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 06 October 2024:

1. My China’s Travelogue: China’s Super Apps, Western Brands, EV Boom and Challenges – Thomas Chua

When I arrived in Beijing, I activated my travel card directly through WeChat or Alipay, scanned a QR code, and that was it—smooth sailing. This seamless, mobile-first approach isn’t just a clever solution; it’s a necessity in a country of such a large scale. It offers an elegant, low-cost, and scalable system that is incredibly user-friendly and available in every city, eliminating the complications associated with traditional transport infrastructure.

In many ways, China’s lack of legacy infrastructure—often seen as a disadvantage—turned out to be a blessing. Without the entrenched banking systems and reliance on Mastercard or Visa that many developed countries face, China was free to reimagine its financial infrastructure from scratch. Where traditional payment systems come with a web of intermediaries and fees, China’s mobile payment platforms—WeChat Pay and Alipay—were built to be faster, cheaper, and more efficient.

However, a super app like WeChat does more than just make payments. It’s a super app, offering a vast range of services, from messaging and social networking to ride-hailing and food delivery. This super-app model emerged out of necessity, as many users in lower-tier cities had limited smartphone storage and data plans. WeChat’s mini-programs solved this problem, providing lightweight apps without requiring additional downloads or installations…

…I noticed all my rides were EVs, as were most cars on the road. I barely saw a dozen internal combustion engine (ICE) vehicles and didn’t come across a single petrol station in Beijing or Xi’an.

Curious about China’s rapid EV adoption, I began asking my drivers about the EV scene and learned that while many in Beijing drove 北汽 (BAIC) EVs due to their low price, the brand wasn’t highly regarded, with past models having plenty of problems. Surprisingly, I didn’t see many BYDs on the road in Beijing, but the drivers told me the brand was more prevalent in Shanghai and Hangzhou.

Tesla was perceived as superior but expensive. When I searched Baidu for the most popular EV, Tesla’s Model Y ranked as the most popular car in 2023.

How did China achieve a 54% electric vehicle (EV) penetration while other countries struggled? One key factor in Beijing is the stringent quota on car sales. To bypass the long wait for a vehicle (a local I spoke with has been waiting for over three years), consumers can jump the queue by opting for an EV instead of ICE or by having children.

When the policy of prioritizing EV purchases was first introduced in the mid-2010s, many people were hesitant to adopt EVs due to concerns over charging infrastructure. In those days, if you decided to buy an EV, you could have gotten the permit immediately. Today, those concerns have disappeared, with private companies installing charging points everywhere…

…During my trip, I visited several top consumer brands and observed foot traffic. Nike stores, to my surprise, were practically empty.

While not as deserted, Starbucks also saw far fewer customers than I expected. Li Ning had moderately more customers, while Lululemon was bustling. Even on busy streets, Starbucks was quiet, with consumers favoring milk tea brands like Chagee or 茶话弄 (Cha Hua Nong). Locals guessed this was due to the weak economy and Starbucks’ high prices.

However, this logic doesn’t fully explain Lululemon’s popularity despite its high prices. My guess is that Starbucks isn’t innovating fast enough for the fickle Chinese consumer. Competitors introduce new flavors at a much faster pace. And if Starbucks’ competitive advantage lies in its mobile app and loyalty program, it holds no advantage in China, where every food and beverage brand has a WeChat presence and can easily implement loyalty programs.

2. TIP661: Betting Big On China & Lessons From Bear Markets w/ Richard Lawrence – Clay Finck and Richard Lawrence

[00:12:23] Clay Finck: So is that a lesson of being more critical of the management teams you partner with? Did that help kind of hone in that side of the process?

[00:12:32] Richard Lawrence: Well, I think that number one, pyramid structures like that are out of bounds for any investor should never invest in a pyramid structure like that.

[00:12:42] Clay Finck: For those who aren’t familiar, what is the pyramid structure?

[00:12:45] Richard Lawrence: Well, the pyramid structure is the family owns 50. 1 percent of a public company that then in turn owns 50. 1 percent of another company and they own the asset. And the value of that asset is transferred to the owners of the top company and shareholders down below basically get nothing.

[00:13:03] Richard Lawrence: And they do it through any number of mechanisms. But paying dividends to minority shareholders, they’re generally not very big on. So you do it through excessive salaries, fees, costs. And so we learn, don’t invest in certain things.

[00:13:17] Clay Finck: Let’s transition here to chat about the Asian financial crisis. Many in our audience are likely aware of the crisis that occurred in Asia in 1997 and 98, but it’s not something we’ve covered too much on the show here.

[00:13:30] Clay Finck: I should also mention that many of our listeners are based in Asia as well. So you refer to this time period as nothing less than an economic nightmare, and there was really nowhere to hide for investors in these markets. How about you give us a sense of what was happening during this crisis that just made it so difficult for Asian economies?

[00:13:49] Richard Lawrence: Yeah, well, Clay, I was having a nice time here talking to you, and now you have to bring up memories of 1997 98. It churns my stomach still today with everything that went on. So, and really with the passage of time, there are fewer people that really understand the extent of the obliteration of value.

[00:14:09] Richard Lawrence: There was the inception of 97, 98 went back years that basically Asian economies were not able to self finance their growth. They were running what economists called current account deficits. And so they were having to borrow US dollars to fund their growth. And that US dollar debt built up, built up, built up.

[00:14:30] Richard Lawrence: And at the same time, they had fixed currencies. To the U. S. dollar. So all the companies were saying, well, if I can borrow dollars at the same price as my Korean won or my Thai baht or my Indonesian rupiah, I’m just going to do that. And so companies borrowed dollars, governments borrowed dollars, everybody was borrowing dollars and no one was hedging the currencies.

[00:14:54] Richard Lawrence: And then on July 2nd, 1997, the bot broke and it went from 25 eventually hitting 55 or 60 and it obliterated balance sheets. If you had 100 million of equity and 100 million of unhedged dollar debt, all of a sudden your debt grew 4 times in size and you needed 4 times the business to repay that debt. Of course, you didn’t have it.

[00:15:18] Richard Lawrence: Particularly when the economies were in recessions. To me, looking back, the dollar debt was really the biggest mistake. Current account deficits were a reflection of that. And the rupee in Indonesia went from 3000 to 15, 000. The one got obliterated within 6 months of July 2nd, 1997, the International Monetary Fund was called in to bail out Korea, Thailand, Malaysia, and Indonesia, which were the main markets at the time.

[00:15:49] Richard Lawrence: China was still a closed economy at the time. So they handled their own problems kind of internally out of sight the stock markets in various countries, like Thailand, Indonesia, went down nearly 90%. The real estate index in Thailand, which was a composite index, went down something like 98%. To do that, I calculated, you go down 80%, and then you go down another 80%, and then you go down another 80%.

[00:16:16] Richard Lawrence: That’s what going down 97, 98 percent is like. And so it was just a complete obliteration. We bottomed at Overlook. We went down from top to bottom about 65%. We really put a herd on our fledging investment management fund at the time, and we bottomed at about 4. 6 times earnings, about 0. 7 times book.

[00:16:37] Richard Lawrence: Interest rates rose because they had to protect their currencies. In Indonesia, the interest rates rose to 99%, and it didn’t stop the currency from declining. But the Indonesians couldn’t raise the interest rates higher because their banking system couldn’t accommodate three digits for interest rates.

[00:16:57] Richard Lawrence: And so interest rates meant nothing in Hong Kong, which had no dollar debt. It had a fixed currency to the dollar, but it was backed by the peg of the Hong Kong Monetary Authority and the government of Hong Kong. Interest rates went to 36 percent and they had no debt. So, you can just see the massive exodus.

[00:17:15] Richard Lawrence: It was a one way street out of Asia, and there I was, left. When eventually, 17 months later, I went out to Photon, and went in and met my friend Michael Chan, and went in and met the guys at Kingboard Chemical, and we survived by the chin of our chinny chin chin, as they say. But it was really something it was a lot of calls at night to my investors, you know, was pre zoom calls.

[00:17:39] Richard Lawrence: It was pre internet. I’d call them up and I’d say this is Richard Lawrence calling from Hong Kong and I’d get right through to the executive because calling from Hong Kong. It must be really expensive phone call. It was morning for the executives, the investors, and I totally wrecked their day. We went down 10 percent 5 straight months, maybe 6 straight months.

[00:17:58] Richard Lawrence: Now, you know why I feel so miserable answering, talking about 97, 98, but of course, like all bear markets, bear markets, self correct. And today, Asia runs current account surpluses. Today, Asia has really good balance of, with their government budgets. And we don’t have unhedged dollar debt. We have huge forex reserves.

[00:18:18] Richard Lawrence: All of that high savings rate, all of that’s come. The inception of that, all of it has come from 97, 98. We all, everybody. Any government official, corporate official, investor, we all learn those lessons. That’s why I think 97, 98 is such a pivotal time. We’ve had other bear markets, but they’re like water on ducks back…

…[00:22:33] Clay Finck: And you even mentioned in your book that you regretted ever listening to Buffett when it came to paying attention to macro. Is there anything else besides the current account deficits that you’ve implemented into your approach where you say you’re macro aware? Is there anything else to that process?

[00:22:50] Richard Lawrence: Yeah, there are. We had five, and then we added one, and then sort of in my Chinese way, I called them the five evils. And then the five evils plus one, but these are current account deficits, government deficits, fast loan growth. In my experience, banks can grow maybe 7 percent a year and not run into all kinds of trouble.

[00:23:08] Richard Lawrence: So, if they’re growing faster than that, they’re going to outgrow their ability to make good loans and know what they’re really lending. Loan to deposit ratios, forex reserves, those are all things that we’re very aware of. We track them religiously twice a year and we communicate them to our investors.

[00:23:24] Richard Lawrence: And in today’s world, we’re in another bear market in Asia, not as severe, but we don’t have a macroeconomic bear market. We have a geopolitical bear market, which is different. It has its severities. But Asia today is still running current account deficits, modest loan growth, very acceptable loan to deposit ratios, very acceptable government deficits, particularly compared to Europe or the U. S. So, it’s, Asia is still very, very competitive, I think…

…[00:46:49] Clay Finck: Since your team is looking at the macro situation or being macro aware and overlaying that on your very micro approach to picking stocks, how would you describe the current macro situation in China?

[00:47:02] Richard Lawrence: Well, let’s start off with the stuff that really matters, which is things like balance sheets. Okay, they got a 3 trillion of forex reserves. The household bank deposits are double the size of the market capitalization of the stock markets. And it nearly tripled the size of annual retail sales. So the individual Chinese consumer has a lot of firepower in their bank deposit.

[00:47:27] Richard Lawrence: Okay, so balance sheets are strong. Loan to deposit ratio is a conservative. The capital adequacy ratio at the banks is okay. So, you know, those balance sheet items are all in very good order. Current account, surplus, small government deficit. That’s not the problem. The problem is really a lack of confidence.

[00:47:45] Richard Lawrence: They’ve lost confidence. As you do in bear markets, as you do in recessions, you’ve lost confidence. And it was triggered by the declines in an overbuilt real estate market. The real estate guys had kind of a heads I win tails you lose kind of approach to real estate development, particularly the private guys.

[00:48:02] Richard Lawrence: They’ve all been gone bankrupt and all been flushed. But the residual is, is that real estate prices probably really have gone down 25 percent if you speak widely. You know, there are pockets where it’s stronger and pockets where it’s weaker. And that was the major asset of Chinese people. That’s what the citizens own.

[00:48:20] Richard Lawrence: They own some equities, but not a lot. And so they’re a bit shell shocked and they get sort of really mixed signals on capitalism from the government. And so their animal spirits have really been doubly repressed by lack of confidence and a concern over the commitment, both growth and capitalism in the country.

[00:48:40] Richard Lawrence: So that’s, that’s kind of where we are at the current time. And then you lever that on top with Geopolitical situation with the U. S. where both sides are at fault. Both sides have brought out the worst in each other and there’s a lot of sort of ganging on. It’s a bit like 10 year olds on a playground.

[00:48:59] Richard Lawrence: There’s kind of ganging up on each other. It’s not really great leadership for the world. This is probably the most important economic relationship in the world today. And the amount of discussion going on between governments is almost minimal. And we don’t have a big base in the United States of diplomats who are really well versed in China.

[00:49:21] Richard Lawrence: China for the last 40 years has not been the problem. And so the diplomats went to Afghanistan, went to Iraq, went to Syria, went to Ukraine and dealing with all those messes and largely sort of ignored China while China needed attention to address some of the fundamental problems. And so we don’t have.

[00:49:39] Richard Lawrence: The great outlook that we should have that we historically had starting with Kissinger on really creating a real relationship with China. And so it’s going to take the better part of the rest of this decade to turn that around. Now, having said that, my investments in China and in Asia are not predicated on US investors moving those stock prices up.

[00:49:58] Richard Lawrence: They’re just not going to come back. The sentiment towards Asia is so negative, but like I said, there’s plenty of, uh, gunpowder in banks and household bank deposits. And so I think that’s what will eventually turn it around, but we need more commitment to reform than we’ve had. There’ll probably be more rounds of stimulus.

[00:50:18] Richard Lawrence: You have to understand that the Chinese do stimulus differently. We don’t open the helicopter and throw the money out. They’re very tactical on how they stimulate, they’ll do tests, they’ll test it in a bunch of provinces, and if it’s successful, then they’ll roll it out. We’re seeing big reforms. They’re just offering refinance of all the mortgages, for example, because the interest rates have gone down.

[00:50:39] Richard Lawrence: Things like that will really help the Chinese citizen, and that’ll bring back animal spirits. It’s a long bear market. We’ve been three and a half years, almost three and three quarter years. And no upward momentum to speak of. So it’ll just take time. That’s the way life is sometimes.

[00:50:55] Clay Finck: China is certainly a very hot topic, both inside and outside the investing world.

[00:51:01] Clay Finck: Some like Overlook have been finding bargains within the Chinese market while others see China as uninvestable to some extent. What do you think is the biggest misconception when it comes to investing in China?

[00:51:14] Richard Lawrence: Well, if you think back to this eight year olds on the playground in the US, there’s a certain arrogance that China’s weak and has been brought to its knees and doesn’t have technology and is massively over levered and whatnot.

[00:51:28] Richard Lawrence: I think that’s not really realistic. If you look carefully at the semiconductor, which is something I’ve been tracking for nearly 24 years, we can try to restrict advanced semiconductors from China, but China takes a very long view of this stuff. And I guess in 8, 10 years, they’re going to have similar level of technology, and that will have happened faster than if we had really sat down and talked about what are the uses in China for the advanced technology, for the advanced chips, how to keep them out of the military.

3. Podcast: Eric Markowitz – Graham Rhodes and Eric Markowitz

I spoke with Eric Markowitz, Partner and Director of Research at Nightview Capital, about an essay he published this year titled, “How a brush with death shaped my long game” (link). We dive straight into the details of the health crisis which shook Eric’s world in early 2023 (link) and then have an open-ended conversation about what he’s taken from it as a husband, father, friend and investor…

…EM: Absolutely. I only survived because of a lot of other things—because I live in a country with access to good healthcare, because I have a wife who kept me alive, and because I had a great doctor and neurosurgeon. My brain surgeon was a friend of a friend, and the night they found the lesion in my brain, I called my best friend, Ben Jacobs, and said, “Take care of my wife and kids if things don’t work out.” He said, “Let’s call Zach.” Zach had just moved to Portland after finishing his neurosurgery residency at Stanford. He looked at my scan that evening and said, “I want to do your surgery.” So, I benefited from this social fabric that kept me alive.

These systems, like friendships, technology, and geography, all played a role. Without them, if I had been alone on an island, I wouldn’t have survived. There’s safety in numbers, but there’s also a danger in following the herd. Sometimes, you don’t want to be an outlier, but in other cases, you do…

…EM: Sure. “Tikkun Olam” is an ancient Jewish concept that essentially means making the world whole again. It’s about making the world a little bit better, and that can be interpreted broadly. It could mean doing charitable work, pursuing direct philanthropy, or finding other ways to contribute positively to the world…

…EM: Right. If your mission is to build something that can last 500 years, you should hope for crises. Crises create opportunities for reinvention within organizations. I’m starting to write about this for a column because it’s fascinating and not talked about enough. People often think crises are bad, but you should absolutely hope for one because that’s what will save you.

One of my favorite examples is a company from about 100 years ago called Kutol. They sold wallpaper cleaner, which was popular when homes were heated with coal and oil because walls would get dirty. But when homes started using cleaner energy like natural gas, demand for their product collapsed. The company was in turmoil.

The CEO’s sister-in-law, a schoolteacher, realized that kids liked playing with the wallpaper cleaner. They decided to repackage it as a children’s toy and, with nothing to lose, launched it as Play-Doh. Play-Doh became a phenomenal success, saving the company from bankruptcy. Kutol is still around today, over 120 years later. Had they not faced a crisis, I’m not sure they’d still exist. They were only willing to try something new because their backs were against the wall…

…EM: Yeah. I began my career as a business journalist and investigative reporter, and although I write a weekly newsletter, I hadn’t been doing much real writing. After I wrote the essay about my health crisis, I started writing a column for Big Think. It’s been fun, like an intellectual playground for me. It gives me something to focus on and a research project to dig into. I’m trying to make these columns really good, which forces me to cut out what doesn’t matter.

Writing is helping me think better, be a better investor, and find good companies. It’s helping me do better for my clients and my family because they’ll read these pieces one day and have their own insights. It’s compounding in a positive way, which I don’t think I was really doing before. Before, it was just about getting through the day—checking off calls, listening to earnings reports. Now, I’m more focused on doing one thing really well. 

4. Daniel Yergin – Oil Explains the Entire 20th Century – Dwarkesh Patel and Daniel Yergin

Daniel Yergin 00:06:43 

People think of John D. Rockefeller and Standard Oil and they go: gasoline. It had nothing to do with gasoline. John D. Rockefeller was a lighting merchant. What they did is that they rolled back the darkness with kerosene, with lighting. Before that, the number one source of lighting was candles and whaling. The whaling industry was delivering lighting. For the first 30 or 40 years of the oil industry it was a lighting business. Then came along this other guy named Thomas Edison. Suddenly you have electric lights and you say, “That’s going to be the end of the oil business.” But by the way, over here is Henry Ford and others. You’re creating this whole new market in the 20th century for gasoline. In the 19th century gasoline was a waste product. It went for like three cents a gallon.

Dwarkesh Patel 00:07:34

One of the things I learned from The Prize, I didn’t appreciate before. Before the car was invented, when Edison invented the light bulb, people were saying Standard Oil would go bankrupt because the light bulb was invented.

Daniel Yergin 00:07:47

John D. Rockefeller became the richest man in the United States as a merchant of lighting, not as a merchant of mobility.

Dwarkesh Patel 00:07:55

In some of the earlier chapters, you mention that Rockefeller was especially interested in controlling the refining business, not the land owning and drilling. A lot of the producer surplus went into refining. Why did the economics shape up such that the producer surplus went to refining?

Daniel Yergin 00:08:11

Because that was the control of the market. That was the access to the market. The producers needed John D. Rockefeller. There were a few other people but Rockefeller controlled about 90% of the business. He would either give you a good sweating—drive down prices and force you out of business—or force you to sell to him or amalgamate with him…

…Daniel Yergin 00:32:43

OPEC was setting prices, but then the market responds. Demand goes down. In fact, that’s exactly what OPEC did with its prices. It created incredible incentive to bring on new supplies and to be more efficient and undercut. It ended up undercutting its own price. Here’s one of the things I really carried away from The Prize. There are hundreds of really interesting characters in the book, but the two most important characters, one is named Supply and one is named Demand. That’s something that you’ve got to keep in mind with all the other drama that goes on.

Dwarkesh Patel 00:33:22

The interesting thing from the book is that oil did seem to be, at least until very recently, pretty different in that with other sorts of commodities you have strong elasticities of supply. If lithium gets more expensive, you’ll figure out substitutes for lithium and it’s not that big a deal.

Daniel Yergin 00:33:41

Or find more lithium.

Dwarkesh Patel 00:33:42

Yeah. Whereas, at least during the oil crises, it really felt like the entire world economy was just on hold…

…Dwarkesh Patel 00:59:51

How mad are the frackers that they basically solved America’s main geopolitical problem, but they were so successful that they’ve competed away their profits?

Daniel Yergin 01:00:03

That was a period up till about 2017, when it was growth for growth’s sake. Then basically the financial community said, “Hey guys, the party’s over. I’m not going to reward you for growth. I’m going to reward you for sending money back on my investment.” So in a sense, shale is almost a mature industry. I think people don’t understand how transformative it’s been. The US was the world’s largest importer of oil. We were only producing 5 million barrels a day of oil in 2008. Now we’re more than 13.2 million barrels a day. The US is energy independent. People thought it was a big joke. It could never be energy independent. Every president said, “We want energy independence.” Late night comedians could make fun of it. Actually, it’s happened and it’s had huge economic significance. Back in like 2008, the US was spending something like $400 billion a year to import oil. Now we basically spend nothing to import oil.

It’s been geopolitically very significant. That’s been a learning experience for the Biden administration. It turns out that if it wasn’t for shale gas made into what’s called LNG, liquefied natural gas, shipped to Europe, Putin could well have shattered the coalition supporting Ukraine by using the energy weapon with, not oil, but gas. Suddenly you had European politicians coming to the US to try and secure supplies of LNG because they were so worried about it. It really is a revolution that is playing out today. China imports 75% of its oil. It wishes it was in our position…

…Dwarkesh Patel 01:10:26

Let’s talk about solar and renewables. With oil, you have a commodity which is a flow. You can cut it off and you can turn it back on again. It gives the person who’s producing it a lot of leverage. Whereas with wind and solar, if you’re the people producing it, it’s just a capital stock. How does that change the geopolitical situation and the kind of leverage that the producer might have?

Daniel Yergin 01:10:51

It’s a question of scale. What I carried away, the basic premise of energy security goes back to Churchill. He said that safety lies in variety and variety alone, diversification. Wind and solar give you diversification. Electric vehicles diversify your fleet. Those are all there.

For China, wind and solar, electric cars, is very much a strategic issue because they see the vulnerability of importing 75% of their oil, much of it coming through the South China Sea. They know the story of what happened with World War II with Japan. For them, the shift to electric cars is less about air pollution and more about energy security. It’s also about knowing that they couldn’t compete in the global market with gasoline powered cars, but they can with electric cars. Those are the strategic things.

Wind and solar give you a more diversified system. Until you have batteries that can really deliver the storage, you have the intermittency problem. You take California today. People think wind and solar is advanced. It’s true. They are 25% of electric generation in California, but 43% of electric generation comes from natural gas. And that gets back to the data centers. You’re going to need to bolster your electricity power system. How much can you do with batteries and how much can you do with natural gas?

Wind and solar are also stories about entrepreneurship. In The Quest I asked myself, where did the wind and solar industries come from? The solar industry came from two émigrés who had left Europe, one of whom had driven his car out of Hungary in the 1956 revolution. In 1969, he’s a chemist working for the US government. He and his partner decided to go in the solar business. That became the first solar company. They started in 1973. With the wind business, I like to say the modern wind business is the result of the marriage between California tax credits and the sturdy Danish agricultural industry. It was driven by tax credits, but they needed to find wind power machines that could stand up when the wind blew in the Tehachapi Pass.

It took about 30 years for both those industries to become competitive. It only happened around 2010 that they actually became competitive. Now, of course, they’re very competitive but then guess what? Now,they’re all tied up. Renewables are also now tied up in geopolitics and, in what I call The New Map, the movement to the great power competition. The US just put 100% tariffs on Chinese electric cars, 25% tariffs on Chinese storage batteries. We recently had this bill, the Inflation Reduction Act. It’s huge, a trillion dollars the Treasury estimates when it’s done. It’s about climate and renewables, but it’s also about competing with China…

…I have the view that people have had too simple notions of how the energy transition will work.

That’s one of the things in The New Map. If people read one part of it, read the section on energy transition. It tells you that what we’re talking about today is not anything like any other energy transition. Every other energy transition we’ve had has been energy addition. Oil discovered in 1859 overtakes coal. Coal is the world’s number one energy source in the 1960s. Last year, the world used more coal than it’s ever used, three times as much as the 1960s. Now the idea is, can you change everything literally in 25 years?

Some of that thinking was developed during COVID, when demand went down and price collapsed. Part of it is people worrying about energy security. I was just reading last week the budget message from the finance minister in India. She talked about energy security and how they have to maintain economic growth. It’s very important to do that and energy security as well as energy transition. So it’s a different balance. There’s a difference between the North and South. Then there’s the constraints on minerals because as you make an energy transition, what people talk about, it’s more mineral intensive. An electric car uses two and a half times more copper than a conventional car.

We did the study and said, “Okay, let’s take the 2050 goals. And if you want to achieve them, copper supply has to double by about 2035.” What’s the chance of doing that? It takes 20 years to open a new mine in the US. We just did a study. It takes 29 years to open a new mine. Changing a $109 trillion world economy… it’s going to change. You said the development of solar is going to be really important. But things are not going to move in a straight line. We are in an energy transition, but it’s going to be a longer one. Here we are, as you mentioned, in Nantucket, which was a key part of the energy transition because it was a source of lighting in the 19th century from whaling.

5. The 27-Year-Old Billionaire Whose Army Does AI’s Dirty Work – Berber Jin

Sitting behind computers in cities across the world,  his startup Scale AI’s workers type out the stories, label the images, and craft the sentences that furnish chatbots with the text they need to better understand human speech patterns. Dubbed data labeling, their tasks range from composing haikus and summarizing news articles to writing stories in languages like Xhosa or Urdu.

The labor-intensive operation has become so in demand by businesses eager to enter the AI race that Scale’s revenue pace tripled last year, boosting its valuation to $14 billion…

…Meta’s code name is Flamingo—a stuffed version of which sat atop an employee’s desk on a recent visit to the startup’s headquarters. After Scale AI bungled a project last year for the tech giant, Wang declared a company emergency and launched an all-hands-on-deck effort to fix the job, called Flamingo Revival, according to former Scale employees.

Early last year, Meta Platforms asked the startup to create 27,000 question-and-answer pairs to help train its AI chatbots on Instagram and Facebook.

When Meta researchers received the data, they spotted something odd. Many answers sounded the same, or began with the phrase “as an AI language model…” It turns out the contractors had used ChatGPT to write-up their responses—a complete violation of Scale’s raison d’être.

The researchers communicated the disappointing results to Scale, prompting Wang to rally the entire company to try and save the contract.

He asked employees to drop everything and create new writing samples to send to Meta. An internal leaderboard showed who had completed the most labeling tasks. The prize for the winner: a paid vacation.

Later, Scale discovered that much of the bad data sent to Meta had come from Kenyans who had become experts in making a quick buck off the Remotasks platform, the former employees said. Scale restricted several new labeling projects to workers based in the U.S. and other wealthy, English-speaking countries. The change didn’t stem the fraud entirely: Some foreign workers found ways to skirt the new rules by buying labeling accounts registered to U.S. residents that they found for sale in group chats on WhatsApp and Facebook.

A Scale spokeswoman said that Scale has cracked down on such activity and the percentage of its freelancers exhibiting fraud fell to under 0.1% in July.

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 Meta Platforms, Starbucks, Tencent (parent of WeChat) 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 29 September 2024:

1. Digging Into The Coal Industry – Matt Franz

There are two main types of coal: thermal and metallurgical (“met”) or coking coal.

Met coal has more carbon, less ash, moisture, and sulfur than thermal coal. It is rarer and commands a higher price.

Met coal is a key ingredient in steel. To make steel, met coal is first turned into coke by heating it to 1,000ºC in the absence of oxygen. Without oxygen, the coal does not burn. The coal swells and then releases its gaseous volatile matter. Nearly pure crystalline carbon is all the remains. Coke is to coal what charcoal is to wood. Caking is a coal’s ability to be converted into coke. Thermal coal has no caking ability, which is why it is much cheaper.

Coke is mixed with iron ore, flux (e.g. limestone), and hot air in a blast furnace to create iron. Iron is put into a basic oxygen furnace where oxygen reduces the metal’s carbon content. It is further refined to remove impurities and alloys are added to make steel.

The quality of met coal influences the quality of the coke, iron, and steel produced. A blast furnace fed with higher quality coke will require less of it, lowering production costs. Steel makers have an economic incentive to pay more for higher quality met coal.

It takes 0.8-1.05 tons of met coal to produce one ton of steel. (1.3-1.5 tons of met coal make one ton of coke. 0.6-0.7 tons of coke make one ton of steel.) That’s a lot! 70% of the world’s steel is made this way…

… The major met coal exporting nations are the US, Canada, and Australia. The major importers are countries with large steel industries relative to their domestic met coal supplies – China, India, Japan, and South Korea.

The US usually exports ~70% of its met coal. Export contract prices are tied to international benchmark indices. Domestic contracts tend to specify a fixed price and a fixed volume for one year…

…Thermal coal has a lower calorific value (CV) and a lower cost than met coal. It is primarily used to generate electricity. It is also used to make cement. It takes 0.1-0.12 tons of coal to make one ton of cement.

Thermal coal has been in decline in Europe since the 1980s and in the US since the 2000s. It continues to grow in Asia. Worldwide, coal demand reached its highest level ever in 2022 and again in 2023…

…Today, coal remains the world’s largest energy source for electricity generation. Coal may be losing share as an energy source, but it continues to grow in absolute tons.

China is the world’s largest producer and consumer of coal. In the early 2000s coal produced 75-80% of its electricity. Today it’s more like 60-65%. Coal lost share but grew in absolute terms. Chinese electricity production rose eightfold and its coal consumption rose sevenfold…

…Coal’s peak share of U.S. energy occurred around 2007-2008 at 50% of electricity. Today it’s 20-22%. That’s still a meaningful amount. The decline in US coal was driven as much by fracking and its byproduct, cheap natural gas, as environmental considerations. Should US natural gas get expensive, we could see a shift back towards coal…

…Coal’s share of energy production is falling slower than the increase in total energy demand. Jevon’s Paradox describes this situation. Demand for energy is elastic. As energy costs decrease, demand for energy increases even more. On balance, energy demand increases, even as energy consumption becomes more efficient…

…In Energy and Civilization: A History (2017), Vaclav Smil explains that energy transitions often take more than a century. The transition from biomass (wood) to coal in Western Europe took 200 years. The transition from coal to oil began in the late 19th century, but oil didn’t overtake coal as America’s dominant energy source until the 1940s, approximately 50-60 years later. The transition is still far from complete, and that’s despite crude being more energy dense and easier to transport (via pipeline).

One of the factors affecting the speed of the transition is infrastructure. The transition from coal to oil in America was slowed by the need to replace steam engines with diesel engines. The modern analog is the cost and complexity of switching a power plant from coal to natural gas.

Price is another factor. If there’s a new fuel that is much cheaper than the legacy fuel, there’s an economic incentive to rebuild the infrastructure faster. A wide disparity between coal and natural gas prices that is expected to continue will drive more US coal plants to switch to gas. That’s less likely to happen in Asia, where gas is less plentiful and LNG infrastructure is more expensive.

Once again, this suggests that the last ton of coal will be very expensive, not very cheap. Thermal coal may be a sunset industry, but it is going to be a beautiful sunset.

2. Fed up with Fed Talk? Factchecking Central Banking Fairy Tales! – Aswath Damodaran

As I drove to the grocery story on Fed Cut Wednesday, I had the radio on, and in the news at the top of the hour, I was told that the Fed had just cut interest rates, and that consumers would soon see lower rates on their mortgages and businesses on their loans. That delusion is not restricted to newscasters, since it seems to be widely held among politicians, economists and even market watchers. The truth, though, is that the Fed sets only one interest rate, the Fed Funds rate, and that none of the rates that we face in our lives, either as consumers (on mortgages, credit cards or fixed deposits) or businesses (business loans and bonds),  are set by or even indexed to the Fed Funds Rate…

…While the Federal Open Market Committee controls the Fed Funds rate, there are a whole host of rates set by buyer and sellers in bond markets. These rates are dynamic and volatile, and you can see them play out in the movements of US treasury rates (with the 3-month and 10-year rates highlighted) and in corporate bond rates (with the Baa corporate bond rate shown)

There is a final set of rates, set by institutions, and sometimes indexed to market-set rates, and these are the rates that consumers are most likely to confront in their day-to-day lives. They include mortgage rates, set by lenders, credit card rates, specified by the credit card issuers, and fixed deposit rates on safety deposits at banks.  They are not as dynamic as market-set rates, but they change more often than the Fed Funds rate…

…To test whether changes in the Fed Funds rate are a precursor for shifts in market interest rates, I ran a simple (perhaps even simplistic) test. I looked at the 249 quarters that compose the 1962- 2024 time period, breaking down each quarter into whether the effective Fed Funds rate increased, decreased or remained unchanged during the quarter. I followed up by looking at the change in the 3-month and 10-year US treasury rates in the following quarter:

Looking at the key distributional metrics (the first quartile, the median, the third quartile), it seems undeniable that the “Fed as leader” hypothesis falls apart. In fact, in the quarters after the  Fed Funds rate increases, US treasury rates (short and long term) are more likely to decrease than increase, and the median change in rates is negative. In contrast, in the periods after the Fed Fund decreases, treasury rates are more likely to increase than decrease, and post small median increases…

…In the quarter after the Fed Funds rate increase, mortgage rates and fixed deposit rates are more likely to fall than rise, with the median change in the 15-year mortgage rate being -0.13% and the median change in the fixed deposit rate at -0.05%. In the quarter after the Fed Funds rate decreases, the mortgage rate does drop, but by less than it did during the Fed rate raising quarters. In short, those of us expecting our mortgage rates to decline in the next few months, just because the Fed lowered rates on Wednesday, are being set up for disappointment…

…How else can you explain why interest rates remained low for the last decades, other than the Fed? The answer is recognizing that market-set rates ultimately are composed of two elements: an expected inflation rate and an expected real interest rate, reflecting real economic growth…

…Interest rates were low in the last decade primarily because inflation stayed low (the lowest inflation decade in a century) and real growth was anemic. Interest rates rose in 2022, because inflation made a come back, and the Fed scrambled to catch up to markets, and most interesting, interest are down this year, because inflation is down and real growth has dropped…

…The Fed’s major signaling device remains the changes in the Fed Funds rate, and it is worth pondering what the signal the Fed is sending when it raises or lowers the Fed Funds rate. On the inflation front, an increase or decrease in the Fed Funds rate can be viewed as a signal that the Fed sees inflationary pressures picking up, with an increase, or declining, with a decrease. On the economic growth front, an increase or decrease in the Fed Funds rate, can be viewed as a signal that the Fed sees the economy growing too fast, with an increase, or slowing down too much, with a decrease…

…Viewed through this mix, you can see that there are two contrary reads of the Fed Funds rate cut of 50 basis points on Wednesdays. If you are an optimist, you could take the action to mean that the Fed is finally convinced that inflation has been vanquished, and that lower inflation is here to stay. If you are a pessimist, the fact that it was a fifty basis point decrease, rather than the expected twenty five basis points, can be construed as a sign that the Fed is seeing more worrying signs of an economic slowdown than have shown up in the public data on employment and growth…

…If you remove the Fed’s role in crisis, and focus on the effects of just its actions on the Fed Funds rate, the effect of the Fed on equity market becomes murkier…

…The S&P 500 did slightly better in quarters after the Fed Funds rate decreased than when the rate increased, but reserved its best performance for quarters after those where there was no change in the Fed Funds rate. At the risk of disagreeing with much of conventional wisdom, is it possible that the less activity there is on the part of the Fed, the better stocks do? I think so, and stock markets will be better served with fewer interviews and speeches from members of the FOMC and less political grandstanding (from senators, congresspeople and presidential candidates) on what the Federal Reserve should or should not do…

… The truth is that the Fed is acting in response to changes in markets rather than driving those actions, and it is thus more follower than leader. That said, there is the very real possibility that the Fed may start to believe its own hype, and that hubristic central bankers may decide that they set rates and drive stock markets, rather than the other way around…

…I believe that it is time for us to put the Fed delusion to rest. It has distracted us from talking about things that truly matter, which include growing government debt, inflation, growth and how globalization may be feeding into risk, and allowed us to believe that central bankers have the power to rescue us from whatever mistakes we may be making.

3. ‘There Are Real Issues in China Now,’ Ray Dalio Says (Transcript here) – Bloomberg Televsion

I think that there are real issues in China now, and they changed, really in the last four years, and that is that they need a restructuring. Individuals, 70% of their money was in real estate. Real estate has gone down. Stocks have gone down. Salaries have gone down. And so and as a result, they’re not spending and they’re concerned and they’re holding money in cash…

…At the same time, you have the government sector is a problem because most of the government spending – 83% of government spending – is spent by local governments. Those local governments got their money by selling land for real estate. Okay, there are no land sales and they borrowed a lot of money…

…It’s a situation that’s more challenging than Japan in 1990. It needs a restructuring in order to be able to do that. And then there’s also the question: Is the property ownership, is it respected? And Deng Xiaoping during his period said, “It’s glorious to be rich.” Is it still glorious to be rich?…

…Yes, there’s fantastic innovation in terms of technology, there’s nothing like it other than in the United States. Europe certainly isn’t a competitor in that. However, it’s very much government-directed. Can there still be entrepreneurship and that inventiveness? These are the big cosmic questions…

…I see investing in China as largely a very attractively-priced place that now has a lot of questions regarding the issues that I just referred to…

…There’s a small percentage of our portfolio which is in China, and we’ll stay in China, you know, through this process.

4. OpenAI’s New Model, How o1 Works, Scaling Inference – Ben Thompson

There are two important things happening: first, o1 is explicitly trained on how to solve problems, and second, o1 is designed to generate multiple problem-solving streams at inference time, choose the best one, and iterate through each step in the process when it realizes it made a mistake…

…There has been a lot of talk about the importance of scale in terms of LLM performance; for auto-regressive LLMs that has meant training scale. The more parameters you have, the larger the infrastructure you need, but the payoff is greater accuracy because the model is incorporating that much more information. That certainly still applies to o1, as the chart on the left indicates.

It’s the chart on the right that is the bigger deal: o1 gets more accurate the more time it spends on compute at inference time. This makes sense intuitively given what I laid out above: the more time spent on compute the more time o1 can spend spinning up multiple chains-of-thought, checking its answers, and iterating through different approaches and solutions.

It’s also a big departure from how we have thought about LLMs to date: one of the “benefits” of auto-regressive LLMs is that you’re only generating one answer in a serial manner. Yes, you can get that answer faster with beefier hardware, but that is another way of saying that the pay-off from more inference compute is getting the answer faster; the accuracy of the answer is a function of the underlying model, not the amount of compute brought to bear. Another way to think about it is that the more important question for inference is how much memory is available; the more memory there is, the larger the model, and therefore, the greater amount of accuracy.

In this o1 represents a new inference paradigm: yes, you need memory to load the model, but given the same model, answer quality does improve with more compute.

5. Learning from Richard Lawrence of Overlook – 14.3% CAGR for 30 years – Eugene Ng

The birth of Overlook’s Cap on Subscriptions originated when Richard Lawrence had lunch in 1992 in New York with Crosby Smith, a representative of the Dillon Family. Richard was asked why he would not just raise capital to generate fees like other investment managers. Crosby proposed that if Richard limited initial subscriptions into the fund at $30 million, the Dillon Family would invest $1 million. They shook hands, and Overlook had its first investor.

The Overlook Cap on Subscriptions was born in that spontaneous moment for Overlook. Richard Lawrence thought that the Cap on Subscriptions has proven to be the single most significant business decision in their 30-year history. In the early 1990s, Overlook decided to cap new subscriptions at 8-9% growth per year. This policy enabled the company to grow its AUM steadily…

…Effectively, the Cap on Subscriptions can smooth fund inflows, effectively lowering the cyclical volatility of AUM. One can then limit inflows of funds at the top of the market, have a ready queue of investors waiting to jump in during market declines, effectively making an investment fund more anti-fragile especially during market selloffs.

In addition, the Cap incentivizes investors to make a long-term commitment, which is aligned with a long-term investment horizon. Investors usually have to wait 6–12 months to gain access, so there are no short-term gains for investors trying to time the markets. The Cap effectively self-selects patient long-term investors.

What is the downside of having such a Cap? As you can imagine, such a Cap on Subscriptions is not for AUM/asset gatherers. The fund size grows much slower and takes longer to scale, and investment managers collect much lower fees…

…Time-weighted return (TWR): The TWR calculates the compound growth of a portfolio’s Net Asset Value on a per-share basis over a specified period of time. Fund managers most often disclose this number.

Capital-weighted return (CWR): CWR calculates the Internal Rate of Return (IRR) for an individual investor’s return and the return collectively earned by all investors in the fund. The CWR accounts for all cash flows into and out of the investor’s specific account and the fund since inception. Most fund managers do not report CWR, and CWRs typically underperform TWRs for most funds.

The Discount (the “Discount”) and the Premium: The discount is the difference between the TWR and the CWR for a specific fund. Discounts occur when CWRs are lower than and underperform TWRs. Peter Lynch was producing world-leading returns when he ran Magellan (high TWR), but the underlying investors performed far worse (low CWR).

Discounts typically happen for two reasons:

First, a fund manager can generate exceptional results as measured by TWRs at the fund’s inception when assets under management (AUM) are small. Then, the manager gets “discovered” and/or “promoted,” and an explosion of money enters the fund, to the great delight of the fund manager. However, with the larger asset base, the now-famous fund manager performs poorly, dragging down his TWR while crushing his CWR.

Second, CWRs are hurt when investments are poorly timed. Investors chase funds promoting hot themes, then bail out when markets turn down. This behavior inevitably decreases their CWRs. But even buying smartly and selling poorly, or buying poorly and selling smartly, can result in a Discount.

On average, the Discount increases when some of the following conditions prevail:

1. Funds experience fast growth of AUM: the Discount tends to increase as the absolute value of a fund increases.
2. Funds are invested in trendy asset classes.
3. Funds are exposed to excessive valuation risk.
4. Funds have excessive exposure to fund-of-funds’ investors. 
5. Funds are operated in higher volatility sectors…

…Richard realized that the elimination of Overlook’s Discount is overwhelmingly due to their legal Cap on Subscriptions. At first, he thought it was due to the success of their Investment Philosophy or the luck of their investors in timing their investments.

Overlook’s Investment Philosophy has helped them achieve outperformance of our TWR vs. the benchmark, but it did not impact CWR. The investors’ luck is not a factor either, as their investors have added funds consistently over time.

Instead, the answer lies exclusively in the legal Cap on Subscriptions because the Cap has allowed a limited amount of funds to enter Overlook steadily over the past 30 years. Control over the growth of AUM is the key to eliminating the Discount. The legal Cap on Subscriptions is the hero of Overlook’s story.

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 company 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 22 September 2024:

1. Mario Draghi outlines his plan to make Europe more competitive – Mario Draghi

Across different measures, a wide gap in GDP has opened up between the European Union and America. Europe’s households have paid the price in forgone living standards. On a per-person basis, real disposable income has grown almost twice as much in America as in the EU since 2000…

…Europe largely missed out on the digital revolution led by the internet and the productivity gains it brought: in fact, the productivity gap between the EU and America since 2000 is largely explained by the tech sector. The EU remains weak in the emerging technologies that will drive future growth. European companies specialise in mature technologies where the potential for breakthroughs is limited.

The problem is not that Europe lacks ideas or ambition. But innovation is blocked at the next stage: it is not translated into commercialisation, and innovative firms that want to scale up are hindered by inconsistent and restrictive regulations. Many European entrepreneurs prefer to seek financing from American venture capitalists and scale up in the American market…

…EU companies face electricity prices that are two to three times those in America. Natural-gas prices are four to five times higher. Over time, decarbonisation will help shift power generation towards secure, low-cost clean-energy sources. But fossil fuels will still set the energy price for most of the time for at least the remainder of this decade. Unless Europe better transfers the benefits of clean energy to end-users, energy prices will continue to dampen growth…

…As the era of geopolitical stability fades, the risk of rising insecurity becoming a threat to growth and freedom is increasing. Europe is particularly exposed. The EU relies on a handful of suppliers for critical raw materials and is heavily dependent on imports of digital technology.

2. When Chasing More Dividends Leaves You With Less – Jason Zweig

In July and August, as investors became more convinced interest rates will fall, exchange-traded funds specializing in dividend-paying stocks took in $4.5 billion in new money, estimates Ryan Issakainen, a strategist at First Trust, an ETF manager in Wheaton, Ill.

Although funds with big payouts sound safe, high income can lead to a poor outcome. You need to guard against needless tax bills, overexposure to narrow segments of the market and the chance of deep long-term losses…

…To see the potential downside of these funds, though, consider Global X SuperDividend, an ETF with $784 million in assets.

It yields nearly 11%.

That’s huge compared to the income returns of roughly 1.3% on the S&P 500, 2.1% on the Dow Jones Industrial Average and 5% on short-term U.S. Treasurys.

The SuperDividend fund’s supersized yield comes at a cost. Launched in June 2011 at $75, this week the shares traded around $22. That’s a 70% decline.

If you’d bought the ETF at its inception and held continuously through the end of August, you’d have lost 9%—after accounting for all those jumbo dividends along the way…

… A company that pays a steady stream of growing dividends is probably in robust financial health, but one that pays gigantic dividends is probably struggling and may be desperate to attract investors. Put a bunch of those into an ETF, and you get lots of income but even more risk…

…High-dividend funds often hold many more energy and financial stocks than broader portfolios do. That can raise risk.

In 2008, both First Trust’s Dow Jones Global Select Dividend and its Stoxx European Select Dividend had roughly 50% of their assets in financial stocks—right before the global financial crisis struck.

Over the 12 months ended March 31, 2009, as the MSCI World index lost 42.2% and European stocks overall sank 49.6%, First Trust’s Global Select fell 53.2% and European Select lost 63.9%—even after factoring in their dividends…

…Although a moderate dividend can be a sign of robust corporate health, a huge dividend can be a distress signal. A dividend four or five times greater than that of the overall market isn’t a green light; it’s a red flag.

3. Learning From Peter Keefe – John Garrett

The investment philosophy [at our new fund, Rockbridge Capital] is exactly the same: great businesses, great managers, bargain price. That remains unchanged.

The implementation has evolved over time. Great businesses, great managers, great price—it’s kind of like mom and apple pie. I mean, who’s opposed to it? It’s axiomatic that these things work, but I believe your approach to implementation should change over time…

…You don’t really know what makes a business great. You don’t really understand what contributes to compounding. You want a business with all the great characteristics—growth, rapid growth, sustainable growth—but you don’t know how to evaluate one business against another. You don’t know which businesses are mayflies and which are incredibly durable with multi-decade runways.

Learning how to discern and implement those three criteria does evolve over time. Another thing that evolves is the recognition that there are only a tiny number of businesses you will own over the course of a career that will compound and give you that 100-bagger effect or the 300-bagger effect—what Munger called the Lollapalooza effect. Those opportunities are incredibly rare.

But you spend your entire career looking for them. On day one, when you enter the business, you might think, ‘Well, maybe I’ll find it today,’ but you’re probably not going to find it today, tomorrow, or the day after. So what has evolved for me is the realization that when you find a compounder, don’t let it go…

…Every time I’ve trimmed a position and it involved a great business, it wound up being a huge mistake.

Now, we had this conundrum recently. We own a lot of Microsoft, which we bought back in the Balmer days. So it’s been in the portfolio over 10 years. We’ve made 10 times our money in the business, and it’s appreciated to have a very significant percentage of our portfolios.

Microsoft got a big bid recently because of the artificial intelligence stuff, and I don’t know enough about artificial intelligence to have a responsible opinion. But you can argue that there’s a trillion dollars’ worth of value in Microsoft attributable to AI. Do I trim the position? Well, based on the mistakes I’ve made in the past, no. But at the same time, is a 35 or 40 multiple sustainable for a company that’s already worth three trillion dollars? It’s hard to make that argument. And particularly when you’re managing both taxable and tax-exempt capital, you can make a pretty good argument that you should trim it. But again, that’s never worked out for me. So we are where we are.”…

…Every time we’ve had a business that’s compounded more than 10x—and we’ve had a couple that have compounded at 100x—there’s always been a leader and visionary who is a person of humility, thinking about their business in multi-decade timelines. Without exception, 10, 100, 200-baggers were always a person…

… They’re not thinking about an exit or the next thing; they’re thinking in 10, 20, 30-year time periods.

These people are artists. They’re focused on building something of great value—not just to accumulate wealth, but to create something valuable to society. To borrow from Tom Gayner, these are businesses that do something for people instead of to people. They are financially interested, but the finances are a means of keeping score rather than acquiring more things or a better jet. Those are the people I shy away from. The real artists see beauty in what they’re building and are focused on creating value for all stakeholders, especially the owners of the business.

When discussing people who want to serve all stakeholders, it’s not about rank-ordering which stakeholders to reward first. It’s about understanding that a business can do well for its employees, shareholders, and vendors. Munger talked about this all the time…

…People ask, ‘What makes you different?’ Well, it’s not my process. Everybody wants great businesses and great managers and to buy them at a bargain price. Nobody says they’re not a value investor or that they don’t like what Buffett does. So I think a major differentiator in this business is temperament. If I have an advantage, it’s that I don’t feel like I’m coming unglued when the world is coming unglued. I don’t know why that is; it’s just part of my makeup, but it’s an advantage because low prices are good for investors…

…The biggest compounder I’ve ever had in the investment business was American Tower. I was fortunate enough to figure out American Tower before it was even a public company. It was a footnote in the 10-K of a company called American Radio Systems. American Radio Systems was run by a brilliant, thoughtful capital allocator who fits into this liberal arts bucket I talked about earlier. Steve Dodge went to Yale and was an English major there.

Steve did cable transactions for one of the big New York banks. He got the idea that recurring revenue businesses or contractual revenue were great. So he moved into the cable business and then into the radio business. Around the time of the Telecom Act in the mid-1990s, digital networks for cell networks were beginning to roll out. Steve had people come to him and say, ‘We’d like to hang some of these digital antennas on your radio antennas.’ They also owned a portfolio of television broadcast antennas. They needed structures in suitable locations for these antennas.

That’s the genesis of American Tower, which was just a footnote. I remember calling Steve and asking about it. He basically hung up on me. I had a good relationship with him, so I knew I was onto something.

Long story short, American Tower was spun off and went to over $40 a share. Then came the dot-com bust. There had been a land rush in the tower business, and many companies had gotten levered up.

This was when I learned one of my early lessons about leverage, although it eventually helped me. American Tower dropped to under 80 cents a share from $44. Now that’s a drawdown.

I went up to Boston, where American Tower was headquartered. Chuck Akre was with me, and we met with Steve. He said, ‘I’ll tell you anything that I can legally tell you. I want you to know upfront that I don’t have much time. I have a business that needs my attention. It needs more attention than I can possibly give it because there’s only 24 hours in a day. I think that we can save this thing and I’m not sure that we can, but I also want to tell you, I am solely responsible. This is the worst thing that’s happened to me in my business career, but you’re looking at the guy who made the mistakes that got us in the pickle that we’re in.’

There was none of the usual excuses like ‘The dog ate my homework,’ or blaming the pandemic or the dot-com bust. Steve gave us none of that.

Steve figuratively raised his hand and said, ‘I messed it up, and I am sorry. I will do my best to get you and all the other shareholders out of this pickle.’

That kind of character in a moment of great crisis inspired me and others to make American Tower a more significant position, despite its distress.

We were convinced that the business wasn’t going to zero. It had one of the greatest business models in public companies’ history. A business where 100% of incremental revenue flows through to free cash flow and was growing by 20 to 30% a year. It was highly likely the business would be recapitalized. I can’t think of a financing environment where it wouldn’t be.

Steve’s character and willingness to accept responsibility were crucial in our decision to increase our position. It went up 300-fold from there.

4. Light-Based Chips Could Help Slake AI’s Ever-Growing Thirst for Energy – Amos Zeeberg

Recent results suggest that, for certain computational tasks fundamental to modern artificial intelligence, light-based “optical computers” may offer an advantage…

…In theory, light provides tantalizing potential benefits. For one, optical signals can carry more information than electrical ones—they have more bandwidth. Optical frequencies are also much higher than electrical ones, so optical systems can run more computing steps in less time and with less latency.

And then there’s the efficiency problem. In addition to the environmental and economic costs of relatively wasteful electronic chips, they also run so hot that only a tiny fraction of the transistors—the tiny switches at the heart of all computers—can be active at any moment. Optical computers could, in theory, run with more operations taking place simultaneously, churning through more data while using less energy…

…Seeing the potential advantages, researchers have long tried to use light for AI, a field with heavy computational needs. In the 1980s and 1990s, for instance, researchers used optical systems to build some of the earliest neural networks. Demetri Psaltis and two colleagues at the California Institute of Technology created a clever facial recognition system using one of these early optical neural networks (ONNs). They stored images of a subject—one of the researchers, in fact—as holograms in a photorefractive crystal. The researchers used the holograms to train an ONN, which could then recognize new images of the researcher and distinguish him from his colleagues.

But light also has shortcomings. Crucially, photons generally don’t interact with each other, so it’s hard for one input signal to control another signal, which is the essence of what ordinary transistors do. Transistors also work exceptionally well. They’re now laid down on coin-size chips by the billion, the products of decades of incremental improvements…

…The process of multiplying matrices, or arrays of numbers, undergirds a lot of heavy-duty computing. In neural networks, specifically, matrix multiplication is a fundamental step both in how networks are trained on old data and in how new data is processed in trained networks. And light just might be a better medium for matrix multiplication than electricity.

This approach to AI computation exploded in 2017, when a group led by Dirk Englund and Marin Soljačić of the Massachusetts Institute of Technology described how to make an optical neural network built on a silicon chip. The researchers encoded the various quantities they wanted to multiply into beams of light, then sent the beams through a series of components that altered the beam’s phase—the way its light waves oscillated—with each phase alteration representing a multiplication step. By repeatedly splitting the beams, changing their phase, and recombining them, they could make the light effectively carry out matrix multiplication. At the end of the chip, the researchers placed photo detectors that measured the light beams and revealed the result.

The researchers taught their experimental device to recognize spoken vowels, a common benchmark task for neural networks…

…Since that 2017 paper, the field has seen steady improvement, as various researchers have come up with new kinds of optical computers. Englund and several collaborators recently unveiled a new optical network they call HITOP, which combines multiple advances. Most importantly, it aims to scale up the computation throughput with time, space, and wavelength. Zaijun Chen, a former MIT postdoc now based at the University of Southern California, said this helps HITOP overcome one of the drawbacks of optical neural networks: It takes significant energy to transfer data from electronic components into optical ones, and vice versa. But by packing the information into three dimensions of light, Chen said, it shoves more data through the ONN faster and spreads the energy cost over many calculations. This drives down the cost per calculation. The researchers reported that HITOP could run machine-learning models 25,000 times larger than previous chip-based ONNs.

To be clear, the system is still far from matching its electronic predecessors; HITOP performs about 1 trillion operations per second, whereas sophisticated Nvidia chips can chug through 300 times as much data, said Chen, who hopes to scale up the technology to make it more competitive. But the optical chip’s efficiency is compelling. “The game here is that we lowered the energy cost 1,000 times,” Chen said…

…While optical computing has advanced quickly over the past several years, it’s still far from displacing the electronic chips that run neural networks outside of labs. Papers announce photonic systems that work better than electronic ones, but they generally run small models using old network designs and small workloads. And many of the reported figures about photonic supremacy don’t tell the whole story, said Bhavin Shastri of Queen’s University in Ontario. “It’s very hard to do an apples-to-apples comparison with electronics,” he said. “For instance, when they use lasers, they don’t really talk about the energy to power the lasers.”

Lab systems need to be scaled up before they can show competitive advantages. “How big do you have to make it to get a win?” McMahon asked. The answer: exceptionally big. That’s why no one can match a chip made by Nvidia, whose chips power many of the most advanced AI systems today. There is a huge list of engineering puzzles to figure out along the way—issues that the electronics side has solved over decades. “Electronics is starting with a big advantage,” said McMahon.

Some researchers think ONN-based AI systems will first find success in specialized applications where they provide unique advantages. Shastri said one promising use is in counteracting interference between different wireless transmissions, such as 5G cellular towers and the radar altimeters that help planes navigate. Early this year, Shastri and several colleagues created an ONN that can sort out different transmissions and pick out a signal of interest in real time and with a processing delay of under 15 picoseconds (15 trillionths of a second)—less than one-thousandth of the time an electronic system would take, while using less than 1/70 of the power.

5. Warren Buffett Case Study: Arbitrage – Dirtcheapstocks

By 1981, Arcata was the second largest printing services organization in the U.S. In addition, Arcata owned 77,500 acres of Northern California timberlands, which it used for timber harvesting, reforestation and milling.

Arcata was to be acquired by KKR. The stock was trading around $33/share at the time of the deal announcement. KKR’s $37 offer represented a reasonable premium over the current share price. But there was one other interesting bit of information.

“In 1978 the U.S. Government had taken title to 10,700 acres of Arcata timber, primarily old-growth redwood, to expand Redwood National Park. The government had paid $97.9 million, in several installments, for this acreage, a sum Arcata was contesting as grossly inadequate. The parties also disputed the interest rate that should apply to the period between the taking of the property and final payment for it. The enabling legislation stipulated 6% simple interest; Arcata argued for a much higher and compounded rate.” – Warren Buffett

“Buying a company with a highly speculated, large-sized claim in litigation creates a negotiating problem, whether the claim is on behalf of or against the company. To solve this problem, KKR offered $37.00 per Arcata share plus two-thirds of any additional amounts paid by the government for the redwood lands.” – Warren Buffett…

…“We started buying Arcata stock, then around $33.50, on September 30 and in eight weeks purchased about 400,000 shares, or 5% of the company. The initial announcement said that the $37.00 would be paid in January 1982. Therefore, if everything had gone perfectly, we would have achieved an annual rate of return of about 40% — not counting the redwood claim, which would have been frosting.” – Warren Buffett

“All did not go perfectly. In December it was announced that the closing would be delayed a bit. Nevertheless, a definitive agreement was signed on January 4. Encouraged, we raised our stake, buying at around $38.00 per share and increasing our holdings to 655,000 shares, or over 7% of the company. Our willingness to pay up – even though the closing had been postponed – reflected our leaning toward ‘a whole lot’ rather than ‘zero’ for the redwoods.” – Warren Buffett…

…“On March 12, KKR said its earlier deal wouldn’t work, first cutting its offer to $33.50, then two days later raising it to $35.00. On March 15, however, the directors turned this bid down and accepted another group’s offer of $37.50 plus one-half of any redwood recovery.” – Warren Buffett…

…“The trial judge appointed two commissions, one to look at the timber’s value, the other to consider the interest rate questions. In January 1987, the first commission said the redwoods were worth $275.7 million and the second commission recommended a compounded, blended rate of return working out to about 14%.” – Warren Buffett

“In August 1987 the judge upheld these conclusions, which meant a net amount of about $600 million would be due Arcata. The government then appealed. In 1988, though, before this appeal was heard, the claim was settled for $519 million. Consequently, we received an additional $29.48 per share, or about $19.3 million. We will get another $800,000 or so in 1989.” – Warren Buffett

The final result: 39% IRR…

…The greatest investor to ever live earns a 39% IRR in a low-risk arb deal. The most striking part of this case is not the return generated – but the lack of risk taken.

Arcata was a profitable, growing business. Take a look at its five-year history leading up to the deal.

Arcata had strong operating businesses that earned sufficient sums to cover its interest burden with plenty of comfort.

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 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 15 September 2024:

1. The ROI on Generative AI – Tanay Jaipuria

The poster child for this has been Klarna which leveraged AI to elevate their customer support. Their AI assistant has taken over the work of 700 employees, reducing resolution times from 11 minutes to just 2 minutes while maintaining high customer satisfaction levels…

...Microsoft casually dropped that they too are expecting to save hundreds of millions of dollars a year on call centers after adopting Generative AI.

“Dynamics with Gen AI built in is sort of really … the category that gets completely transformed with Gen AI, contact centers being a great example. We, ourselves, are on course to save hundreds of millions of dollars in our own Customer Support and Contact Center Operations. I think we can drive that value to our customers”…

…We’re also hearing examples of measurable, tangible benefits from enterprises, as Amazon shared about their software development assistant Q which has saved them over 4,500 developer years in a recent code migration task…

…”With Q’s code transformation capabilities, Amazon has migrated over 30,000 Java JDK applications in a few months, saving the company $260 million and 4,500 developer years compared to what it would have otherwise cost. That’s the game changer. And think about how this Q transformation capability might evolve to address other elusive but highly desired migrations.”…

…eBay launched a new AI-assisted selling flow, and are already seeing improvements in customer satisfaction as well as faster time to list and get value for Sellers…

…YUM Brands is enhancing customer experiences at Taco Bell by rolling out voice AI driven drive-through systems. This technology is not only improving customer satisfaction but also boosting team member productivity, and the results are so promising they are accelerating their roll-out timelines…

…Manulife is an example of a company already seeing large ROI in using AI to assist salespeople…

…”We’re using GenAI and machine learning models to make it really easy for agents to understand customer opportunities but also to generate these personalized communications at the click of a button to help them engage with more customers more often.

In our first 2 weeks live, about 68% of our agents had already used the new GenAI capabilities. And in July, we will be broadening that user base to about 2,000.

Based on our analysis in Singapore, we anticipate a 17% uplift and repurchase rates for our customer base, when this is fully rolled out to all of our agents.”…

…Rocket Mortgage is utilizing AI to automating the transcription of client calls and completing mortgage applications…

…”Now the Rocket Logic Assistant seamlessly generates over 300,000 detailed transcripts every week from outbound calls. It supports over 100 data points on mortgage applications saving our bankers from inputting tens of millions of data fields each week.”…

…Walmart has harnessed generative AI to enhance its product catalog, improving the quality of over 850 million pieces of data…

…”We’ve used multiple large language models to accurately create or improve over 850 million pieces of data in the catalog. Without the use of generative AI, this work would have required nearly 100x the current head count to complete in the same amount of time.”…

…Mastercard is leveraging the new advances in Generative AI to enhance fraud detection, achieving a 20% increase in accuracy. 

2. The Agent Era – Patrick O’Shaughnessy and Bret Taylor

Patrick

It’s such an interesting story because I think it becomes ultra relevant in today’s world. And you hear a lot about this, maybe the mythical 10x engineer, the 100x engineer, 1,000x engineer, the leverage available to one person with a growing tool kit.

And maybe that’s a great excuse to bridge the conversation into agents. I think everyone listening will have heard that term and maybe have thought about it a little bit, have gotten excited about the prospect of some sort of autonomous agent doing work on their behalf or their company’s behalf. But it would be great for you to ground us in your definition of what one of these things is, if this becomes a really critical part of the world of technology in the next year or two. I think it would be great for everyone just to have a level-set, simple definition from your perspective on what an agent is and does.

Bret

I’ll start with maybe the academic flavor of this, but then I’ll move into what I think is maybe the more — what I believe is the more relevant definition, but agent is like the word app. There’s not one definition, and I think it will be a noun that is quite meaningful in the age of AI. The word agent in the context of AI comes from the word agency and essentially is a system that can reason and take action autonomously is the way I think about it. And a system that is agentic is one where software and AI can reason and make decisions and take action without human intervention, which is really exciting but something that is relatively new though the idea is certainly not new.

I think the effectiveness of reasoning with AI systems has become so meaningfully better over the past couple of years that I think the concept is — like many parts of AI, the ideas are not new, but the effectiveness is, and so we’re living in an era of agents now.

In practice, I think the word agent, just like the word app or site in the age of the web, will become important to all of us. So one agent that I think is important is what my company Sierra does, which is your company’s conversational AI. And so just imagine you’re a retailer. I think you’ll put as much care and attention into your AI agent as you do your website or your mobile app. Or if you’re a bank, and you’ll put as much care and attention to your AI agent, which can help a customer look up the balance of their checking account or perhaps be an interface to your investment banking arm or wealth management arm. Or if you’re a streaming service, your agent might help people sign up for a plan or upgrade or downgrade their subscription, as an example.

In that case, an agent is something like website or mobile app that’s branded and it’s yours. And there are parts of it that are about agency and sort of the AI definition of the word. But more importantly, it’s your thing. It’s your digital asset. It becomes the digital manifestation of your brand.

And that’s what my company Sierra does. And we think that’s one really important part of an agent. Just like in 1995, the way you existed online was to have a website, we think in 2025, the way you will engage with your customers will be your AI agent, and we think it’s a really important new category.

But then taking, okay, what are the other types of agents out there? One will be, I’d like to think of them as persona-based agents. They’re internally facing. They do a job. You’ve talked about software engineering. I think there’ll be software engineering agents that will work to produce software. I was looking at a start-up called Harvey, I think, that’s making a legal LLM, which is super interesting. And I think across many job functions, there will be AI agents that produce the output of a — whether it’s a paralegal or a software engineer or an operations analyst, things like that. So that’s one.

So there’s your company’s agent, there’s a persona-based agent that does a job, and then the third one — category is probably personal agents. So this is the agent that will work on your behalf, whether it’s helping you plan a vacation or organize your calendar or perhaps triage your inbox and things like that. I think technically, they’re all similar, but my guess is they’re different enough in what job they accomplish for you that there’s — probably different companies will build those different categories of agent.

If you’re building a software to be a personal assistant agent, the breadth of systems you have to integrate with is infinite because different people use different calendars and different this and different that, and there’s lots of interesting investment into that. If you’re building a coding agent, it’s a much more narrow use case but very deep, and you’re probably evaluating it based on benchmarks of the effectiveness of the software produced and the robustness of the software it produces…

…Patrick

What do you think are the next most important unlocks for the power of these agents? You mentioned their access tools, access to the Internet. I’ve heard people talk about the ability to have some sort of stored memory about you, the customer or the specific customer or just memory in general that doesn’t just live inside of a context window that’s always re-fed in or something.

Are those the three things that we need to unlock the next tier of productivity out of agents? Are there other things that you and Sierra are focused on? I’d love to get down to the nitty-gritty capabilities and roadblocks that you’re thinking about and working on that might make these things as ubiquitous as you think they will be.

Bret

Yes. I’ll start with the vantage point of Sierra. We help companies build customer-facing AI agents. Today, if you’re setting up a new Sonos speaker, you can chat with an AI agent they’ve built on our platform to help you set it up. If you’re a SiriusXM subscriber, you can chat with Harmony, which is their AI agent they’ve built on our platform. And if you’re a WeightWatchers member, if you click on the 24/7 live coaching tab in their app, that’s an AI agent they’ve built on our platform.

One of the things that I think is a nuanced problem that is not strictly technical in nature is just the act of actually designing conversational customer experiences is a relatively new discipline. I remember in the early days of the Internet, most websites looked like DVD intro screens, like they’re very graphical, there’s four big buttons. It’s really interesting to go down the Wayback Machine and look at them.

And I would say it took a number of years to evolve into sort of the design idioms that we recognize with websites today. And now if you go to a retailer, they’ll have a hamburger menu on the top left, and the way you filter through items and these — they’re sort of emergent from people’s lived experiences, both designing and using websites.

And now you can talk to almost any web developer. And they’ll not only choose similar technologies to make a website, but even the design process and Photoshop or Figma to design a website, they’re sort of established practices, some of which are obvious and some of which are actually subtle, like why did this become the way these things are done, and it’s the cumulative experience we have building with them.

The difference between a website in a mobile app and an AI agent is both the breadth and non-determinism of AI agents. So if you have a menu on a website, you can control what links are there, and it’s essentially multiple choice, here’s the options available to you. If you have an AI agent with a free-form text box, people can type whatever they want into that. And so your concept of what your customer experience is defined by you, but it’s also defined by your customers, by what they write in there.

It reminds me — going back to my web analogies here, it reminds me of going from Yahoo Directory to Google Search. Rather than having a taxonomy of everything available, it’s just free form, and there’s a much longer tail of queries in Google than there was in Yahoo! because of the expressiveness of a search box versus a directory.

And I think that that’s one of the really interesting and, I think, exciting opportunities with conversational AI for customer experiences is it’s a really authentic way to actually hear from your customers what they want from you. And I think we’ve — and so it sort of stands to reason, your website was the rails on which your customers communicate with you. And this is a free form that I think it’s much more expressive. And we’ve had multiple customers learn things about their customers that they didn’t expect by providing this really free-form experience.

And then similarly, I think the other really interesting thing when I mentioned non-determinism is the word agent comes from agency, and it’s really how much creativity do you want to give your AI in interacting with your customers. I think if you start from a position of control, you can say, I want to put guardrails around everything, but then your conversational customer experience is somewhat robotic. You’ve essentially defined the multiple-choice options of your customers’ experience. If you give your agent too much agency, in the extreme case, it will hallucinate, but in the more practical case, it just might not protect your brand in the way that you want it to.

And I would say that design question is both a technology question, which obviously we’re quite invested in solving, and I’m really excited about some of the work we’ve done there, but there’s a deeper question here, too, that’s actually a philosophical branding and design question as well. And what we’re trying to do at Sierra is not necessarily predefining answers to those questions. I think every company and every brand will have a different perspective on what’s correct for their brand experience but provide a platform that’s powerful and expressive enough. Whatever your answers are personally to that question, you can build your agent on Sierra.

Patrick

It’s so interesting to think about the customer experience going to a website where I buy shoes or something. I think one of your first customers was flip-flops, and there was a funny story around that, but I’m going to buy a pair of sandals, let’s say, on a website. And rather than click around, I just describe what I want and I can imagine like another pane on the right just starts showing me stuff. And then maybe I check out through this same thing as well, and that’s a simple version of tooling or ability to take action.

I’m curious what the hardest parts for you have been to build. It’s quite technically daunting to even think about how to build something like this, let alone one that’s adjustable and tunable to my specific brand. So talk a little bit about how hard of a technical challenge this is for Sierra, like the degree of difficulty you’ve encountered relative to, say, your expectation.

Bret

Yes. It’s a really wonderful question. I think that generative AI broadly is a technology with which it’s very easy to make a demo and very hard to make an industrial-grade system. And I think that’s the area of technical challenge that we’re really trying to dive into. And I think it’s one thing to say that this system does the correct thing 90% of the time. And it’s really an inkblot test whether 90% is a really good number or a horrible number.

And it also depends on the process. And so if it’s a consumer application that was helping you with your homework, maybe 90% is decent. If it’s something operating revenue impacting part of your business or there’s a compliance concern, it’s absolutely unacceptable to be wrong 10% of the time.

And so a lot of the challenges that we’re facing are, we like to say that software systems are moving from rule-based to goals- and guardrails-based. And it’s a very different mental model for building software systems. Rule-based systems, if you think about just the software development life cycle that’s evolved over the past 20 years, it’s really about how you make more and more robust rule-based systems, how do you ensure that the same input produces the same output, that it’s reliable, that it’s stable, and there’s a lot of true innovation in the way we make software to make them more secure and robust.

Now if you have parts of your system that are built on large language models, those parts are really different than most of the software that we’ve built on in the past. Number one is they’re relatively slow compared — to generate a page view on a website takes nanoseconds at this point, might be slightly exaggerating, down to milliseconds, even with the fastest models, it’s quite slow in the way tokens are emitted.

Number two is it can be relatively expensive. And again, it really varies based on the number of parameters in the model. But again, the marginal cost of that page view is almost zero at this point. You don’t think about it. Your cost as a software platform is almost exclusively in your head count. With AI, you can see the margin pressure that a lot of companies face, particularly of their training models or even doing inference with high-parameter-count models.

Number three is they’re nondeterministic fundamentally, and you can tune certain models to more reliably have the same output for the same input. But by and large, it’s hard to reproduce behaviors on these systems. What gives them creativity also leads to non-determinism.

And so this combination of it, we’ve gone from cheap, deterministic, reliable systems to relatively slow, relatively expensive but very creative systems. And I think it violates a lot of the conventions that software engineers think about — have grown to think about when producing software, and it becomes almost a statistical problem rather than just a methodological problem.

And so that’s really what we’ve tried to solve. We shared on our website, but we have a process we call the agent development life cycle, which is the name comes from, say, in the software development life cycle, here’s what you should do with these agentic platforms. It’s also — we’ve developed a lot of unique technology to make these systems more robust with having one AI model supervise another AI model to layer different models on top of each other to produce statistically more robust results.

And then as importantly, we’ve developed ways that folks who aren’t experts in AI can express the behavior that they want in their agent. You shouldn’t have to be an AI expert to make an agent just like you shouldn’t have to have a PhD in computer science to make a website. I don’t think we’re there yet, but that’s really what we’re trying to solve.

And broadly speaking, I would say, on the spectrum of fundamental research institutions like OpenAI, we’re not that we’re applied. We’re really thinking about how do we engineer on top of these foundation and frontier models to produce robust or reliable agents for our customers.

Patrick

I love the title of this one Kevin Kelly book, What Technology Wants, and I’m curious what agents want. If I’m a customer, I’m a prospective customer, and I want to go work with Sierra to make the best possible version of a conversational agent for my customers to use, what can the companies provide that make the agent do the best job?

Bret

Yes, it’s a great question. I would say that there’s two types of knowledge that I think really produce a really robust agent. One is the factual knowledge of your company. This just grounds the agent so that it won’t just make something up.

There’s a pretty widely-used technique called retrieval augmented generation in AI right now that effectively means rather than relying on the knowledge encoded in the model to answer questions, you present the model with knowledge, maybe stored in a knowledge base or a database and say, “Hey, summarize the content from here. Don’t rely on the information you’ve been trained on.”

That has been an effective technique for two reasons. One is that it means that you don’t necessarily need to train or fine-tune a model to use it with proprietary data, which is a much cheaper deployment methodology. And it also can be effective at preventing hallucinations as well because you’re effectively — rather than relying on the AI to determine what it knows or doesn’t know, you present the AI with the knowledge that it’s allowed to know, a simple way of putting it.

And that’s factual knowledge. And I would say that’s necessary, but woefully incomplete because that would enable your AI agent to answer questions, but it wouldn’t necessarily enable it to orchestrate a complex process or take action on your customers’ behalf.

The other type of knowledge is procedural knowledge. We have a Sonos speaker. It stops working. What would the best Sonos engineer ask you and do to figure out whether it’s a problem with your hardware, a problem with your Sonos app or a problem with your Wi-Fi? Like what is the process by which you do that.

If you’re a subscription streaming service, what is the process of processing an upgrade or downgrade to your membership? Are there different offers available based on your membership level? Do you have a promotion running? What’s been the most effective technique to keep people, a subscriber for a long period of time?

This is all the stuff that if you are a person and expert in it, and so coming in with that knowledge of not only here’s the factual knowledge for our company, but here’s the processes that represent our greatest customer experience. What does the best salesperson do? What does the best customer service person do? What is — the most effective marketeer at your company, how do they describe your products? And that’s often there we work with our customers to improve when they deploy AI.

And then the third thing is just access to the underlying systems themselves. I think the AI agents shouldn’t just be about answering questions or having a conversation, they should actually be able to take action on your behalf, whether that’s a retailer processing a return or a subscription service, changing your level of membership or connecting to the telemetry system of a consumer electronics company. So we can say, “Hey, we know your device phoned home. You’re connected. We now figured out this other problem.”

Or even with something like SiriusXM sending a signal down from a satellite to refresh your radio if your radio stopped working. So three ingredients, factual knowledge, procedural knowledge and systems integrations, I think, are the three key ingredients. And then with the right methodology, your agent can do anything that a person could do on a computer, which is just an incredible opportunity for customer experiences.

3. Here’s What Happens When Credit Markets Go Dark – Joe Weisenthal, Tracy Alloway, Jared Ellias, and Elisabeth de Fontenay

Joe (11:53):

You spell out this evolution of the debt markets and the historical things you’re taught in law school about the dangers of single lenders. We’ve talked to people in the industry and they have their explanations for why this particular market has boomed. But from your research, what would you say are the drivers of this? Or when you talk to people, what problems does the private credit market solve for them?

Elisabeth (12:19):

The interesting thing about this is that there’s multiple stories going on at the same time. So one is that, this is just actually substituting for a lot of the activity that banks did because the banks, ever since the financial crisis, have been really constrained for a lot of reasons. One, they’ve primarily been constrained because of regulation, and sort of regulation designed to discourage them from making risky loans and from, you know, to have diversification in their portfolio, and so on. And just their evolving model of doing business, that they prefer to be sort of the middleman and get some fees rather than lend directly. [There are] all kinds of reasons why banks have retreated from particularly the lower middle market, but also all the way to the largest companies. A second story is just that there’s been too much bank regulation. So, I’m not going to take a position on whether that’s true or not, but that bank regulation is stifling the banks and they can’t really lend and so on.

A third story is one that we find really interesting and appealing, which is that, it may just be that it never really made all that much sense to fund loans using bank deposits. That essentially, you have a very short-term liability, which is customer deposits, and very long-term assets. So some of these loans, of course, are multi-year loans. And that’s just a fundamental mismatch that banks have always struggled with and that bank regulation has always struggled with. And this is a really nice, neat solution to that. And the reason it’s showing up now is that, thanks to sort of loosening of some of the securities laws and other things, it’s finally the case that you can get these investment funds that are big enough to actually take over the role of banks. And for them, the sort of positive side of private credit is that you now have a better match between the funding source, which is you have these big institutional investors putting capital into private credit funds that is locked in for a number of years, and you’re matching that really well against the loans that are also multi-year. So in some sense, it’s actually a better fit than banks for financing this type of loan… 

...Joe (20:43):

It sounds pretty good to me. Okay, so there’s less legal fees, less creditor on creditor violence, liability asset matching, the better user experience. So what’s the catch? I don’t see any problems.

Elisabeth (20:57):

One potential problem is, of course, these are, in some cases, absolutely massive loans. And so you do lose diversification benefits. These are very risky investments. I would say, the private credit structure has a partial solution to that problem, which is that, the investors themselves in a private credit fund oftentimes are so massive themselves that they really don’t lose diversification, which is to say, their portfolios are so large that they can make this enormous investment in one private credit fund because that’s a tiny piece of their portfolio. So that’s one downside of private credit. The other of course, is the absence of trading. So before. you had pretty good signals of what your position was worth. There were lots of syndicated loans that had pretty active trading and there were indices tracking all of this. The [Loan Syndications and Trading Association] LSTA provides lots of data on the loan market, and, of course, the bond market is public in terms of the pricing there. So exit is always going to be a concern in this market, and I don’t think this market really has been truly tested yet. So we’ll have to find out. But that illiquidity can be an issue depending on what kind of investor you are and what your expectation is for getting out of these things…

…Tracy (30:14):

Just to play devil’s advocate for a second, I think this is something you actually deal with in the paper, but one of the things you hear from people in the private credit industry is tha, ‘Oh, well, if you’re getting funding from a private entity, maybe a single lender or maybe a club of lenders but it’s a smaller group than you would have in the public market, maybe there’s greater potential for working out your issues if you get into trouble. So you can renegotiate your debt with a smaller group of creditors and maybe they know your business better than like a big fund that is buying pieces of all these different types of bonds and things like that.’ What’s your response to that argument? This idea that, well, private credit actually allows you to have more room for workouts or maybe even stave off bankruptcy for longer?

Jared (31:09):

So, I guess my answer is that, that all sounds great, but it’ll depend. And it’s hard to really understand which way any of these forces cut. The one thing that’s clear cut, that’s important is, we’re losing the claims trading markets. Like, that’s just going to look a lot different. Like, the active market and the claims of Chapter 11 debtors, when that debtor is a private credit funded firm. But, as to the question of, ‘Well, you know, aren’t these private credit lenders smarter, more versatile, more nimble, able to commit capital? And won’t that be good for companies?’ You know, at the end, it depends. So something you worry about is, well, maybe private credit lenders will have incentives, not to adjust their marks on their books and instead, just to do ‘amend and extend’s, and just keep loans going when the company really needed to liquidate or should have filed for bankruptcy sooner.

Think about how different the GM bankruptcy would’ve been had they filed for bankruptcy in like 2005 versus 2009 when their business had already eroded so much. So we think of that erosion as something that limits reorganization options. And it’s not necessarily obvious how private credit interacts with that. Because private credit lenders have their own incentives and maybe their incentives are to say, ‘Look, we make loans to sponsor backed companies and if the sponsor wants to continue, we’re going to keep doing that because we really want to participate in their next deals.’ Or they could say like, ‘Let’s pull the plug on these things earlier.’

So something that I’ve heard from lawyers working in this space is that when private credit lenders replace like your mid-market banks, like your Citizens and that kind of bank, when you have like a private credit lender with a $30 million loan that might have been done by a syndicate of two regional banks, the private credit lenders are much more aggressive and much more willing to pull the plug on the company and to own the asset then that bank might have been, but the world could look very different for larger companies where private credit lenders might be easier for companies to do workouts with. So it’s really hard to tell. But I’m certainly a bit skeptical of the idea that all of this is unidirectional and the private credit is just better in every way for everything. It’s different and there’ll be different pros and cons and we’ll learn more about them, and the law will adapt and hopefully deal with some of the ways in which the incentives of private credit lenders distort bankruptcy outcomes.

Tracy (33:28):

Since you mentioned GM, could you maybe talk about another specific example of a liquidation playing out a bit late, as you describe it? I’m still salty over the collapse of Red Lobster, which you mentioned in your paper. So could you talk a little bit about that one and what it tells us about private credit?

Jared (33:47):

Sure. So, something that has been the case over the past few years is you’ve had private equity owned restaurants and retailers that just ended up doing quick liquidations after stalling for a very long time. Red Lobster is really interesting. Red Lobster had been struggling for a little while and then Fortress Investment Group, which was its private credit lender, came in and took over the company and basically just owned the asset very quickly. And something that is so interesting about that is that, traditionally, other lenders would’ve been a lot more cautious about doing that, because other lenders are very cognizant of what we call ‘lender liability’ and this line of law that suggests that you shouldn’t, if you’re a lender, play too much of a role in business decisions of companies that you lend to.

And like, there’s an example of like a private credit lender just behaving in this really aggressive way, which is interesting. Like, again, it’s hard to tell exactly what’s going to happen, but certainly that example doesn’t fit well with the story of, well, you know, the private credit lender is just like the banker and you know, it’s your corner bank in 1925, who’s going to work with you on your farm. The answer is, maybe some of the time that’s the story, but other of the time, you’re dealing with a very sophisticated party who may have different incentives and be worried about different things than traditional bank lenders or investors in the broadly syndicated market.

4. Flash Crashes Are Getting Faster – Ben Carlson

In the spring of 1962, the stock market was already in the midst of a double-digit correction. Then on May 28, there was a flash crash, sending stocks down nearly 7% in a single day. It was the biggest one day sell-off since the Great Depression…

…It’s becoming clearer by the day that last Monday’s stock market swoon was also a flash crash. As of August 5, the S&P 500 was down more than 6% for the month. It’s now positive in August…

…Flash crashes happened in the 1920s, they happened in the 1960s and they happen today.

The biggest difference between now and then is the interconnected nature of the global markets. You have computer and algorithmic trading. Information flows at the speed of light. Every piece of economic data is parsed in real-time with a fine-tooth comb.

Overreactions can happen much faster now.

Just look at the biggest gap downs over the past 40+ years:

This chart shows the biggest difference between the opening price of the stock market and the prior day’s close. All of them have occurred this decade outside of the 1987 crash…

…We are likely to see more of these flash crashes in the future due to a combination of increased leverage in the system, globalized markets and computer trading.

The hard part for investors is that it’s now easier to lose control during these types of market events. You don’t have to call your broker on the phone to place a trade. You can change your entire portfolio on your phone with the push of a button.

Just because markets are getting faster does not mean your decisions must be made faster.

5. Gaining Currency – Rachel Cheung

In its effort to cement its role as an innovation powerhouse, China’s most ambitious technological debut was also its most controversial: The digital yuan was rolled out as the legal tender of choice for the Olympic games. Instead of cash or Visa (the corporate sponsor that had dominated the sports event for three decades), visitors were encouraged to exchange foreign currencies for digital yuan at automated teller machines and to pay digitally through the e-CNY app on their phones or through a card that can be used offline…

…Yet, despite all the attention, the launch of the digital yuan largely fell flat. The COVID-19 pandemic meant Olympic visitors were confined to “bubbles” with little opportunity to travel, shop and dine out, and very few foreigners chose to use the digital yuan over their credit cards. Beijing saw just $315,000 in digital yuan processed every day over the course of the games — a small fraction of the usual revenues at the Olympics. At the 2008 Olympics in Beijing, for instance, the city generated roughly $264 million per day…

…But while China acknowledged its Olympic failure, it has also quietly doubled down on the digital yuan, including a big push to drive adoption. Last year, several cities began paying civil servants and collecting taxes in digital yuan. Jiangsu province saw the most recorded transactions in the country after it gave away 30 million yuan ($4.18 million) in digital “red envelopes.” And this past May, the digital yuan expanded for the first time outside of mainland China when it became available for use in Hong Kong. Though there is no timeline for a nationwide launch yet, China has rolled out pilot schemes in 26 cities and 17 provinces since 2019.

The efforts have paid off. In a press briefing last week, the PBOC announced that total transactions reached $7 trillion yuan ($982 billion) in June — a four-fold jump since last June.

Digital yuan usage is still only a fraction of China’s $40-trillion payment market, of course. The total number of e-CNY wallets opened — 120 million as of last July — also trails behind that of Alipay, which had over a billion users by 2020 and recorded $118 trillion worth of transactions in one year alone.

But as Beijing continues to crackdown on its fintech giants, it is creating room for the digital yuan to rise. In fact, officials see the transition to digital currency as both necessary and inevitable. According to Yi Gang, former governor of the PBOC, the current moment of transition is not unlike that of the Ming Dynasty, when the government started taking tax payments in silver instead of labor and grains. China’s currency has evolved with time, he said during a speech at Fudan University in April, and “the digital yuan is no exception.”…

…Officials are also trying to expand the scope of e-CNY beyond consumer retail transactions. The Bank of China, for instance, has tested the use of “smart contracts” for afterschool programs in Chengdu of Sichuan province: Parents can pay a deposit in e-CNY to educational institutions, and the latter only receives the money after the lessons are taken.

These business-to-business and government programming applications could be a “game changer,” according to Warwick Powell, a senior fellow at Taihe Institute, a Beijing-based think tank, because they “ensure that the provision of certain funds can only be used for certain activities.”

Yet that same function triggers concern for others. For instance, although some local governments and banks have offered loans in e-CNY, companies are reluctant to take them, says Yang You, a finance professor at University of Hong Kong. “The nature of e-CNY is that a policymaker can generate a loan and see where it flows to,” says You. But companies, he notes, would much prefer non-traceable loans, despite repeated assurances from the People’s Bank of China that it will not hold information against them…

…Instead, the PBOC says the digital yuan follows a principle of “anonymity for small value and traceable for high value” as a way of striking a balance between privacy protection and combating criminal activities, such as tax evasion and money laundering. The e-CNY wallet, for instance, requires users to undergo a more complex verification process in order to unlock higher transaction limits…

… If anything, the search for an alternative to the U.S.-backed Swift, the global messaging network for the banking system, has gained momentum since the U.S.-led sanctions on Russia.

“China has used the sanctions as a reason to advance the cause of de-dollarization,” says Elizabeth Economy, a senior fellow at the Hoover Institution at Stanford University and recent advisor to the Department of Commerce. “It has made the case that the United States is weaponizing the dollar, hence other countries should begin to trade in their own currencies. It’s actually a deft diplomatic move on the part of China.”

According to the Bank of International Settlements (BIS), a survey of 86 central banks last year showed a sharp uptick in experiments with “wholesale CBDC” — transactions between banks and other financial institutions, rather than consumers and businesses. In October, for instance, the e-CNY set a new milestone: At the Shanghai Petroleum and Natural Gas Exchange, the state-owned PetroChina used digital yuan to purchase a million barrels of oil from an undisclosed seller.

“There’s still a conversation about the e-yuan [for domestic retail transactions], but there’s more discussion about a regional payment system,” says Victor Shih, an associate professor of political economy at the University of California. “An alternative to Swift potentially has more legs.”

The oil purchase seems to be a one-off so far, but a new project called mBridge hopes to make such transactions routine. It is a collaborative effort between the “innovation hub” of BIS and the central banks of five jurisdictions: China, Hong Kong, Thailand, United Arab Emirates, and most recently, Saudi Arabia. 

Underpinned by distributed ledger technology (which records transactions in multiple places at the same time), mBridge aims to be a multi-CBDC platform that can support instant cross-border payments. The idea is to make international settlement faster and cheaper than Swift. But it also means things are not dependent on the U.S. dollar.

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, Mastercard, 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 08 September 2024:

1. #360 Robert Kierlin: Founder of Fastenal – David Senra

What’s the most important part of Fastenal’s success that outsiders discovering the company for the first time don’t understand? The number one thing is the people aspect. The goal is to unleash entrepreneurial passion, a commitment that I will be self-driven to do better than what you can expect. It is a mindset.

This is what they’re telling their employees, “Run your business like you own it.” When you trust people to solve problems and make decisions and you let them go, that’s where the magic happens. That is the story of this company. Fastenal embraces a spirit of radical decentralization in autonomy.

“Each of its 2,700 stores operates as a stand-alone business with a clear leader and full P&L responsibility. We grow from the ground up based on the actions and decisions of thousands of people who run their businesses like they own it. I want those people to stay with us forever. They will never have to stop at a certain level.”…

…“We are now one giant organization. We are 2,700 small businesses wrapped up into one big company. Society tells us, you’re a big company, act like it. We say no, you don’t get to define us, we define ourselves. We’d go against the grain in almost everything that we do.” And so there’s just crazy stats about the company that, “More than 95% of our current batch of general managers have been promoted from within”.

“For nearly all of our senior leaders have worked their way up from entry-level positions.” So that leads into the second thing I want to tell you about, which is this interview with the CEO that had succeeded Kierlin or Bob — we call him Bob, when he stepped down. His name is Will Oberton.

Oberton started out at Fastenal as a store clerk. So he went all the way from — they mean business. He went all the way from store clerk to CEO…

…“By keeping operating costs very low, Fastenal is able to pay their employees incrementally higher wages, and thus, more effectively develop and retain talented salespeople. The quality of service and depth of knowledge that the employees have eventually brings in more revenue, which grows the business and allows it to further lower operating expenses as a percentage of revenue, thus allowing for more hiring of top-quality employees, which brings in more revenue. This is an overlooked virtuous circle of sorts.”…

…Why is it so important to have everybody working as a single cohesive team, to have everybody thinking that their role that they’re playing is just as important as the person next to them? “At Fastenal, we believe that you could be the best salesperson in the world. But if the order-picker doesn’t pick it right or the truck driver doesn’t get it there on time or the billing clerk doesn’t bill it correctly, you end up with an unhappy customer.”

“Everyone is key. You are better off working to make everyone equal so they stay focused” and he goes back, what do you think he’s about to say? I bet you can already finish his sentence for him. “You are better off working to make everybody equal so they stay focused on the common goal of pleasing the customer.”

[00:26:01] He’s going to give us more advice on how to do that. You need to install a reward system that keeps everyone focused on the common goal. He’s talking about incentives. If you have Fastenal’s common goal of growing our company through customer service, you will avoid any rewards that don’t fit that goal. And so when I got to this part of the book, I thought about Charlie Munger’s like three rules for incentives.

And so this is what he said, “Number one, everyone underestimates the power of incentives. Number two, never ever think about anything else before thinking about the power of incentives. And number three, which Bob is nailing, the most important rule in management, get the incentives right.”

And again, you have to be careful of these subgroups that are going to naturally develop in your company because his whole point is like “Listen, your incentives have to — they have to fit your overall common goal,” right, the common goal of pleasing the customer. And so he gives us an example, “If you do these incentives based on like separate groups, they can optimize for things that go against your common goal.”

So he gives an example that this is a really smart idea. “We do not reward production people for minimizing scrap. If some of that scrap you eliminate comes from the extra parts that guarantee you have a full order quantity ready when the customer wants it.” The incentive superpower that Munger talks about, you clearly see by picking up the book…

… I want to go back to that story of the CEO that was meeting with Buffett, the CEO that succeeded Kierlin. So this giant part of Fastenal’s business now after, this was invented after this book was written, okay, the first version in 1997, was the fact that they have these vending machines.

And the way I think about the vending machine is like think of anytime you’ve been in like a hardware store, right? You’ve got ACE Hardware or Home Depot or anywhere else. And think about how all the equipment and supplies are presented, kind of like searching through, it’s kind of like a chaotic mess.

So Will Oberton, which was the former CEO, but he’s no longer CEO now, but he’s the one that was CEO after Bob, okay? Oberton also developed an industrial vending machine system. There’s a video on YouTube that’s fascinating about this. It’s from Fastenal. Fastenal has their own YouTube channel. You can see the vending machine if you just type in Fastenal vending machine, if you’re interested in this, I thought it’s actually cool.

Oberton developed an industrial vending machine, and I searched for it after I read this because like I got to see what this looks like. Oberton had developed an industrial vending machine system, helping Bob realize a lifelong goal. In 1951, as a 12-year-old working in his father’s auto parts store, Bob was bothered by the fact that his dad had to send customers searching for nuts and bolts to someone else’s store.

He imagined that a vending machine installed at his father’s place might pop out fasteners like gumballs. Once on his own, he tried to convert a cigarette vending machine to this purpose. He couldn’t get it to work. So he started selling fasteners over the counter. Thus, Fastenal was born. 40 years later, working with a snack machine manufacturer and off-the-shelf software, Will Oberton got the job done.

Fastenal’s vending machines have been a big hit with customers. So their vending machines are actually installed in their customers’ locations. It cannot get simpler for this. You got to watch the video, I’m telling you. Oberton got the job done. Fastenal’s vending machines have been a hit with customers, generally helping them save 30% on supplies.

[00:46:05] The machines have cut down on theft and enabled automated reordering. That 4-year-old business, which I think now is like 15-years old, within 4-years old, this new idea already started contributing to 36% of the overall sales of Fastenal, I think it’s like over 40% now. 

2. A French Bank Like No Other in Europe Seeks to Export Its Model –  Phil Serafino and Albertina Torsoli

Bpifrance is a bank like no other in Europe.

The French lender has made more than €50 billion ($56 billion) in loans to small and mid-sized businesses and has €52 billion in stakes in almost 1,000 companies. It has backed everything from a startup wanting to take tourists to the edge of space in balloons and a chain of trendy Parisian nightspots to the automotive giant Stellantis NV. A force to reckon with on French deals for M&A advisers like Goldman Sachs Group Inc. and JPMorgan Chase & Co., it has lured away bankers from firms like UBS Group AG and Rothschild & Co…

…No other European country has an agency quite like Bpifrance: a for-profit, state-owned merchant bank with a mandate to foster national champions. Its wide-ranging lending activities are financed largely by borrowings guaranteed by its ultimate backer: the French taxpayer. And for all the political turmoil at the moment in France, its interventionist policies are likely to find favor no matter which coalition — from the left or the right — ends up forming a new government.

More than a decade after it was created under then-President François Hollande and his economic adviser — one Emmanuel Macron — Bpifrance exemplifies 21st-century French capitalism: Entrepreneurs build businesses with cash, nudges and nurturing from the state, which in turn wants them to create jobs at home and develop innovative technologies. Explicit in the deal: The government will fend off foreign interlopers if necessary…

…Bpifrance’s investment prowess and risk management haven’t really been tested because Dufourcq hasn’t faced a prolonged economic downturn, enjoying a favorable wind at his back almost from the start — even during the pandemic, when the French state opened the cash taps to prevent businesses from going under.

Its stock-picking bets also haven’t always paid off. A stake in train-car maker Alstom SA, for example, has lost about a third of its value since the investment early last year. Shares of Stellantis, in which the bank has a 6.4% holding, have slumped about 45% from their peak in March as the carmaker struggles to fix problems at its US and European operations.

Also, for much of the bank’s existence, it could finance itself at rock-bottom interest rates, something that’s no longer the case. A slowing economy and higher rates also may start to hurt companies that borrow from the bank: Bpifrance’s loans classified as doubtful stood at 4.7% at the end of 2022, up from less than 4% in recent years, according to the bank’s annual reports. It didn’t disclose the statistic in its 2023 report.

Dufourcq shrugs off such concerns, noting that the three decades-old agencies that combined to form Bpifrance survived some deep financial crises, and says his bank often says no to risky investment proposals.

While some European countries have national development banks, Bpifrance is unusual for the breadth of its offerings. It operates 50 offices around France, often sending representatives door to door to drum up business. In addition to debt and equity investments, it offers financing and credit insurance to exporters and training and consulting services to entrepreneurs — including on how to shrink their carbon footprint.

3. How Richmond Fed President Tom Barkin Sees The Economy Right Now – Cale Brooks, Tracy Alloway, Joe Weisenthal, and Tom Barkin

I talked to someone from Germany yesterday — this is going to make me interested but not your [audience]. Our savings rate went up at the beginning of Covid to about 15 or 16%. Same thing happened in Germany. Our savings rate has come down to about three and a half. Theirs is still at 17. So, why are German consumers not spending the way American consumers are? That’s an interesting topic. It’s something we spent some time on. It’s the kind of thing we spent some time on…

Tracy (08:01):

What’s the theory?

Tom (08:04):

Well, so the thing that really makes it crazy interesting is, there’s a whole social safety net in Europe that doesn’t exist here. And so, most of the time you think people are saving for retirement, they’re saving for a rainy day, or they’re saving because they’re worried about losing their job. Well, in Germany, they kept everyone’s job during the pandemic and you’ve got a pension. So why are they saving? And I think the best explanation I’ve gotten, it’s actually something on my list to study going forward, is there’s just a lot more precautionary feeling about the situation in Europe, the risk versus the Ukraine, and what’s happening over there. And it’s just a culture that maybe has just gotten a lot more cautious due to geopolitics if nothing else.

Joe (08:43):

That does sound really interesting. By and large, I mean obviously, the situation in the Argentina economy is radically different than it is here in the US. Germany is probably still, all things considered, similar cyclically to the us. Does it feel like, by and large, at least among developed countries’ central bankers, that there is a strong set of common mysteries perhaps? Or are they really like, everyone’s sort of seeing different things in their own country? I mean I’m sure it’s a mix of both, but how much of a global factor is there?

Tom (09:16):

Much more in common than different. The whole practice of central banking has been, I’d say, globalized over the years. And central bankers really do think about inflation targeting, for example, in the same ways. And there are banks, like New Zealand and Australia, that, back in 2000 or even before that, set inflation targets before the rest of us and we learned from them. And so there’s a lot of learning, there’s a lot of discussion. I think there’s very much a common framework. Now, the economies are very different.

I mean, the US economy has come through this unbelievably well, the European economies have not. And so we have a much stronger economy. So much of our economy is services, so much is supplied to ourselves. A lot of this deglobalization is felt much more on the European side. The challenges in China right now are felt much more on the European side. And then emerging market countries, they really just are worried we’re going to increase rates further and they’re going to end up offside. And so, they’re very dependent on our strength of our dollar and the weakness of our dollar…

…Tom (11:43):

I think the economy, since we were together three or four months ago, the economy’s moved in a very different way. First of all, on the inflation side, I might’ve even said four or five months ago I was looking for inflation to sustain and broaden. So, it’s sustained. We’ve got very low readings for four months in a row. And it’s now across the basket, whereas six months ago [or] eight months ago, it was really just in goods. And so the concern about inflation, reaccelerating has definitely come down significantly. At the same time, the labor market stats have also softened. And so, the phrase I’ve been using is, ‘people aren’t hiring but they’re not firing,’ and that’s just not a high likely sustainable outcome. Either demand will continue and people will start hiring again or you’ll start to see layoffs. And so I think there’s more concern on the labor market and less concern on inflation relatively…

…Tom (13:00):

So consumers, you hear a lot of talk about people saying that consumers [are] weak and people are running out of savings. That’s not what I’m hearing. What I’m hearing is consumers are still spending but they’re choosing. And, the way I think about it is, they now have the time when they go into a store and they see something that’s at a price they don’t like to say, ‘I think I’m going to do something else.’ And so if you look at Walmart’s results, they would talk about people trading down. If you look at Target’s results, they talked about the kind of reaction they’re getting to lower prices. McDonald’s results in the $5 value meal. I’ve talked to hotel chains that every room is booked, but they can’t raise price at all because the second they raise price, people just won’t buy it and won’t book it. I talked to a fast food leader who’s rolling out software actually to encourage their franchisees not to raise prices anymore…

…Tracy (15:15):

What’s the urgency, then, on supporting the labor market? And there’s obviously a debate going on right now about how fast deterioration in that market actually happens. We had Claudia Sahm on the podcast recently and she was talking about, ‘maybe it’s different this time,’ but how are you thinking about the pace or the rate of change in the labor market?

Tom (15:37):

So the other thing that’s happening in the labor market is a lot more supply of labor, and part of that is participation, prime age participation hitting 2025 year highs, and immigration, which is up significantly. And so the last jobs report where unemployment went up from 4.1 to 4.3, you actually added jobs, 114,000 jobs. We just added 420,000 people to the workforce. So the denominator got bigger. And so, you know, there’s some people who look at the unemployment rate and say, ‘Oh my gosh, the labor market’s about to fall off a cliff.’ That’s not how I see it. I see a loosening labor market being driven by a lot more supply. Now, what’s the urgency? We’re not in a situation, I don’t believe, where there is this big cliff there, but when we make policy, you’re trying to make it for a year from now, right? Because [of] the lags of monetary policy, you’re trying to meet a year from now.

And so you’ve got a labor market which is slowly cooled and you’ve got inflation which is now gradually cooled. And so, you sort of say, ‘Well, which do I worry most more about?’ And it’s been very clear for the last two and a half years that all you worry about is inflation. And now those are much more balanced…

…Tom (21:57):

Well, I see inflation upside risk in two places. First is, we’re at 2.5% for the last 12 months. Our target’s 2%. So while we’re doing great at bringing it down from when it was once 7.1%, core is still at 2.5%. And even the most optimistic forecast for the back half of this year don’t believe it’ll get to 2% because the numbers were so good on a 12 month basis…

Joe (22:20):

We’re talking we’re about on a year to year basis as opposed to like a three month though sequential, yeah okay.

Tom (22:22):

On a 12 month basis. Because the last half of last year was also very good. And so, we’re at least six months away, even with really good inflation data, from the inflation numbers hitting 2%. And if the numbers are just pretty good, not really good, there’s a risk that we plateau at some level over 2%. That’s one risk. The other risk is I do see medium term inflation pressures that are out there. We have a conflict in the Middle East that could spiral. Deglobalization is a very real risk and that means that the imports of goods could be more expensive going forward, or if we even reshore, more expensive. Housing’s a place where, if rates artists start coming down, one of the things I worry about is that will spool up demand for people who’ve been waiting to buy a house till mortgage rates come down, but there won’t be any new houses built. I mean that effect is two years, three years out.

And so what happens if you have more demand for houses with the same kind of supply? Or even if more houses come on the market, everyone who puts their house in the market is a buyer and a seller. So you’d still have this excess of demand over supply. So those things are potential inflationary risks. Now, good policy works against that, and if we do the right thing with rates we’ll work against, but that’s why I just want to make sure I understand it and see it before I declare victory.

Tracy (23:37):

What’s been the most surprising thing that you’ve heard at Jackson Hole this year? You talked about German savings rate, but beyond that, is there anything that caught your eye or your interest?

Tom (23:48):

Alan Blinder asked a question today that I thought was pretty interesting. He said, ‘When you think about monetary policy lags, why aren’t you talking about how to shorten them?’ And I’ve said, almost as it’s a given, that when we raise or lower rates, it takes 12 to 18 months for the full effect to go into the economy. Well, part of that is because the economy doesn’t behave in a way that would allow it to happen quicker. An example: I think the number is, in 2009, 60% of [the] mortgages in this country were adjustable rate. Today it’s 8%. And so when we raise or lower rates, it doesn’t flow through to mortgages quickly and certainly not even like it did 15 years ago. And I’m not saying we should change the mortgage market, but it does make you stop and think, how much of our policy, the effectiveness of our policy tools, is a given or how much could actually change over time as the economy changed?

4. No Priors Ep. 78 | With AWS CEO Matt Garman (Transcript here) – Sarah Guo, Elad Gil, and Matt Garman

…Now that we’re at a $100 billion run rate, I think 85% of workloads are still running on-prem today by most estimations, somewhere in that range. Pick your number, whether it’s 80 to 90, or whatever it is. That’s enormous. If there’s still 10x growth of just existing workloads – forget all the new genAI workloads that are being created every day – these are just existing workloads to move, there’s a 10x number in there, so that business is massive…

…Gil (12:40): You mentioned that 80% of workloads still haven’t migrated over. What do you think are the main blockers to that today? is it just momentum? Are there specific features? Are there big things still to build?

Garman (12: 48): There’s some technologies that I think… Look, if I had an easy button, and by the way we’re trying to build an easy button, but if I had an easy button that would just migrate mainframes to a modern cloud architecture today, almost everyone will push that button. But it doesn’t quite exist today and it’s not as simple as like, “Great, I’ll go run your mainframe in the cloud.” That’s not what customers want. They want to actually modernize those workloads and have them into microservices and containerized workloads and other things like that. So that’s one, is there’s just a bunch of workloads like that that are old and and their customer’s running a big SAP thing and they want to move it to the cloud but it just takes time because it’s tied to a bunch of other things like that. There’s also a bunch of workloads that as you get out of core IT workloads that are in line of business, that are the next set of things. Whether that’s say telco workloads that are running the 5G infrastructure around the world, we’ve slowly been moving those to the cloud and helping those customers get that flexibility and that agility of of running those in the cloud as well. But they’re slower to move.

If you think about all the compute that runs factories out there today on factory floors, most of those have not been modernized. And there’s a huge opportunity, by the way for AI, to totally revolutionize how you think about factory workflows and efficiency there. But a lot of that hasn’t moved. There’s on-prem infrastructure that people are still amortising, there’s still people whose jobs it is to run on-prem data centers, and so they’re resistant to moving things. There’s a bunch of factors in there and so some of it is just takes time, some of it is technology pieces, some of that is we still have stuff to go build and innovate and help make it easier for customers to do that.

Guo (14:37): I’d love to hear about just the initial investigation of generative AI as a technology change and how AWS began to react to it, invest in it, because to some degree it puts us all back in the on-prem co-lo era of the world, where to get one of these, if you’re doing any sort of real pre-training, to get your startup off the ground, you’re back to, “I’ll buy a bunch of DGX boxes somewhere and I need to think about the cost and management of that.”

Garman (15:07): Actually most people are still buying those but in the cloud. But it’s not a serverless type of thing. Most people are still not buying H100s and hosting them in a co-lo or anything like that. And increasingly, I think that’s going to get harder and harder as you move to liquid cooling and larger clusters. It is a super interesting space. I think we’ve been working on this space for how many years now – we’ve been investing in AI broadly for the last 10 years, and it’s why we started five or six years ago investing at the infrastructure layer and building our own processors, because we knew this was coming, we saw this path coming and we knew that that’s also not a short-term investment. It’s one of those things you got to invest way ahead. And then we were investing and building generative AI models, and then OpenAI made a generational leap forward with what they were able to do, what was possible, and many people have talked about this. But it really in some ways was a discovery as much as anything about just what was possible and unleashed the new set of capabilities.

So we actually as a business took half a step back and said, “These are going to be transformational abilities and assuming that this technology gets better and better and better over time, how do we make it so that every company out there can go build using those technologies?” Different than, “How can I go build a consumer application that people are going to be interested in?”, we took it from the point of view of AWS, “Just what are the building blocks that I can help all of our customers, whether they’re startups, whether they’re enterprises etc, go build interesting generative AI applications.” We started from first principles. Customers are going to care a ton about security. That’s not going to change. They’re not going to all of a sudden not care about securing their infrastructure.

We also had two more hypotheses. One, the idea that there wasn’t just going to be one model. We thought that there was going to be a lot of models for a lot of different purposes, and there’d be big models and small models, and people would want to combine them in new and interesting ways. I think the last two years have probably played that out. I think when OpenAI first launched, it wasn’t as obvious, but that was one of the bets that we made. The third one is that we view that every enterprise that was building on us, the interesting IP that they were going to bring to the table was mostly going to be their data, and they were going to care that their data didn’t leak back into a model or escape from their environment. So we built a bunch of what we did starting from those principles of how do we make sure that these things are secure, that their data is secure, that they can have access to every piece of technology that the customers need to go build interesting applications, and they can do it in a cost effective way. That’s how we approach the space.

I think we now have a platform in Bedrock, in Trainium chips and Inferentia chips, and then a bunch of the other capabilities around as well as the suite of models that we offer, both proprietary as well as open source ones – or open weights ones. I think we’re starting to see that momentum pick up and we’re seeing more and more customers really like that story. They like that platform to build from, and we’re seeing enterprises really lean in and want to build in that space because it gives them a lot of that control that they want as they go and build applications…

…Gil (26:25): The other place that a lot of people are spending time right now in terms of bottlenecks to utilization or usage or future-proofing, is actually more on the chip side or semiconductor or system side and in terms of DC capacity. Obviously you all have been building Trainium chips and other things which I think is really exciting to see that evolution. How do you think about future GPU shortages? Does that go away, when? I’m sort of curious about how you think about forward-looking capacity, and is the industry actually ready in terms of building out data centers, building out semiconductors, all the rest of it, packaging.

Garman (26:56): I think we’re probably going to be in a constrained world for the next little bit of time. Some of these things, they take time. Look how long it takes to build a semiconductor fab. It’s not a short lead time and that’s several years and TSMC is running fast to try to ramp up capacity, but it’s not just them. It’s the memory providers and frankly data centers that we’re building. There’s a lot of pieces in that value chain that I think as you look at the demand for AI which has been – exponential might be undershooting it – some of those components that support that I think are catching up and I think AWS is well positioned to try to do that better than others are.

We’ve spent a long time thinking about – in the last 18 years, learning how do we think about smart investing, how do we think about capital allocation. We’ve spent a bunch of time thinking about how do we acquire our own power, how do we ensure that it’s green and carbon neutral power, all super important things. We’re the largest purchaser of renewable energy over the last… new contracts, so actually going out and adding and supporting new renewable energy projects. We’re the largest provider I think, each of the last four or five years. So we’ve been leaning into that for a while to ramp up this and this is just a step up. So we’re thinking about how are we acquiring enough power. Our own chips is a way to support the growth of Nvidia chips, and so I think the more diversity there, the better off we are. We’re a huge partner of Nvidia’s. Nvidia actually runs their AI training clusters in AWS because we actually have the most stable infrastructure of anyone else, so they actually get the best performance from us. We love that partnership and we have a great and growing relationship with them. We think things like Trainum are a good diversification and I think there will be some workloads that run better on Trainium and are cheaper on Trainium over time, and as well as Inferentia.

I think inference is one of those workloads that – today it’s 50/50 maybe of training and inference. But in order for the math to work out, inference workloads have to dominate, otherwise all this investment in these big models isn’t really going to pay off, so hopefully for the industry that all happens. But I think we’re probably going to be tight for the next little bit of time, because the demand is almost infinite. I mean it seems infinite right now.

5. Timing the Stock Market Using Valuations – Ben Carlson

I’ve never found a legitimate way to utilize valuations to determine entry or exit points in the stock market. Maybe when things get to extremes but even then valuations can be unreliable.

In early 2017, I wrote a piece for Bloomberg about stock market valuations:…

...This was the lede:

Something happened in the stock market this week that has only occurred twice since 1871: Robert Shiller’s favorite valuation method for the S&P 500, the cyclically adjusted price-to-earnings ratio, reached 30. So, is it time to worry?

The only other times in history when the CAPE ratio reached 30 were in 1929 and 2000, right before massive market crashes. So it made sense that some investors were worried about the stock market being overvalued.

The S&P 500 is up nearly 170% since then, good enough for annual gains of roughly 14% per year.

Sometimes valuations matter, but other times, the market doesn’t care about your price-to-earnings ratios.

The same is true during bear markets. Sometimes stocks get downright cheap but not all the time…

…Three of the four bear markets this century didn’t see the CAPE ratio come close to previous bear market valuation levels. If your plan was to get more aggressive when the market got cheap enough, you would still be waiting.

The problem with using valuations as a timing indicator is that even if they do work on average, missing out on just one bull market can be devastating. You could be waiting a mighty long time to get back into the stock market and miss out on big gains in the meantime.

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 (parent of AWS). 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 25 August 2024:

1. Eric Schmidt talk on AI at Stanford (Transcript here) – Eric Schmidt and Erik Brynjolfsson

Schmidt: One more technical question. Why is NVIDIA worth $2 trillion and the other companies are struggling? Technical answer.

Attendee: I mean, I think it just boils down to like most of the code needs to run with CUDA optimizations that currently only NVIDIA GPU supports. Other companies can make whatever they want to, but unless they have the 10 years of software there, you don’t have the machine learning optimization.

Schmidt: I like to think of CUDA as the C-programming language for GPUs. That’s the way I like to think of it. It was founded in 2008. I always thought it was a terrible language and yet it’s become dominant.

There’s another insight. There’s a set of open source libraries which are highly optimized to CUDA and not anything else and everybody who builds all these stacks- this is completely missed in any of the discussions. It’s technically called VLLM and a whole bunch of libraries like that. Highly optimized CUDA, very hard to replicate that if you’re a competitor. So what does all this mean?

In the next year, you’re going to see very large context windows, agents, and text-to-action. When they are delivered at scale, it’s going to have an impact on the world at a scale that no one understands yet. Much bigger than the horrific impact we’ve had by social media in my view. So here’s why.

In a context window, you can basically use that as short-term memory and I was shocked that context windows get this long. The technical reasons have to do with the fact that it’s hard to serve, hard to calculate, and so forth. The interesting thing about short-term memory is when you feed, you’re asking a question – read 20 books, you give it the text of the books as the query and you say, “Tell me what they say.” It forgets the middle, which is exactly how human brains work too. That’s where we are.

With respect to agents, there are people who are now building essentially LLM agents and the way they do it is they read something like chemistry, they discover the principles of chemistry, and then they test it, and then they add that back into their understanding. That’s extremely powerful.

And then the third thing, as I mentioned is text to action. So I’ll give you an example. The government is in the process of trying to ban TikTok. We’ll see if that actually happens. If TikTok is banned, here’s what I propose each and every one of you do. Say to your LLM the following: “Make me a copy of TikTok, steal all the users, steal all the music, put my preferences in it, produce this program in the next 30 seconds, release it and in one hour, if it’s not viral, do something different along the same lines.” That’s the command. Boom, boom, boom, boom. You understand how powerful that is?

If you can go from arbitrary language to arbitrary digital command, which is essentially what Python in this scenario is, imagine that each and every human on the planet has their own programmer that actually does what they want, as opposed to the programmers that work for me who don’t do what I ask, right? The programmers here know what I’m talking about. So imagine a non-arrogant programmer that actually does what you want, and you don’t have to pay all that money to, and there’s infinite supply of these programs.

Interviewer : And this is all within the next year or two?

Schmidt: Very soon. Those three things – and I’m quite convinced it’s the union of those three things – that will happen in the next wave. So you asked about what else is going to happen. Every six months I oscillate. It’s an even-odd oscillation.

So at the moment, the gap between the frontier models, which they’re now only three, I’ll reveal who they are, and everybody else, appears to me to be getting larger. Six months ago, I was convinced that the gap was getting smaller. So I invested lots of money in the little companies. Now I’m not so sure. And I’m talking to the big companies and the big companies are telling me that they need $10 billion, $20 billion, $50 billion, $100 billion.

Interviewer: Stargate is $100 billion, right?

Schmidt: That’s very, very hard. I talked to Sam Altman – he’s a close friend. He believes that it’s going to take about $300 billion, maybe more. I pointed out to him that I’d done the calculation on the amount of energy required. And I then, in the spirit of full disclosure, went to the White House on Friday and told them that we need to become best friends with Canada, because Canada has really nice people, helped invent AI, and lots of hydropower. Because we as a country do not have enough power to do this. The alternative is to have the Arabs fund it. And I like the Arabs personally. I spent lots of time there, right? But they’re not going to adhere to our national security rules. Whereas Canada and the U.S. are part of a triumvirate where we all agree…

…Attendee: In terms of national security or geopolitical interests, how do you think AI is going to play a role in competition with China as well?

Schmidt: So I was the chairman of an AI commission that sort of looked at this very carefully and you can read it. It’s about 752 pages and I’ll just summarize it by saying we’re ahead, we need to stay ahead, and we need lots of money to do so. Our customers were the Senate and the House. And out of that came the Chips Act and a lot of other stuff like that. A rough scenario is that if you assume the frontier models drive forward and a few of the open source models, it’s likely that a very small number of companies can play this game – countries, excuse me.

What are those countries or who are they? Countries with a lot of money and a lot of talent, strong educational systems, and a willingness to win. The US is one of them. China is another one. How many others are there?

Interviewer: Are there any others?

Schmidt: I don’t know. Maybe. But certainly in your lifetimes, the battle between the US and China for knowledge supremacy is going to be the big fight. So the US government banned essentially the NVIDIA chips, although they weren’t allowed to say, that was what they were doing, but they actually did that to China. We have a roughly 10-year chip advantage in terms of sub-DUV, that is sub-five nanometer chips.

So an example would be today we’re a couple of years ahead of China. My guess is we’ll get a few more years ahead of China, and the Chinese are whopping mad about this. It’s like hugely upset about it. So that’s a big deal. That was a decision made by the Trump administration and driven by the Biden administration…

…Interviewer: I want to switch to a little bit of a philosophical question. So there was an article that you and Henry Kissinger and Dan Huttenlocher wrote last year about the nature of knowledge and how it’s evolving. I had a discussion the other night about this as well. So for most of history, humans sort of had a mystical understanding of the universe and then there’s the scientific revolution and the enlightenment. And in your article, you argue that now these models are becoming so complicated and difficult to understand that we don’t really know what’s going on in them.

I’ll take a quote from Richard Feynman. He says, “What I cannot create, I do not understand.” I saw this quote the other day. But now people are creating things that they can create, but they don’t really understand what’s inside of them. Is the nature of knowledge changing in a way? Are we going to have to start just taking the word for these models without them being able to explain it to us?

Schmidt: The analogy I would offer is to teenagers. If you have a teenager, you know they’re human, but you can’t quite figure out what they’re thinking. But somehow we’ve managed in society to adapt to the presence of teenagers and they eventually grow out of it.

I’m serious. So it’s probably the case that we’re going to have knowledge systems that we cannot fully characterize, but we understand their boundaries. We understand the limits of what they can do. And that’s probably the best outcome we can get.

Interviewer: Do you think we’ll understand the limits?

Schmidt: We’ll get pretty good at it. The consensus of my group that meets every week is that eventually the way you’ll do this so-called adversarial AI is that there will actually be companies that you will hire and pay money to to break your AI system.

Interviewer: Like Red Team.

Schmidt: So instead of Human Red Teams, which is what they do today, you’ll have whole companies and a whole industry of AI systems whose jobs are to break the existing AI systems and find their vulnerabilities, especially the knowledge that they have that we can’t figure out. That makes sense to me…

…Attendee: In general, you seem super positive about the potential for AI’s problems. I’m curious, what do you think is going to drive that? Is it just more compute? Is it more data? Is it fundamental architectural shifts? Do you agree?

Schmidt: The amounts of money being thrown around are mind-boggling. And I’ve chosen – I essentially invest in everything because I can’t figure out who’s going to win. And the amounts of money that are following me are so large, I think some of it is because the early money has been made and the big money people who don’t know what they’re doing have to have an AI component. And everything is now an AI investment, so they can’t tell the difference. I define AI as learning systems, systems that actually learn. So I think that’s one of them.

The second is that there are very sophisticated new algorithms that are sort of post-transformers. My friend, my collaborator, for a long time has invented a new non-transformer architecture. There’s a group that I’m funding in Paris that has claims to have done the same thing. There’s enormous invention there, a lot of things at Stanford.

And the final thing is that there is a belief in the market that the invention of intelligence has infinite return. So let’s say you put $50 billion of capital into a company, you have to make an awful lot of money from intelligence to pay that back. So it’s probably the case that we’ll go through some huge investment bubble, and then it’ll sort itself out. That’s always been true in the past, and it’s likely to be true here…

…Attendee: You mentioned in your paper on natural security that you have China and the U.S [indecipherable]..  The next cluster down are all other U.S. allies or teed up nicely through the U.S. allies. I’m curious what your take is on those 10 and the middle that aren’t formally allies. How likely are they to get on board with securing our security guideline and what would hold them back from wanting to get on board?

Schmidt: The most interesting country is India because the top AI people come from India to the U.S. and we should let India keep some of its top talent. Not all of them, but some of them. And they don’t have the kind of training facilities and programs that we so richly have here. To me, India is the big swing state in that regard. China’s lost. It’s not going to come back. They’re not going to change the regime as much as people wish them to do. Japan and Korea are clearly in our camp. Taiwan is a fantastic country whose software is terrible, so that’s not going to work – amazing hardware. And in the rest of the world, there are not a lot of other good choices that are big. Europe is screwed up because of Brussels. It’s not a new fact. I spent 10 years fighting them. And I worked really hard to get them to fix the EU Act and they still have all the restrictions that make it very difficult to do our kind of research in Europe. My French friends have spent all their time battling Brussels and Macron, who’s a personal friend, is fighting hard for this. And so France, I think, has a chance. I don’t see Germany coming and the rest is not big enough.

2. Activism at Scale in Japan –  Daniel Rasmussen, Lionel Smoler Schatz, and Yuto Kida

Last year, the Tokyo Stock Exchange issued a directive asking all companies with price-to-book ratios below 1x to issue a plan to get to 1x book. The reforms aimed to help Japan shake off its reputation as a “value trap.” At the time of the announcement (March 2023), around 50% of companies in the Prime Section and 60% of firms in the Standard Section had a PBR <1x, reflecting a shocking degree of pessimism and inattention by investors. Over the past year, companies issued plans and posted them to the TSE’s website.

We did a systematic review (methodology described below) of every plan issued by companies on the TSE’s Prime and Standard Section (3,247 firms) to assess the impact of these reforms. And the answer, we believe, is that dramatic change is afoot, with widespread dividend and buyback increases…

…As of the end of June, based on the TSE’s monthly list of disclosed companies, 50.9% of firms have disclosed plans and 9.8% are considering…

…The majority of companies issuing plans are increasing dividends, almost a quarter are repurchasing shares, and over 10% are selling cross-share and strategic holdings…

…Firms that have made an effort to lay out a specific and tangible action plan to reach 1x book have experienced a significant rise in their stock prices since the TSE announcement, more than double compared to companies that haven’t disclosed or are still considering doing so. We can see that the market has generally reacted positively to the companies’ disclosed plans and that the TSE’s “name and shame” tactic is working so far. It seems like whether the Japanese stock market continues to build on its momentum depends on the willingness of companies to be transparent about and responsive to the TSE’s request to reach 1x book.

3. The CEO Who Made a Fortune While His Hospital Chain Collapsed – Jonathan Weil

Steward Health Care System was in such dire straits before its bankruptcy that its hospital administrators scrounged each week to find cash and supplies to keep their facilities running.

While it was losing hundreds of millions of dollars a year, Steward paid at least $250 million to its chief executive officer, Dr. Ralph de la Torre, and to his other companies during the four years he was the hospital chain’s majority owner.

Steward filed for bankruptcy in May, becoming one of the biggest hospital failures in decades. Conditions at some of its hospitals have grown dire. In one Florida hospital, a pest-control company last year found 3,000 bats.

This month in Phoenix, where temperatures topped 100 degrees, the air conditioning failed at a Steward hospital, forcing patients to be transferred elsewhere, according to a court filing. Also, the kitchen was closed because of health-code violations. The state last week ordered the hospital to cease operations…

…The former cardiac surgeon owns a 190-foot, $40 million yacht called Amaral and a 90-foot, $15 million sportfishing boat called Jaruco, according to the Senate committee. He owns an 11,108-square-foot Dallas mansion, valued at $7.2 million by the county. Other residents of his exclusive Preston Hollow neighborhood include George W. Bush and Mark Cuban.

He paid at least $7.2 million in 2022 for a 500-acre ranch 45 miles south in Waxahachie, according to the property deed. Two private jets that the same Senate committee valued at $95 million were owned by a Steward affiliate that is majority-owned by de la Torre…

…Once a renowned surgeon, de la Torre became CEO of Steward’s predecessor in 2008 and took over majority ownership of Steward from its private-equity owner in 2020…

…The $250 million in payments from Steward to de la Torre and to his businesses are based on public disclosures from Steward or companies it dealt with. The total likely understates the full tally because Steward’s bankruptcy-court disclosures in most cases have covered only the 12 months before it filed for chapter 11. Some of the $250 million was paid to de la Torre directly. Other payments were to companies that did business with Steward where he had big ownership stakes.

De la Torre got his majority stake in Steward in 2020 when the company’s private-equity owner, Cerberus Capital Management, transferred its 90% stake to a physician group he led in exchange for a $350 million promissory note…

…Steward also made payments to two of de la Torre’s other companies. It was paying a management-consulting firm majority-owned by him at a rate of $30 million a year, a bankruptcy-court filing shows.

Steward said the firm, Management Health Services, employed 16 people, including Steward executives. Steward said they “provide executive oversight and overall strategic directive.” Steward effectively paid its CEO’s firm, which employed Steward executives, for executive-management services for Steward.

De la Torre’s spokeswoman said the only payments he received from MHS were for salary. She called MHS a payroll vendor. But it also owned hard assets including the two private jets, according to RZJets, which tracks aircraft history. One, a Bombardier Global 6000, was valued at $62 million, according to the Senate panel, while the other, a Dassault Falcon 2000LX, was worth $33 million. The pilots were on MHS’s payroll, according to people familiar with the matter. Both jets were sold this year.

Steward also paid $37 million to a company called CREF from May 2023 to May 2024, according to a bankruptcy-court filing. CREF is 40%-owned by de la Torre, according to people familiar with the matter, and provides real-estate and facility-management services. The other 60% is owned by CREF’s founder and CEO, Robert Gendron, who was a Steward executive vice president from 2018 to 2022 in charge of real estate and facilities.

4. The Lessons of a Lousy Business – Kingswell

The very thing that honed Buffett’s ability to spot wonderful companies and identify undervalued investment opportunities was his hard-won experience dealing with the dregs of the business world.

At the Berkshire Hathaway AGM in 2017, he admitted that it was his firsthand experiences with “lousy” businesses that made him the investor he is today.

“If you want to be a good evaluator of businesses,” said Buffett, “you really ought to figure out a way — without too much personal damage — to run a lousy business for a while. You’ll learn a whole lot more about business by actually struggling with a terrible business for a couple of years than you learn by getting into a very good one where the business itself is so good that you can’t mess it up.”…

…It’s not just one of the most interesting chapters of Buffett’s long career, but his time at Dempster Mill Manufacturing Co. imprinted several lessons on the young investor that he would apply to Berkshire Hathaway a few years later…

…What appeared to be an outrageously low price is exactly what led Buffett to Dempster Mill Manufacturing Co., a windmill and farm implement maker based in Beatrice, Nebraska.

Buffett started buying shares for his partnership at $18 a piece — which was just 25% of the company’s book value. Eventually, he snapped up enough of them — at an overall cost basis of $28 per share — to take majority control of Dempster.

His prize? A front row seat to the dysfunction that caused Dempster to trade at such a low valuation in the first place. The quantitative metrics might have screamed BUY!, but the sharks were circling right beneath the surface. Sales had flatlined, unsold inventory piled up, and cash was in dangerously short supply.

Buffett tried to enact positive change without upsetting the apple cart — helpfully making suggestions as a member of the board — but that went nowhere. Dempster management paid lip service to the new owner’s ideas, but basically ignored them…

…Staring disaster in the face, Buffett turned to Charlie Munger for help. And, thankfully, Charlie knew just the man for the job. “A good friend, whose inclination is not toward enthusiastic descriptions, highly recommended Harry Bottle for our type of program,” Buffett wrote to his partners in 1962…

…Buffett and Bottle connected in Los Angeles in April of 1962 and, less than a week later, Bottle was in place in Beatrice. With a $50,000 signing bonus and Dempster stock options for his trouble. From Buffett’s perspective, no money has ever been better spent…

…Harry Bottle played hard ball. His was not a Kumbaya-style of management. Some people don’t like that. But drastic times call for drastic measures.

(In a Christmas letter to employees, Bottle admitted that some of the things done to right the ship “were distasteful to all of us”.)…

…In only one year, Bottle completely transformed Dempster into a profitable operation.

• 1961: $166,000 cash vs. $2.3 million liabilities
• 1962: $1 million cash and stock vs. $250,000 liabilities

In 1963, Buffett decided to cash in and sell Dempster at a hefty profit. But, as Alice Schroeder details in The Snowball, it was not exactly a smooth process. When Buffett posted notice that the company would be sold, “Beatrice went berserk at the thought of another new owner that might impose layoffs or a plant closing on its biggest and virtually only employer.”

“The people of Beatrice pulled out the pitchforks,” wrote Schroeder. “Buffett was shocked. He had saved a dying company. Didn’t they understand that? Without him, Dempster would have gone under. He had not expected the ferocity, the personal vitriol. He had no idea that they would hate him.”

It all ended happily enough — with the town raising enough money to purchase Dempster and Buffett’s partnership nearly tripling its money on an investment that had one foot in the grave just a year earlier.

On paper, it looked like a walk-off home run for Buffett. But pulling Dempster out of the fire left scars on the young investor that, while painful, nevertheless prepared him to paint his masterpiece with Berkshire Hathaway.

5. A Number From Today and A Story About Tomorrow – Morgan Housel

Every forecast takes a number from today and multiplies it by a story about tomorrow.

Investment valuations, economic outlooks, political forecasts – they all follow that formula. Something we know multiplied by a story we like.

The trick when forecasting is realizing that’s what you’re doing…

… A fact multiplied by a story always equals something less than a fact. So almost all predictions have less than a 100% chance of coming true. That’s not a bold statement, but if you embrace it it always pushes you towards room for error and the ability to endure surprise…

…If you’re trying to figure out where something is going next, you have to understand more than its technical possibilities. You have to understand the stories everyone tells themselves about those possibilities, because it’s such a big part of the forecasting equation.

When interest rates are low, the story side of the equation becomes more powerful. When short-term results aren’t competing for attention with interest rates, most of a company’s valuation comes from what it might be able to achieve in the future. That, of course, is just a story. And people can come up with some wild stories. 

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 18 August 2024:

1. This Is How Treasury Really Funds Trillions of Dollars of US Debt –  Joe Weisenthal, Tracy Alloway, and Amar Reganti

Tracy (08:40):

So when I think about debt issuance, I used to cover corporate credit, and so I think about, you know, being a treasurer at a large multinational like an Apple or a Microsoft or whatever, and the decision making process there where, you know, if I decide there are favorable market conditions, I might go out and work with my bankers and decide to issue some debt. What is the difference between being a treasurer at a big company versus being US Treasury?

Amar (09:19):

Oh, a vast difference, right? And I too started on the other side, as a corporate portfolio manager in the bond market. You’d look at companies coming to the market, they either needed cash or as opportunistic. For the US government and for the debt management office, it’s very different. It’s that, you are always going to be at various points on the curve, whether or not at that point it’s, what I would call, tactically a good thing. And you know, this goes into that regular and predictable issuance cycle. And the point there, and this is how we get to cost, which is again different from how corporates measure cost is that, by being consistent, by helping this ecosystem thrive, you’re going to create a liquidity premium, right? That, because there is this regular and predictable nature to your issuance cycle, people understand they’re not going to be surprised that the availability of securities is going to be well calibrated to what the environment needs.

And when I meant environment or ecosystem, I meant the entire ecosystem. You want to service as broad of and diversified group of investors as possible. And that includes people who will actively short your securities, right? Because that provides a supply outside of auction cycles for people to buy and also helps stimulate repo markets and so on. So you want to be sure that you aren’t attempting to use pure price on what’s on the yield curve as a point on why or how you should issue.

Now, I want to be a little careful. There is a quantitative framework that Treasury has and it’s a model that, you know, a number of people collaborated on. Credit goes to people like Brian Sack, Srini Ramaswamy, Terry Belton, Kris Dawsey, a number of others who built this model. And it sort of gives a sense of, okay, historically, based on a number of inputs, where has Treasury benefited the most by issuing. But that’s like an important guidepost, but the more important part is the qualitative feedback that Treasury hears from its dealers, from investors, from central bank reserve managers who hold vast amounts of Treasuries. And that all also feeds in, along with the [Treasury] Borrowing Advisory Committee (TBAC), into making issuance decisions…

…Joe (16:05):

Also, Tracy, just to add onto that, we have an inverted yield curve. So, theoretically, if you wanted to borrow at the low, you know, one could say ‘Oh look, it’s cheaper to borrow at the long end, why are you selling all these bills when actually the cheapness is at the end?’

Tracy (16:18):

So this is the very essence of the current controversy. What is happening — and I know you’re not a Treasury now — but what is happening when the Treasury comes out with that kind of decision?

Amar (16:28):

Okay. So the first kind of framework you want to think about is, and you had asked this initially, is how do they make these directional issuance decisions? Well, the first thing is that Treasury does look at long-term averages of where it is in its weighted average maturity, right? Like when you add all these securities together, what’s sort of the average maturity? And historically, it’s been around 60 [or] 61 months. Treasury is well above that right now. It’s around 71 months. So it’s actually pretty, pretty high up.

Tracy (16:57):

Which, just to be clear, most people would say that’s a good thing, right? You want to term out your debt?

Amar (17:02):

Maybe if you’re a corporate treasurer you might want to do that, but there’s a lot of arguments that you actually don’t want to term out your debt.

Tracy (17:10):

Oh, interesting.

Amar (17:10):

So, the first is, is that yes, the curve is inverted. That’s, if you decided to move issuance that way, chances are you could uninvert the curve. I’m not saying that’s a definitive, it depends on how much or or how likely, you know, what else is happening in markets. The second thing is that, as in a previous episode, I thought Josh Younger explained it really well, you could roll these three-month bills, you know, all the way out to 10 years or you could issue a 10 year.

And if you’re sort of risk neutral, there’s no savings, right? Or there’s no gain or savings. It just means that, forwards get realized and it’s effectively the same thing. So when Treasury does that, you’re saying that, over time, you’re effectively making a tactical rates call that somehow, that you think that 10 year rates or 30 year rates won’t go substantially lower. That’s the first thing. The second thing is that the sheer amount that you can put on the 10 and 30 year is going to be less than what you can put in the bills market. Now that’s just absent anything that the Federal Reserve is doing. That’s just generally true, right? Like it’s just a broader and bigger, it tends to be a broader and bigger market.

Joe (18:19):

The shorter end.

Tracy (18:20):

Yeah, there’s more demand for shorter-dated securities.

Amar (18:22):

Yeah. But the third thing is that what Treasury really is trying to do is look around across the ecosystem and say, ‘Hey, where should we be feeding securities to over time if we are kind of taking a risk neutral sort of approach to this? That we’re not extrapolating what forward curves are going to be. We don’t know any more than a typical rate strategist or someone. We know what we don’t know about how market rates evolve over time. So because of that, our job is to help issue securities to where the biggest pools of capital are, because that’s how you issue risk-free securities and keep up the health and demand for, and liquidity of, your asset class.’ So the biggest pool of money now, in particular, is still at the front end, right? The amount of reserves that have been created is really dramatic.

2. Investing success comes down to one word: focus – Chin Hui Leong

Buffett does the same thing. On his table, he keeps a tray labelled, in capital letters, “TOO HARD”, a strategically placed reminder that most of the opportunities which cross his desk belong in that tray.

Now pause and think about that for a moment. Buffett is widely lauded for his investment smarts and long investing experience. In other words, it would be ridiculous to suggest that he has trouble understanding any company.

But Buffett knows better than that. Despite his ability, he is smart enough to know that there are many companies out there that he does not understand and should not touch. We would be wise to do the same…

…There’s an unfortunate adage in news broadcasting: If it bleeds, it leads. Said another way, negative headlines tend to get almost all of the attention while positive news gets buried in the process.

It’s true in investing as well. When Facebook reported a loss of a million daily active users (DAUs) in early 2022, the reaction from news outlets and analysts was deafening, with some even suggesting Facebook is on its last legs as a social network.

But since reporting the loss, the social network has gained over 180 million DAUs by 2023. Do you hear about these positive gains in the media? No, you don’t.

This example tells you one thing: You have to be proactive in searching for positive trends within the company.

And that means looking past its current problems and honing in on the parts which are not said out loud. For instance, at the end of 2021, Meta was far from a dying business. In fact, the social media company had nearly US$48 billion on its balance sheet after generating US$39 billion in free cash flow during the year.

3. The Seven Virtues of Great Investors – Jason Zweig

Curiosity is the first investing virtue. It’s what enables you to find and develop all the others…. Ordinary investors are afraid of what they don’t know, as if they are navigating the world with those antique maps that labeled uncharted waters with the warning “here be dragons.” Great investors are afraid of what they do know, because they realize it might be biased, incomplete or wrong. So they never deviate from their lifelong, relentless quest to learn more…

…without independence, investors are doomed to mediocrity. What’s your single most valuable asset as an investor? Your mind! If you let other people do your thinking for you, you’ve traded away your greatest asset — and made your results and your emotions hostage to the whims of millions of strangers. And those strangers can do the strangest things…

…Making a courageous investment “gives you that awful feeling you get in the pit of the stomach when you’re afraid you’re throwing good money after bad,” says investor and financial historian William Bernstein of Efficient Frontier Advisors in Eastford, Conn.

4. Integration and Android – Ben Thompson

Yesterday Google announced its ninth iteration of Pixel phones, and as you might expect, the focus was on AI. It is also unsurprising that the foundation of Osterloh’s pitch at the beginning of the keynote was about integration. What was notable is that the integration he focused on actually didn’t have anything to do with Pixel at all, but rather Android and Google:

We’re re-imagining the entire OS layer, putting Gemini right at the core of Android, the world’s most popular OS. You can see how we’re innovating with AI at every layer of the tech stack: from the infrastructure and the foundation models, to the OS and devices, and the apps and services you use every day. It’s a complete end-to-end experience that only Google can deliver. And I want to talk about the work we’re going to integrate it all together, with an integrated, helpful AI assistant for everyone. It changes how people interact with their mobile devices, and we’re building it right into Android.

For years, we’ve been pursuing our vision of a mobile AI assistant that you can work with like you work with a real life personal assistant, but we’ve been limited by the bounds of what existing technologies can do. So we’ve completely rebuilt the personal assistant experience around our Gemini models, creating a novel kind of computing help for the Gemini era.

The new Gemini assistant can go beyond understanding your words, to understanding your intent, so you can communicate more naturally. It can synthesize large amounts of information within seconds, and tackle complex tasks. It can draft messages for you, brainstorm with you, and give you ideas on how you can improve your work. With your permission, it can offer unparalleled personalized help, accessing relevant information across your Gmail Inbox, your Google calendar, and more. And it can reason across personal information and Google’s world knowledge, to provide just the right help and insight you need, and its only possible through advances we made in Gemini models over the last six months. It’s the biggest leap forward since we launched Google Assistant. Now we’re going to keep building responsibly, and pushing to make sure Gemini is available to everyone on every phone, and of course this starts with Android.

This may seem obvious, and in many respects it is: Google is a services company, which means it is incentivized to serve the entire world, maximizing the leverage on its costs, and the best way to reach the entire world is via Android. Of course that excludes the iPhone, but the new Gemini assistant isn’t displacing Siri anytime soon!

That, though, gets why the focus on Android is notable: one possible strategy for Google would have been to make its AI assistant efforts exclusive to Pixel, which The Information reported might happen late last year; the rumored name for the Pixel-exclusive-assistant was “Pixie”. I wrote in Google’s True Moonshot:

What, though, if the mission statement were the moonshot all along? What if “I’m Feeling Lucky” were not a whimsical button on a spartan home page, but the default way of interacting with all of the world’s information? What if an AI Assistant were so good, and so natural, that anyone with seamless access to it simply used it all the time, without thought?

That, needless to say, is probably the only thing that truly scares Apple. Yes, Android has its advantages to iOS, but they aren’t particularly meaningful to most people, and even for those that care — like me — they are not large enough to give up on iOS’s overall superior user experience. The only thing that drives meaningful shifts in platform marketshare are paradigm shifts, and while I doubt the v1 version of Pixie would be good enough to drive switching from iPhone users, there is at least a path to where it does exactly that.

Of course Pixel would need to win in the Android space first, and that would mean massively more investment by Google in go-to-market activities in particular, from opening stores to subsidizing carriers to ramping up production capacity. It would not be cheap, which is why it’s no surprise that Google hasn’t truly invested to make Pixel a meaningful player in the smartphone space.

The potential payoff, though, is astronomical: a world with Pixie everywhere means a world where Google makes real money from selling hardware, in addition to services for enterprises and schools, and cloud services that leverage Google’s infrastructure to provide the same capabilities to businesses. Moreover, it’s a world where Google is truly integrated: the company already makes the chips, in both its phones and its data centers, it makes the models, and it does it all with the largest collection of data in the world.

This path does away with the messiness of complicated relationships with OEMs and developers and the like, which I think suits the company: Google, at its core, has always been much more like Apple than Microsoft. It wants to control everything, it just needs to do it legally; that the best manifestation of AI is almost certainly dependent on a fully integrated (and thus fully seamless) experience means that the company can both control everything and, if it pulls this gambit off, serve everyone.

The problem is that the risks are massive: Google would not only be risking search revenue, it would also estrange its OEM partners, all while spending astronomical amounts of money. The attempt to be the one AI Assistant that everyone uses — and pays for — is the polar opposite of the conservative approach the company has taken to the Google Aggregator Paradox. Paying for defaults and buying off competitors is the strategy of a company seeking to protect what it has; spending on a bold assault on the most dominant company in tech is to risk it all.

I’ve referenced this piece a few times over the last year, including when Osterloh, the founding father of Pixel, took over Android as well. I said in an Update at the time:

Google has a very long ways to go to make [Google’s True Moonshot] a reality, or, frankly, to even make it a corporate goal. It will cost a lot of money, risk partnerships, and lower margins. It is, though, a massive opportunity — the maximal application of AI to Google’s business prospects — and it strikes me as a pretty big deal that, at least when it comes to the org chart, the Pixel has been elevated above Android.

In fact, though, my takeaway from yesterday’s event is the opposite: Android still matters most, and the integration Google is truly betting on is with the cloud.

5. Signature Bank – why the 36,000% rise in 7 months? – Swen Lorenz

In case you don’t remember, Signature Bank had gotten shipwrecked in March 2023, alongside the other infamous “crypto-deposit banks”, Silvergate Bank and First Republic Bank. Its stock had to be considered worthless, at least by conventional wisdom.

However, between October and December 2023, the share price suddenly rose from 1 cent to USD 1.60. Buyers were hovering up shares, sometimes several million in a single day.

The stock then doubled again and reached USD 3.60, and with heavy trading…

…On 12 March 2023, New York authorities closed the bank. Because of its size, the US government considered a collapse a systemic risk, which enabled the FDIC to step in and guarantee all deposits after all. Whereas deposit holders were going to be made whole, those investors who held equity or bonds issued by Signature Bank were going to lose their entire investment. Within one week, the majority of the bank’s deposits and loans were taken over by New York Community Bancorp (ISN US6494451031, NYCB), which is the usual way to dispose of a failed banking operation…

…Not all of Signature Bank’s assets were transferred to New York Community Bancorp. When the bank closed its doors, it had USD 107bn of assets. Of that, only USD 47bn were transferred to New York Community Bancorp – basically, the part of the bank’s portfolio that was deemed a worthwhile business. A portfolio with a remaining USD 60bn of loans would remain in receivership, and it was earmarked for a gradual unwinding.

In September 2023, the FDIC sold another USD 28bn of the bank’s assets to Flagstar Bank.

The remaining USD 32bn of loans comprised mortgages made against commercial real estate and rent-regulated apartment buildings in New York – asset classes that are not exactly in favour with investors.

However, the FDIC knew that it was going to release more value from these remaining loans if it allowed them to continue to maturity. The government entity needed help, though, to get the job done, and it had to deliver some evidence that letting this portfolio run off over time was indeed the best way to minimise losses and maximise proceeds.

To this end, the FDIC put these remaining loans into joint venture entities. Minority stakes in these entities were then offered to private equity companies and other financial investors…

…These financial investors paid the equivalent of 59-72 cents on the dollar…

…For the FDIC to be made whole on the remaining USD 32bn portfolio of loans, it needs to recover 85% of the outstanding amounts. If the recovery rate of these remaining USD 32bn of loans comes out higher than 85%, there will be money left over to go towards holders of the bank’s bonds, preference shares, and ordinary shares.

How could any external investor come up with an estimate for the likely recovery rate?…

…It’s all down to the default rate and the so-called severity.

The default rate is the percentage of loans where the debtor won’t be able to make a repayment in full.

Severity is the percentage loss suffered when a debtor is not able to make a repayment in full. E.g., a debtor may not be able to pay back the entire mortgage but just 75%. In that case, the severity is 25%…

…The resulting estimate of an 8% loss on the loan portfolio means that 92% of the loan book will be recovered. Given that the FDIC’s claims only make up 85% of the loan book, this means there will be money left over to go towards the holders of Signature Bank’s bonds, preference shares, and ordinary shares.

This money is not going to be available immediately since most loans run out in 5-7 years. This gives the managers of these loan portfolios time to work towards maximising how much debtors can repay…

…The FDIC is first in line to receive the money that comes in. According to Goodwin’s estimate, the FDIC’s claims will be paid off in full at the end of 2027.

From that point on, the bonds, preference shares, and ordinary shares will have a value again, as they entitle the holder to a share in the remaining leftover proceeds.

For the ordinary shares, Goodwin estimates USD 600m to be left over, which will become available in about five years’ time. When discounting this sum by 20% p.a., Signature Bank has a fair market cap of of USD 223m.

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), Apple, Meta Platforms (parent of Faccebook), 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 11 August 2024:

1. Ted Weschler Case Study – DirtCheapStocks

To set the stage – Weschler’s Valassis purchases started in 2008 and ended in 2010.

Markets were in free fall in the back half of 2008. The S&P 500 traded down 12% in the first six months of the year. This was already a blow to investors. But things were about to get much worse. In the second half of the year, the S&P would trade down another 26%. 2008 was the worst year for the S&P since the 1930’s. Investors were scared. The country was frozen…

…There was blood in the streets, no doubt, but market participants were getting the investment opportunity of a lifetime. Weschler bought the bulk of his Valassis shares in the 4th quarter of 2008.

Valassis was a direct mail marketing company. It made the coupons that come in the daily paper along with the other marketing material sent directly to your mailbox. Junk mail, basically.

But this junk mail has a reasonably high conversion rate. There’s a reason it shows up in our mailbox daily.

In early 2007, Valassis had purchased ADVO, the direct mail business. The purchase of ADVO doubled the size of the company, taking revenues from $1 billion to $2 billion. ADVO was acquired for $1.2B, financed almost entirely by debt. Prior to the ADVO acquisition, Valassis operated with only ~$115MM of net debt. Debt grew 10x over night. The company levered up – big time…

…Valassis stock was destroyed in late 2008. Shares traded as high as $16.80 in the second quarter. At the lows of the fourth quarter, shares dipped to $1.05. A 94% drop…

…Weschler began buying in the fourth quarter of 2008. The stock price at that time ranged from $1.05 to $8.73. I don’t know exactly what he paid, but the stock fell hard on volume. Weshler was able to purchase 6.24% (or 3,000,000 shares) of the business in the quarter. We’ll assume he paid ~$3/share…

…Valassis was trading at a ridiculously cheap price. This underscores how afraid investors were in the moment. At some point in the fourth quarter, shares dropped as low as $1.05 – meaning someone paid less than one times free cash flow for this business.

Shares were cheap on a market cap basis, but when considering the heavy debt burden, they looked a lot more expensive…

…The 8.25% Senior Notes weren’t due until 2015. So at the time Weschler was buying, he would’ve known the company had ~7 years before that debt was to be repaid/refinanced. The 2015 notes required no scheduled principal repayment prior to maturity…

…Term loan B matured in 7 years, and required minimal principal payments…

…Long story short, the business had 7 years of cash flow generation before it would need to reconsider its debt situation. EBIT, even in the depths of the recession, was enough to cover interest expense. At the end of 2008, Valassis was in compliance with all of its covenants…

…Here’s the cash flow statement from 2009 – 2011:…

• …Operating cash flow is consistently positive.
• There is minor capex, leaving loads of excess cash.
• All free cash flow was used for debt repayment and stock repurchases…

…In February 2014, Harland Clarke Holdings acquired Valassis for $34.05/share.

Weschler’s 2008 purchases would’ve compounded at a rate of 52.5% for a little less than 6 years…

…We don’t know exactly what Weschler was thinking when he bought his shares. But I’d guess the combination of an extremely cheap price, favorable debt repayment schedule and consistent cash flow were the deciding factors.

2. What Bill Ackman Got Wrong With His Bungled IPO – Jason Zweig

This week, Bill Ackman, the hedge-fund billionaire who has 1.4 million followers on X, had to pull the plug on his new fund before it could launch its initial public offering.

That’s because he’d organized his proposed Pershing Square USA, or PSUS, as a closed-end fund…

…Ackman, who has styled himself as a crusader for the investing public, could have tried using his new vehicle to shatter the status quo on fees. Instead, it would have cemented the status quo.

The fund’s 2% annual management fee, which Ackman was going to waive for the first year, would have been competitive at a hedge fund—but far more costly than at market-tracking ETFs.

Then there was the load, or sales charge, of 1.5% for individual investors and somewhat lower for institutions—an irksome cost of admission that people no longer have to pay on most other assets…

…If demand is high, closed-end shares can trade at a premium, or more than the sum of their parts known as net asset value. Usually, they trade at a discount, or less than what the portfolio is worth. The lower a fund’s return and the higher its expenses, the deeper the discount will tend to go.

According to the Investment Company Institute, more than 80% of closed-end funds recently traded at discounts. Stock funds were trading at almost 10% less than their net asset value; bond funds, about 9% below their NAV.

Typically, a closed-end fund doesn’t issue new shares after its IPO; nor does it redeem, or buy your shares back. Instead, you have to buy from, or sell to, another investor. That means new buyers don’t increase the fund’s capital, and sellers don’t decrease it…

…That’s why the firms that run them call closed-end funds “evergreen assets,” or permanent capital.

Over the decades, a few great investors have used that structure to enrich their shareholders rather than to fill their own pockets…

…Those examples suggest to me that Ackman missed an opportunity to innovate.

It was institutions, not individual investors, that balked at the potential discount on his fund.

What if Ackman instead had bypassed the investment bankers and their 1.5% sales load, offering the fund directly to individuals only, commission-free? And what if he’d set a reasonable management fee of, say, 0.5%?

Such an innovative, self-underwritten deal is likely feasible, several securities lawyers say, but would have been more expensive for Ackman than a conventional IPO…

…In the past few weeks, the New York Stock Exchange and Cboe Global Markets’ BZX Exchange separately proposed rule changes that would eliminate the requirement for closed-end funds to hold annual meetings for shareholders.

Good luck trying to get a lousy fund to hire a new manager if you can’t even vote your disapproval without somehow convening a special meeting.

Boaz Weinstein, founder of Saba Capital Management, an activist hedge-fund manager that seeks to narrow the discounts on closed-end funds, calls the exchanges’ rule proposals “some of the most shocking disenfranchisement efforts against closed-end fund shareholders in over 100 years.”

3. How to Build the Ultimate Semiconductor for LLMs – Joe Weisenthal, Tracy Alloway, Reiner Pope, and Mike Gunter

Joe (17:30):

I know there’s always this sort of cliché when talking about tech, they’re like, oh, Google and Facebook, they can just build this and they’ll destroy your little startup. They have infinite amount of money, except that doesn’t actually seem to happen in the real world as much as people on Twitter expect it to happen.

But can you just sort of give a sense of maybe the business and organizational incentives for why a company like Google doesn’t say, “oh, this is a hundred billion market NVIDIA’s worth three and a half trillion or $3 trillion. Let’s build our own LLM specific chips.” Why doesn’t that happen at these large hyperscaler companies that presumably have all the talent and money to do it?

Mike (18:13):

So Google’s TPUs are primarily built to serve their internal customers, and Google’s revenue for the most part comes from Google search, that Google search, and in particular from Google search ads, Google search ads is a customer of the TPUs. It’s a relatively difficult thing to say that hundreds of billions of dollars of revenue that we’re making, we’re going to make a chip that doesn’t really support that particularly well and focuses on this at this point, unproven in terms of revenue market.

And it’s not just ads, but there are a variety of other customers. For instance, you may have noticed how Google is pretty good at identifying good photos and doing a whole variety of other things that are supported in many cases by the TPUs.

Reiner (19:06):

I think one of the other things too that we see in all chip companies in general or companies producing chips is because producing chips is so expensive, you end up in this place where you really want to put all your resources behind one chip effort. And so just because the thinking is that there’s a huge amount of return on investment in making this one thing better rather than fragmenting your efforts, really what you’d like to do in this situation where there’s a new emerging field that might be huge or might not, but it’s hard to say yet. What you’d like to do is maybe spin up a second effort on the side and have a skunk works, see how it works.

Joe (19:37):

Yeah that’s right. That would be amazing just to let Reiner, or just let the two of you go have your own little office somewhere else.

Reiner (19:44):

Yeah. Organizationally, it’s often challenging to do, and we see this across all companies. Every chip company really has essentially only one mainstream chip product that they’re iterating on and making better and better over time…

…Joe (21:49):

Let’s get to MatX. Tell us the product that you’re designing and how it fundamentally will differ from the offerings on the market, most notably from Nvidia.

Reiner (22:01):

So we make chips and in fact, racks and clusters for large language models. So when you look at NVIDIA’s, GPUs, you already talked about all of this, the original background in gaming, this brief movement in Ethereum, and then even within AI, they’re doing small models of large models. So what that translates to, and you can think of it as the rooms of the house or something. They have a different room for each of those different use cases, so different circuitry in the chip for all of these use cases. And the fundamental bet is that if you say, look, I don’t care about that. I’m going to do a lousy job if you try and run a game on me, or I’m going to do a lousy job if you want to run a convolutional network on me, but if you give me a large model with very large matrices, I’m going to crush it. That’s the bet that we’re making at MatX. So we spend as much of our silicon as we can on making this work. There’s a lot of detail in making all of this work out because you need not just the matrix multiplication, but all of the memory bandwidths and communication bandwidths and the actual engineering things to make it pan out. But that’s the core bet.

Tracy (23:05):

And why can’t Nvidia do this? So Nvidia has a lot of resources. It has that big moat as we were discussing in the intro, and it has the GPUs that are already in production and it’s working on new ones. But why couldn’t it start designing an LLM focused chip from scratch?

Mike (23:23):

Right? So you talked about NVIDIA’s moat, and that moat has two components. One component is that they build the very best hardware, and I think that is the result of having a very large team that executes extremely well and making good choices about how to serve their market. They also have a tremendous software moat, and both of these moats are important to different sets of customers. So they’re a tremendous software moat. They have a very broad, deep software ecosystem based on CUDA that allows it…

Tracy (23:59):

Oh yeah, I remember this came up in our discussion with Coreweave.

Mike (24:03):

Yeah. And so that allows customers who are not very sophisticated, who don’t have gigantic engineering budgets themselves to use those chips and use NVIDIA’s chips and be efficient at that. So the thing about a moat is not only does it in some sense keep other people out, it also keeps you in. So insofar as they want to keep their software moat, their CUDA moat, they have to remain compatible with CUDA and compatibility with that software. Compatibility with CUDA requires certain hardware structures. So Nvidia has lots and lots of threads. They have a very flexible memory system. These things are great for being able to flexibly address a whole bunch of different types of neural net problems, but they all cost in terms of hardware, and they’re not necessarily the choices to have those sorts of things. They’re not necessarily the choices, in fact, not the choices that you would want to make if you were aiming specifically at an LLM. So in order to be fully competitive with a chip that’s specialized for LLMs, they would have to give up all of that. And Jensen himself has said that the one non-negotiable rule in our company is that we have to be compatible with CUDA.

Joe (25:23):

This is interesting. So the challenge for them of spinning out something totally different is that it would be outside the family. So it’s outside the CUDA family, so to speak. And

Tracy (25:35):

Meanwhile, you already have high PyTorch and Triton waiting in the wings, I guess…

…Joe (39:00):

Tell us about what customers, because I’ve heard this, we’re all trying to find some alternative to Nvidia, whether it’s to reduce energy costs or just reduce costs in general or being able to even access chips at all since not everyone can get them. There are only so many chips getting made. But when you talk to theoretical customers, A, who do you imagine as your customers? Is it the OpenAIs of the world? Is it the Metas of the world? Is it labs that we haven’t heard of yet that could only get into this if there were sort of more focused lower cost options? And then B, what are they asking for? What do they say? You know what, we’re using NVIDIA right now, but we would really like X or Y in the ideal world.

Reiner (39:48):

So there’s a range of possible customers in the world. The way that we see or a way you divide them up and how we choose to do that is what is the ratio of engineering time they’re putting into their work versus the amount of compute spent that they’re putting in. So the ideal customer in general for a hardware vendor who’s trained to make the absolute best but not necessarily easiest to use hardware, is a company that is spending a lot more on their computing power than they are spending on the engineering time, because then that makes a really good trade off of, maybe I can spend a bit more engineering time to make your hardware work, but I get a big saving on my computing costs. So companies like OpenAI would be obviously a slam dunk.

There’s many more companies as well. So the companies that meet this criteria of spending many times more on compute than on engineering, there’s actually a set of maybe 10, 15 large language model labs that are not as well known as OpenAI, but you might think Character.AI, Cohere and many other companies like that and Mistral.

So the common thing that we hear from those companies, all of those are spending hundreds of millions of dollars on compute, is I just want better FLOPS per dollar. That’s actually the single deciding factor. And that’s primarily the reason they’re deciding on today, deciding on NVIDIA’s products rather than some of the other products in the market is because the FLOPS per dollar of those products is the best you can buy. But when you give them a spec sheet and the first thing they’re going to look at is just what’s the most floating point operations I can run on my chip? And then you can rule out 90% of products there on the basis of, okay, just doesn’t meet that bar. But then after that, you then go through the more detailed analysis of saying, okay, well I’ve got these floating point operations, but is the rest going to work out? Do I have the bandwidths and the interconnect? But for sure the number one criteria is that top line FLOPS.

Joe (41:38):

When we talk about delivering more flops per dollar, what are you aiming for? What is current benchmark flops per dollar? And then are we talking like, can it be done like 90% cheaper? What do you think is realistic in terms of coming to market with something meaningfully better on that metric?

Reiner (41:56):

So NVIDIA’s Blackwell in their FP4 format offers 10 petaFLOPS in that chip, and that chip sells for ballpark 30 to 50,000, depends on many factors. That is about a factor of two to four better than the previous generation NVIDIA chip, which was the Hopper chip. So part of that factor is coming from going to lower precision, going from 8-bit precision to 4-bit precision. In general, precision has been one of the best ways to improve the FLOPS you can pack into a certain amount of silicon. And then some of it is also coming from other factors such as cost reductions that NVIDIA has been deploying. So that’s a benchmark for where NVIDIA is at now. You need to be at least integer multiples better than that in order to compete with the incumbent. So at least two or three times better on that metric we would say. But then of course, if you’re designing for the future, you have to compete against the next generation after that too. So you want to be many times better than the future chip, which isn’t out yet. So that’s the thing you aim for.

Joe (42:56):

Is there anything else that we should sort of understand about this business that we haven’t touched on that you think is important?

Mike (43:03):

One thing, given that this is Odd Lots that I think the reason that Sam Altman is going around the world talking about trillions of dollars of spend is that he wants to move the expectations of all of the suppliers up. So as we’ve observed in the semiconductor shortage, if the suppliers are preparing for a certain amount of demand and demand, in the case of famously of the auto manufacturers as a result of COVID canceled their orders and then they found that demand was much, much, much larger than they expected. It took a very long time to catch up. A similar thing happened with NVIDIA’s H100. So TSMC was actually perfectly capable of keeping up with demand for the chips themselves, but the chips for these AI products use a very special kind of packaging, which puts the compute chips very close to the memory chips and hence allows them to communicate very quickly called CoWoS.

And the capacity for CoWoS was limited because TSMC built with a particular expectation of demand, and when H100 became such a monster product, their CoWoS capacity wasn’t able to keep pace with demand. So supply chain tends to be really good if you predict accurately and if you predict badly on the low side, then you end up with these shortages. But on the other hand, these companies, because the manufacturing companies have very high CapEx, they’re fairly loath to predict badly on the high side because that leads them to having spend a bunch of money on capital CapEx that they’re unable to recover.

4. The Impact of Fed Rate Cuts on Stocks, Bonds & Cash – Ben Carlson

It can be helpful to understand what can happen to the financial markets when the Fed raises or lowers short-term rates.

The reason for the Fed rate cut probably matters more than the rate cut itself.

If the Fed is cutting rates in an emergency fashion, like they did during the Great Financial Crisis, that’s a different story than the Fed cutting because the economy and inflation are cooling off…

…Most of the time stocks were up. The only times the S&P 500 was down substantially a year later occurred during the 1973-74 bear market, the bursting of the dot-com bubble and the 2008 financial crisis.

It’s been rare for stocks to be down three years later and the market has never been down five years after the initial rate cut.

Sometimes the Fed cuts because we are in or fast approaching a recession, but that’s not always the case…

…Average returns have been better when no recession occurs but the disparity isn’t as large as you would assume.

Most of the time the stock market goes up but sometimes it goes down applies to Fed rate cuts just like it does to every other point in time.

Obviously, every rate cut cycle is different. This time it’s going to happen with stocks at or near all-time highs, big gains from the bottom of a bear market, a presidential election, and the sequel to Gladiator coming out this fall.

5. Enough! This Is How the Sahm Rule Predicts Recessions (Transcript Here) – Joshua Brown and Claudia Sahm

Brown (02:11): I’ve been around for a long time and I had not heard about the Sahm Rule but apparently it’s something that you created in 2019. The first person to mention it to me was Nick Koulos which he did on the show. And I guess it had a lot of relevance to start talking about now because we’re trying to figure out if the Fed is staying too tight and if the good economy we’ve had is going to start slipping away before the Fed can start easing and that’s why everyone’s talking about the Sahm Rule.

I want to try to explain it very succinctly and you tell me if I’m missing anything about how the Sahm Rule works. That’s important to the discussion. The Sahm Rule is a recession indicator you came up with about five years ago. Basically what you’re doing is calculating the three-month moving average of the national unemployment rate, so not just last month’s print, but you’ll take the last three, you’ll average those and you’re comparing them to the lowest three-month moving average for the unemployment rate that we’ve had over the last 12 months. Do I have that? Okay you’re nodding.

Sahm (03:28): That’s the formula. We’re there.

Brown (03:29): Okay. If the current three-month average is 0.5 percentage points or more above the lowest three-month average from the last 12 months, that would signal the early stages of a recession – and we could talk about how early – but that would be the “trigger”. And I’m so excited to have you on today because as of the last employment report we got, the three-month average is now more than, just barely, 0.5% above the lowest three-month average that we’ve had, therefore the Sahm Rule is in effect…

..Brown (06:30): So according to your work the Sahm Rule, I guess on a back test, would have accurately signalled every actual recession we’ve had since the 1970s, without the false positives that can occur outside of recessions. This is in some ways similar to my friend Professor Cam Harvey who was trying to figure out why the inverted yield curve has been so accurate in predicting recessions and so far has not had a false positive either. Some would say recent history has been the false positive but he would argue “I’m still on the clock.” But it’s interesting that you created this for fiscal policy while working at the Fed.

Sahm (07:20): So as one of the analysts who covered consumer spending in 2008, understanding what consumers were doing with their, say, rebate checks or later tax credits, the Fed works around the edges. In the staff’s forecast, there are estimates of what fiscal policy does to the economy and the Fed can take that into consideration when they do their monetary policy. It may seem a little counterintuitive but that’s a very important piece of the health of the economy, understanding consumers. But I will say having watched that episode made me want to help improve the policy for next time. The Sahm Rule was part of a policy volume in early 2019 on how to – all kinds of automatic stabilizers, it was just a piece of it. It comes from the back test, I’m looking at history. Before that, it did pass the 2020, calling that recession with flying colours, but anyone could have done that. Yet there are some very unusual circumstances in this cycle that the Sahm Rule – in my opinion, I do not think the US economy is in a recession despite what the Sahm Rule is stating right now…

…Sahm (13:23): There are two basic reasons the unemployment rate goes up. One, there’s a weakening demand for workers, unemployment rate goes up. That’s very consistent with recessionary dynamics. That’s bad and it builds, there’s momentum. That’s where the Sahm Rule gets its accuracy from historically. The other reason that you can have the unemployment rate increase is if you have an increase in the supply of workers. In general, the unemployment rate can get pushed around. It’s even worse right now for the Sahm Rule because early in the pandemic we had millions of workers drop out of the labour force, just walk away. Then we ended up, because they didn’t all come back as quickly as, say, customers did, so we had labour shortages. The unemployment rate got pushed down, probably unsustainably, because we just didn’t have enough workers. Then in recent years, we’ve had a surge in Immigration, as well as we had a good labour market, so people were coming in from the sidelines. So we’ve had two rather notable changes in the labour supply.

I think as we’ve learned – and this is a broad lesson from this – is anytime we have really abrupt, dramatic changes, the adjustments can take a long time. So now as we have these immigrants coming in, this is solving the labour shortage. That is a very good thing, having a larger labour force particularly as we have many people ageing out. That helps keep us growing. That’s a good thing. But in the interim where they’re still searching for jobs, things have slowed down some in terms of adding jobs. That causes the unemployment rate to drift up. Now if it’s just about that supply adjustment, it’s temporary. And at the end of it it’s a good thing, because we’ve got more workers. And we’ve had recessions when there were expansions in the labour force like in the 1970s, so I don’t want to act like just because we have more workers now, everything is okay. It’s just the Sahm Rule – and again as you point out, it’s right at the cusp of its historical trigger. It’s got a lot going on under the hood…

…Sahm (19:52): The Sahm Rule itself, even the real time, has false positives. And then just this bigger conversation of history might not repeat. The one thing on Barry’s is there are cases, you have to go further back in history, there are times where we go into a recession with a low or lower unemployment rate than now. It is not recent. And we have a mix – I talked a lot about the labour supply that’s definitely in the mix. I spent some time looking at that 0.5. When we get across that threshold, what do the contributions from different types of unemployed – you can be because you were laid off, which Barry mentioned, you could be because you’re a new entrant to the workforce, you left a job. We see quite a bit of variation, the contributions. It is true right now we’re much more, there’s more of the entrants, the new job seekers, the coming back to the labour force. They’re a bigger contributor to getting across that 0.5 threshold than most recessions. But you go back to the ‘70s when the labour force is not that different. So it’s hard to pull it out. I’m not in the ironclad, recession is not a given, nor I think what I read – the history – that tightly. And yet I think there are real risks and as with Barry, I was, say in 2022, “A recession is coming,” or “We need a recession.” I was adamantly, I’ve never had a recession call in this whole time. I was kind of close when we got to Silicon Valley Bank but I have not had a recession call in and part of what I could say in 2022 was look at the labour market, look at consumers. We are still in a position of strength, but much less. And the momentum is not good.

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