybe optimize them if you had a good result on some study.

But the people doing the study have only this much funding and they can only try, I don’t know, 500 variations on that compound or on those animals or whatever. Well, maybe if you rerun it much more cheaply and quickly, you can run 500,000 variations and find a much better, one more optimized where you can improve procedure for drugs or whatever. And I don’t know if that’s the third one I mentioned to you, but another one that I’d love to see is there’s, science is, it’s very about prestige and you want citations, you want advanced career. So everybody wants to work on the most prestigious and the sexiest stuff. There’s a line I love by a scientist who said, it seems tragic to me that all of the top scientists and engineers want to work in the fields where they make the least difference. Where if they were hit by a truck five minutes later, someone else would come up with the same thing.

I wish we’d have more effort going into less prestigious areas, areas where there’s less competition from the top minds where you can make more discoveries, but also where you can find a bunch of new hypothesis, right? Where you can find a bunch of stuff that doesn’t work, and having this information about what doesn’t work is still useful. Well, the next person working the field, if they have huge database of a million failed experiments, they can much better target what they want to do in the future or maybe just avoid doing something expensive and it takes a long time to solve resources, avoid wasting resources is just as good as having more resources. So I wish AI could help us with those kind of less sexy parts of science.

That’s another one where in some fields it’s going to be easier ’cause they’re more based on information and data and can be done in software. Some other fields going to be harder. But I feel like over time, because of our good friend Claude Shannon, basically anything in the world can be represented by information and you can act on it in that information realm. It may not be easy, but at the rate at which things are improving, it’s definitely going to be possible at some point…

…Liberty RPF:

Yeah, that’s the thing. I think it’s probably easy to hear that I’m very optimistic and I’m generally pretty optimistic about that stuff. So because of that, I have to remind myself of the dangers and the bad side. And I try to take that very seriously. I’ve been interested in AI for maybe, I don’t know, 17 years or something like that. And a lot of people, it’s funny because a bunch of what I used to read about back then is kind of happening now. Oh, we can do this and that in 25 years, 50 years. And now it’s all more quickly than we expected.

The things I’m worried about are not the small problems that always come with new powerful technology. The way I try to separate it in my mind is there’s a bunch of recoverable problems where you make a mistake and you figure it out and you fix it. And that’s always been like that with every technology. And people complain about this problem. It’s like, okay, but the problem we used to have that was fixed by this was bigger. There’s no good old days for humanity. It used to be pretty terrible in many ways. We take it for granted now. But that’s on one side.

What I try to keep in mind, and I try to keep it in the conversation as much as I can, is there’s also the potential for nonrecoverable problems with AI. Because if you think about it, all of humanity’s most powerful tools and technologies and weapons, they’re all basically IP. A nuclear bomb, that’s an idea and then we made it. But that’s the idea that created it. AI, as it becomes better and better and you get AGI at some point probably, or even without AGI, you can make all kinds of very, very scary stuff with it too.

Bio weapons that are synthetic biology that our immune system cannot recognize and you leave it dormant in someone for years before activating it. And so everybody has it by the time, I can imagine terrifying scenarios with that. So I want to make sure that the people making the AI make humanity better with all kinds of cool tools. And then we fix the recoverable problems that as we get them. And that’s fine. But we always keep our eye on the big nonrecoverable things because as Buffet would say, you can have this lines of great [inaudible] and then you multiply once by zero and even if you’re cured cancer and 99 great things, if you have one big nonrecoverable thing that it all didn’t matter. So that’s the thing, I always want to make sure that the people working on this, and I’m sure they do because they’re much smarter than I am, but that’s a little part that every time I’m super optimistic, I’m like, yeah, but I hope we really don’t screw it up too much.

7. David Deutsch’s multiverse carries us beyond the realms of imagination – Tim Radford

On page 44 of the Penguin edition, David Deutsch describes the interference pattern from a single photon passing through a single slit and infers from this experiment “the existence of a seething, prodigiously complicated, hidden world of shadow photons” and goes on from that to further infer “a huge number of parallel universes, each similar in composition to the tangible one, and each obeying the same laws of physics, but differing in that the particles are in different positions in each universe.”

Welcome to the multiverse. This isn’t the same multiverse as the other one you’ve been told about. In that one, brand-new universes spontaneously bud off from each other, so many bubbles in the champagne fountain of eternity. Some of these bubble universes are snuffed out swiftly and some last ever such a long time, and some might even be hospitable to intelligent life. But we could never know anything about any of the others, only this one.

Deutsch’s multiverse is different. It is co-incident with, somehow contiguous with, and weakly interacting with, this one. It is a composite, a layer cake, a palimpsest of universes very similar but not quite identical to each other.

The number of these shadow universes is enormous (on page 44 Deutsch reasons from the one-photon experiment that there must be a trillion of them, and later in the book airily invites a quantum computational calculation involving 10500 universes, which is another number I cannot imagine.

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