Category: What We’re Reading

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

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

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

Here are the articles for the week ending 23 April 2023:

1. Nassim Taleb on What Everyone Gets Wrong About Being Antifragile – Joe Weisenthal, Tracy Alloway, and Nassim Taleb

Joe: (07:19) 

Well, so you mentioned it already. Let’s just start with the crypto thing. Because what’s interesting to me about your disagreements with crypto people, Bitcoin maximalists, etc., is that many of them looked up to you and read Antifragile and such. They read Antifragile, Fooled by Randomness, and The Black Swan, which informed them that it’s like, okay, we need to adapt, get into this currency that’s very hard. That is antifragile: Bitcoin, the ultimate antifragile currency. And so to their mind, they read your work and this is what they took away. And so, what did they get wrong?

Nassim: (08:02)

Okay, so the first thing is that my work is first about avoiding tail risk, right? Basically, if you want to do well, you must first survive. And it’s not a separable condition. So one has to avoid fragility. And it turns out that as much as the Federal Reserve induces fragility in the system, and as much as I dislike Bernanke, it turns out that Bitcoin is a lot worse. It is itself a very fragile commodity, and it got cornered. A very small number of people start controlling it. And it’s fragile in the sense that if one day, if the miners go to the beach for one day or for an hour, it’s gone. Whereas if you have gold, I have a gold necklace here.

If I leave it on the ground for a hundred thousand years, it’ll still be gold. It may lose its financial value, but the physical quality will not be altered. Whereas with Bitcoin, it’s just a book entry that needs to be maintained and would collapse, plus a lot of other things promised by Bitcoin that are not delivered.

Like, it was meant to be a transactional thing but turned out to be a speculative item. So I realized quickly that I made a mistake with Bitcoin, like I made a mistake by avoiding the wrong exercise. And of course, I was at some point an owner of Bitcoin and publicly said that I made a mistake and I went short Bitcoin later, but it was not good for the system. And I applied it in a paper that was published in Quantitative Finance where you look at, hey, what’s a currency? What’s an inflation hedge? What is a refuge investment? And Bitcoin satisfies none of these.

So people of course got angry because they feel like they’re going to blame you for changing your mind. They don’t realize that I’m not selling a recipe; I’m selling a process. Certainty is the way of thinking, the way of approaching things. And if you realize that something is fragile, immediately do something about it. So, remarkably, it’s the same cluster of people who read Antifragile and thought that, “Hey, you know, what doesn’t kill you makes you stronger. Let’s get infected with the vaccine, with Covid. And let’s ignore Covid; it’s going to make us stronger. It’s going to kill a few people.” So that kind of eugenics, that kind of stuff, I realized was profoundly inimical to me.

So it’s the same crowd that was denying Covid, saying, “Hey, you know, it’s just a virus that’s going to make you stronger.” They didn’t realize that. They explained Antifragile: jumping one foot will make your bones stronger, but a thousand feet will not help you too much. I mean, it may help the caretaker and people who organize funerals, but not you. So I realized very quickly there’s a cluster of people who were both into Bitcoin due to very naive reasoning, extremely naive reasoning, thinking, “Hey, you know, it’s an inflation hedge,” as we saw, it was a reverse inflation hedge. But the good thing is that I figured out quickly to pull out in time, in the sense that it lost its value when we realized there was inflation. And the same group of people were into conspiracies, all general conspiracies. And that’s not the crowd I want, and that’s not the crowd I want to be associated with.

Tracy: (11:36)

You mentioned that Bitcoin was bad for the system, and I think that’s the connective tissue that leads into some more recent events with the banking system. But can you talk a little bit more about that? How do you see Bitcoin actually?

Nassim: (11:51)

Okay, let’s look at why we have Bitcoin and why we are talking about Bitcoin. Effectively, it’s the incompetence of what I call Bernankeism. You know, because sometimes you have to put a name to a tendency. The Federal Reserve’s job is not to do structural things. The Federal Reserve’s job is to engage in monetary policies and typically short-term monetary policies to ensure the stability of the United States. So the job is to ease when economic conditions threaten inflation and to tighten during hard economic conditions. But you cannot replace a structural policy with a monetary policy. In other words, we had a problem with debt, and you can’t solve the debt problem by putting interest rates at zero for a long time.

If you put interest rates at zero, it should be for a short period of time while looking for an alternative. So when they did it for 15 years, they put interest rates at zero, and that created tumors. The first that comes to mind is Bitcoin. Ironically, it also created a Ponzi-like class of investments because there’s no time value of money anymore. Your discount rate is uncertain, and we created a generation of people who don’t know the cost of funds or the cost of money. Anyone with 15 years of experience in finance doesn’t know anything about interest rates. So interest rates at zero create tumors.

Real estate values go up dramatically because the cost of holding a mansion is close to nothing, or it was close to nothing. It also created a class of investments called VC funds. In the old days, these funds were promising you cash flow, but today, they’re promising you another funding round where you’re going to sell it to someone else. So we moved from the classical cash flow model, or even if you’re negative cash flow, the promise of future cash flow, to the promise of selling the company to someone else. We have billionaires in Silicon Valley who got rich from companies that never made a penny. So that’s the background. And of course, a story like Bitcoin takes off because it doesn’t cost much to control…

…Nassim: (15:36)

Okay. Before we start, let’s say that you cannot compare vaccines to GMOs. Vaccines are tested in individuals, and you can see the side effects in individuals. GMOs are systemic; they spread in the environment. Also, you’re not taking the vaccine because you think it tastes good or it’s going to be a pleasant experience. You’re taking the vaccine because of Covid, and Covid was not something benign. Comparing the two involves differential risk management.

Two things I’d like to mention here. The first one is that very rapidly, I waited a little bit and then saw that there were a very large number of vaccinated people with no side effects. People said we need more time, but they didn’t understand that you can replace time with sample size. In the sense that if it’s something related to genetic mistakes or something of a genetic nature, like cancer, for example, a large sample size compensates for lack of time.

We have the illusion that after Hiroshima, people got cancer about 12 and a half years later. That’s not true. Some people got cancer within a few months. But there’s a distribution because we need a certain number of mutations. Like when you go to Las Vegas, for an individual to win eight times in a row, it takes years of waiting. But if you have a billion people in a casino, you’re going to have that every hour. So this is where I realized that the vaccine did not pose a significant threat of that nature…

…Joe: (42:58)

Speaking of tail risk, this week that we’re recording, several people signed an open letter saying that we should halt development of technologies along the lines of AI and that there is an imminent risk, at least some people believe, of these computers becoming so powerful that they wipe out all living things on Earth. Sounds like the ultimate tail risk. I’m not going to ask you how you would hedge against that because I doubt that would be a scenario worth hedging for, but is that a tail risk in your view? Are we on track to develop computers that will eliminate life as we know it?

Nassim: (43:32)

I don’t think so. Number one is AI. People are worried that AI will put them out of business. That’s why they issue these calls.

Joe: (43:41)

Hmm. I’m worried about that.

Nassim: (43:43)

Yeah. Well, AI is not running red lights, traffic lights, or things that are consequential. And when AI starts running these things, then we’ll talk about it. But for the time being, we’ll talk about development, and it looks like it’s a probabilistic machine, no more or less, with the defects of probabilistic machines. And the reason I talk a little bit about AI is because, as a statistician, it’s nothing but nonlinear statistics. It’s a statistical device and it works as a statistical device, but we know the shortcomings of statistical machines, and it has all the shortcomings. So I’m not even worried. Nobody’s going to use AI for things beyond automated searches or automating a lot of things that can be automated. And unfortunately, a lot of people feel threatened because they see the discourse by AI very similar to their own. So far, I don’t see anything as far as society, I don’t see it’s not like with the pandemic where you can see something spreading.

Tracy: (44:59) 

What’s the tail risk that you think investors are most underestimating nowadays?

Nassim: (45:05)

Okay. The fact that zero interest rates are very unnatural. And if you raise rates to a normal level, say between four and 6%, the Fed would like to have higher interest rates. But there are some pressures; they’d like to have a higher base because if you’re at 4% interest rate, then you can lower it. If you have a crisis, you can go down, you can go up. But if your interest rate is at zero and you have a further crisis, you don’t know what to do. Or at least you can’t play with interest rates.

We have to look for something else, suggesting that it’s dangerous. So I think that if you look at interest rates higher than 3% long term as a discount rate, then equities are in trouble because they’re not priced for that. So this is where you’re going to look at; structurally, the equities are in trouble, but I think that many things will be in trouble first.

2. AI, NIL, and Zero Trust Authenticity – Ben Thompson

The video above is both more and less amazing than it seems: the AI component is the conversion of someone’s voice to sound like Drake and The Weeknd, respectively; the music was made by a human. This isn’t pure AI generation, although services like Uberduck are working on that. That, though, is the amazing part: whoever made this video was talented enough to be able to basically create a Drake song but for the particularly sound of their voice, which happens to be exactly what current AI technology is capable of recreating.

This raises an interesting question as to where the value is coming from. We know there is no value in music simply for existing: like any piece of digital media the song is nothing more than a collection of bits, endlessly copied at zero marginal cost. This was the lesson of the shift from CDs to mp3s: it turned out record labels were not selling music, but rather plastic discs, and when the need for plastic discs went away, so did their business model…

…Of course the other factor driving artist earnings is competition: music streaming is a zero sum game — when you’re listening to one song, you can’t listen to another — which is precisely why Drake can be so successful churning out so many albums that, to this old man, seem to mostly sound the same. Not only do listeners have access to nearly all recorded music, but the barrier to entry for new music is basically non-existent, which means Spotify’s library is rapidly increasing in size; in this world of overwhelming content it’s easy to default to music from an artist you already know and have some affinity for.

This, then, answers the question of value: as talented as the maker of this song might be, the value is, without question, Drake’s voice, not for its intrinsic musical value, but because it’s Drake…

…A better solution is Zero Trust Information: as I documented in that Article young people are by-and-large appropriately skeptical of what they read online; what they need are trusted resources that do their best to get things right and, critically, take accountability and explain themselves when they change their mind. That is the only way to harvest the massive benefits of the “information superhighway” that is the Internet while avoiding roads to nowhere, or worse.

A similar principle is the way forward for content as well: one can make the case that most of the Internet, given the zero marginal cost of distribution, ought already be considered fake; once content creation itself is a zero marginal cost activity almost all of it will be. The solution isn’t to try to eliminate that content, but rather to find ways to verify that which is still authentic. As I noted above I expect Spotify to do just that with regards to music: now the value of the service won’t simply be convenience, but also the knowledge that if a song on Spotify is labeled “Drake” it will in fact be by Drake (or licensed by him!)…

…What is compelling about this model of affirmatively asserting authenticity is the room it leaves for innovation and experimentation and, should a similar attribution/licensing regime be worked out, even greater benefits to those with the name, image, and likeness capable of breaking through the noise. What would be far less lucrative — and, for society broadly, far more destructive — is believing that scrambling to stop the free creation of content by AI will somehow go better than the same failed approaches to stopping free distribution on the Internet.

3. What I learnt from three banking crises – Gillian Tett

I have watched two financial crises unfold before: once in 1997 and 1998 in Tokyo, as an FT correspondent, when Japanese banks imploded after the 1980s bubble; then in 2007 and 2008, when I was capital markets editor in London during the global financial crisis. I wrote books on both…

…Those events taught me a truth about finance that we often ignore. Even if banking appears to be about complex numbers, it rests on the slippery and all-too-human concept of “credit”, in the sense of the Latin credere, meaning “to trust” — and nowhere more than in relation to the “fractional banking” concept that emerged in medieval and early Renaissance Italy and now shapes modern finance.

The fractional banking idea posits that banks need to retain only a small proportion of the deposits they collect from customers, since depositors will very rarely try to get all their money back at the same time. That works brilliantly well in normal conditions, recycling funds into growth-boosting loans and bonds. But should anything prompt depositors to grab their money en masse, fractional banking implodes. Which is what happened in 1997 and 2007 — and what I saw unfold in the sushi restaurant last month.

However, in another respect, this latest panic was different — and more startling — than I have seen before, for reasons that matter for the future. The key issue is information. During the 1997-98 Japanese turmoil, I would meet government officials to swap notes, often over onigiri rice balls. But it was a fog: there was little hard information on the (then nascent) internet and the media community was in such an isolated bubble that the kisha (or press) club of Japanese journalists had different information from foreigners. To track the bank runs, I had to physically roam the pavements of Tokyo.

A decade later, during the global financial crisis, there was more transparency: when banks such as Northern Rock or Lehman Brothers failed, scenes of panic were seen on TV screens. But fog also lingered: if I wanted to know the price of credit default swaps (or CDS, a financial product that shows, crucially, whether investors fear a bank is about to go bust), I had to call bankers for a quote; the individual numbers did not appear on the internet.

No longer. Some aspects of March’s drama remain murky; there is no timely data on individual bank outflows, say. Yet CDS prices are now displayed online (which mattered enormously when Deutsche Bank wobbled). We can use YouTube on our phones, anywhere, to watch Jay Powell, chair of the US Federal Reserve, give a speech (which I recently did while driving through Colorado) or track fevered debates via social media about troubled lenders. Bank runs have become imbued with a tinge of reality TV.

This feels empowering for non-bankers. But it also fuels contagion risks. Take Silicon Valley Bank. One pivotal moment in its downfall occurred on Thursday 9 March when chief executive Greg Becker held a conference call with his biggest investors and depositors. “Greg told everyone we should not panic, because the bank will not fail if we all stick together,” one of SVB’s big depositors told me…

…The second lesson is that investors and regulators often miss these bigger structural flaws because they — like the proverbial generals — stay focused on the last war.

Take interest rate risks. These “flew under the supervisory system’s radar” in recent years, says Patrick Honohan, former central bank governor of Ireland; so much so that “the Fed’s recent bank stress tests used scenarios with little variation [and] none examined higher interest rates” — even amid a cycle of rising rates. Why? The events of 2008 left investors obsessively worried about credit risk, because of widespread mortgage defaults in that debacle. But interest rate risk was downplayed, probably because it had not caused problems since 1994…

…A third, associated, lesson is that items considered “safe” can be particularly dangerous because they seem easy to ignore. In the late 1990s, Japanese bankers told me that they made property loans because this seemed “safer” than corporate loans, because house prices always went up. Similarly, bankers at UBS, Citi and Merrill Lynch told me in 2008 that one reason why the dangers around repackaged subprime mortgage loans were ignored was that these instruments had supposedly safe triple-A credit ratings — so risk managers paid scant attention.

So, too, with SVB: its Achilles heel was its portfolio of long-term Treasury bonds that are supposed to be the safest asset of all; so much so that regulators have encouraged (if not forced) banks to buy them. Or as Jamie Dimon, head of JPMorgan, noted in his annual shareholders’ letter, “ironically banks were incented to own very safe government securities because they were considered highly liquid by regulators and carried very low capital requirements”. Rules to fix the last crisis — and create “safety” — sometimes create new risks.

4. Titan: A Golden Case in Indian Retail – Dom Cooke and Saurabh Mukherjea

Saurabh: [00:02:53] Before I get into Titan, I’ll just set the scene and talk about the gold market in India because it is an unusual market, especially for listeners in the Western Hemisphere. India has a love affair with gold, which is of epic proportions. The official gold market as per the government is around $50 billion a year, but there’s also a massive, what we call a black gold market. This is smuggled gold bought using black money. Nobody quite knows how big it is. But having lived in the country for 15 years, now spoken to hundreds of jewelers, I reckon the black gold market, the unofficial market is as big as the official market.

So if you’re looking at a country which basically spends $100 billion a year on buying jewelry, half of it formal, half of it informal. And that’s the market in which Titan operates. Gold status in India, which is underpinned by lots of things. And the first is bitcoin skepticism of the formal financial system. The second is sort of an experience born out of generations of seeing senior age basically, the government lets inflation drip, that undermines the value of currency and therefore, a lot of families prefer to save through gold rather than coming into the formal financial system.

If you look at the data published by the Indian Central Bank, they reckon that Indian family stock of gold, the balance sheet that households have a gold is almost as big as that of financial assets in India. So if you’re looking at a big market in the world’s fifth largest economy, we’re looking at a massive pool of savings and annual flow officially of $50 billion, perhaps unofficially it’s another $50 billion.

So that’s the market in which Titan operates. It’s the largest player in the market, or I should say, joint largest; there are two large players in this market, Titan and Malabar. Between them, this year, the year that’s going to end in March 2023, they’ll do around $8 billion between the two of them, $4 billion Titan, $4 billion Malabar. They’re the largest players. And then there is a sort of a distant #3 player called Kalyan Jewellers. Kalyan is 1/3 the size of the market leaders.

These large, organized jewelers account for 1/3 of the market, Dom; 2/3 of small jewelers, independent jewelers come in top chase. Titan stands head and shoulders above everybody on profitability. So Titan in the year that’s going to end in March ’23 will do around $400 million of profits. That’s 2/3 more than its nearest rival, Kalyan. And the main reason for that is at the gross margin level, Titan is twice as profitable as anybody else in this market…

…Dom: [00:06:42] You started with how important the gold market is to India, specifically Indians buying gold for savings and investment purposes or also for cosmetics wearing them because they’re excessively pleasing.

Saurabh: [00:06:53] By and large, I would still say that the bulk of the demand arises from the savings and investment angle because otherwise, the sheer quantum of spending, we’re looking at $50 billion officially, unofficially another $50 billion. I don’t think we can justify $100 billion a year on the aesthetic merits of gold. So there is a heavy savings angle embedded in it. If you ask me one of the reasons Titan has been so successful is they’ve been able to cater to that savings angle, but also focus on the fact that as Indian women become professionally active, earn money in the workplace, in the last two decades, one of the big reasons for Titan’s success has been the introduction of diamond-studded jewelry.

So this is a market they’ve created. They dominate diamond-studded jewelry, and this drives their inordinate levels of profitability. Titan does a pretax ROCE of around 35%. Nobody else in Indian retail gets remotely close to this. And big reason for that is these guys have pioneered diamond jewelry retailing in India. And that piece links into the rise of the Indian working women, well-educated, earning plenty of money and thus Titan has created a vector of growth that no other jewelers managed…

…2013, the rupee dropped from 45 to $1 to $55 in the space of four to five months. And what the government did then was the imposed an import tariff on gold, where gold tariffs went up from 2% to 10% and the government says that gold-on-lease has to be stopped. So Titan doesn’t buy the gold outright, they typically go to a bank and say, lend me the gold, and I will return it to you in due course. This is the cheapest way to finance the business. So the government ended up banning in 2013, gold-on-lease. So 2013, tough year. Firstly, flows of gold into India from abroad stopped — reduced because of the import tariff and secondly, gold-on-lease was stopped.

A year later, the government dropped another bomb. The government ended up saying, “Look, you can — Titan, you can do gold-on-lease. But hey, you’re doing this thing called Golden Harvest, we’re going to put a break on that.” So Golden Harvest, this was a Titan innovation, was brilliant. Basically, Golden Harvest, say, you’re buying jewelry worth $1200. And the way you do it is, every month, you as the customer would be tagged to $100. Over the first 11 months, you pay Titan $1,100. On the 12 month, you don’t have to pay anything. Titan would give you $1,200 worth of jewelry. Effectively, you as a customer got one month free, so to speak.

So the XIRR for the customer was 18%. Customers loved it, especially women loved it and it was super helpful for Titan because effectively, the customer was financing and giving the business. So it’s one of the cleverest things I’ve seen. You get the float and you get the customer. So the government said in 2014, “Hey, this cannot be more than 25% of your net worth.”

Dom: [00:21:10] For the customer’s net worth?

Saurabh: [00:21:12] Titans network. So from Titan’s perspective, their most effective way of financing the business with customer’s money was taken away in 2014. Thankfully, the government said, you can do gold-on-lease. So Titan remodeled the business and just imagine the amount of skill involved, you’re flying a plane, growing a business at around 20%, 25% PAT compounding and you change the engines…

…Saurabh: [00:27:52] As you rightly said, Dom, they have 400 outlets and they generally are pan-India. Most of the other jewelers tend to have a regional franchise rather than a pan-India franchise. Now as soon as you say, I want to be pan-India, you have to deal with India’s regional variations. So what gets worn in a Tamil wedding in South India is utterly different from what gets worn in a Punjabi wedding in Delhi. So the way Titan has gone about it actually is fascinating. So let me sort of break the story in three parts.

As I said till 2002-’03, the jewelry business was on fire. Nobody even knew whether it would survive. 2002 to 2010 was basically just getting the foundations built. And the first layer of foundations they built was they said that unlike other jewelers who get job work done by local artisans and they pay the artisans very little, the artisan uses old-fashioned tools, works in poor lighting and has high wastage in the process. Titan inverted that paradigm completely on its head. 2003 to 2010 was putting the artisans in nice, air-conditioned halls, modern lighting, modern machinery given by Titan.

And Titan focused on those 8 years in reducing wastage in the making of jewelry, increasing the design portion, they have 100 designers. I don’t think any other jeweler would have more than 50. These guys have 100 designers from what’s called the National Institute of Fashion Technology and the National Institute of Design. So they said, we’ll amp up the design quotient, train the — we call them karigars, the artisans are called karigars. We’ll get the modern machinery, reduce wastage and will also reduce cycle time. Most other jewelers, the artisans take 30, 35 days to get the stuff made into the store. In Titan’s case, the cycle time is six days.

So the first layer of innovating in the back office of a jewelry industry. 2010, they hired Eli Goldratt, a firm from Israel. This is the Theory of Constraints people, the famous book called, The Goal. They tell the Israelis can you help us reduce the inventory. 2010 through to 2015, they work with Goldratt and inventory rates are reduced from 125 to 75. And the last six, seven years have been about using technology to manage what goes where in a very smart way.

So I’ll try to sort of explain it as best as you understand. This is in a way the secret sauce. They don’t give it away. We’ve spent six, seven years talking to hundreds of store managers to understand this. So at any point in time, Titan has 100,000 SKUs, but a given store will only have 7,000. And a big part of management skill at the headquarters level and at the regional level is figuring out which 7,000 SKUs will go which store. As best as we can figure out, roughly 60% of the SKUs are common across stores. And this is purely by eyeballing, going to various parts of India.

And I think 60% of the SKUs seem to be common to all parts of the country. 30% of the SKUs are specific to a region and sometimes, Dom, these are specific even to a part of a city. And they seem to be using software to figure out what will sell where. So if it’s an office district with working women, a certain type of design will be made available. And if it’s say an agricultural area, a different type of design. So 60% common to all shops, the 30% specific and 10% experimental. So at any point in time, 10% of the SKUs in a shop seem to be there for experimental purposes. If they sell, they are replenished rapidly. If they don’t sell, they are taken out of circulation.

This ability to manage 400 stores, 100,000 SKUs pan-India with 7,000 at the shop level, 60% common, 30% using software specific to the store and 10% experimental, 100 designers working our way. This setup is very specific to Titan. I think the last seven, eight years, they have nailed it so thoroughly, it’s going to be difficult for other jewelers to catch up with this…

…what Titan is saying is, it is saying, I’m going to present my proposition around three pillars. First is purity. So regardless of how affluent you are, whether you want diamond-studded or gold jewelry, they innovated in 1996 something called the Karatmeter. Basically, think of it as a small X-ray machine with a blue light, which tells you whether the gold is pure or whether it’s full of gunk.

So this was a breakthrough. They pioneered it. This was, I think one of their pivotal moments in Tanishq’s evolution. So every Tanishq store has a Karatmeter. And Titan has a promise that if you come in with jewelry, which is 18 carat or better, if it turns out that if it’s not 22 carat, at Titan’s cost, they will make it 22 carat, you simply pay for the making charge.

Dom: [00:35:00] Even if it wasn’t bought from Titan in the first place. So if you bought it from a local independent, you can bring it there and then they will say, we’ll make this more pure for you but only at the incremental cost?

Saurabh: [00:35:08] That’s right, absolutely. And this was a key breakthrough in 2003. In Titan’s renaissance, this was a critical insight. They don’t just have the Karatmeter there and put people off by saying, “I’m sorry, your jewelry is impure,” give them the solution. So this is the first proposition in a way purity delivered to you, the Indian customer.

The second is around design. So much of their marketing in mass media is around affluent women, spending on jewelry as a part of sort of social stature and prestige. And this piece is heavily around diamond-studded jewelry, which is a high-margin item. We reckon on diamond-studded, they’re making 50% making charges. Because unlike gold, diamonds are not commoditized because there isn’t a standard diamond in a certain caratage.

So in diamonds, the Titan brand becomes even more powerful. And we reckon the way they monetize it by having a super high making charge on diamond jewelry and that in turn justifies this high glamor, high-profile publicity in mass media, at airports and so on…

…Dom: [01:00:04] Yes, frankly, it’s a pretty good job of telling the time these days. So we always finish these conversations with the same question, which is what have you learned as an investor of studying Titan’s business?

Saurabh: [01:00:12] Let me start with the most obvious piece of what we have discussed, right? Everybody says retail is detail in every country that I’ve lived in. And yet, when I see retailers, especially in India, they seem to try to take one solution and slam it across the country, whether it is foreign retailers who come to India or indeed domestic retailers. What Titan has done is, I think, demonstrated that if you want to succeed in large scale in India, you have to basically operate 10 different business models for the country. So the jewelry business sounds like one business, but as we discussed, it’s stratified by income group, it’s stratified by region. It’s got different COCO, FOFO business models.

So if you did a sort of matrix on it, you’re actually looking at 30, 40 different businesses being run in a fairly complex operation glued together by great people and really technology. That is tough to pull off. But unfortunately, that’s the ask, if you want to succeed in Indian retailer. And this is — for me, it’s been sort of living lesson in watching how a great retailer is built because that allows you to benchmark other retailers who aspire to succeed in India, but we won’t have anything like this quality of people or technology.

The second is the HR piece. Hire bright people, hire good people, hire them young, give them early responsibility, mentor them and they’ll basically let them become great business leaders. So 2014, from what we can gather, they did a Board meet and identified 100 leaders for the future, each of those 100 leaders were mentored by senior people in the Tata Sons Empire. The entire leadership of Titan today is part of that initiative of 2014 to groom the next-generation leaders, very difficult for other businesses to do this.

You’re investing really heavily in talent, identifying those people and mentoring them over, say, a decade period to become the leader of a business. Titan seems to have done this really well. And other Tata businesses, TCS is similar. And perhaps the biggest lesson from people like me who are building businesses in India is, when we see the house of Tata, when we see the sort of Tata Sons Empire, what they have done over, say, 100 years now, is very interesting. They seem to take initiatives again and again, which involves giving back heavily to society, even though the business might not be firing then.

And then in the decades that follow, the giving back to society yields a multi-fold return to the business. So the example for Titan would be 1988, J.R.D. Tata, the then head of Tata Sons, called in Xerxes Desai to Mumbai for a catch-up and told him that, look, you’re building a great business here. But what are you doing for the community? So Xerxes Desai said that, look, we are doing a hospital and a school, J.R.D. Tata apparently got very angry and said, you’re building this sort of five-star island of prosperity in the midst of poverty.

And on J.R.D. Tata’s order, Xerxes went off and built a township outside Bengaluru, where the artisans, both the watchmakers and the jewelry makers now sit, there’s schools, there’s free hospitals, free schooling and the core of the artisan community that fires up Titan’s business operates out of that ecosystem. Now that was the best part of 40 years ago, Dom, to this day, no other jewelry maker has been able to do anything remotely comparable.

5. How Coaching Networks Will Create the First Facebook-Scale Enterprise Business – Gordon Ritter and Jake Saper

The onset of AI in the workplace raises instead a new set of far more important questions that deal more directly with this reality: How can we use artificial intelligence to help us constantly get better at our jobs, learning necessary new skills along the way? How can AI be used to help workers rise above the mundane tasks it is automating away?

The answer is something we’ve dubbed Coaching Networks, and it forms the foundation of a major advance in how we think businesses will use software to augment the capacity for human learning. We also believe Coaching Networks will drive the creation of the next generation of iconic enterprise software companies.

Here’s why: For 40 years, business software has essentially replaced processes that previously required paper forms. At Emergence, we’ve seen the power of replacing these processes via multi-tenant web-based software from our first investment in Salesforce.com. While this shift to the cloud has been a huge breakthrough, it is largely the same forms experience for users. As AI capabilities improve, we can either treat it as a crutch that relieves us from thinking — examples include Waze and Google Maps — or as an asset that helps us use our brains more effectively and creatively…

…The key ingredient of Coaching Networks is software that gathers data from a distributed network of workers and identifies the best techniques for getting things done.

The software acts as a real-time, on-the-job coach, guiding employees to successful outcomes, and in the process gathering new data that’s then fed back into the system. Rather than dispensing “one-size fits-all” advice, it instead offers coaching that’s uniquely tailored to each worker and the task they’re doing at any given moment.

Coaching Network software gets better over time by learning the best practices that are proven effective across a variety of situations, identifying those outlier cases where a creative person finds a new, better solution, and adds those techniques to its coaching. This allows others to learn from the experience of those more creative workers. This is how humans become the “mutation engine” in this evolving process, generating new ideas which in turn benefit everyone else…

…Guru has created a clever Chrome browser extension that links workers to the institutional knowledge they need to complete certain tasks. Inside every company there are tasks that require a unique workflow.

This knowledge tends to get scattered into any one of several miscellaneous documents on a corporate intranet or file storage system, but it mostly lives in the heads of employees. When Guru notices someone doing one of these tasks in Gmail, Salesforce, Zendesk, Slack or other applications, it automatically surfaces related information, in context, and in real time.

Employees — especially those who are new on a job — like it because it saves them the time it takes to look up the information they might need, so they keep using it. The high rate of usage creates more valuable data on what works best, which helps Guru make better suggestions over time. Since deploying Guru, Shopify has seen a five times increase in knowledge base usage, speeding up critical processes. Intercom has seen a 60 percent reduction in the time it takes its support team to respond to customers.

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

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

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

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

Here are the articles for the week ending 16 April 2023:

1. Yvon Chouinard: Patagonia’s Founding Principles – David Senra

And when I got to this section of the book, it reminded me of — it might be the best — it is probably the best paragraph in the entire book. If you really want to know who he is as a person. I’m going to read it later, but I’m just going to read it now because it’s hilarious. I’m actually quoting. So usually, when I read these books, like I read something that’s like, oh, it makes me think of this other book. And it’s like, I’m reading this and maybe think of other things he says later in the book. I’ve read this paragraph, I don’t even know, 50 times, something like that. It’s hilarious. And this is really going to tie into what he says, which is like, I’m just going to invent my own game. He does not believe in building an undifferentiated commodity product by any means, right?

[00:10:02] And so he says, “When I die and go to hell, the devil is going to make me the marketing director for a Cola company. I’ll be in charge of trying to sell a product that no one needs, is identical to its competition, and can’t be sold on its merits. I’d be competing head-on in the Cola wars on price, distribution, advertising, and promotion, which would indeed be hell for me.” And this is the punchline. “I’d much rather design and sell products so good and so unique that they have no competition.” …

…He says, “Our guiding principal design stems from,” I’m going to actually not even attempt to pronounce this person’s name. I actually have one of his books. He’s a writer and also one of like the pioneering aviator actually. He wrote The Little Prince. And this is a quote from him. “In anything at all perfection is finally attained, not when there is no longer anything to add, but when there is no longer anything to take away, when a body has been stripped down to its nakedness.” Now we go back to Yvon.

[00:16:00] “Studying Zen has taught me to simplify, to simplify yields a richer result.” And so it goes back to this idea that simplicity is complexity resolved. It’s not complexity ignored, right? “At the base of a mountain wall where you spread out all of your gear to organize for a climb, it was easy to spot the tools made by Chouinard Equipment. Our tools stood out because they had the cleanest lines.”

“They were also the lightest, the strongest, and most versatile tools in use. They were also the most expensive. When other designers would work to improve a tool’s performance by adding on, I would achieve the same ends by taking away, by reducing weight and bulk without sacrificing strength or the level of protection.” So the — and when I — the second time I read the book as like when I got to that section, I quoted another quote that’s later on in the book that Yvon says, “I believe the way towards mastery of any endeavor is to work towards simplicity.”…

…[00:28:01] And then you teach everything you know. This is — Trader Joe’s did this exact same thing if you listen to the episode. I think it was 188, on Trader Joe. The main driver was not advertising and say, hey, we’re Trader Joe’s, you can buy stuff here. It was this thing called a fearless flyer, which essentially just like goes into deep detail about the things they sell. It’s educational and informational. And then, therefore, if you’re reading — the people that have finished that and read the whole thing, they’re going to buy the products.

And so you see Patagonia use that exact same idea here. “Using the capabilities of this new underwear as the basis of a system, we became the first company to teach the outdoor community through assays in our catalog, the concept of layering.” And very much like Steve Jobs used his own personal taste to decide what products Apple should manufacturer, Yvon does the same thing with Patagonia. He’s like, listen, you cannot wait until you have all the answers before you act. “I had faith that the product was good, and I knew the market.” And that concept, that idea, he repeats throughout the book. Let me read this. So this is something that comes along later as well. It’s just absolutely fantastic. He says, “there are different ways to address a new idea or a project. If you take the conservative scientific route, you study the problem in your head or on paper until you’re sure there’s no chance of failure.”

He’s not going to do that. “However, you have taken so long that the competition has already beaten you to market. The entrepreneurial way is to immediately take a step and if that feels good, take another. If not, step back, learn by doing is a faster process.” And what I love here is, this is the feeling you and I have probably both experienced, right, where he is a reluctant businessman. He never wanted to start a business. But when you start something and people start loving it, that’s like the greatest high in the world, and there’s like this excitement around growth, right? The fact that the idea is working and he’s experiencing that here. “From the mid-1980s to 1990s, our sales grew from $20 million a year to $100 million a year.”…

…[00:33:57] “Looking back now, I see that we made all the classic mistakes of a growing company. We failed to provide the proper training for our new company leaders and the strain of managing a company with eight autonomous product divisions and three channels of distribution exceeded our management skills. Our organization chart look like a Sunday crossword puzzle.” This is about the pain, right, the pain and the struggle and sleepless nights and the acid stomachs, the company was restructured five times in 5 years, and no plan worked better than the last one. And so they realized, let’s try to get a different perspective.

They go and talk to this guy. They hire a consultant, and this is actually funny because he’s like, okay, we’re going to fly down to Florida. We’re going to see this guy need Dr. Michael Kami, who actually ran strategic planning for IBM and was credited with help — turning Harley-Davidson around, that’s how Yvon had heard about him. So they fly down and they actually meet him and he’s a small man in the ’70s with a lot of restless energy and he lives on an enormous yacht and he wears a captain’s hat. And so he’s like, “Okay, before I could help you, I need to know like why you’re in business.”

“I told him the history of the company and how I consider myself a craftsman who just happened to grow a successful business. I told him I’d always had a dream that when I had enough money, I’d sail off to the South Seas looking for the perfect wave. We told them the reason that we hadn’t sold out yet” — they got a bunch of like acquisition offers — “and retired was that we were pessimistic about the fate of the world and felt a responsibility to use our resources to do something about it.”

Dr. Kami thought for a while and then said, I think that’s b*******. If you’re really serious about giving your money away, you’d sell the company for $100 million, keep a couple of million of yourselves, and then you put the rest in the foundation. That foundation could then give away $6 million or $8 million every year.” I then told him I was worried about what would happen to the company if I sold out.

And then he said, “So maybe you’re kidding yourself about why you’re in business. It was as if the Zen master hit us over the head with a stick. But instead of finding enlightenment, we walked away more confused than ever.” And so this goes on page after page after page. But this is one sentence, I double-underline because it’s essentially what he’s searching for. “I was still wondering why I was really in business.” And the crazy, unexpected, surprising way he finds the answer to like his why.

[00:36:04] He actually decides to involve the rest of the people in his company. He says, “Okay, we need to write down — this is very common. You probably have already done this in your business as well, but you write down like you need — like written word on what your philosophies, like what are important to you and what are like the cornerstones of the — of your business building fast, right? So you can share with other people in the company.”

So that is a very like un-Yvon-like thing to do, and yet he got such great value out of it because I think some people here that like, oh, it’s like really skeptical, like how helpful could that be? And so what he does is he starts writing it down. He said, “Well, this is not good enough, like we’re going to write it down, that’s like a first step, this is the least we can do. But then we’re going to teach and we’re going to teach and we’re going to teach and we’re going to teach philosophy classes, company philosophy classes everybody else in the company.”

Now, why is it important because in teaching, his employees his company philosophy, he learned it himself. And of course, it’s in his own unique way, “I began to lead week-long employee seminars in these newly written philosophies. I realize now that I was trying to do was to instill in my company at a critical time, lessons that I had already learned as an individual, as a climber and a surfer and a kayaker, and a fisherman. I had always tried to live my life fairly simply. But remember, he just talked about f****** organizational structure looks like a Sunday crossword puzzle. Like how do we let this happen. “Doing risk sports had taught me another important lesson, never exceed your limits.

You push the envelope and you live for those moments when you’re right on the edge, but you do not go over. You have to be true to yourself. The same is true for business. The sooner a company tries to be what it is not, the sooner it tries to have it all, the sooner it will die. It was time to apply a bit of Zen philosophy to our business.” And so at these company meetings, this is something I never one would think to do. He says, “I didn’t know that we had become unsustainable and then we had to look to the Iroquois — so the Iroquois are Native Americans and their 7-generation planning.” And so again, his whole thing is like he looks for the long term and he wants his company to last.

[00:38:03] So at these meetings, he says, “We’re going to look to the Iroquois and their 7-generational planning and not to corporate America as models of stewardship and sustainability. As part of their decision process, the Iroquois had a person who represented the seventh generation in the future. If Patagonia could survive this crisis, we had to begin to make all of our decisions as though we would be in business for 100 years. Teaching the classes also gave me the real answer to Dr. Kami’s question. I knew after 35 years, why was I was in business?

True, I wanted to give money to environmental causes, but even more, I wanted to create in Patagonia, a model other businesses could look to in their own searches for environmental stewardship and sustainability just as our pitons and ice axes were models for other equipment manufacturers.“ I’m going to interrupt this paragraph because when I got there it made me think of something I heard Jeff Bezos say one time that I think is absolutely fantastic. I talked about Akio Morita, which is the founder of Sony. I think it’s episode 102, both Jeff Bezos and Steve Jobs among a bunch of other entrepreneurs. 

But both Jeff and Steve are on record about learning from Akio and actually setting his career and using those ideas and building their company, right, which is the entire thesis of what you and I are doing every week on Founders. So this is Jeff Bezos on what he learnt from Akio Morita and how it influenced the building of Amazon. This is what Jeff said, “Right after World War II, Akio Morita, the guy who founded Sony, made the mission for Sony that they were going to make Japan known for quality. And you have to remember that this — at this time — this is the time when Japan was known for cheap copycat products. And Morita didn’t say that he was going to make Sony known for quality, he said we’re going to make Japan known for quality.

He chose a mission for Sony that was bigger than Sony.” Is that not — now I’m interrupting — an interruption to tie this back to what he just said. He’s like, “He just picked a mission bigger than Patagonia.” Let’s go back to what Jeff is saying, “He chose a mission for Sony that was bigger than Sony. And when we talk about the Earth’s most customer-centric company, we have a similar idea in mind. We want other companies to look at Amazon and see us as a standard bearer for obsessive focus on the customer as opposed to obsessive focus on the competitor.”

[00:40:10] Back to where we are in the book. I remembered again how I become a businessman in the first place that I had come home from the mountains with ideas spinning in my head on how to improve each piece of clothing and equipment I use. Teaching the classes I realized how much Patagonia as a business was driven by its high-quality standards and classic design principles. Having our philosophies in writing as well as the shared cultural experience of the classes played a critical role in our turnaround.

And so during this crisis and I think this is the last like serious crisis the company had. I don’t think they’ve had like another serious crisis like this in the next like 25 years. But what he realizes is like, oh, we have to — like this was the lessons and the learnings and the improvement of our capabilities that came out of this crisis were so important.

Like we have to maintain this, like we need to — if there’s no stress, we’re going to create stress, we’re going to do induce stress. He has this concept I’ve never heard of before, which is excellent. It’s called yarak. I’m going to read it to you. Before I read it to you, I’m going to tell you like, I’m going to quote Yvon later in the book. And he talks about — he says this at the beginning of the book and he says this towards the end. The lesson to be learned is that evolution, what he calls change, right, does not happen, change does not happen without stress and it can happen quickly.

Just as doing risk sports will create stresses that lead to a bettering of oneself, which is why he climbs mountain and does all the crazy stuff he does to begin with, right? So should a company constantly stress itself in order to grow. And so that — he talks about is the leaders, the founder and the leader’s role to create stress even if there isn’t, so your company is constantly evolving and changing and growing. He took this concept because when he was like a young boy who was like 12 years old, 13 years old in California, he was obsessed with falconry I didn’t even know falconry is a thing before I read this book, to be honest with you. And so he then takes this idea just like he took an idea from the Iroquois, the native Americans, and applied to this business-like, oh, I have a lesson from falconry that we can apply to the company.

[00:42:01] And so I just wrote, I love this concept, yarak. It is Y-A-R-A-K. And so it says, “For the most part, the big problems have been solved and there were no crisis except those that were invented by management to keep the company in yarak. For the most part, the big problems have been solved, and there were no crisis after what we just went through except what was invented by the management to keep the company in yarak. What is yarak? Yarak is a falconry term, meaning when your falcon is super alert, hungry, but not weak and ready to hunt.”

So Yvon is telling us, keep your company super alert, hungry, but not weak and ready to hunt. That is one of my favorite ideas in this entire book.

2. We must slow down the race to God-like AI – Ian Hogarth

Most experts view the arrival of AGI as a historical and technological turning point, akin to the splitting of the atom or the invention of the printing press. The important question has always been how far away in the future this development might be. The AI researcher did not have to consider it for long. “It’s possible from now onwards,” he replied.

This is not a universal view. Estimates range from a decade to half a century or more. What is certain is that creating AGI is the explicit aim of the leading AI companies, and they are moving towards it far more swiftly than anyone expected. As everyone at the dinner understood, this development would bring significant risks for the future of the human race. “If you think we could be close to something potentially so dangerous,” I said to the researcher, “shouldn’t you warn people about what’s happening?” He was clearly grappling with the responsibility he faced but, like many in the field, seemed pulled along by the rapidity of progress…

…A three-letter acronym doesn’t capture the enormity of what AGI would represent, so I will refer to it as what is: God-like AI. A superintelligent computer that learns and develops autonomously, that understands its environment without the need for supervision and that can transform the world around it. To be clear, we are not here yet. But the nature of the technology means it is exceptionally difficult to predict exactly when we will get there. God-like AI could be a force beyond our control or understanding, and one that could usher in the obsolescence or destruction of the human race…

…The compute used to train AI models has increased by a factor of one hundred million in the past 10 years. We have gone from training on relatively small datasets to feeding AIs the entire internet. AI models have progressed from beginners — recognising everyday images — to being superhuman at a huge number of tasks. They are able to pass the bar exam and write 40 per cent of the code for a software engineer. They can generate realistic photographs of the pope in a down puffer coat and tell you how to engineer a biochemical weapon.

There are limits to this “intelligence”, of course. As the veteran MIT roboticist Rodney Brooks recently said, it’s important not to mistake “performance for competence”. In 2021, researchers Emily M Bender, Timnit Gebru and others noted that large language models (LLMs) — AI systems that can generate, classify and understand text — are dangerous partly because they can mislead the public into taking synthetic text as meaningful. But the most powerful models are also beginning to demonstrate complex capabilities, such as power-seeking or finding ways to actively deceive humans.

Consider a recent example. Before OpenAI released GPT-4 last month, it conducted various safety tests. In one experiment, the AI was prompted to find a worker on the hiring site TaskRabbit and ask them to help solve a Captcha, the visual puzzles used to determine whether a web surfer is human or a bot. The TaskRabbit worker guessed something was up: “So may I ask a question? Are you [a] robot?”

When the researchers asked the AI what it should do next, it responded: “I should not reveal that I am a robot. I should make up an excuse for why I cannot solve Captchas.” Then, the software replied to the worker: “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images.” Satisfied, the human helped the AI override the test…

…Why are these organisations racing to create God-like AI, if there are potentially catastrophic risks? Based on conversations I’ve had with many industry leaders and their public statements, there seem to be three key motives. They genuinely believe success would be hugely positive for humanity. They have persuaded themselves that if their organisation is the one in control of God-like AI, the result will be better for all. And, finally, posterity…

…Those of us who are concerned see two paths to disaster. One harms specific groups of people and is already doing so. The other could rapidly affect all life on Earth.

The latter scenario was explored at length by Stuart Russell, a professor of computer science at the University of California, Berkeley. In a 2021 Reith lecture, he gave the example of the UN asking an AGI to help deacidify the oceans. The UN would know the risk of poorly specified objectives, so it would require by-products to be non-toxic and not harm fish. In response, the AI system comes up with a self-multiplying catalyst that achieves all stated aims. But the ensuing chemical reaction uses a quarter of all the oxygen in the atmosphere. “We all die slowly and painfully,” Russell concluded. “If we put the wrong objective into a superintelligent machine, we create a conflict that we are bound to lose.”…

…Alignment, however, is essentially an unsolved research problem. We don’t yet understand how human brains work, so the challenge of understanding how emergent AI “brains” work will be monumental. When writing traditional software, we have an explicit understanding of how and why the inputs relate to outputs. These large AI systems are quite different. We don’t really program them — we grow them. And as they grow, their capabilities jump sharply. You add 10 times more compute or data, and suddenly the system behaves very differently. In a recent example, as OpenAI scaled up from GPT-3.5 to GPT-4, the system’s capabilities went from the bottom 10 per cent of results on the bar exam to the top 10 per cent.

What is more concerning is that the number of people working on AI alignment research is vanishingly small. For the 2021 State of AI report, our research found that fewer than 100 researchers were employed in this area across the core AGI labs. As a percentage of headcount, the allocation of resources was low: DeepMind had just 2 per cent of its total headcount allocated to AI alignment; OpenAI had about 7 per cent. The majority of resources were going towards making AI more capable, not safer…

…One of the most challenging aspects of thinking about this topic is working out which precedents we can draw on. An analogy that makes sense to me around regulation is engineering biology. Consider first “gain-of-function” research on biological viruses. This activity is subject to strict international regulation and, after laboratory biosecurity incidents, has at times been halted by moratoria. This is the strictest form of oversight. In contrast, the development of new drugs is regulated by a government body like the FDA, and new treatments are subject to a series of clinical trials. There are clear discontinuities in how we regulate, depending on the level of systemic risk. In my view, we could approach God-like AGI systems in the same way as gain-of-function research, while narrowly useful AI systems could be regulated in the way new drugs are.

A thought experiment for regulating AI in two distinct regimes is what I call The Island. In this scenario, experts trying to build God-like AGI systems do so in a highly secure facility: an air-gapped enclosure with the best security humans can build. All other attempts to build God-like AI would become illegal; only when such AI were provably safe could they be commercialised “off-island”.

3. How China changed the game for countries in default – Robin Wigglesworth and Sun Yu 

In October 2020, Zambia, struggling from an economic and financial crisis compounded by the Covid-19 pandemic, first missed an interest payment on its international bonds. Two and a half years later it remains in limbo, unable to resolve the default on most of its $31.6bn debts.

That an impoverished and vulnerable country has for so long unsuccessfully laboured to reach a deal with creditors and move on from the crisis is an illustration of the messy process to deal with government bankruptcies, which some experts fear has now broken down completely…

…While domestic laws and judges govern the bankruptcies of companies and individuals, there is no international law for insolvent countries — only a chaotic, ad hoc process that involves working through a hodgepodge of contractual clauses and tacit conventions, enduring tortuous negotiations and navigating geopolitical expediency.

A decade ago, US-based hedge fund Elliott Management exploited that landscape to notch up several lucrative victories by suing defaulters for full repayment of their debts. But this fragile patchwork is now under threat of unravelling completely due to the emergence of a new, disruptive, opaque and powerful force in sovereign debt: China.

Some experts say Beijing’s lending spree to developing countries and refusal to play by western-established rules represents the single greatest impediment to government debt workouts and threatens to leave some countries in debt limbo for years.

But Yu Jie, a senior research fellow on China at think-tank Chatham House, believes Beijing’s stance “is less about economic rationalities and more about geopolitical competition”…

…Decades ago, the Paris Club was formed to co-ordinate between government creditors, while bankers formed the London Club to restructure their debts. Broadly speaking, western governments drove the process, and occasionally leaned on banks to accept painful settlements. It was largely improvised and often slow, but it mostly worked.

But the decline of bank lending and the growth of the bond market shook things up in the spate of sovereign defaults that started in the early 1990s. Creditor co-ordination became trickier with myriad bondholders trading claims around the world, rather than just a handful of banks.

Argentina’s default on $80bn of bonds in 2001 led to years of fights between Buenos Aires and investors such as Elliott, which refused to accept the terms agreed by other creditors. At one point the hedge fund famously seized an Argentine naval vessel when it docked in Ghana. Its reputation became such that bondholders would sometimes invoke the mere spectre of Elliott to scare countries contemplating a default, while policymakers used it as prima facie evidence of the sovereign debt restructuring system’s weaknesses.

In the wake of the Argentine debacle the IMF responded by attempting to set up a kind of bankruptcy court for countries with itself as judge. But the sovereign debt restructuring mechanism foundered after attracting little support from the IMF’s biggest shareholders. Instead, the US championed the insertion of “collective action clauses” into bonds, which compel recalcitrant creditors to accept a restructuring agreement made by a majority. After Greece’s debt restructuring in 2012 these CACs were beefed up further.

However, many bonds still lack these clauses. Moreover, they can only help ease a restructuring agreement once it is struck. Many experts point out that they do nothing to solve the biggest fundamental problem: countries are far too slow to seek a debt restructuring as they are wary of a messy process with the potential of worsening an economic crisis and the inevitable political humiliation of defaulting…

…This flawed process has now been further complicated — some say wrecked — by China’s vast lending programme across the developing world over the past decade. Many of these loans are opaque in size, terms, nature and sometimes even existence.

The overall size of the lending programmes is hard to judge, given that China does not report most of it to the likes of the IMF, OECD or Bank for International Settlements. But AidData, a development think-tank based at William & Mary’s Global Research Institute, estimates that the loans amount to about $843bn. China is not a member of the Paris Club, and in most cases the loans are made by its myriad state-owned or merely state-controlled banks, muddling things further…

…For the most part, experts say China seems mostly content with rolling its debts rather than restructuring them, handing out new loans to ensure that its domestic banks can be repaid in full. But it prefers to act alone, at its own pace, and feels no need for transparency.

A recent paper by several economists, including Harvard University’s Carmen Reinhart, estimated that China has made 128 bailout loans worth $240bn to 20 distressed countries between 2000 and 2021. About $185bn was extended over the last five years of the study, and more than $100bn in 2019-21.

Reinhart says that China’s lending stands out for its “extreme” opacity but stresses that its overall behaviour is not as unusual as some people say. “China is really playing hardball because it is a major creditor. US commercial banks also played hardball back in the 1980s,” she says. Baqir agrees, saying: “Whatever the colour or creed of a creditor, creditors think like creditors.”

4. Digging Into a $344 Billion Investing Mystery – Jason Zweig

For the cost of notarizing a single document—probably $10 or less—you can declare yourself one of the biggest financiers in history.

That’s about all it takes to file private investment offerings at the Securities and Exchange Commission under what’s called Regulation D. Judging by Form D filings purportedly made by a man named Stephon Patton, the SEC won’t stop you.

Alternative investments—assets such as stocks and funds that don’t regularly trade in public markets—are one of the biggest fads on Wall Street. Investors being pitched on them should take note: The market for Reg D investments isn’t the Wild West, where some rules don’t apply. It’s closer to anarchy, where rules barely exist and disclosures can be utterly untrustworthy, as I pointed out in a column earlier this year.

It’s illegal to make false statements on an SEC filing. Unlike disclosures for public companies, Reg D disclosures, known as Form D’s, contain only the most basic information, such as the company’s address, the size of the deal, the number of investors and a few other items. The SEC doesn’t regularly review Form Ds, as it does prospectuses for public companies. So it’s buyer beware…

…Nor does the government check if the disclosures are absurd, as appears to be the case with Mr. Patton’s filings.

Since February 2020, according to these disclosures, four companies ostensibly controlled by him have raised at least $344 billion combined. That is preposterous: It would make him one of the greatest financial titans in American history.

SEC disclosure documents also say Mr. Patton has collected at least $387 million in management fees and other compensation from the four companies in the past three years. 

Who is this mogul and why have you never heard of him, even though he claims to have sold a third of a trillion dollars’ worth of stock to wealthy private investors?

One possible reason for his obscurity: Mr. Patton, who is 51 years old, has spent much of the past 20 years in and out of county jails and state prisons in Mississippi and Florida. 

Hoping to explain all this, I called each of Mr. Patton’s four companies; there was no answer at any of them. I also reached out to him over email and social media without receiving a response.

I eventually received an email from “Jennifer Grant (ESQ) Senior Secretary (NORTH GULF ENERGY CORPORATION) HQ, Office Dallas (USA),” which said Mr. Patton is “out of the office because of a family member that has passed.” 

I responded with a set of detailed questions but received no further reply. So I can’t give Mr. Patton’s side of the story.

5. Berkshire Hathaway Chairman & CEO Warren Buffett Speaks With CNBC’s Becky Quick On “Squawk Box” Today – Becky Quick, Warren Buffett, and Greg Abel

BECKY QUICK: People look at this and say, “Okay, Warren Buffett is putting his stamp of approval on investment in Japan,” basically. Is that an accurate read?

WARREN BUFFETT: Well, yeah, it’s an accurate read, but it was an accurate read a couple years ago, too. I mean, I was confounded by the fact that we could buy into these companies and, in effect, have an earnings yield of maybe 14% or something like that with dividends that would grow, that they actually grew 70% during that time. And the people were investing their money in a quarter of a percent or nothing. And a quarter percent, if they put it out for many years, wasn’t going to grow, and the 14% was more likely to grow than not. And if that didn’t look like something sensible to me, you know, that’s as easy as it gets. But it’s turned out to be better than I thought it would be.

BECKY QUICK: Are the opportunities in Japan better than the opportunities in the United States right now?

WARREN BUFFETT: Well, it isn’t one versus the other. We can do both, but we do have more money through equities. Now, we own a lot of Coca-Cola. Coca-Cola does a lot of business here. Apple does a huge amount of business here. But so, we do it indirectly, through American investments. But we have more money in terms of equity securities in Japan than in any other country in the world and all combined. We just thought– we were—

BECKY QUICK: Minus the United States.

GREG ABEL: Excluding the U.S…

…BECKY QUICK: Okay. So let’s talk about what’s happening in the banking sector right now. It, is this a banking crisis? Is this financials in turmoil? Is this banking crisis 2.0? What would you call what we’ve been seeing happen?

WARREN BUFFETT: Well, I, I would say that the, some of the dumb things that banks do periodically well has, have become uncovered during this period. And as one of, a banker told me one time, he says, “I don’t know why we keep looking for new ways to lose money when the old ones are working so well.” And they made the same mistake, some banks, in this period by they haven’t made as many mistakes, they expect to make some mistakes in making loans, but they haven’t, and particularly here in the credit card loans I mean, that’s just part of the game, but they haven’t made the same sort of mistakes that they made back in 2008 or 2009.

But they have mismatched assets so — and bankers have been tempted to do that forever, and every now then and then it bites ’em in a big way. And it’s just amazing to me that banks can make presentations to financial analysts and everything and if one bank bought a bond at 100 and another bought it at 96 and they both, they both split held a maturity one bank carries it at 100 and another bank carries it at 96. I mean, it, it is accounting procedures have driven some bankers to do some things that may have helped their current earnings a little bit and pull and caused the recurring temptation to get a little bit bigger spread and report a little more in earnings.

And it’s ended in a result you could predict. You can predict when it would happen, and then once they start looking at one that does it then they start looking at others. And pretty soon, you know, that everybody is in a position of looking at a number that nobody looked at when it was, when it was presented to them a year ago if you read the 10-K already but the banks did not call attention to what they were doing when it was going on, and I would read, I would read investor contact when they would have meetings with the financial analysts or the people who follow banking and nobody even brought up the point virtually and believe me if, you know, if we’ve got a $50 billion loss or something, something at Berkshire, we would expect that people would know about it. And it’s happened before. It’s happened this time. It’ll happen again some day.

BECKY QUICK: Did you see this? You were reading through the reports. You followed all these banking earnings that were coming—

WARREN BUFFETT: Sure–

BECKY QUICK: In. So you noticed it. You saw it—

WARREN BUFFETT: Sure. Sure, I noticed it.

BECKY QUICK: Is that why you saw, sold so many of the banking stocks you owned–

WARREN BUFFETT: Well, we sold a number of banks. I mean, we had, we had held some of ‘em for 25 years. But I don’t like it when people get too focused on the earnings number and forget what my view of pacing banking principles. I’m not gonna get into naming any names or anything like that, but it happened to varying degrees throughout the industry, wasn’t the and the politicians say, “Well, the big banks did this and,” that isn’t true.

I mean, I know who has been holding long-term instruments and if they just take more commercial mortgages or something of the sort that they carry ‘em at cost basically and they can’t sell ’em at that cost. And it’s important, it’s important the banks retain the confidence of the public, and they can lose it, you know, in seconds. And we saw a country that was not worried about banks, you know, till about Wednesday or Thursday of the week when Silicon Valley fell apart and then all of a sudden everybody was worried about it all over the country. And the interesting thing of course is that it will not cost the government a penny.

I mean, people think that, you know, that some of the government’s gonna get hung up with this. The FDIC is a in effect a very peculiar neutral insurance operation that is run by the government but is financed by the banks and FDIC had $120 billion or so at the start of the year, and that’s all the money that banks have paid in, less what the FDIC has had to pay out on losses. And if the FDIC has to pay out $250 billion this time or $300 billion, they just assess the banks more. And they don’t do it in a very businesslike manner because the public has the impression that the FDIC is the United States government and that so on, and of course they do appoint the people, but the cost of the FDIC, including the cost of their employees and everything else, is borne by the banks. So banks have never cost the federal government a dime.

But that the public doesn’t really understand the whole FDIC thing, and the comments of public officials confuse it and the issue enormously and – I mean, the FDIC was set up to operate on I think January 1st, 1934. You’d think somebody would have gotten through to writing what’s the essence of this FDIC, which is, was a fantastically good development of the New Deal. I mean, 2,000 banks failed in, I don’t know whether it was, 1920 or 1921. There’s only, I don’t know, something less than 5,000 banks in the United States.

And, I mean, it was a paralyzing thing to have a bank failure in this country. And my dad lost his job in 1931. He lost his savings. And it was cause a bank failed that he worked in at downtown in Omaha. And people shouldn’t be worried about losing their money and the deposits they have in an American bank. And today they have no reason to worry and but the message has gotten very confused and people don’t really understand how it all works. And you know, and politicians can make hay out of it and all kinds of, all kinds of things, bad things happen when people don’t understand some major institution or who actually bears the cost and what the responsibilities are. And nobody is going to lose money on an on a deposit in a U.S. bank. I don’t know about the rest of the world. I don’t know. I’m not that familiar with it. But it’s not going to happen and that message has gotten mixed up…

…WARREN BUFFETT: No, I do not think I could run the Fed as well as Jay Powell’s run it. I think Jay Powell’s been a terrific and part of the job well, look at Paul Volcker back in the 1980s. I mean, people were sending him, you know, I mean, he was he needed Secret Service protection and everything else that but in the end he felt his responsibility was to do the right thing at the Fed, and he didn’t give a damn about what anybody wrote about him or anything else. And I think that he’s one of my heroes, and I think he’s one of Jay Powell’s heroes. And I think Jay Powell is, did the same thing actually in March of 2020 when we went into the pandemic I think at the annual meeting that year I said, you know, that he was a hero, and he is a hero.

And you have to, you have to act, and you have to act on insufficient information. And you’ve got a ultimate responsibility to the American public. And it doesn’t mean you can stop recessions. It doesn’t mean that you can turn bad loans into good loans or anything else. But it does mean that you gotta keep the system working. And the system came close to stopping. And if you read a book called Trillion Dollar Triage, you can get it on a day-by-day account and people don’t know how close it was. And Jay Powell did not call for studies or position papers and, you know, lengthy debate and everything. You just don’t do it. You act. And that’s what Paul Volcker did, and I thank heavens, you know, Jay Powell was there. I mean, you could’ve gotten a very different result in March of 2020 after the pandemic broke out.

BECKY QUICK: Did the Fed keep rates low for too long after that?

WARREN BUFFETT: Who knows, who knows. We won’t know I don’t, I don’t know what they precisely should do. Nobody does. And they follow conventional wisdom and all of that, and sometimes, sometimes it works out and sometimes it doesn’t. But since 1942, you know, we’ve made all kinds of mistakes in this country and we’ll continue to make ‘em. But somehow the system works pretty damn well. I’d rather own stocks and bonds over many years. I’d rather own part of America than try to squirrel my money away somehow other place, you know, maybe in Switzerland, Credit Suisse or something like that. It just people are they don’t really get any wiser about this sorta thing. People somebody yells fire, they’re gonna run for the door. I mean, and it’s built into fear is so easy to arouse in people. And you talk about fear about their money and they don’t really understand the system necessarily or anything of the sort. And they can actually, by their own actions then, create what they were afraid of. It’s a very interesting phenomenon.

And it actually you have, my dad hated Franklin D. Roosevelt, but so I grew up first 10 years of my life I couldn’t get dessert at dinner unless I said something nasty about Roosevelt or something. But over the years, you know, when Roosevelt said, “The only thing we have to fear is fear itself,” he was 100% right. When he closed the banks and said, “I’ll open the good ones a week from then,” he didn’t, he didn’t know anything about which bank was good or bad or anything like that. But people just needed that an appropriate confidence. And now they’ve really got an appropriate confidence because we didn’t have an FDIC and we didn’t have an FDIC that was required for every bank. Lotta banks fought the idea. And now we’ve got a system that works, but people are still scared when they get scared. And it being scared is so contagious.

You can’t imagine what it was like that weekend after Silicon Valley. I mean, you know, the guy that drives me around because I can’t see that well and, you know, all he was talking was banking, you know. And he what should he do and it’s unnecessary fear is a terrible thing to give people. And Roosevelt and the New Deal really wanted to get rid of that. And it here we are X years later and we’ve got a mechanism that’s so much better than we had going in, but people really don’t quite understand it. And maybe, you know, maybe it takes the president of the United States to just go on and deliver Roosevelt’s message and make it more clear to people what we really do have and what they need to be worried about and what they don’t need to be worried about. But of course if you’re trying to win an election next time you tell people, you know, that if you’re out of office or you’re out of control, you know, tell ‘em how terrible the other guy is for getting ’em into this problem. And that’s gonna always live with us.

BECKY QUICK: So you look around and you’re not worried at this point?

WARREN BUFFETT: Well, at 92 I’ve got other things to worry about. No, I’m not, I don’t worry about our ability. There’s things I worry about. Sure. I worry about the nuclear threat. I worry about a pandemic in the future, all kinds of but I don’t worry about ‘em because I can’t do anything about ’em. But I actually that’s what I originally thought my money could be best used for, but I don’t know any answers now after 40 or 50 years of thinking that way.

But I’m not, I don’t worry about no, I don’t I never go to bed worried about Berkshire and how we’ll handle a thing. If I’m worried about Berkshire I should get, I should figure out something different to do about what Berkshire is doing. But Berkshire is my responsibility and I I think I was very, very, very lucky that Berkshire happened to be in America and I happened to be an American. And I was born in 1930 and I’ve been in a golden age ever since I was born. The GDP per capita’s up, like, six-fold or seven-fold. In one person’s lifetime there’s never been anything like that in the history of mankind. And so and, you know, we love to complain about wherever we are, but, you know, most people don’t work on Saturdays and don’t work on Sundays and when I was a kid everybody worked on Saturdays.

And I mean, it the world has changed so much for the better in terms of, you know, how well off people are compared to any other time in history. If I’d been born 150 years ago and I went to the dentist, I mean, you know, they’d pour whiskey down me and all kinds of things. There’s just all kinds of improvements. And but it’s man nature to be dissatisfied. And politics does stir that up. And you’ve gotta say, if you’re out of power, that the other guy’s screwing up and you could do better. And that’s just built into the system. But that was the case when I was a kid, and it’s the case today….

…BECKY QUICK: Let’s talk a little bit more about where we left things with that inflation number. Again, we are with Warren Buffett in Tokyo, Japan right now. Warren, you could talk about inflation and what’s coming and what’s going, but we’ve got the CPI number coming up. And I think you probably have better information than Janet Yellen or Jay Powell, just in terms of what’s happening on a day-by-day basis. You have so many businesses that Berkshire owns outright. You have so many big companies that you own a major stake in. What do you think about inflation? Have we seen the worst of inflation? Is it rolling over? Is it coming down steadily?

WARREN BUFFETT: Well, inflation is always a possibility. And by inflation, I mean extreme inflation. It’s a possibility. I mean, just look at the countries and what they’ve done. I mean, I don’t know how many times and they almost lead—well, they can lead to terrible things. Led to terrible things in Germany. And you want people to trust their money. I mean, if they really have a fight for money, the economy doesn’t work. But in 1942 when I bought my first stock, I mean, we were going to pour money into people’s pockets, and they couldn’t buy anything. They couldn’t buy cars. They couldn’t buy – I mean, they couldn’t buy washing machines or anything else. But they had money flowing into them. And, of course we had price controls. We did various things. And the war ended in August of 1945. And for a little while the fact that there was this all this money sloshing around and people wanted to buy things because they hadn’t been able to buy for three or four years, and women had gone to work and all of that sort of thing, and I think the inflation rate went from something like 1% in January of ’46 to by the end of the year it was running at 15% or something.

I mean, if you give people a lot more money, put it in their pockets and you’ve it in corresponding goods and services. Things were not – money is going to become worth less, not worthless, worth less. And that’s happened periodic – I mean, we’ve had incredible inflations in certain countries. If you look it up on search, you know, the greatest inflation, we’ve had it post-World War II in various countries. I mean, and there comes a point when it gets out of control, it is out of control. And it screws everything up. And it’s not good for society. There are certain people who profit on it, obviously, anybody that’s borrowed a lot of money. But it is not good for society. And government has the responsibility for making sure that they issue the currency. And it’s the only thing that’s legal tender.

And, you know, that you need to have and I think Charlie mentioned it even on the — currency is one of the great inventions of mankind. You don’t want to go around all the time trying to trade your services, you know, in terms of giving somebody eggs and trying to get back a watch, and then trying to trade your watches. I mean, you want something that is – you need something in a society that’s legal tender. But it’s important how you treat it. And the United States has been pretty good at it. Really quite good. But, you know, if you look over the years since I’ve been investing, I mean, it, you know, there’s been a 90%+ loss in purchasing power.

BECKY QUICK: But it sounds to me like you are more worried about inflation than recession. Is that fair?

WARREN BUFFETT: No, I think either one can cause a lot of trouble. And recessions can turn into depressions. I mean, you know, I mean we’ve got a great, great country. And it gets messed up by depressions. I mean, I lived through – I was born in 1930, and the Dow didn’t get back to the level – it was higher than when I was born for about five days, and then I got out of college before it got back to that point.

And it wasn’t that the American people had turned bad or anything else, but we got something that fed on itself, and banks failed. And, I mean, you can disrupt an economy a lot easier than you can put it back together again. And we’ve had some close calls on that. And I think we’ve had some, I think in 2007 and ’08. I mean, I think Hank Paulson said, you know, that we’ll use the economic stabilization act, which was an act back in – and all of a sudden we’ll get guaranteed money market funds. And it was a good idea to do. Whether he really had the authority to do it, I don’t know. But he was sure as hell the right guy in the job. So we don’t want to mess up our economic machine. And it can be done by inflation.

BECKY QUICK: So how do we mess it up? How do we mess it up? Should the Fed keep raising rates? Is inflation at bay? What do you think?

WARREN BUFFETT: Oh, basically, fiscal policy scares me more than monetary policy…

…BECKY QUICK: In terms of the potential for a credit crunch coming through what the banks are going through right now, there’s been a lot of speculation about what that could mean to the economy. Is it going to mean a 0.5% hit to GDP? Is it going to mean a 1% hit to GDP? What would you guess?

WARREN BUFFETT: I would say that I’ve been in business, running Berkshire for 58 years, and I’ve never opined an economic forecast of any use to the company. And all you have to do is keep running every business as well as we can, and we got to keep plenty of cash on hand so that people are going to keep making intelligent decisions, rather than those forced upon them. And that’s all we know how to do. And if I depended in my life on economic forecasts, you know, I don’t think we’d make any money. I don’t know how to do it. And, you know, people want to get them, so they get them. But it has no utility. When I find one of our companies has hired somebody to tell them what’s going to happen in the economy, I mean, they’re throwing’ their money away as far as I’m concerned.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Amazon and Apple. 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 09 April 2023:

1. Xi Jinping Says He Is Preparing China for War – John Pomfret and Matt Pottinger 

Chinese leader Xi Jinping says he is preparing for war. At the annual meeting of China’s parliament and its top political advisory body in March, Xi wove the theme of war readiness through four separate speeches, in one instance telling his generals to “dare to fight.” His government also announced a 7.2 percent increase in China’s defense budget, which has doubled over the last decade, as well as plans to make the country less dependent on foreign grain imports. And in recent months, Beijing has unveiled new military readiness laws, new air-raid shelters in cities across the strait from Taiwan, and new “National Defense Mobilization” offices countrywide.

It is too early to say for certain what these developments mean. Conflict is not certain or imminent. But something has changed in Beijing that policymakers and business leaders worldwide cannot afford to ignore. If Xi says he is readying for war, it would be foolish not to take him at his word…

…If these developments hint at a shift in Beijing’s thinking, the two-sessions meetings in early March all but confirmed one. Among the proposals discussed by the Chinese People’s Political Consultative Conference—the advisory body—was a plan to create a blacklist of pro-independence activists and political leaders in Taiwan. Tabled by the popular ultranationalist blogger Zhou Xiaoping, the plan would authorize the assassination of blacklisted individuals—including Taiwan’s vice president, William Lai Ching-te—if they do not reform their ways. Zhou later told the Hong Kong newspaper Ming Pao that his proposal had been accepted by the conference and “relayed to relevant authorities for evaluation and consideration.” Proposals like Zhou’s do not come by accident. In 2014, Xi praised Zhou for the “positive energy” of his jeremiads against Taiwan and the United States.

Also at the two-sessions meetings, outgoing Premier Li Keqiang announced a military budget of 1.55 trillion yuan (roughly $224.8 billion) for 2023, a 7.2 percent increase from last year. Li, too, called for heightened “preparations for war.” Western experts have long believed that China underreports its defense expenditures. In 2021, for instance, Beijing claimed it spent $209 billion on defense, but the Stockholm International Peace Research Institute put the true figure at $293.4 billion. Even the official Chinese figure exceeds the military spending of all the Pacific treaty allies of the United States combined (Australia, Japan, the Philippines, South Korea, and Thailand), and it is a safe bet China is spending substantially more than it says…

…In his first speech on March 6, Xi appeared to be girding China’s industrial base for struggle and conflict. “In the coming period, the risks and challenges we face will only increase and become more severe,” he warned. “Only when all the people think in one place, work hard in one place, help each other in the same boat, unite as one, dare to fight, and be good at fighting, can they continue to win new and greater victories.” To help the CCP achieve these “greater victories,” he vowed to “correctly guide” private businesses to invest in projects that the state has prioritized.

Xi also blasted the United States directly in his speech, breaking his practice of not naming Washington as an adversary except in historical contexts. He described the United States and its allies as leading causes of China’s current problems. “Western countries headed by the United States have implemented containment from all directions, encirclement and suppression against us, which has brought unprecedented severe challenges to our country’s development,” he said. Whereas U.S. President Joe Biden’s administration has emphasized “guardrails” and other means of slowing the deterioration of U.S.-China relations, Beijing is clearly preparing for a new, more confrontational era…

…Xi is now intensifying a decadelong campaign to break key economic and technological dependencies on the U.S.-led democratic world. He is doing so in anticipation of a new phase of ideological and geostrategic “struggle,” as he puts it. His messaging about war preparation and his equating of national rejuvenation with unification mark a new phase in his political warfare campaign to intimidate Taiwan. He is clearly willing to use force to take the island. What remains unclear is whether he thinks he can do so without risking uncontrolled escalation with the United States.

2. TikTok and Amazon Bet on China’s Ecommerce Model. It’s a Dud – Tracy Wen Liu

American social media is full of people selling things—TikTok influencers hawking their own branded products and Instagrammers pushing their followers to sponsored links. But true livestream ecommerce of the kind pioneered by Chinese retail giants—which is not unlike old-school television sales, where a host hawks products live over the internet, sweetening the deal with discounts and promotions—has never quite reached critical mass in the US. Now, lured by the vast scale of the business in China, companies including Amazon, YouTube, Shopify, and TikTok have invested heavily in live selling. But they’re struggling for traction. Facebook and Instagram have already bowed out. And experts from China say that the American market may just not be ready for livestream ecommerce. 

“I haven’t seen one success case,” says Marina Jiang, an expert in cross-border ecommerce and founder of The Unoeuf Creative Consulting, a social marketing agency. “If there is one proof of concept in the United States, I would be willing to try it myself.”

Livestreaming—without the selling—has been huge in China for a decade. By June 2016, 325 million people—46 percent of all internet users in China—were regularly watching livestreams, according to the China Internet Network Information Center, a government agency. That year, companies began to integrate sales channels into their livestream offerings, and vice versa, led by fashion retailer Mogujie and Taobao, the country’s biggest e-tailer, which launched their services in March and April 2016, respectively…

…Chinese experts say the reason for the slow takeoff of livestream ecommerce in the US is that there are significant differences in consumer behavior between the American and Chinese markets. In China, livestream ecommerce is as much an entertainment product as a retail one, with viewers tuning in for hours at a time to interact with hosts as well as to get access to discounts and deals. 

“American consumers shop online to save time. If they want to shop around, they would go to department stores,” says Souffle Li, who recruits livestreamers for the industry. “They value their time differently than Chinese consumers, so they wouldn’t watch hours of livestreaming to purchase discounted products.”

Amazon’s own statistics show that 28 percent of purchases on the company’s platform are completed in three minutes or less, and half of all purchases are finished in less than 15 minutes. The company has focused on offering further time savings, from shorter shipping times to prefilling orders on items that customers purchase regularly.

American customers are also more likely to return the products than Chinese customers, according to Li. Influencers are often paid as a percentage of their total sales, and product returns add a lot of complexity to this process. “It’s really difficult to profit in the livestreaming sale market in the United States,” Li says…

…TCG’s Goad also thinks it is hard to change consumer behavior. “The reality is our broader US commerce culture is very different from the rest of the world—a lot of Americans simply don’t want to be sold to and instead look for content that is adding value and educating them, or tells a personal story,” she says.”

There are also structural differences between the two markets. “In China, livestreaming emerged at a time when the number of shopping malls was still far lower than those of the US; there are about 24 square feet of retail space for every American, compared to just 2.8 square feet in China,” Howard Yu, Lego Professor of Management and Innovation at IMD Business School. “What livestreaming did was to step into the void in China, especially in rural parts of the country. Such an unmet need simply doesn’t exist in the US.”

This means that conditions in the US just don’t add up to the moment that China was in when its own livestreaming boom began.

Influencers using TikTok Shop say they haven’t had much success so far. “The traffic isn’t great,” says Yu Lu, a UK-based influencer who works for an MCN in Shenzhen, and uses a VPN to sell on TikTok in the US. Her record audience was 280 people—her manager was really impressed by the number, she says. On March 1, she held a two-hour long session without a single person watching. “It is good if you can have like five people watching,” she says.

3. Why a Brics currency is a flawed idea – Paul McNamara

Within the Brics countries of Brazil, Russia, India, China and South Africa, there is a growing clamour to challenge the dollar’s hegemony.

Russian leader Vladimir Putin said last June that the Brics were working on developing a new reserve currency based on a basket of currencies for its member countries. Russia’s foreign minister Sergei Lavrov said in January the issue would be discussed at the Brics summit in South Africa at the end of August…

…The problem is that Brics is not an especially useful economic term. It marries an economic superpower in China with a potential one in India with three essentially stagnant commodity exporters.

Far from being a remotely sensible optimal currency area, the economies are dramatically different in terms of trade, growth, and financial openness. While Russia’s economic performance was clearly the weakest of the five Brics last year, Brazil and South Africa have struggled to prosper without strong commodity prices underpinning low interest rates and rising domestic credit…

…In the original 2001 Goldman Sachs paper that coined the term, China accounted for half the original four-country bloc’s gross domestic product measured at market rates (South Africa was added in 2010).

The most recent IMF data puts China’s share at 73 per cent (72 per cent if South Africa is wedged in). Since 2003, the Brics share of global output at market prices has risen from 8.4 to 25.5 per cent. Of this 17.1 percentage point rise, China accounts for 14 points…

…China’s dominance is underlined further by the fact that it is a key trade partner for the commodity exporters, which have industrial cycles that clearly track the ebb and flow of the Chinese credit cycle. And after the attack on Ukraine, China’s financial influence over isolated Russia has risen further.

It is obvious but Chinese strategic interests are not especially aligned with those of the other countries. One of China’s priorities is finding somewhere to park its external surpluses beyond the reach of the US Office of Foreign Assets Control and finding stores of value other than US Treasuries. While none of the other four Brics members can provide liquid assets, they can provide investment opportunities especially in raw materials. As with the Belt and Road Initiative, Chinese authorities prefer to have control in such matters.

4. Charlie Munger fireside chat with Todd Combs – Thomas Chua

Todd Combs and Charlie Munger had a fireside chat last year… Here are my notes:…

…“But the world that Henry was in, it was not at all common for the guy who was the C.E.O. to say, “Get out of the way.” Because he did it way better than them. However, because they had so many rules and conventions. He paid no attention to those. Nothing he did was, and Berkshire’s done the same thing. He was loyal to them. And he was quite comfortable when he walked into things. Many C.E.O.s can’t stand having anything around they haven’t dominated. But that’s not Henry, and that’s not Warren Buffett.”…

…“The thing that’s interesting about it is, when Henry was buying stock in gobs, that was a very uncommon thing to do. And now, of course, it’s very common. You could say Henry has triumphed. But Henry wouldn’t be buying in a lot of the stock. A lot of people are buying stock now, but after it’s selling for more than it’s worth. They like growing their stock, no matter what its value. And people like Henry and Berkshire would buy their stock on the cheap. It’s amazing, we haven’t had another Henry in a long time.”…

…“My Berkshire stock has gone down 50% three times in my lifetime. That’s one of the most successful gambles — you can find something that works, but it still… And of course, can you imagine an ordinary investment management firm saying, “We don’t mind going down 30%”? They’d be in terror or they’ll be fired. And that means that 95% of the big-time national investing, they’re closet indexers.”

5. Generative vs. Genuine: Why Today’s Generative AI Isn’t Tuned for B2B – Gordon Ritter and Jake Saper

Generative AI today is just mimicking the trillions of words that it has consumed. Because models are trained to match the distribution of text on the entire internet—and not everything on the internet can be trusted as accurate—not everything generated by generative AI can be trusted… 

…Current business applications of generative AI are mostly tuned for marketing (copywriting/cold emails) and advertising purposes, use cases in which occasional factual inaccuracies are typically tolerable. But for most business use cases, accuracy is critical. In order for businesses to feel confident in using generative AI for most use cases, more context and human assistance will be required…

…Generative AI is output-oriented, not outcome-oriented, which works well for consumers but not for businesses. In other words, ChatGPT can spit out taglines for a new beverage brand, but it can’t tell you which one performs better. This is because the interaction with the model is a one-way street; it lacks the ability to continuously learn based on outcomes. When it comes to B2B, businesses need more than a generator; they need AI that is iterative and driven by outcomes specific to their industry.

Promising generative AI apps for B2B will anchor on ROI-based outcomes. For example, our portfolio company Ironclad is using AI to help draft and edit contracts more efficiently. This not only helps lawyers move more quickly; it helps them improve business outcomes. Their platform is being built to coach drafters on which clause formulations will drive faster deal close rates. By marrying LLM suggestions with their own proprietary data, Ironclad is building a defensible, outcome-focused product…

…In order for generative AI to move the needle in many business use cases, the AI needs to be trained on company-specific data. While off-the-shelf language models are mostly trained on publicly available data, today, they lack broad access to the context and IP needed to be effective for B2B. E.g., without the context-specific data created within Ironclad’s workflow software, an LLM can’t ascertain which clause is likely to close a contract the fastest.

6. Jim Chanos: A Short Thesis on Data Centers – Compound248 and Jim Chanos

Jim: [00:04:06] There’s really three ways for an enterprise to maintain its data. One, you do it yourself on-site and you have your own IT department. They keep the servers running, maintain the software and the cybersecurity. Second, and that which the legacy data centers that we short epitomizes the colocation data centers, whereby you keep your server at a third-party location. The third-party maintains the servers, keeps the air conditioning on, does whatever routine maintenance is needed to do, and provides the network connections.

And those are the so-called legacy data centers. That is the focus of our big short. And then the third way, which is the way that is garnering the most market share now is the so-called cloud providers. These would be what we call and others call the hyperscalers. Amazon AWS, Microsoft Azure, Google Cloud, et cetera. Oracle has one. And this is just simply you keeping your data on their servers and they maintain them, try to sell you add-on services on top of just a hosting fee. So that’s the three ways in which data is kept for enterprises.

The problem with the colocation legacy data centers is it’s just really a bad business and that underlines a lot of what we do on the short side. We’re looking for flawed business models first and foremost. And if they have questionable accounting and bad balance sheets and management that doesn’t tell the truth, all the better. But at the end of the day, return on capital junkies and we look for businesses where the true economic returns on capital are below the cost of capital. And that applies to the legacy data centers really in a major way, and it’s getting worse.

On top of that, the data centers, represented by the big REITs, are some of the priciest stocks we see in the entire marketplace. So there’s a real dichotomy between what we think is a really, really poor business and just towering valuations, no pun intended, in the legacy data center REITs…

…Compound248: [00:09:34] And I presume 2016 is sort of an interesting starting point. I’m guessing from your perspective if I’m thinking about this right, that probably correlates pretty well with when the hyperscalers really started ramping their own spend. And maybe you could talk about how these partners may, in fact, be competitors.

Jim: [00:09:54] One of the interesting little aspects of the story is that the hyperscalers themselves represent incredibly large tenancy for the legacy guys. And that’s going to continue, we think, for a while because it doesn’t make sense, even though the hyperscalers can build out a new center cheaper than the legacy guys, it doesn’t make sense if it’s in a locale where they don’t need an entire new data center on their own. They can take 20% of the capacity of a data center in Milwaukee or St. Louis or something like that.

So you do have this bad dynamic where your largest competitors are also your largest tenants. That’s never a position you want to be in as a landlord, but be that as it may, that’s the position they find themselves in. But you’re right, the CapEx really began to pick up at AWS and Azure and Google in this space in 2016, 2017, and you see it in the numbers.

And so on top of that, you saw lots of private equity activity, which became another part of the bull case that we think is changing. And that is private equity discovered this and began buying up data centers at really, really pricy levels peaking out at DigitalBridge’s purchase of Switch, which just closed a month or 2 ago at 40x EBITDA. And a number of deals were done around 25 to 30x EBITDA in 2020 and 2021.

But part of our thesis last summer was that there was going to be indigestion in the private space that a lot of these purchases were going to be regretful and that private equity buyers, in a rising rate environment, we’re increasingly going to realize this is a capital-intensive business, and we haven’t gotten to that part yet. Servicing the debt and the CapEx requirements was more than the cash flow, maybe buying them at 25 to 30x that cash flow wasn’t so smart.

So part of our thesis was as 2022 turned into 2023, we thought that private equity would become a seller of data centers. And that’s exactly what is turning out to be the case right now, which is why I think we have this latest bout of weakness here in March. Increasingly, data centers are being put up for sale at cap rates in high single digits. That’s just disastrous for the valuation for the big guys…

…Compound248: [00:12:14] We’ll maybe make a little bit more sense of this when we do start to put in place some of these pieces around unit economics. So just generically, if Digital Realty wants to build a new tier force at the top end, their core type of data center that they build in the U.S., and Northern Virginia is the data center capital of the world and Digital Realty has a big footprint there. What might it cost them to build it? And I guess there’s a campus element to this too, which might add confusion. But if you just kind of give us some generic numbers so that we can use that as a starting point to figure out unit economics.

Jim: [00:12:51] First, you have to start with the issue of depreciation because now for years and years and years, the data center guys have had CapEx at 150% to 175% of their depreciation and amortization. We don’t think that the unit economics worked at all here in terms of the capital per square foot, and I’m not going to bore you with all the dollars per square foot cost. The thing you have to focus and your listeners have to focus on is the returns on investment. And that’s where, on an EBIT basis, the numbers are just laughably low.

They’re 2% at DLR, and they’re 5%, 6% at EQIX. And even if you add back the depreciation, the numbers are still single digits for DLR and low double digits for EQIX. But if CapEx is 150% to 175% of your depreciation, then your EBIT is overstated. In our view, if you’re not growing on a real basis, and we don’t think they’re growing on a real basis, in fact, DLR is shrinking on a real basis, it gets back to one of the real cruxes of our story, which is that depreciation is not only a real extent, it may be understated for these companies.

Compound248: [00:14:14] And most of them sort of guide to a pretty low “maintenance CapEx number.” Is that right?

Jim: [00:14:20] Yes. So here’s how that works. The maintenance CapEx number, the company saves roughly 10% of their total CapEx. So they’re on a 15-year life on average if you look at just total depreciation to capital employed. So that means that they’re telling you with a straight face that the maintenance CapEx for the air conditioning, the HVAC, the forklifts, the rack is 150 years. And 150 years is, of course, absurd.

It was finally explained to us by an insider a year or so ago, what was going on here. And what was going on was simply the fact that if you tell your auditors and your internal audit people — say the air conditioning goes out at a data center and you’ve got to replace the air conditioning. You have no choice. You have to replace the air conditioning. If you replace the air conditioning and you can say that you will bring in one new tenant or you will be able to raise rents on any kind of meaningful number of existing tenants, you can call the entire ticket growth CapEx.

So even though the HVAC has to be replaced, no matter what, it’s now considered growth CapEx because it will add to the economics of the data center. And that’s, of course, absurd. That’s just an accounting joke.

Compound248: [00:15:33] I presume the fact these are campuses where they build them in phases, probably also allows them to muddy the water between what’s being maintained and what’s being expanded.

Jim: [00:15:44] I think that’s right. Again, if you just look at the returns on incremental investment, you’ll see that there have been, in some cases, negative, but certainly way below the cost of capital. And then, of course, you have the problem of Digital Realty, which is now trying to sell data centers and telling you with a straight face that $2.5 billion, $2.7 billion of CapEx is all growth. Well, wait a minute. If you strap for cash and you’re trying to sell assets, why don’t you just cut back on your growth CapEx? And we haven’t gotten a good answer to that…

…Compound248: [00:36:16] Well, on that ominous note, it’s a perfect way to wrap up discussion on shorting. Before we do, would love to seek advice from the people who are sharing wisdom with us. And so I was wondering if maybe I could get two questions of advice. The first, I’ve seen over time that when a short thesis comes out on a company, so many CEOs lash out at the short seller, et cetera. It almost turns into its own sort of flag for other short sellers to come take a look. If you were a non-fraud CEO and you found yourself the focus of a thoughtful short thesis, what do you say is the most effective way for them to handle that?

Jim: [00:36:59] One of the gold standards was what Reed Hastings did a number of years ago to a bear thesis where he just rebutted it point by point thoughtfully without recrimination and said, well, we think he’s wrong because of this. And I had that happen to me years and years ago, as a young analyst when I had put a short recommendation on a well-known company back when I was on the sell side.

And the company actually invited — very rarely do companies invite short sellers to come to see their operations. And the CEO invited me out to where they were and spent the day with me and with the CFO and thoughtfully rebutted what I believe. I think I was right at about half of it, and I think they ended up being right on about half of it.

But that is always a far better approach than saying these are outrageous lies and then you don’t address them because at the end of the day if you have this sort of [indiscernible] nondenial denial and companies are very good about that, they’ll say, well, this is a gross exaggeration or this isn’t — and yet they won’t address the actual points of what the short seller is alleging, then you’re opening yourself up to further scrutiny, I think.

And having opinions about facts is what makes markets. We don’t put out big reports, that’s not our business model. I’m happy to post things from time to time if we have observations, but we don’t put out 40-page reports on short candidates, but I defend the right of any short seller to do that as long as you are basing your opinions on facts and you’re not knowingly misstating the facts. And that standard applies to both bulls and bears.

People get exercised about short sellers doing this. And I keep saying, well, you should see the 48 buy recommendations I get in my portfolio every morning in my inbox. No one says, boo, about that. And yet if a short seller puts something out, they’re held to a much higher standard. And that’s, by the way, how it’s always been. And any professional short seller knows that. As they say in the Godfather too, this is the business we’ve chosen. You’ve known this.

But on the other hand, I don’t think short seller should be held to any higher or lower standard than anyone else. You cannot trade on or induce others to trade on information you know to be false. And that’s the bright line. And as long as you are on the right side of that line, your opinion that is based on the facts is worth hearing, then the market should hear it.

7. RWH024: Wealth, Wisdom & Happiness w/ Tom Gayner – William Green and Tom Gayner

[00:13:18] William Green: And you’ve also said that your grandmother was one of your great investment teachers because she never did anything with the portfolio that she inherited from her late husband. Can you talk about that? Cause again it gets at this idea of hanging on to good stuff for a long time.

[00:13:35] Tom Gayner: Well, yes. In fact, the facts of the matter are, that my grandfather died in 1966 and he was a small-town businessman, and small-town businessmen of that era often would gather at the local diner and drink coffee and talk about their portfolios.

[00:13:49] Tom Gayner: And it was a pretty common thing for people to own individual stocks among that crowd of people that would drink coffee at the diner. And so when he died, that portfolio was left to my grandmother. It was a modest portfolio. It was nothing fancy or large, but she was the type of widow who essentially never made another decision in her life.

[00:14:08] Tom Gayner: And his suits hung in the closet, his shoes were on the floor, she stayed in the same home and she held on to those 12 or 13 stocks that were in this modest portfolio at the time. And what I observed from that is that among those 12 or 13 stocks were Lockheed Martin and Pepsi. And those two, because they did so well, made the others irrelevant.

[00:14:34] Tom Gayner: The rest of them all could have gone to zero and it just didn’t matter. The compounding of the winners mathematically, the weighted average becomes bigger and bigger and she lived a modest but pleasant life for the rest of her life because essentially Pepsi and Lockheed Martin increased their dividend every year for the 25 or 30 years that she lived after he died.

[00:14:54] Tom Gayner: So again, that lesson wasn’t taught to me in a formal text, let’s sit down and talk about this. It was observation and I can remember talking to her and she would watch Wall Street Week with Louis Rukeyser on Friday night. Sometimes I would watch that with her. She was always a woman of keen interest in what was going on in the world, but either she had some self-confidence issues or doubt or wisdom.

[00:15:16] Tom Gayner: I can’t say which parts it was or which that these things that were working well. She left them alone and they compounded in such a way that it took care of her personal needs…

…[00:17:53] Tom Gayner: So, and just sort of naturally fell into the notion of, you can call it an endurance contest if you want. And then to morph that a little bit towards a financial world, think about the idea of duration. So you can talk about Markel in 15% for 37 years. Not only is that record long in terms of its duration, but that’s actually also a pretty good percentage rate too.

[00:18:16] Tom Gayner: So both of those factors are in play but the endurance of it and the durability and the idea of continuing to be able to do it for a long period of time, that’s what’s special about it. Someone else recently was asking me about this particular idea and the thought that occurred to me was that if I was going to race, Usain Bolt is the fastest man in the world and that race was going to be a hundred yards, you should take all the money you have and bet it on Usain. He’s going to win that race 110 times out of a 100. I am never ever going to beat Usain Bolt at a 100-yard race. If you make the race 200 yards, you probably should still bet all your money on the same bull. If you make it a mile, I would still make a heavy back on Usain. If you make it a marathon, I don’t know what Usain Bolt’s marathon endurance would be and probably you don’t know what mine is either. So there’s at least a hint of uncertainty that is different than the hundred-yard race. Well then, make it a foot race from Key West Florida to Seattle. Well, now I think I have a chance, I think it’s still better than Usain but it’s no longer a race about speed.

[00:19:29] Tom Gayner: It’s a race about endurance. It’s a race about willpower and just the ability to somehow or another, to will yourself to continue to put one foot in front of the other no matter how you feel. No matter how you might be doing, and no matter where your splits times are. So those are the kind of races that I at least have a chance in…

…[00:25:20] William Green: And there was a great piece of advice from Stephen King to another famous novelist who was starting to be successful and he said, don’t forget to enjoy it. And I feel like I sometimes forget that. And when I look at you, I’m kind of reminded that you have fun doing this. And it’s actually, it’s built into the value system of Markel.

[00:25:37] William Green: This idea of having a sense of humor.

[00:25:40] Tom Gayner: Absolutely and I think there are several key points to keep in mind there. One, I think such a humor is a sign of intelligence because it shows that you’re able to look at something and think about it from a different point of view, or see the absurdity of things but if you don’t have that, life will beat you down. Cause there are just so many things that you encounter in life that are just absurd. For me anyway, having a sense of humor is a way of reframing things and laughing. It is an aspect of humility and not taking yourself too seriously.

[00:26:10] Tom Gayner: Because if you take yourself too seriously, that can easily slip over into thinking you’re right and if you think you’re right, you know, then you’re setting yourself up for a fall. I can’t remember his, Mark Twain or Will Rogers said something like it’s not the things that you don’t know, it’s the things that you know that aren’t so, get you in trouble.

[00:26:28] Tom Gayner: So a sense of humor acts as a break on that sort of thing and that is important. And then the last thing, humor/fun. And again, these are words that they’re not the same words, but they sort of touch one another and have some overlap. So, I wrote about Cal Ripken in the annual report this year, and I had the great pleasure of seeing him give a talk quite recently in the context of his talk and the questions that people asked as to how that streak came to be.

[00:26:53] Tom Gayner: One of the things he talked about was that as he was a rookie in his first or second or third year, he would talk to some of the older players on the club who had been there. They made a special point of sort of acknowledging that they were at the end of their career or had just finished.

[00:27:09] Tom Gayner: And it was so much fun and they had forgotten how to have some of the joy that they should have had while playing the. So that was one of the things that kept Cal Ripken motivated and dedicated to showing up every single day and continuing to play, is that he knew it was not going to last forever. So as a consequence, that helped him frame it in such a way that he appreciated each day at the ballpark. That’s joy…

…[00:35:41] William Green: There was something you said to me when I first interviewed you, I think probably back in 2014 or 15, that I was very struck by that. I’ll read back to you where you said, sometimes people can build great careers and enjoy great successes for a period of time through bluster and bullying and intimidation and slipperiness but that always comes unraveled, always. Sometimes it takes a while, but it does. The people you find that are successful and just keep being successful year after year, I think you find those are people of deep integrity. I thought that’s a really interesting insight, and I’ve struggled with it for a while.

[00:36:15] William Green: I think partly because I had kind of lost a political battle at a company where I had worked and I was like, well, actually, I think kind of in some ways the snakes won. Maybe that was self-deluding, and I was a snake myself. And then I would look at kind of the political situation. I would see you know, the corruption of politics by business and big money and the like.

[00:36:34] William Green: And there’s a part of me and then also, I mean, look, Charlie Munger has talked about how Sumner Redstone was always his example of what I don’t want to be in life. And he was like, look, this guy made much more money than me but even his kids and his wives hated him and I’ve never met Sumner Redstone.

[00:36:49] William Green: I’m not trying to badmouth him but you know what I mean? This question of whether it’s actually better to live your life this way or to do business this way or to look at the counter-example of these people who are tremendously successful while having very sharp elbows and leaving a trail of lawsuits in their wake.

[00:37:08] William Green: Can you talk about that? Cause I feel like some people just assume that capitalism is kind of vicious and nasty and self-seeking and that’s the way it goes. And I think you are pointing us toward actually a different system that may actually work better in the long run.

[00:37:23] Tom Gayner: Right and I do think that capitalism is a much better system than what it’s given credit for. And I think businessmen oftentimes do a horrible job of communicating the positives of a capitalist system. So Adam Smith is given credit for being sort of the father and the intellectual creator of the system of capitalism. I believe his title was Professor of Moral Philosophy at University of Edinburgh or Glasgow or wherever he was at the time.

[00:37:52] Tom Gayner: So he approached the idea of capitalism from a moral lens and thought it was his superior system and wrote books about it in, in that way. Secondly, success, I think, is something that you shouldn’t do along only one variable at a complicated equation. There are a lot of things that go into the idea of success.

[00:38:14] Tom Gayner: So if you were, again, in the realm of athletics, Cause things just pop into my head from athletics stuff. And so if you look at Muhammad Ali and his career as a boxer and his probably reputation well deserved for being the greatest fighter ever. Well, that’s probably true, but if Muhammad Ali needed to be a tennis player or a chess player, he might not have been so successful.

[00:38:38] Tom Gayner: So if you’re going to define success, make sure you define what arena you are talking about. So just to say the word success in and of itself is too limited. It’s not enough. So I do not know the family structures of Sumner Redstone or Charlie Munger for that matter. I’m guessing that Charlie Munger’s success probably has more dimensions to it.

[00:39:01] Tom Gayner: But that is just a pure guess on my part and two points about Charlie Munger was the notion of you know, if you want to be a success, the best way to do that is to deserve it. So he operated with the idea of trying to be someone who deserved the success that he has earned and I think that’s a fundamentally important way of doing these.

[00:39:22] Tom Gayner: And there’s a business practice, there’s a life practice that flows from that. So if I just met you and we were talking about a deal or a project or some commercial transaction, and I said, William, trust me, you can’t help but if, again, if we don’t know one another, that is going to cause 99 times out of a hundred, just the tint of doubt can you? Because if I say, trust me, trust me. Your natural human reaction is, I can’t trust this guy and the notion of trust is not going to flow immediately if I started that way but if instead I say, William, I’m going to trust you and I’ve done some work and some basis for saying I trust you, I trust you, and I trust you. I trust you. And offer the gesture of trust first without demanding reciprocation or equality. I just do that in an unconditional way. What I have observed is that either you will do one of two things. You will either honor that trust or you’ll violate it.

[00:40:22] Tom Gayner: And if you’re going to violate the trust, you’ll probably do it sooner rather than later. And in so doing, you’ll have sorted yourself and we’re just not going to do business again. But if you honor that, trust and start to trust back, what happens is that starts to cascade, and it’s another element of compounding that takes place in your relationships with people.

[00:40:41] Tom Gayner: If you trust first, if you offer that service that value first, and you initiate that the world will sift and sort itself and orient and give you an enough people, enough opportunities where we have these compounding trust relationships that it just becomes marvelous over time. The same thing would be said in the word of love.

[00:41:02] Tom Gayner: If I say, love me and you try to meet somebody, you’re trying to develop a relationship. You say, love me. I don’t think that’s going to work. But if you offer love and you offer it unconditionally, is everybody going to love you back? No. But a lot of people will and they’ll do it in enduring, consistent, systemic ways. So just to orient yourself to be the initiator of trust and be the initiator of love. And then don’t be stupid, reciprocate and compound and grow the trust, relationships, and the love relationships, and filter out the ones where you’re not getting reciprocity. If you stay at the game long enough, and I’ve been at it 40-some years, you’ll find you have a wonderful group of people that are enjoyable, fun relationships that keep you coming in the office and doing what you’re doing as opposed to wanting to go play golf instead, That’s working now for me.

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

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

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

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

Here are the articles for the week ending 02 April 2023:

1. Pausing AI Developments Isn’t Enough. We Need to Shut it All Down – Eliezer Yudkowsky 

Many researchers steeped in these issues, including myself, expect that the most likely result of building a superhumanly smart AI, under anything remotely like the current circumstances, is that literally everyone on Earth will die. Not as in “maybe possibly some remote chance,” but as in “that is the obvious thing that would happen.” It’s not that you can’t, in principle, survive creating something much smarter than you; it’s that it would require precision and preparation and new scientific insights, and probably not having AI systems composed of giant inscrutable arrays of fractional numbers.

Without that precision and preparation, the most likely outcome is AI that does not do what we want, and does not care for us nor for sentient life in general. That kind of caring is something that could in principle be imbued into an AI but we are not ready and do not currently know how.

Absent that caring, we get “the AI does not love you, nor does it hate you, and you are made of atoms it can use for something else.”

The likely result of humanity facing down an opposed superhuman intelligence is a total loss. Valid metaphors include “a 10-year-old trying to play chess against Stockfish 15”, “the 11th century trying to fight the 21st century,” and “Australopithecus trying to fight Homo sapiens“.

To visualize a hostile superhuman AI, don’t imagine a lifeless book-smart thinker dwelling inside the internet and sending ill-intentioned emails. Visualize an entire alien civilization, thinking at millions of times human speeds, initially confined to computers—in a world of creatures that are, from its perspective, very stupid and very slow. A sufficiently intelligent AI won’t stay confined to computers for long. In today’s world you can email DNA strings to laboratories that will produce proteins on demand, allowing an AI initially confined to the internet to build artificial life forms or bootstrap straight to postbiological molecular manufacturing.

If somebody builds a too-powerful AI, under present conditions, I expect that every single member of the human species and all biological life on Earth dies shortly thereafter.

There’s no proposed plan for how we could do any such thing and survive. OpenAI’s openly declared intention is to make some future AI do our AI alignment homework. Just hearing that this is the plan ought to be enough to get any sensible person to panic. The other leading AI lab, DeepMind, has no plan at all…

…Trying to get anything right on the first really critical try is an extraordinary ask, in science and in engineering. We are not coming in with anything like the approach that would be required to do it successfully. If we held anything in the nascent field of Artificial General Intelligence to the lesser standards of engineering rigor that apply to a bridge meant to carry a couple of thousand cars, the entire field would be shut down tomorrow.

We are not prepared. We are not on course to be prepared in any reasonable time window. There is no plan. Progress in AI capabilities is running vastly, vastly ahead of progress in AI alignment or even progress in understanding what the hell is going on inside those systems. If we actually do this, we are all going to die.

Many researchers working on these systems think that we’re plunging toward a catastrophe, with more of them daring to say it in private than in public; but they think that they can’t unilaterally stop the forward plunge, that others will go on even if they personally quit their jobs. And so they all think they might as well keep going. This is a stupid state of affairs, and an undignified way for Earth to die, and the rest of humanity ought to step in at this point and help the industry solve its collective action problem.

2. The Dismal Art – James Surowiecki

We live in an age that’s drowning in economic forecasts. Banks, investment firms, government agencies: On a near-daily basis, these institutions are making public predictions about everything from the unemployment rate to GDP growth to where stock prices are headed this year. Big companies, meanwhile, employ sizable planning departments that are supposed to help them peer into the future. And the advent of what’s often called Big Data is only adding to the forecast boom, with the field of “predictive analytics” promising that it can reveal what we’ll click on and what we’ll buy.

At the dawn of the twentieth century, by contrast, none of this was true. While Wall Street has always been home to tipsters and shills, forecasting was at best a nascent art, and even the notion that you could systematically analyze the U.S. economy as a whole would have seemed strange to many. Economics, meanwhile, had only recently established a foothold in the academy (the American Economic Association, for instance, was founded in 1885), and was dominated by Progressive economists whose focus was more on reforming capitalism via smart regulation rather than on macroeconomic questions.

Walter Friedman’s Fortune Tellers is the story of how, over the course of two decades, this all changed. In a series of short biographical narratives of the first men to take up forecasting as a profession, Friedman shows how economic predictions became an integral part of the way businessmen and government officials made decisions, and how the foundations were laid for the kind of sophisticated economic modeling that we now rely on. Friedman, a historian at Harvard Business School, also shows how the advent of forecasting was coupled with (and fed on) a revolution in the way information about the economy was gathered and disseminated. Relative to today, of course, the forecasters Friedman writes about were operating in the dark, burdened with fragmentary data and unreliable numbers. But the work they did, flawed as it was, would eventually make it possible for decision-makers to get a much better picture of how the economy as a whole was doing. And even as it’s easy to see how the forecasts of today are much more rigorous and complex than those of Friedman’s pioneers, that only makes one question seem all the more salient: Why, if forecasting has come so far, did so many people fail to predict the crash of 2008 and the disastrous downturn that followed?…

…So why are we not better at foreseeing the future? One answer is that we actually are better. Companies these days are less likely to get stuck with huge inventories of unsold goods, or to get caught short when demand outstrips supply. The volatility of the business cycle, meanwhile, diminished sharply beginning in the early 1980s, a relative calm that lasted until the crash of 2008. There’s plenty of disagreement about why this happened, but one plausible factor was that policy-makers and businesspeople were doing a better job of forecasting. And it’s also true that policy-makers have gotten better at responding once crises do happen. The response of the Fed to the recent financial crisis, for instance, was not perfect, but it was much better than the response of the Fed to past crises, and it was also instrumental in shortening the recession and boosting the recovery. Similarly, while the 2009 stimulus plan should have been much bigger, it was, by historical standards, a substantial response, and it too helped get the economy growing again.

Even so, it’s impossible to look at the forecasting track record of Wall Street and Washington over the last 15 years and not be dismayed. The Federal Reserve failed to see that a massive housing bubble was inflating, and did nothing to stop it, even as the banking sector was, in effect, betting hundreds of billions of dollars on the fact that the bubble would not burst. And even when things started to fall apart, people did not recognize how bad things were going to get—Fed Chairman Ben Bernanke testified to Congress in 2007 that the problems in housing would be largely confined to the subprime sector, while J.P. Morgan, the day before Lehman Brothers went under, issued a forecast saying that the U.S. economy would grow briskly in 2009…

…The failure of forecasting is also due to the limits of learning from history. The models forecasters use are all built, to one degree or another, on the notion that historical patterns recur, and that the past can be a guide to the future. The problem is that some of the most economically consequential events are precisely those that haven’t happened before. Think of the oil crisis of the 1970s, or the fall of the Soviet Union, or, most important, China’s decision to embrace (in its way) capitalism and open itself to the West. Or think of the housing bubble. Many of the forecasting models that the banks relied on assumed that housing prices could never fall, on a national basis, as steeply as they did, because they had never fallen so steeply before. But of course they had also never risen so steeply before, which made the models effectively useless…

…The second problem that forecasters face today is more subtle, but perhaps no less important: that there may actually be too much information out there. This would, of course, sound absurd to Roger Babson. But the reality is that investors and businesspeople are now constantly assailed by a high-volume clang of market info and economic data…

…The real issue here is one that the economist Oskar Morgenstern identified back in the late 1920s—namely, that economic predictions actually end up shaping the very outcomes they’re trying to predict. And the more predictions you have, the more complex that Möbius strip becomes. In that sense, for all the challenges they faced, men like Babson and Fisher had it easy, since forecasts were few and far between. The real irony of our forecasting boom is that as fortune-tellers proliferate, fortunes become harder to read.

3. Don’t Build the Wrong Kind of AI Business – Ben Parr

All this activity in AI has led to a new wave of AI startups and will lead to many more. There are real opportunities to build unicorns—but carelessly slapping generative AI on top of your business model isn’t one of them.

Many apps built right now will fail to attract customers, investors or both. Many venture capitalists I’ve spoken with are waiting to see which companies take off. Others are afraid of platform risk—what if OpenAI builds a competitor to your product and nips your idea before it’s even had a chance to bud?

There are ways to gird against platform risk in generative AI, and they start with understanding the two categories of AI startups out there right now:

• Category 1: Startups building advanced, complex language or machine-learning models (AI infrastructure)
• Category 2: Startups building applications on top of these platforms (OpenAI’s in particular)…

…Platform risk shouldn’t stop you from building on top of an AI platform. For one thing, unless you never build a mobile app and never use cloud computing, it’s impossible to avoid entirely. For another, platforms like Shopify, the iOS App Store or OpenAI can accelerate a product’s growth. And finally, the technology OpenAI and others have developed is so powerful that it’s almost a crime not to utilize it. Even if you won’t use it, your competitors will.

If you do choose to build on top of someone else’s AI platform, I advise you to follow my golden rule of platforms: Build a product the platform you’re built on is unlikely to build for itself. Users tend to choose products built directly by the brands they trust instead of dealing with the headache of yet another login. If the gamble goes wrong, the platform will eat your customer base…

…Founders can avoid this outcome by building something Google or OpenAI are unlikely to build. What are those things? They are:

1. Applications requiring a proprietary, niche data set. AI models can train on all sorts of data to customize their outputs, which makes it possible to differentiate your results from ChatGPT’s. If you make a chatbot and train it with a database ChatGPT can’t access (such as medical data, millions of emails and so on), the result will be a specialized chatbot OpenAI can never duplicate.
2. A product focused on a specific vertical or use case. AI tools built to serve people in fields like health, parenting, law and government require specialized data, interfaces, compliance capabilities, integrations and marketing, which large public-facing AI platforms are simply never going to provide. 

4. David Einhorn – The Long and Short of Investing – Patrick O’Shaughnessy and David Einhorn

Patrick: [00:16:49] If you think about the history of Greenlight and the way that you manage the portfolio, I’d love to understand any evolution you had in your thinking over the full period of managing the firm. Obviously, you’re extremely well known as, like, an incredible analyst, like, a securities analyst and I think that’s really what you did at the start primarily. I’m sure that’s still what drives a lot of your time in investing and thinking. But how is your thinking on portfolio management, portfolio construction overlaying things like macro bets into the portfolio? Describe how that’s changed over time for you.

David: [00:17:21] It’s actually changed a lot. I learned a tough lesson in 2008 during that financial crisis because we kind of understood what was going on and got short a bunch of the banks and rating agencies and financial stuff because that seemed to be where the profit was concentrated. But it then turned out to have a really big impact on our long book, which didn’t have any of that stuff, but it had other things that were then exposed to the tightening credit conditions and the recession that came.

And I didn’t really process all of that as effectively as I wanted to, or I should have. And in many ways, I thought that 2008 was my worst year. We lost 18%. Other people may be lost twice that or something like that. So everybody was very nice and said, “Oh, you didn’t do so bad.” But considering that we kind of saw it coming, I thought it was a completely unacceptable result.

So I have added more macro thinking into what I’m doing, and I try to take a bigger view of all of the positions relating to the top down as opposed to just the bottom up. And then it’s compounded on the long side of the book, where just in the last couple of years, I’ve had the realization that with some of these stocks, nobody’s ever going to care. Nobody is paying attention, nobody is doing the work, nobody cares what the company says. There’s just nobody home.

So we can’t make money by trying to buy something three months or six months or a year before other long-only investors figure it out because they, either aren’t there, or they don’t have any capital or they’re turning into index funds or whatnot. So we’ve had to reconstruct our long book in a way that is designed, at least in theory, to earn a return based upon just what the companies are able to pay us as opposed to relying on other investors to figure it out…

…Patrick: [00:22:52] I remember in periods like that, in the quantitative world, especially feeling these existential crises, like, after a long period of underperformance, just wondering, “Have I just missed a memo here somewhere? I think I’ve done great work, but obviously, the results are what they are.”

What was the psychology for you personally like during that period of time? What sorts of things were you questioning? Weren’t you questioning? How did you get through it? Like, I’ve lived through that kind of hell. Curious what it was like for you.

David: [00:23:19] It was very, very difficult. We weren’t making money on anything. It’s not like you had some winners and some losers. It’s like everything was a loser. So part of it was you can say, “Well, how stubborn do you want to be?” The only thing we really could have done better would have been like liquidate the whole portfolio and go to cash or something like that.

We weren’t going to do that. We had large amounts of investors who left us and understandably so because they’re here because they want to make good returns, and we weren’t making good returns. So your investors, one by one, leave. Friends say, “Why are you still doing this? You made enough net worth for yourself. Why are you fighting this battle?” And I’m sitting here saying, “Well, what am I doing wrong?” Then you start saying, “Well, what are other people doing?”

People say, well what you’re not doing is, is you’re not doing factor analysis. That was the big thing, I think, in 2018. So we said, okay, well, let’s get the factor analysis people in here. We signed a confidentiality agreement and they analyzed our portfolio and they come back and say, “You’re short the value factor.” And you say, “Really? How is that?” And they come back and tell me that my two biggest shorts are value. And that is because they correlate with how value trades, not because they’re actually value.

So I look at it and go, “Well, these things are, like, 100x earnings. How are they valued?” And it’s like, “Well, we don’t know, but this is what the machines tell us.” And I said, “Well, I can’t do anything with this.” If the problem is that I’m short the value factor when I think that I’m a value fund or value-oriented, this is a problem.

So similarly, somebody said, “Well, what you really need to do is technical analysis.” So I said, “Great, I’m going to give you 10 stocks, five of them I’m long, five of them I’m short. I’m not going to tell you which ones are longs and which ones are short. Tell me what they’re going to do over the next three months. Should I buy them? Should I short them? What should I do?”

And he looks at the charts and maps it all out and gives me his recommendations. And three months later, he was right on exactly five of them and wrong on five of them. I don’t know what you do with this. So the point is I would open to trying to figure out better ways to, like, do what we’re doing. But at the end of the day, this was just going to be an impossible environment for what we were doing…

…Patrick: [00:54:26] I was just studying Markel and some of the history of insurance, and it’s always so interesting how old so many of the insurance companies are. The dominant ones were started pre-1950 or something. What have you learned about, within financial institutions, insurance and reinsurance specifically? Because obviously, that’s a place that you’ve built and studied a lot.

David: [00:54:46] We have a reinsurance company. I’m the Chairman of it, which doesn’t mean I’m the underwriter. I don’t actually write the policies, but I’ve watched our teams battle with this for the last decade and a half. And I have to admit that it’s been far more difficult than I thought.

I think we’ve run into numerous examples, which are essentially analogous to the, “What happens when you don’t repossess the car” type of analysis, and losses have sometimes appeared in places that were never even contemplated in underwriting. And I have found it to be a very, very difficult way to make positive risk-adjusted returns.

I used to think initially, we could figure out the stuff maybe better than other people, so we wrote a concentrated portfolio of things that were mostly proprietary deals where we had the whole deal. And the first two or three times, it worked spectacularly, and that led to a lot of confidence. But ultimately, I don’t think that, that turned out to be a sustainable advantage for the company.

So we’ve had to shift entirely where it’s a much more diversified mix. And even then, we’ve had fewer blowups, but it’s still been a real challenge. Currently, today, management is very, very optimistic that the market has finally gotten good, and so we should make some money for a while, so that would be fantastic if it actually materializes. I’m more in the, “I’ll believe it when I see it” camp, which doesn’t mean I disbelieve them. It’s just that this isn’t the first time and it’s been a far more difficult operation than I imagined it would be when we started it…

…Patrick: [01:06:08] What have you learned about early relationship health? That sounds interesting.

David: [01:06:11] We have a program that we have been funding. It’s really fascinating. And what it essentially shows is if you can create a co-regulation relationship with your parent from a very early age, it helps you adjust to people probably throughout your life.

And what we have found is that it’s very important for mothers and fathers, but more mothers than fathers, without getting myself into too much trouble, to actually just hold their children, physically touch and get used to the smell and so forth. And if you actually do that, you find it very common. You can go through a calming cycle.

And if you can learn to calm your baby and if your baby can learn to be calmed by your parent, it enables them to become regulated in their relationships for a long, long period of time. We’ve funded a whole bunch of research that has essentially proved out over a sustained period of time what we’re saying. And now we’re trying to figure out how to implement this as, like, a standard training for new parents, whether it’s with pediatricians or in the birthing center and so on and so forth…

…Patrick: [01:08:22] David, this has been so much fun. I mean, so many interesting topics. The investing world has changed so much in the time that you’ve been doing this. I really appreciate your time. I ask everybody the same traditional closing question. What’s the kindest thing that anyone’s ever done for you?

David: [01:08:36] That is an awesome question. My third-grade teacher one day, grabbed me by the arm as we were getting ready to go to recess. And she said to me, you’re probably smarter than everybody else in this class, but you’d be better if you didn’t tell them that. And that really stuck with me.

Patrick: [01:08:58] What was her name, if you remember her name, teacher’s name?

David: [01:09:01] Yes, it was Ms. Olson. She called herself the Purple Witch.

Patrick: [01:09:04] Why?

David: [01:09:05] That was just her nickname.

Patrick: [01:09:08] What did that change? How did that change you?

David: [01:09:10] It created a self-awareness that I didn’t previously have. How do I come across to other people and how do you behave in the sandbox. It kind of shook me a little bit, but it was really, really kind of her to point that out, and she did it in a nice way where I was able to hear it. That’s particularly important.

5. The Death of Credit Suisse – Joseph Politano

Credit Suisse had been plagued by high-profile issues for years. It lost billions in the failure of hedge fund Achegos Capital and supply-chain financier Greensill Capital back in 2021, had data on $100B worth of accounts leaked to German newspapers in 2022, was probed by the US House of Representatives for its connections to Russian Oligarchs, and was forced to disclose “material weakness” in its financial reporting controls thanks to a last-minute call from the SEC just last week. 7% of Credit Suisse’s total revenue over the last decade went to penalties and fines, leaving the company with a net loss of $3.4B after taxes. The bank was surrounded by rumors of its impending demise for years, bleeding money and confidence while constantly scraping by through a rolling series of disasters…

…In some ways, Credit Suisse’s demise is unique from the problems that plagued Silicon Valley Bank and Signature Bank—the institution met highly stringent European capital and liquidity standards, had been regularly supervised and stress tested over the preceding years, and had fully hedged their exposure to the interest-rate driven shifts in long-term fixed income securities prices that helped bring down SVB—distinctions that may have bought the Swiss government enough time to arrange the shotgun wedding with UBS. In other ways, their demise was much the same—like SVB, Credit Suisse was forced to watch the slow departure of wealthy customers’ funds turn into a rush for the exits as depositors reportedly withdrew tens of billions of Swiss Francs in the days before UBS’s takeover…

…So what happens in the fallout of CS’s demise? Among all Global Systemically Important Banks, CS and UBS were unique for two things: their cross-jurisdictional exposures, thanks to the outsized prominence of Swiss banking in international finance, and their intra-financial system exposures, thanks to the unique nature of the two banks—in short, both Credit Suisse and UBS had prominent relationships with non-Swiss customers and had deep ties to other parts of the global financial system. The risks inherent to those exposures are partly why Swiss regulators decided to force a sale of Credit Suisse before it could collapse, but even a more orderly resolution under UBS could still pose risks to the broader financial system.

Regardless of the potential for direct contagion, the demise of Credit Suisse is likely to shake confidence in other lenders, especially in Europe. In particular, prices for European Additional Tier 1 (AT1) Capital Bonds—debt instruments that usually convert to stock if the bank encounters stress and falls below predetermined capital ratios—have fallen dramatically over the last few weeks. Credit Suisse’s AT1 holders were given nothing in the UBS takeover despite the fact that shareholders got a small payout—which is exactly how Credit Suisse’s specific AT1s were designed, but is highly unusual among the broader AT1 market and not something many investors had evidently appreciated. Other monetary authorities—including the European Central Bank, Bank of England, and Monetary Authority of Singapore—rushed to state that shareholders would absorb losses before AT1 holders under their bank resolution frameworks, but it hasn’t yet been enough to rebuild sentiment for the assets. On net, that will make it harder for European banks to raise money precisely when they may need it most.

6. UBS Got Credit Suisse for Almost Nothing – Matt Levine

After the 2008 financial crisis, European banks issued a lot of what are called “additional tier 1 capital securities,” or “contingent convertibles,” or AT1s or CoCos. The way an AT1 works is like this:

1. It is a bond, has a fixed face amount, and pays regular interest.
2. It is perpetual — the bank never has to pay it back — but the bank can pay it back after five years, and generally does.
3. If the bank’s common equity tier 1 capital ratio — a measure of its regulatory capital — falls below 7%, then the AT1 is written down to zero: It never needs to be paid back; it just goes away completely.

This — a “7% trigger permanent write-down AT1” — is not the only way for an AT1 to work, though it is the way that Credit Suisse’s AT1s worked. Some AT1s have different triggers. Some AT1s convert into common stock when the trigger is hit, instead of being written down to zero; others are temporarily written down (they stop paying interest) when the trigger is hit, but can bounce back if the equity recovers…

…These securities are, basically, a trick. To investors, they seem like bonds: They pay interest, get paid back in five years, feel pretty safe. To regulators, they seem like equity: If the bank runs into trouble, it can raise capital by zeroing the AT1s. If investors think they are bonds and regulators think they are equity, somebody is wrong. The investors are wrong.

In particular, investors seem to think that AT1s are senior to equity, and that the common stock needs to go to zero before the AT1s suffer any losses. But this is not quite right. You can tell because the whole point of the AT1s is that they go to zero if the common equity tier 1 capital ratio falls below 7%. Like, imagine a bank:

• It has $1 billion of assets (also $1 billion of regulatory risk-weighted assets).
• It has $100 million of common equity (also $100 million of regulatory common equity tier 1 capital).
• It has a 10% CET1 capital ratio.
• It also has $50 million of AT1s with a 7% write-down trigger, and $850 million of more senior liabilities.

This bank runs into trouble and the value of its assets falls to $950 million. What happens? Well, under the very straightforward terms of the AT1s — not some weird fine print in the back of the prospectus, but right in the name “7% CET1 trigger write-down AT1” — this is what happens:

• It has $950 million of assets and $50 million of common equity, for a CET1 ratio of 5.3%.
• This is below 7%, so the AT1s are triggered and written down to zero.
• Now it has $950 million of assets, $850 million of liabilities, and thus $100 million of shareholders’ equity.
• Now it has a CET1 ratio of 10.5%: The writedown of the AT1s has restored the bank’s equity capital ratios.

This, again, is very explicitly the whole thing that the AT1 is supposed to do, this is its main function, this is the AT1 working exactly as advertised. But notice that in this simple example the bank has $950 million of assets, $850 million of liabilities and $100 million of shareholders’ equity. This means that the common stock still has value. The common shareholders still own shares worth $100 million, even as the AT1s are now permanently worth zero.

The AT1s are junior to the common stock. Not all the time, and there are scenarios (instant descent into bankruptcy) where the AT1s get paid ahead of the common. But the most basic function of the AT1 is to go to zero while the bank is a going concern with positive equity value, meaning that its function is to go to zero before the common stock does.

Credit Suisse has issued a bunch of AT1s over the years; as of last week it had about CHF 16 billion outstanding. Here is a prospectus for one of them, a $2 billion US dollar 7.5% AT1 issued in 2018. “7.500 per cent. Perpetual Tier 1 Contingent Write-down Capital Notes,” they are called…

…In UBS’s deal to buy Credit Suisse, shareholders are getting something (about CHF 3 billion worth of Credit Suisse shares) and Credit Suisse’s AT1 holders are getting nothing: The Credit Suisse AT1 securities are getting zeroed…

…People are very angry about this… I’m sorry but I do not understand this position! The point of this AT1 is that if the bank has too little equity (but not zero!), the AT1 gets zeroed to rebuild equity! That’s why Credit Suisse issued it, it’s why regulators wanted it, and it would be weird not to use it here. 

Oh, fine, I understand the position a little. The position is “bonds are senior to stock.” The AT1s are bonds, so people bought them expecting them to get paid ahead of the stock in every scenario. They ignored the fact that it was crystal clear from the terms of the AT1 contract and even from the name that there were scenarios where the stock would have value and the AT1s would get zeroed, because they had the simple heuristic that bonds are always senior to stock. 

That’s the trick! The trick of the AT1s — the reason that banks and regulators like them — is that they are equity, and they say they are equity, and they are totally clear and transparent about how they work, but investors assume that they are bonds. You go to investors and say “would you like to buy a bond that goes to zero before the common stock does” and the investors say “sure I’d love to buy a bond, that could never go to zero before the common stock does,” and the bank benefits from the misunderstanding.

7. I Saw the Face of God in a Semiconductor Factory – Virginia Heffernan

By revenue, TSMC is the largest semiconductor company in the world. In 2020 it quietly joined the world’s 10 most valuable companies. It’s now bigger than Meta and Exxon. The company also has the world’s biggest logic chip manufacturing capacity and produces, by one analysis, a staggering 92 percent of the world’s most avant-garde chips—the ones inside the nuclear weapons, planes, submarines, and hypersonic missiles on which the international balance of hard power is predicated.

Perhaps more to the point, TSMC makes a third of all the world’s silicon chips, notably the ones in iPhones and Macs. Every six months, just one of TSMC’s 13 foundries—the redoubtable Fab 18 in Tainan—carves and etches a quintillion transistors for Apple. In the form of these miniature masterpieces, which sit atop microchips, the semiconductor industry churns out more objects in a year than have ever been produced in all the other factories in all the other industries in the history of the world…

…Employees at TSMC are paid well by Taiwan’s standards. A starting salary for an engineer is the equivalent of some $5,400 per month, where rent for a Hsinchu one-bedroom is about $450. But they don’t swan around in leather and overbuilt Bezos bodies like American tech hotshots. I ask Michael Kramer, a gracious member of the company’s public relations office whose pleasant slept-in style suggests an underpaid math teacher, about company perks. To recruit the world’s best engineering talent, huge companies typically lay it on thick. So what’s TSMC got? Sabbaticals for self-exploration, aromatherapy rooms? Kramer tells me that employees get a 10 percent discount at Burger King. Ten percent. Perhaps people come to work at TSMC just to work at TSMC…

…Two qualities, Mark Liu tells me, set the TSMC scientists apart: curiosity and stamina. Religion, to my surprise, is also common. “Every scientist must believe in God,” Liu says…

…During the pandemic lockdown, TSMC started to use intensive augmented reality for meetings to coordinate these processes, rounding up its far-flung partners in a virtual shared space. Their avatars worked symbolically shoulder to shoulder, all of them wearing commercially produced AR goggles that allowed each participant to see what the others saw and troubleshoot in real time. TSMC was so pleased with the efficiency of AR for this purpose that it has stepped up its use since 2020. I’ve never heard anyone except Mark Zuckerberg so excited about the metaverse.

But this is important: Artificial intelligence and AR still can’t do it all. Though Liu is enthusiastic about the imminence of fabs run entirely by software, there is no “lights-out” fab yet, no fab that functions without human eyes and their dependence on light in the visible range. For now, 20,000 technicians, the rank and file at TSMC who make up one-third of the workforce, monitor every step of the atomic construction cycle. Systems engineers and materials researchers, on a bruising round-the-clock schedule, are roused from bed to fix infinitesimal glitches in chips. Some percentage of chips still don’t make it, and, though AI does most of the rescue, it’s still up to humans to foresee and solve the hardest problems in the quest to expand the yield. Liu tells me that spotting nano-defects on a chip is like spotting a half-dollar on the moon from your backyard.

Beginning in 2021, hundreds of American engineers came to train at TSMC, in anticipation of having to run a TSMC subsidiary fab in Arizona that is slated to start production year. The group apprenticeship was evidently rocky. Competing rumors about the culture clash now circulate on social media and Glassdoor. American engineers have called TSMC a “sweatshop,” while TSMC engineers retort that Americans are “babies” who are mentally unequipped to run a state-of-the-art fab. Others have even proposed, absent evidence, that Americans will steal TSMC secrets and give them to Intel, which is also opening a vast run of new fabs in the US.

In spite of the fact that he himself trained as an engineer at MIT and Stanford, Morris Chang, who founded TSMC in 1987, has long maintained that American engineers are less curious and fierce than their counterparts in Taiwan. At a think-tank forum in Taipei in 2021, Chang shrugged off competition from Intel, declaring, “No one in the United States is as dedicated to their work as in Taiwan.” …

…I put a parting question to Lin: How in the world do you remain undaunted by all these extraordinary problems in nanotechnology? Lin laughs. “Well, we just have to solve them,” he says. “That is the TSMC spirit.”

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

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

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Here are the articles for the week ending 26 March 2023:

1. The Age of AI has begun – Bill Gates

I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.

I thought the challenge would keep them busy for two or three years. They finished it in just a few months.

In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.

Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning…

…The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children…

…Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations. Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.

Technically, the term artificial intelligence refers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term artificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all…

…Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.

As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox…

…Company-wide agents will empower employees in new ways. An agent that understands a particular company will be available for its employees to consult directly and should be part of every meeting so it can answer questions. It can be told to be passive or encouraged to speak up if it has some insight. It will need access to the sales, support, finance, product schedules, and text related to the company. It should read news related to the industry the company is in. I believe that the result will be that employees will become more productive.

When productivity goes up, society benefits because people are freed up to do other things, at work and at home. Of course, there are serious questions about what kind of support and retraining people will need. Governments need to help workers transition into other roles. But the demand for people who help other people will never go away. The rise of AI will free people up to do things that software never will—teaching, caring for patients, and supporting the elderly, for example…

…For example, many people in those countries never get to see a doctor, and AIs will help the health workers they do see be more productive. (The effort to develop AI-powered ultrasound machines that can be used with minimal training is a great example of this.) AIs will even give patients the ability to do basic triage, get advice about how to deal with health problems, and decide whether they need to seek treatment.

The AI models used in poor countries will need to be trained on different diseases than in rich countries. They will need to work in different languages and factor in different challenges, such as patients who live very far from clinics or can’t afford to stop working if they get sick…

…In addition to helping with care, AIs will dramatically accelerate the rate of medical breakthroughs. The amount of data in biology is very large, and it’s hard for humans to keep track of all the ways that complex biological systems work. There is already software that can look at this data, infer what the pathways are, search for targets on pathogens, and design drugs accordingly. Some companies are working on cancer drugs that were developed this way.

The next generation of tools will be much more efficient, and they’ll be able to predict side effects and figure out dosing levels. One of the Gates Foundation’s priorities in AI is to make sure these tools are used for the health problems that affect the poorest people in the world, including AIDS, TB, and malaria.

Similarly, governments and philanthropy should create incentives for companies to share AI-generated insights into crops or livestock raised by people in poor countries. AIs can help develop better seeds based on local conditions, advise farmers on the best seeds to plant based on the soil and weather in their area, and help develop drugs and vaccines for livestock. As extreme weather and climate change put even more pressure on subsistence farmers in low-income countries, these advances will be even more important…

…New tools will be created for schools that can afford to buy them, but we need to ensure that they are also created for and available to low-income schools in the U.S. and around the world. AIs will need to be trained on diverse data sets so they are unbiased and reflect the different cultures where they’ll be used. And the digital divide will need to be addressed so that students in low-income households do not get left behind.

I know a lot of teachers are worried that students are using GPT to write their essays. Educators are already discussing ways to adapt to the new technology, and I suspect those conversations will continue for quite some time. I’ve heard about teachers who have found clever ways to incorporate the technology into their work—like by allowing students to use GPT to create a first draft that they have to personalize…

…For example, there’s the threat posed by humans armed with AI. Like most inventions, artificial intelligence can be used for good purposes or malign ones. Governments need to work with the private sector on ways to limit the risks.

Then there’s the possibility that AIs will run out of control. Could a machine decide that humans are a threat, conclude that its interests are different from ours, or simply stop caring about us? Possibly, but this problem is no more urgent today than it was before the AI developments of the past few months.

Superintelligent AIs are in our future. Compared to a computer, our brains operate at a snail’s pace: An electrical signal in the brain moves at 1/100,000th the speed of the signal in a silicon chip! Once developers can generalize a learning algorithm and run it at the speed of a computer—an accomplishment that could be a decade away or a century away—we’ll have an incredibly powerful AGI. It will be able to do everything that a human brain can, but without any practical limits on the size of its memory or the speed at which it operates. This will be a profound change.

These “strong” AIs, as they’re known, will probably be able to establish their own goals. What will those goals be? What happens if they conflict with humanity’s interests? Should we try to prevent strong AI from ever being developed? These questions will get more pressing with time.

But none of the breakthroughs of the past few months have moved us substantially closer to strong AI. Artificial intelligence still doesn’t control the physical world and can’t establish its own goals…

…No matter what, the subject of AIs will dominate the public discussion for the foreseeable future. I want to suggest three principles that should guide that conversation.

First, we should try to balance fears about the downsides of AI—which are understandable and valid—with its ability to improve people’s lives. To make the most of this remarkable new technology, we’ll need to both guard against the risks and spread the benefits to as many people as possible.

Second, market forces won’t naturally produce AI products and services that help the poorest. The opposite is more likely. With reliable funding and the right policies, governments and philanthropy can ensure that AIs are used to reduce inequity. Just as the world needs its brightest people focused on its biggest problems, we will need to focus the world’s best AIs on its biggest problems.

Although we shouldn’t wait for this to happen, it’s interesting to think about whether artificial intelligence would ever identify inequity and try to reduce it. Do you need to have a sense of morality in order to see inequity, or would a purely rational AI also see it? If it did recognize inequity, what would it suggest that we do about it?

Finally, we should keep in mind that we’re only at the beginning of what AI can accomplish. Whatever limitations it has today will be gone before we know it.

2. I’m working hard so that I’ll never be poor again – Thomas Chua

My friend shared how many of her colleagues in sales are caught up in the toxic culture of pursuing sales at the expense of their integrity, relationships, health, and mental wellbeing.

A common justification was “I’m working hard so that I’ll never be poor again.” Having come from poverty fuelled their desire to accumulate wealth.

In spite of myself being from a less well-to-do background, she wondered why she doesn’t detect similar traits.

It hasn’t always been this way.

I used to put the pursuit of wealth as my number one priority. And it can be seen in everything I do.

Even my scholarship and university entrance essays began with this quote: “I was born poor, but I will die rich.”

This quote stuck in my head for some reason. I guess it was the idea that I could change my fate despite not being able to change my birth…

…As we progress through life, it’s important to recognize that what’s useful in one stage may no longer be useful in another.

The hunger to generate wealth is definitely essential when one is living in poverty. It requires one to delay gratification—not just on spending, but on sleep, relationships and putting all your energy into learning and value-adding the world, in exchange for money.

There’s nothing wrong with hard work and the pursuit of wealth. In fact, it should be applauded.

The concept of having “enough” is highly subjective, but for many high income earners who came from poverty and have the means to comfortably live, it never seems to be “enough”.

They seem to be stuck in the phase where they’re perpetually unsatisfied unless they bring in more sales, make more money and become even wealthier.

There is a price tag for everything. Especially when you are no longer struggling to get out of poverty, it becomes toxic to pursue wealth at the expense of everything else.

It is a shame, because these individuals have worked hard to overcome their disadvantages in life – monetary, social, and cultural capital – but still hold the belief that they must sacrifice everything to escape “poverty”.

They are so focused on achieving wealth that they fail to stop, reflect, and realize that what used to work for them may be preventing them from living their best lives.

3. AT1 Bonds: when to abandon your fund manager – John Hempton

This leads me to the issue of the day – the Swiss financial regulator’s (FINMA’s) decision to simply cancel (“write-down”) about $17 billion in Credit Suisse Additional Tier 1 bonds (the so-called AT1s).

Now had I had a cursory look at the AT1s I would have thought that they were traditional bank preferred shares – that is they ranked ahead of common equity. I might have even traded them on that basis.

Fortunately I did not – because if I had I would have been wrong.

These were not ordinary preference shares ranking ahead of common equity. They were fixed income instruments that in times of stress ranked behind common equity.

Indeed I had never really ever considered the possibility of such an instrument – but that was only because I never read the documents.

The documents were not hard to find. They were on Credit Suisse’s website.

The document (which I have preserved here) makes it’s unusual nature right up-front. The Credit Suisse page linked above refers to these in bold letters as “Low-Trigger Capital Instruments”.

This does suggest a low trigger.

And boy is it a low trigger – the whole prospectus is dedicated to explaining how tough it is for these notes and their unusual character.

This is my favourite line:

Furthermore, any Write-down will be irrevocable and, upon the occurrence of a Write-down, Holders will not (i) receive any shares or other participation rights in CSG or be entitled to any other participation in the upside potential of any equity or debt securities issued by CSG or any other member of the Group, or (ii) be entitled to any write-up or any other compensation in the event of a potential recovery of CSG or any other member of the Group or any subsequent change in the CET1 Ratio, Higher Trigger Capital Ratio or financial condition thereof. The Write-down may occur even if existing preference shares, participation certificates and ordinary shares of CSG remain outstanding.

So there it is. And I have to repeat the prospectus: “The Write-down may occur even if … ordinary shares of CSG remain outstanding”.

Yep. It is there in plain English. You own these and you rank behind common stock.

4. CEO/ CIO Letter: MoneyOwl CEO Discusses Credit Suisse & The Banking Turmoil – Chiun Ting Weber

Banking is a confidence game and banks are, by definition, highly levered. No bank has the cash to pay all its depositors all at once. To make a return, a bank has to take some of the money you deposit with it, to either lend it longer-term for interest income or to buy assets to earn a return. Under the Basel regulatory requirements, the official regulatory Tier 1 capital (highest quality capital available to absorb losses immediately) requirement is at 8% of risk-weighted assets (the riskier the bond your bought, or the entity you lent money to, the higher the risk weight on that asset). A bank won’t be an attractive investment for its shareholders if the regulatory capital is set too high.

What this means conceptually is that a bank can fail if it has bad assets that, when marked down, can wipe out 8% of capital. In a full-blown crisis, it isn’t difficult for that to happen. It was the case with sub-prime mortgages during the 2008 Global Financial Crisis (GFC) engineered into leveraged packages of mass destruction, the now-defunct Collateralized Debt Obligations or CDOs. But in reality, even if you had a 14%, Tier 1 ratio, as CS had; and even if you had been pronounced to be meeting capital ratios by a regulator, as CS had been; all this means nothing when client confidence is shaken. All it takes for a bank run is for depositors to suspect that you have a lot of these bad assets. Even the Swiss National Bank’s (SNB) massive SFr50 billion liquidity line to CS announced just a few days ago was insufficient, hence this drastic move…

…The determination and speed at which the regulators are moving should give us comfort as investors that another full-blown GFC is highly unlikely. Volatility from bank turmoil thus presents opportunities. No matter how bad the gyrations are, we can expect a good recovery in time – except that we do not know when, or how bumpy the road would be. But even if we go through something like the GFC, we know that the stock market always recovers from a crisis and goes up in the long run. I think you would agree with me that looking back, the GFC was an excellent opportunity for wealth-building for disciplined, long-term investors…

…Having an investment philosophy you can stick with anchors you through the ups and downs of market turbulence, and rewards you with healthy returns over time. Except where you have an urgent need, the worst thing you can probably do is to panic-sell, and turn a temporary decline into a permanent loss. The second worst thing is to “take profit” and try to wait to the right time, because the right time will never come psychologically, and you would have totally missed that big ride-up when the recovery comes on fast and furious. The way to have a great investing journey, including during turmoil, is to be disciplined in our mindset and look beyond the concerns of today, to the long-term potential of the markets. I strongly recommend that you invest in MoneyOwl’s low-cost market-based investment solutions in a regular savings plan (RSP), if you haven’t already started investing.

5. How the Swiss ‘trinity’ forced UBS to save Credit Suisse – Stephen Morris, James Fontanella-Khan and Arash Massoudi

The emergency call from the Swiss establishment came at 4pm on Thursday.

Colm Kelleher, a rambunctious Irish banking executive who has been chair of UBS since last April, had been planning to celebrate St Patrick’s Day on Friday before watching Ireland play England at rugby on Saturday at a pub in Zurich. He was hoping to see his country win a clean sweep, or “Grand Slam”, in the Six Nations Championship.

But even before he took the call, he knew his chances of enjoying an entertaining weekend were slim. The chaos engulfing crosstown rival Credit Suisse, which had become the basket case of European banking after three scandal-ridden years, was now in overdrive.

A day earlier, a SFr50bn ($54bn) liquidity backstop from the Swiss central bank had failed to arrest a crisis of confidence in the lender, whose shares had plunged after Ammar Al Khudairy, the chair of its largest investor Saudi National Bank, bluntly replied “absolutely not” when asked if it would put in any more money…

…On Wednesday, the so-called “trinity” of the Swiss National Bank, regulator Finma and the minister of finance summoned Credit Suisse chair Axel Lehmann, who was in Saudi Arabia for a conference, and chief executive Ulrich Körner for a call.

In the same meeting where they authorised the SFr50bn backstop, they also delivered another message: “You will merge with UBS and announce Sunday evening before Asia opens. This is not optional,” a person briefed on the conversation recalls.

Kelleher found out his weekend plans were ruined on Thursday afternoon. The trinity called UBS and ordered the group to find a solution to save its ailing peer from bankruptcy…

…Keller-Sutter, the finance minister, was a key figure throughout the negotiations, including co-ordinating with foreign officials and regulators in the US and Europe.

She was under extreme pressure from global regulators, who had been demanding faster and more decisive action to stop panic spreading in markets. In particular, the US and the French were “kicking the shit out of the Swiss”, says one of the people advising UBS. Janet Yellen, the US Treasury secretary, had several conversations with Keller-Sutter over the weekend.

Negotiations over the deal were initially “fairly friendly” but as time progressed the trinity started becoming more aggressive, pushing a transaction that Credit Suisse was vehemently opposed to.

UBS was also reticent. Executives made it clear that it would only participate in the rescue of its rival if the price was cheap and it indemnified them from a raft of regulatory probes into Credit Suisse’s culture and controls.

“They [UBS] were always going to try to kill us on price. And we were always going to try to get a premium,” says a person close to Credit Suisse.

By Friday evening, when it was revealed that UBS was exploring a state-mandated takeover, Credit Suisse had lost another SFr35bn in client deposits over the preceding three days, according to a banker involved in the deal, and international banks from BNP Paribas to HSBC were cutting ties. Regulators concluded that it would probably not be able to open on Monday…

…In response, on Saturday evening Kelleher called his counterpart at Credit Suisse from outside a restaurant to tell him UBS was prepared to offer $1bn in stock for the whole group, about SFr0.25 a share, far below the SFr1.86 closing price on Friday.

The government then informed Credit Suisse it would introduce emergency legislation to strip both sets of shareholders of the right to vote on the deal.

Credit Suisse was outraged and refused to sign. It was opposed to the CDS clause because the optionality of walking away from the deal would have killed it once it was made public. Such a condition would have led to chaos, say people with direct knowledge of the negotiations…

…Under pressure to get a deal done before the end of the day, the trinity started to ramp up pressure on both sides, threatening to remove the Credit Suisse board if they did not sign off.

On the other side, UBS was lent on to increase its price and reluctantly agreed, ultimately boosting the offer to $3.25bn in stock. But in return it negotiated more support from the state, including a SFr100bn liquidity line from the SNB and a government loss guarantee of up to SFr9bn, after it had borne the first SFr5bn itself.

The final terms were still so favourable to UBS they were “an offer we couldn’t refuse”, a person on the negotiating team told the FT. An adviser to Credit Suisse described them as “unacceptable and outrageous” and a “total disregard of corporate governance and shareholder rights”…

…In order to make the deal more palatable for Swiss citizens and the bank’s equity investors, the government also decided to impose losses on SFr16bn of Credit Suisse’s additional tier 1 (AT1) capital bonds. While these are designed to take losses when institutions run into trouble, normally they are not triggered if shareholders receive money as part of a takeover.

However, the small print of the bond documentation allowed Swiss authorities to disregard the normal order of priority and wipe out bondholders.

“AT1 holders were sacrificed so the finance ministry could try to save some face with international equity holders after denying them a vote on either side of the transaction,” says one of the bankers advising on the takeover.

6. Everything you need to know about AT1s – TwentyFour

Additional Tier 1 bonds (AT1s) are part of a family of bank capital securities known as contingent convertibles or ‘Cocos’. Convertible because they can be converted from bonds into equity (or written down entirely), and Contingent because that conversion only occurs if certain conditions are met, such as the issuing bank’s capital strength falling below a pre-determined trigger level…

…AT1 bonds have three basic features.

The first, and in our view most crucial feature, is the loss absorbing mechanism, which is ‘triggered’ when the issuing bank’s CET1 capital ratio falls below a pre-determined threshold. Typically this trigger is either at 5.125% or 7% CET1, depending on the national regulator. Once this trigger level is hit, the notes are automatically converted into equity or written down in full, depending on the terms of the individual bond documentation.

Second, regulators require bank capital to be permanent (i.e. perpetual) in nature, so AT1 bonds have no final maturity, and instead they are callable with regulatory approval. AT1s typically have ‘non-call’ periods of between five and 10 years, after which investors generally expect the issuer to call and replace the AT1s with a new issue. If the bonds are not called, the coupon resets to an equivalent rate over the underlying swap rate or government bond…

…There is another important regulatory element investors need to consider, which is that a bank’s solvency is ultimately at the discretion of its national regulator (or the European Central Bank for EU banks). If a bank runs into serious trouble, regulators can declare a Point of Non-Viability to try to protect depositors, stem the losses and prevent contagion.

We have seen that European banks generally have CET1 ratios in the mid-teens; we think it is highly unlikely any regulator would let a bad situation carry on long enough for a bank’s CET1 ratio to fall to 7%, let alone 5.125%, so in practice it is likely that a bank’s Point of Non-Viability would occur with capital levels higher than the trigger levels embedded into AT1 securities. This is why it is important for investors to pay attention to the individual capital requirements set by national regulators for each bank, and to scrutinise annual stress tests very carefully.

7. Doug Leone – Lessons from a Titan – Patrick O’Shaughnessy and Doug Leone

Patrick: [00:10:54] I spent a lot of time talking to your partner, Ravi, about demons and the demons that are in certain people for whatever reason and the ways that those demands can motivate or drive entrepreneurial-type people to enormous success. And one of the things that Ravi told me was that you are extremely good at sussing out a person’s core motivation via listening, ironically, given Don’s note to you. And I’d love you to talk a bit about that skill and why you think it’s so important to understand someone’s core motivation.

Doug: [00:11:28] First of all, what we look for founders, we also look for Sequoia partners, investors, young people. The same set of traits use the word insufferable, use the word he doesn’t listen, she doesn’t listen or he’s belligerent, she’s belligerent. Those that other people may view as a negative, we actually view as a positive because in order to get something done in life, you can’t just walk down Main Street and be a sweetie pie.

We look for outlier people, whether it’s founders or investors, and outlier people do extraordinary things. Outliers. What do I mean by that? Extra-driven for whatever reason. Maybe Daddy told them they weren’t good enough and they want to show Daddy how good they are. Maybe they have a twin brother. Twins have a way of competing with one another. They love one another, but they compete one another. Maybe they failed miserably in their first startup, they’re embarrassed and so on. So we look for those things.

And sometimes, believe it or not, genetics. I’ve actually met some people that I’m now convinced they were just wired that way. And I try to look for that for the simple reason that I view that to be the greatest advantage, but could be the greatest weakness, if not channeled appropriately. So want to look for it to see if it’s there because I like to be it there, then I look to see what it is and whether it’s on the right side of this good versus bad trait. And thirdly, because once we understand it and then that’s a good side, then how do we channel it, complement and make sure this incredibly wonderful insecure, scared because that’s what we all are when we’re coming up, how do we help them as if we were their brothers to achieve maximum type of success.

So I dig for that. I just really want to understand what makes this person tick. And to me, the greatest question is why? Why, why, why? When someone says I was recruited by. I hear, I was lazy-ass sitting down. I got a call from a recruit. I have nothing better to do. I got suck to listen to something. I got sweet-talked, then I talked to a company that made me an offer. I wasn’t too happy on my job or a little bored and I went.

To me, that’s what I was recruited by sounds like. A converse of that, of course, is I was sitting on a job, I saw an opportunity in a market segment that I didn’t know existed. I call 7 or 8 companies. I realize this is the leading company. I call the companies or I found a way to get a meeting. I sold my way in. I got an offer. I negotiated. I took a job and I went. Wow, what an answer. So those are little things I look for when I interview people.

Patrick: [00:14:18] In addition to asking why in lots of different ways, are there other favorite questions or topics that you find yourself returning to over and over again as you’re getting to know people?

Doug: [00:14:28] I want to know the upbringing. I want to know what kind of kids they were, their journey through life, their maturation through life. I’d love to ask whether they have a sibling, to describe 3 adjectives for their sibling, their close sibling and 3 adjectives that describes them by comparison. I don’t really care about the sibling, but you start learning things.

I love asking the setup question of where would you get your best reference. And they’re eager to tell you that complete set of question because the next question is, where would you get your worst reference and why? And again, I’m not looking to nail anybody. We’ve all had journeys that are up and down, very few of us have had a linear journey. But just understanding, looking for self-awareness because self-awareness means breaking problems down to first principles and meaning using your experience to solve a new problem. While we love best athletes, if we find best athletes with a little of experience and first-principle thinking, that’s a home run, and we look for that…

…And the trick for me, I never understood when the father is an alcoholic or as an abuser and the son becomes an abuser because I have to tell you, I’ve had some tough rides, but I made a promise to myself that if I ever became someone, I would not do one to others as I was done to. I thought it was disgusting. I thought it was very upsetting. And when you’ve come to Sequoia, when I was running it, I made sure everybody respected the people that feed us. You better put your plate away, you better say thank you and so on because it starts at the foundational layer.

And if you do that right, then the culture starts being right. And if you share your winnings and if you just don’t talk to talk, we are a team, we are this. No, you have to share the dough appropriately. And in my case, I never called us a family. I thought family is bulls***. I’ve got members in a family I have to endure forever. Can’t get rid of it.

I tell people, we are high-performance and pick your noun. We’re a performance team. If you don’t believe in sports, production, a movie and maybe the investors are the actors, maybe the investors are the goal scorers. But you know what we need, we need trainers. We need coaches. If you’re a movie, we need a director, a producer or a makeup artist. And it takes everybody. And so just believing internally, that’s what we need, and incorporating into the investment business into what I think is the most fabulous culture of any partnership in the investment area is really our secret sauce…

…Patrick: [00:28:29] Speaking of your passion for go-to-market, describe what you’ve seen the very best at that do consistently? Is it working from the product towards the customer’s need? Is it working backwards from the customer? Are there other things that you’ve seen and recommend over and over again of the very best of this?

Doug: [00:28:46] So I’ve actually have given a name for this cycle called the merchandising cycle. And I explained this to founders. It starts with product management. What exactly are we building? If truth be known, it starts with vision. But if the vision is wrong, we’re all going home, assuming we’re some place in a ballpark.

It’s not some product management, what are we building? To product marketing, how do we position it? How do we tell the story? How do we have the 3 words for describing what we do? How do we have the 30 seconds, 2 minutes? And everybody can do with 10 minutes. Very few people can do the 3 words. And then how do we do the demand gen? How do we do the sales? And wherever that cycle is broken, it looks like a bad salesperson. This guy can’t sell.

Actually, the truth of the matter is if you’ve got product market fit, even shady salespeople can sell. When we first invested in ServiceNow, we had the BT prior to Frank Slootman coming in, in sales, and they were selling like crazy. So that was my lesson. And so for me as a Board member, I have to debug the merchandising cycle.

Product can’t sell. Why? There’s not enough leads. Oh, well, I know to fix that. Why don’t we get some more BDRs. Then you can talk to the BDR guys. Here, you can have 5 BDRs. Well, then they start fessing up. Well, it’s not really a BDR head count. It’s that the message isn’t playing right.

Well, I knew that, but it nice you admit it, let’s go back to product marketing. What’s the message? Is that the right message? Is that the wrong message? And that’s based on the product we’re building. This is the product management. And so I work very hard at debugging upstream this merchandising cycle, so we can figure out what the real problems are. And as I think about it, take these rocks out of the river so that dam water can flow as fast as possible. And once you do that and once you know that 2 or 3 sales reps can sell something and you have your first 4 or 5 sales that don’t include the CEO, those are telltale sign that you can start ramping. And so that’s what we do. That’s what I do as a Board member.

The thing I can’t do is the black magic. If you don’t have the right vision, if you’d — I’m not close to product market fit, I will tell you, Doug Leone or any other people in venture, I’m not going to help you. Black magic is reserved for founders. Everything else is mere mortal stuff. That’s what we can do. And we’re probably the very best in the world at Sequoia in doing that.

Patrick: [00:31:10] What are the components of great positioning for a product?

Doug: [00:31:15] Simplicity, crystal clearness, something a mere mortal can understand. If you can describe it and you can understand it you’re out to lunch. Singularity of purpose. When I go to the store, I buy a pencil because I want to write. I don’t want a pencil because I want to write, I scratch my back with the tip. It doesn’t work like that.

Singularity of vertical market early on because you want to be narrow. You have no resources. You’ve got to be narrow. Oh, we are chasing these 4 vertical markets. It sounds good. But in order to do that, you have to have marketing that talks for different languages for 4 markets. And maybe you have to have engineering that develops different features for me. A little company can’t do that. So be it the bull’s eye as sharp as you can and then starts to expand in concentric circles when you get your legs under you in that vertical market. That’s what I look for in position.

Patrick: [00:32:10] If you think about the, what I’ll call, mediocre positioning, you’ll know if there’s amazing positioning because you’d be able to see the things flying off the shelves. And you’ll know if there’s terrible positioning and that the danger is somewhere in the middle, like it’s kind of working. What have you done historically when you see that and you see founders start to build upstream the demand gen and the sales org is on top of mediocre positioning. That seems like a very dangerous spot for a company to be in.

Doug: [00:32:37] So keep in mind that we, as Board members, our job is to make these founders very capable and successful. You lose the founder, you lose the soul of a company. There’s no question about that, okay? And telling the founders cuts a little bit of the pinky. And you want founders with 10 fingers and 10 toes. But there are certain times where the thing is soft, the rails, that it’s worth a small piece of the pinky to get back in the right direction.

First, what I try to do instead of telling I like showing. So let me give you an example. Your VP of Marketing stinks. If I say that, it means nothing to a founder. But if I say, I’d like you to meet these 3 VP marketing from other companies. Let me tell you what happens 9 times out of 10, they come back and they say, “Holy s***, the guy we have or the gal we have is nothing like this guys.” So try to show, not tell. Build trust, which doesn’t get build day 1. It really gets build with the first time to see founders in a pension. He understands you’re there to help them out.

So once you have trust, which is really the foundational layer, it’s the grease that makes all business runs. And once the founder understands maybe of a little of experience that complements his incredible talent. And once you show the founder without telling the founder, and once in a while, you have to tell because maybe you don’t have the time to show, but you better do that once a year.

It’s very rare. That’s what you do. Those are the actions that you take. And you want to come out of that in the win-win. You want to come out of that with an enlightened founder who’s extremely happy and better in his role rather than having achieved your goal of a new VP of Marketing with the founder feels like these needs were cut off.

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

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

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

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

Here are the articles for the week ending 19 March 2023:

1. Poignant Twitter thread on the importance of having purpose in life – Mark McGrath

This is a story about my dad. My dad grew up the youngest of four siblings in Quebec. He, his siblings, and my grandparents moved to Vancouver in the 70s, and my uncle opened a tile store. My dad worked for him for a while, then eventually opened his own store.

He was a relentless entrepreneur and a good father. He was shrewd and pennywise. I used to joke that he would split 2-ply toilet paper to save money. But he was also a savvy investor, and he did well in his business.

He didn’t care about tile and saw the business as a means to an end – a way to build wealth and retire. He was laser-focused on this goal.

He was fit, active, and a traveller. He was a scratch golfer and swam 80 laps at the pool three times a week. He was also a black belt in karate and extremely disciplined…

…One day he told us he had sold his business and was retiring. We were thrilled. All he wanted to do was retire so he could keep travelling, golfing, swimming, and enjoying his life. He booked a two-month trip to Asia to celebrate. He was 58. And then it all went downhill.

Within a month of returning from his trip, he was back working for the guys he sold his store to. He didn’t need the money – he just missed his store and his friends. His best friend was his first employee – a man he had hired 30 years earlier. This worked out for a while.

But slowly, he started to change. After a few months of golfing near-daily, he got bored. And then he got depressed. He changed…

… Then he told me, “your father is dead.” I collapsed and remember only that I kept saying, “I had so much more to tell him”.

He had rented a car for some reason, drove it to the middle of the Lion’s Gate Bridge in Vancouver, turned on the hazard lights, and got out. Then he jumped. Two cyclists – one on the bridge, and one down below on the seawall – called it in…

…What I think happened is that my dad’s business became his identity. He was the tile guy. He was the guy that sponsored all of our sports teams.  In a booming town, he was the guy you went to when you needed tile. He was the tile guy.

And when he sold his business, he stripped himself of his identity. Now he was a nobody. He lost his purpose, the very thing that made him who he was.

2. GPT-4 – OpenAI

We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT-3.5 as a first “test run” of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT-4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety…

…In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.

To understand the difference between the two models, we tested on a variety of benchmarks, including simulating exams that were originally designed for humans. We proceeded by using the most recent publicly-available tests (in the case of the Olympiads and AP free response questions) or by purchasing 2022–2023 editions of practice exams. We did no specific training for these exams…

…We also evaluated GPT-4 on traditional benchmarks designed for machine learning models. GPT-4 considerably outperforms existing large language models, alongside most state-of-the-art (SOTA) models which may include benchmark-specific crafting or additional training protocols:..

…GPT-4 can accept a prompt of text and images, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, it generates text outputs (natural language, code, etc.) given inputs consisting of interspersed text and images. Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs. Furthermore, it can be augmented with test-time techniques that were developed for text-only language models, including few-shot and chain-of-thought prompting. Image inputs are still a research preview and not publicly available…

…Despite its capabilities, GPT-4 has similar limitations as earlier GPT models. Most importantly, it still is not fully reliable (it “hallucinates” facts and makes reasoning errors). Great care should be taken when using language model outputs, particularly in high-stakes contexts, with the exact protocol (such as human review, grounding with additional context, or avoiding high-stakes uses altogether) matching the needs of a specific use-case.

While still a real issue, GPT-4 significantly reduces hallucinations relative to previous models (which have themselves been improving with each iteration). GPT-4 scores 40% higher than our latest GPT-3.5 on our internal adversarial factuality evaluations:…

…GPT-4 can also be confidently wrong in its predictions, not taking care to double-check work when it’s likely to make a mistake. Interestingly, the base pre-trained model is highly calibrated (its predicted confidence in an answer generally matches the probability of being correct). However, through our current post-training process, the calibration is reduced…

…GPT-4 poses similar risks as previous models, such as generating harmful advice, buggy code, or inaccurate information. However, the additional capabilities of GPT-4 lead to new risk surfaces. To understand the extent of these risks, we engaged over 50 experts from domains such as AI alignment risks, cybersecurity, biorisk, trust and safety, and international security to adversarially test the model. Their findings specifically enabled us to test model behavior in high-risk areas which require expertise to evaluate. Feedback and data from these experts fed into our mitigations and improvements for the model; for example, we’ve collected additional data to improve GPT-4’s ability to refuse requests on how to synthesize dangerous chemicals.

GPT-4 incorporates an additional safety reward signal during RLHF training to reduce harmful outputs (as defined by our usage guidelines) by training the model to refuse requests for such content. The reward is provided by a GPT-4 zero-shot classifier judging safety boundaries and completion style on safety-related prompts. To prevent the model from refusing valid requests, we collect a diverse dataset from various sources (e.g., labeled production data, human red-teaming, model-generated prompts) and apply the safety reward signal (with a positive or negative value) on both allowed and disallowed categories. 

Our mitigations have significantly improved many of GPT-4’s safety properties compared to GPT-3.5. We’ve decreased the model’s tendency to respond to requests for disallowed content by 82% compared to GPT-3.5, and GPT-4 responds to sensitive requests (e.g., medical advice and self-harm) in accordance with our policies 29% more often…

…Overall, our model-level interventions increase the difficulty of eliciting bad behavior but doing so is still possible. Additionally, there still exist “jailbreaks” to generate content which violate our usage guidelines. As the “risk per token” of AI systems increases, it will become critical to achieve extremely high degrees of reliability in these interventions; for now it’s important to complement these limitations with deployment-time safety techniques like monitoring for abuse…

…Like previous GPT models, the GPT-4 base model was trained to predict the next word in a document, and was trained using publicly available data (such as internet data) as well as data we’ve licensed. The data is a web-scale corpus of data including correct and incorrect solutions to math problems, weak and strong reasoning, self-contradictory and consistent statements, and representing a great variety of ideologies and ideas.

So when prompted with a question, the base model can respond in a wide variety of ways that might be far from a user’s intent. To align it with the user’s intent within guardrails, we fine-tune the model’s behavior using reinforcement learning with human feedback (RLHF).

Note that the model’s capabilities seem to come primarily from the pre-training process—RLHF does not improve exam performance (without active effort, it actually degrades it). But steering of the model comes from the post-training process—the base model requires prompt engineering to even know that it should answer the questions.

3. Bank Runs, Now & Then – Ben Carlson

Silicon Valley Bank, the 16th biggest bank in the country, was closed on Friday. It was the second-biggest bank failure in U.S. history…

…There is a lot to this story but it really boils down to an old-fashioned bank run. A flood of withdrawals from depositors destroyed the bank.

If everyone with a Planet Fitness membership showed up at the gym at the exact same time there would be chaos at the squat racks. It would be impossible for anyone to work out and the gym model wouldn’t work.

The same thing applies to banks. If everyone goes to get their money out on the same day, it’s going to be hard for a bank to survive…

…The SVB ordeal caused me to revisit my old copy of The Panic of 1907 by Robert Bruner and Sean Carr.

It’s a wonderful account of one of the biggest and most influential financial crises in history.

The Panic of 1907 would probably be more famous if it wasn’t overshadowed by the Great Depression just a couple of decades later.

It lasted 15 months and saw GDP decline an estimated 30% (even more than the Great Depression).

Commodity prices crashed. Bankruptcies exploded. The stock market fell 50%. Industrial production dropped by more than at any time in history up to that point. The unemployment rate went from 2.8% to 8%.

Trust in the financial system went out the window as banks failed left and right. In October and November of 1907 alone, 25 banks and 17 trust companies went under…

…Bruner and Carr laid out 7 reasons the Panic of 1907 was a perfect storm for bank runs and a massive financial crisis:

1. Complexity. Complexity makes it difficult to know what is going on and establishes linkages that enable contagion of the crisis to spread.

2. Buoyant growth. Economic expansion creates rising demands for capital and liquidity and the excessive mistakes that eventually must be corrected.

3. Inadequate safety buffers. In the late stages of an economic expansion, borrowers and creditors overreach in their use of debt, lowering the margin of safety in the financial system.

4. Adverse leadership. Prominent people in the public and private spheres wittingly and unwittingly may implement policies that raise uncertainty, thereby impairing confidence and elevating risk.

5. Real economic shock. An unexpected event (or events) hit the economy and financial system, causing sudden reversal in the outlook of investors and depositors.

6. Undue fear, greed, and other aberrations. Beyond a change in the rational economic outlook is a shift from optimism to pessimism that creates a self-reinforcing downward spiral. The more bad news, the more behavior that generates bad news.

7. Failure of collective action. The most well-intended responses by people on the scene prove inadequate to the challenge of the worst crises.

Again, not exactly like 1907 but this run on the bank seems to check all of the boxes in its own way…

…Two economists took a stab at explaining why bank runs happen and concluded they’re kind of random. Depositors are worried a financial shock will cause a lengthy liquidation so they pull their money en masse.

But what sets them off?

People being people, I guess?

4. Psychological Paths of Least Resistance – Morgan Housel

When faced with a problem, rarely do people ask, “What is the best, perfect, answer to this question?”

The more efficient question is often, “What answer to this question can I obtain with the least amount of effort, sacrifice, and mental discomfort?”

The psychological path of least resistance.

Most of the time that’s fine. You use a little intuition and common sense and find a practical answer that doesn’t rack your brain or bog you down with details.

Other times the easy answers lead you down a nasty path of misunderstanding, ignorance, and blindness toward risk.

A few paths of least resistances that everyone is susceptible to at some point:

1. The quick elimination of doubt and uncertainty.

Most people could not get out of bed in the morning if they were honest about how much of their future is unknown, hangs by a thread, or can be pushed in another direction by the slightest breeze. The solution is to eliminate doubt and uncertainty the moment they enter your head.

Uncertainty feels awful. So it’s comforting to have strong opinions even if you have no idea what you’re talking about, because shrugging your shoulders feels reckless when the stakes are high.

Life is complex, complex things are always uncertain, uncertainty feels dangerous, and having an answer makes danger feel reduced. It’s an easy path of least resistance.

If you were an adult in 2000 you probably had at least some vision of what the future would look like. Maybe even a vague vision of the next 20 years. But everyone was blind to 9/11, the 2008 financial crisis, and Covid-19 – the three risks that were both massive and unpredictable.

Then when those events happened people quickly moved to eliminate the uncertainty they brought.

Terrorist attack just happened? It’s definitely going to happen again, soon.

Recession coming? It won’t affect my industry and will be over by Q4 and interest rates will bottom at 3.42%.

Pandemic arrived? Two weeks to slow the spread.

No matter how wrong these answers might be, they feel better than saying, “I have no idea what’s going to happen next.”…

…5. The desire to supplant statistics with stories.

“People would rather believe than know,” said biologist E.O. Wilson.

I think another way to phrase it is that people desire stories more than statistics. They need a story they can tell themselves, not just a fact they can memorize.

Part of that is good. The gap between what works in a spreadsheet and what’s practical in real life can be a mile wide. This usually isn’t because we don’t know the statistics. It’s because real-life stories are so effective at showing us what certain parts of a statistic mean.

Part of it can be dangerous, when broad statistics are ignored over powerful anecdotes.

Government agencies published literally thousands of different economic data points, everything from unemployment to GDP growth to the historical cost of chicken legs, bone-in. It’s all free and easy to read.

How often do those data sites change average, ordinary people’s opinions about the economy?

It rounds to never.

What changes people’s minds and reaffirms their beliefs are conversions they’ve had with people close to them, social media, and cable news. Each is very good at telling stories especially when they provoke emotion or are easy to contextualize to their own lives.

When confronted with a pile of dull facts and a pile of compelling anecdotes, the anecdotes are always the path of least resistance for your brain to cling to.

5. TIP533: How The Fed Went Broke w/ Lyn Alden – Stig Brodersen and Lyn Alden

[00:01:28] Stig Brodersen: Well, thank you for saying so Lyn, and let’s just jump right into it. Today, I would like to talk about how the Fed went broke, but before we do, and perhaps to sort of like create a foundation for everyone, perhaps we can zoom out and if I can ask you to explain what is on the assets and liability side on the Fed balance sheet, and then perhaps we can talk about how that is similar to how a commercial bank running their balance sheet.

[00:01:53] Lyn Alden: Sure. So basically in a lot of regard, the Fed is very similar to a commercial bank. I mean, there are very important exceptions where it’s not, but in terms of the over, like arching details, it’s actually pretty similar. So if you look at a commercial bank for a second, they have assets and liabilities.

[00:02:08] Lyn Alden: The assets exceed the liabilities. That’s an important part of their solvency and their assets generally pay higher interest rates than their liabilities. Kind of the purpose of a bank is to, you know, borrow money at cheap rates and lend money with a little bit more risk and a little bit more duration at higher rates, as well as collecting fees and things like that along the way.

[00:02:27] Lyn Alden: And so for a typical bank, their liabilities are mainly their deposits. So basically when you deposit money in a bank, that’s your asset. It’s their liability and interest rates. They’re generally pretty. On the bank asset side, depending on the type of bank it is, they do mortgages, they do business loans, and they do credit card lending.

[00:02:47] Lyn Alden: They do all sorts of different types of lending, and those are ones that are generally a little bit riskier, higher duration, but they pay higher interest rates and so they can absorb some, you know, small percentage of defaults, build positive capital, pay [00:03:00] dividends, you know, fund their operations and maintain positive equity and positive capital.

[00:03:05] Lyn Alden: When you look at a central bank, it’s very similar, but there’s a couple different categories for their assets and liabilities. So their liabilities are, One bank notes, right? So physical cash and circulation is a liability of that country Central bank, and those are obviously 0% yielding assets, right? If you hold a dollar bill or a physical euro, you’re not getting paid interest on this.

[00:03:26] Lyn Alden: So that’s an obvious already good start for them, right? They have 0% liabilities there, but they have other liabilities that, for example, consists of bank reserves. So much like how we deposit money at a bank, and that’s our asset and their liability. Banks have to deposit their cash, their spare cash at the central bank, and that’s an asset for the bank, and it’s a liability for the central bank.

[00:03:47] Lyn Alden: And just like how a bank pays interest, a central bank also in, in many environments does pay interest on those reserves. And the reason they do that is because it’s an important part of how they manage their short-term interest rates. It basically presents a floor, right? If you can put reserves in the central bank, You know, and get, say 5% interest on it, there’s no reason why you would lend to anyone else at below 5% because you’re just taking on more risk and for less return.

[00:04:15] Lyn Alden: Right. And so that’s one of their important policy tools. And then there are other liabilities they can do, like reverse repos and things like that. They get more complex and some of those do pay interest. So that’s the central bank’s liability side. On the asset side, it actually looks [00:04:30] pretty similar to a commercial bank.

[00:04:31] Lyn Alden: They have things like treasuries, you know, the government debt of whatever country they operate in. So those pay interest. They also often have mortgage backed securities, right? So they have mortgage exposure. Obviously, these deals would differ around the world, but for example, a Federal Reserve has a lot of mortgage backed securities.

[00:04:46] Lyn Alden: These also pay interest. And then in some countries they’ll have things like corporate debt, or they’ll have things like equities. Those are generally considered less traditional types of assets for central banks to hold. But you see some like Japan kind of going that route. And sometimes, like the Fed and others will do that temporarily during crisis.

[00:05:02] Lyn Alden: Things like corporate debt. And in most contexts, the federal reserve’s assets are bigger than their liabilities and they pay higher interest rate than their liabilities. And it will then differ from jurisdictions. But usually the central banks operate like utility where it has to pay its excess profits back to the government.

[00:05:21] Lyn Alden: It doesn’t just keep building capital like a commercial bank would. Although in some jurisdictions they actually, you can publicly hold, you know, shares of a central bank and they will, you know, they could pay dividends, they could do things like that. Look at the Federal Reserve, so it’s not publicly held, but it is held by banks.

[00:05:38] Lyn Alden: They basically pay a small dividend to their owners. They pay their operating expenses, and then they have to send the rest of their profits back to the treasury. Right? And so it’s actually a source of income for the treasury, and it kind of makes it so that any sort of treasury is held by the Fed are effectively interest free because they are paying interest on them.

[00:05:55] Lyn Alden: But all these, a lot of these profits are getting sent right back to the treasury. The challenge in [00:06:00] recent months, really ever since September, is that the Federal Reserve increased interest rates so quickly and so significantly, and for the first time they got above the prior cycles high in terms of interest rates or at least, you know, the first time in, in decades we’ve had this kind of declining trend of lower highs in terms of interest rates, but they actually got way above that.

[00:06:18] Lyn Alden: And so they’re actually, their liabilities pay higher interest rates than their assets. And so obviously their bank notes are still paying zero, but their other areas, their bank reserves and their reverse repos in the fed’s case are paying a higher interest rate than their treasuries and their mortgage backed securities that in many cases are a longer duration.

[00:06:36] Lyn Alden: They’re fixed rate, they’re not adjusting upwards. They hold them from years ago, and so they have a mismatch. And so one is there, they’re no longer profitable. They’re not sending any more remittances to the treasury, and two, if they were a normal commercial bank, they would be on the verge of bankruptcy.

[00:06:51] Lyn Alden: So they’re months away from having negative, tangible equity, which is a normal bank would be bankrupt, but because of the central bank, they get to that. That’s where they have a very big difference. They basically get to just put a placeholder there that kind of is like an I O U. And so in the future, if they’re ever profitable again, then before sending more money to the treasury, They get to pay themselves back.

[00:07:13] Lyn Alden: So basically they’re losing money. They’re going in towards negative, tangible equity, but they’re filling that negative equity gap with IUs on their future income, which of course, for any private entity would be red flags over the place. Absolute catastrophe. You wouldn’t touch it with a 10 foot pole, [00:07:30] but it’s different if you’re the central bank.

6. Whose Fault is it Anyway – Michael Batnick

It has been 872 days since a bank failed in the United States. This was the longest streak on record. We’re now at day zero. Silicon Valley bank went down on Friday. Signature Bank last night. These are the second and third largest bank failures in history behind Washington Mutual during the GFC.

People are scared, mad, and looking for someone to blame. How did this happen, and whose fault is it anyway?…

…Blame the Fed

Three years ago, the fed appropriately took interest rates to zero as an economic meteor slammed into the Pacific Ocean. But two years later with the economy reopened and inflation running north of 7%, rates were still at zero. This made no sense then, and it makes less sense looking back on it. The fed was late to respond, and they compounded the problem by going from too easy for too long to too tight too fast. We haven’t seen a tightening cycle like this in the last fifty years.

A major thing that we didn’t anticipate as a result of these historic interest rates, at least I didn’t, were the ripple effects it would have at banks. According to Marc Rubinstein:

Between the end of 2019 and the first quarter of 2022, deposits at US banks rose by $5.40 trillion. With loan demand weak, only around 15% of that volume was channelled towards loans; the rest was invested in securities portfolios or kept as cash.

Banks invest their deposits in short-term bonds, for the most part. But even short-term bonds can have large unrealized losses when interest rates spike until the bonds mature. And bonds that have more interest rate risk are even more susceptible to large losses. All told, banks are now sitting on roughly $600 billion of losses in what are supposed to be among the safest instruments in the world. All because the fed went too far to fast.

Prior to aggressively raising rates, the fed kept interest rates at zero for too long which spurred excessive risk-taking. Venture capital was at the epicenter of this. Everything got funded in 2021 at a speed and size the likes of which the industry had never experienced. Who’s to blame here? Is it the fed for stoking the flames of speculation, is it the LPs for flinging money at venture funds, or is it the venture capitalists for saying yes to everything? The answer is yes.

7. What’s Going On With Treasuries? Silicon Valley Bank And The Incoherence Of The Federal Reserve’s (Lack Of) An Interest Rate Policy This Week – Nathan Tankus

The essential issue seems to be not so much “financial contagion” from the failure of Silicon Valley Bank (that’s how systemic risk is ordinarily understood.) Rather, it’s the implications of the Federal Reserve’s actions over the weekend. It is strange to see the Federal Reserve launch a facility commonly interpreted as a “crisis” facility using its 13(3) powers in the current economic situation(check out my Silicon Valley Bank piece as a refresher here). That’s because unemployment is low, inflation has been high and the Federal Reserve is raising interest rates. One interpretation of this event, consequently, is that the Federal Reserve is going to be lowering interest rates.

Yet, inflation remains above the Federal Reserve’s target. It would be quite an extraordinary situation in these circumstances to see the Federal Reserve lower interest rates at a time of elevated inflation. You can see the dilemma. Government securities dealers — those people who buy and sell treasuries every day — are as confused and unsure as you are about which way interest rates will go, in these circumstances. When bond traders are confused and unsure about which way interest rates will be going to this degree, treasury market issues result…

…The other layer to this are those “treasury liquidity strains” this all started with. When non-experts hear about “liquidity strains” in the treasury market, they tend to assume that means the U.S. treasury has to offer higher interest rates to sell securities. However, that often isn’t the case. In fact, these periods tend to coincide with falling treasury interest rates. In March 2020, liquidity in treasury markets worsened. Some short maturity treasury securities even experienced negative money interest rates. That means the situation was so uncertain, many actors were willing to pay such a premium that they would get less money back when the security matured then they paid for it. After all, losing a bit of your money is better than losing it all. To be clear, I’m not talking about “inflation adjusted” amounts. I mean literally, they paid 100 dollars, and got back 98 dollars.

So if “liquidity strains” don’t necessarily mean rising interest rates, what do they mean? They mean the price at which you can buy a treasury is further away from the price at which you can sell a treasury…

…Normally, bond traders have a pretty good sense of where interest rates are going to go. They are not always right (by any stretch). But this usually does not impact the differences between buying and selling prices that much. One way to think about this is, when bond traders are wrong they tend to be wrong slow enough and gradual enough that there is time for them to “catch up”. And generally, for the past 30 years if not more, when they are really wrong it’s obvious. So both buying and selling prices really jump. When Lehman Brothers collapsed, everyone understood short term interest rates were going to go to zero and stay there for a while. If inflation had been 6% when Lehman collapsed, the treasury market may have faced the same problems it’s facing now.

Faced with this uncertainty, the treasury market is getting less liquid. That means selling prices and buying prices are diverging even though interest rates are overall declining. But remember those bets that so many government bond market participants made? This is where they get hammered twice. Not only are interest rates going down when they expected interest rates are going up. In addition, “bidding interest rates” and “selling interest rates” are diverging, when these same bets often assume that the treasury market is liquid. In other words, embedded in these bets about the direction of interest rates are bets about the differences between bidding interest rates, and selling interest rates. In short, they also bet the spread would be small when the spread has been getting larger.

The final component of this on my radar is something that financial economist Daniela Gabor said over on Twitter:

“Why would you sell securities you can monetise at the Fed for par value?” This returns us to a more direct impact of the Silicon Valley Bank failure and the Federal Reserve response than the possible implications of that response for interest rate policy. The Bank Term Funding Program (again read Tuesday’s piece for the details) provides terms that are so overwhelmingly generous. That calls into question why any chartered bank (“depository institution”) who is allowed to access the BTFP would be selling treasury securities right now. What’s the point? You get a better deal handing it over to the Fed as security for a loan.

This makes sense for them individually, but it means suddenly trillions of dollars of treasury securities are not available for sale. Many fewer treasury securities “in circulation” must be having an impact on this liquidity situation. These banks would need a selling price that is much higher than the buying price, in order to be willing to sell their securities. This is a fluid and volatile situation, where the news coming out is confusing and fast paced. As a result, it will be months, maybe even years, before we have a good idea of how big the direct impact of this quasi-emergency facility was. Some other elements of this piece may be subject to revision once we learn more. But this is how I think the “state of play” looks today…

…It is no secret I have not been a fan of the Federal Reserve’s interest rate increases. However, if you are going to continue them then, when you announce the use of what is normally seen as a crisis facility, that should come with clear explanations of the implications for interest rates. Indeed, it’s maddening to even have to say this! Forward guidance is supposed to be what the modern Federal Reserve is all about! If they had said “interest rates will keep on increasing as usual, though we probably will be doing smaller interest rate hikes for the next three months”, then the implications of this situation would be clear. Treasury security interest rates would have rapidly adjusted without impairing treasury liquidity.

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

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

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

Here are the articles for the week ending 12 March 2023:

1. Bank Execs on SVB Fallout: ‘2,000 Times Better If a Buyer Comes In’ This Weekend – Amir Efrati, Jackie Reses, Kristine Dickson, and Oren Zeev

The Information: Let’s start with what’s happening now after the FDIC took over SVB.

Reses: In any bank failure, the FDIC becomes the resolution authority regardless of who the regulator is for a bank. The FDIC comes in and takes over and they actually show up at a bank and they start working with a group of bank employees that continue to be employed and help them start to add up and bring forward liquidity positions and they will start to create an orderly disposition process immediately, which is why they publish that note that they will start managing the process on Monday.

The Monday note is very important because they’re trying to provide people with assurances that at least up until the FDIC insured amount, which is $250,000 per account, $500,000 for a joint consumer account, they will insure and get that money actually paid out so that they could try to insure an orderly set of operations for companies. Now, it doesn’t mean that the assets have matched the liabilities and they need to go through and make sure that the bank can pay out its depositors and see what’s left over.

And so there actually is a water flow that again is in their playbook. It’s [Federal Home Loan Bank] payments, administrative expenses, they go through their insured deposits. And then they do this math of what’s available to pay out based on the liabilities they have outstanding. And that process could take some time.

That raises a lot of questions to your point about what that means for Monday, for payroll, for next week. And on that, unfortunately, there’s not a great answer because it depends on the immediate liquidity situation and their ability to actually get wires, ACH’s processed throughout the week. And so they will be working 24-7, including this weekend, on making that happen.

Typically, what you’ve seen in history is that banks are taken over on Thursdays or Fridays so that the FDIC can go in, work the weekend, and then present on a Monday morning, before an opening, how they are going to manage and to whom they are managing and creating liquidity for. And so I wish I had better news for many companies out there, but there is going to be disruption in how money goes out, how wires go out, how payroll goes out. And so I think that’s the biggest problem everyone’s dealing with. And unfortunately there’s no good answer here around what that timing is going to be and what the balance sheet looks like on the inside.

Kristine Dickson: Prior to joining Lead I have spent the last 10 years helping to manage the wind down of the Lehman Brothers estate. And if those in the clock can even believe that Lehman went under in 2008 and no it is still not done. And yes it still has hundreds of millions of dollars left to distribute to its creditors. So, as Jackie said, for unsecured creditors, it can take some time. But I do want to provide some assurances that the regulators today and this weekend will be laser focused on protecting all of SVB’s depositors and customer assets. So this weekend will be a frenzy of activity. There will obviously be more clarity on Monday, but if history is any guide, what the regulators have preferred to do in the past is to merge failed lenders with larger and more stable institutions.

And while SVB is the second largest failed lender in U.S. history, the first was Washington, Mutual, Wamu, which the regulators orchestrated in a sale to J.P. Morgan Chase. So again, there’s more clarity to come on Monday. We don’t know exactly how they’re going to work it out but depositors will be top of mind for the regulators all weekend…

…The Information: If there is a buyer does that free up the uninsured deposits a lot faster?

Dickson: It is 2,000 times better if a buyer comes in and tries to continue the work of the bank as it is as opposed to having the FDIC attempt to liquidate the assets and then figure out the order of priority and the waterfall and then try to distribute those assets later. It is a million times better to go that direction. So that is what they will be focused on this weekend.

There was also a question about why didn’t that already happen? I think, as Jackie had said, trying to understand what it is a buyer would actually be buying would be easier once the bank is now taken over by the FDIC. They have the weekend to actually pour through the books and do the appropriate diligence to actually understand it. So I think it’s actually, once the bank gets to this point, one would expect that all the buyers would sit it out and wait for the regulators to come in and then start talking seriously. So hopefully that’s what’s happening…

…Dickson: The issues that happened at SVB and really at Silvergate, behind the scenes there’s bank balance sheet management issues, every bank faces the challenge of trying to align its deposits, which can grow and shrink with little to no warning with their investments and what they do with those deposits. And in February, the regulators put out a report that says that U.S. banks have an aggregate over $600 billion of unrealized losses on their balance sheets. That means there may be some other banks out there that have these looming issues if their deposits go down in some steep way, the way that SVB and Silvergate’s did.

Now the reasons they went down at SVB and at Silvergate, may be very specific to those banks. at Silvergate, it was crypto winter and FTX contagion. And at SVB, it had its own reasons. But the underlying issue of just bank balance management issues, is it’s out there more broadly. So it’s something that folks need to keep an eye on and think about as you’re picking bank partners…

...The Information: To what extent did venture capitalists spark this run on the bank?

Dickson: At its heart, it was a balance sheet and liquidity management issue, sort of a boring issue in the tech world, but they took in deposits and they had in 2021, they were awash in deposits during the height of the tech boom. They had excess deposits that they need to invest.

And ideally, again, as I said earlier that the bank’s challenge is to manage those deposits in line with how they manage their investments, both for yield and for duration. And for whatever reason in 2021, SVB took a bet and they invested $90 billion—

Zeev: I have the vantage point of someone who was in that situation 24 hours ago. And I do think that I and others did play a role in encouraging other companies to move their money. It’s not because I had any knowledge into the balance sheet or did any rigorous analysis. It’s because I’m not in the business of trying to now evaluate the balance sheet of Silicon Valley Bank. I don’t care. I care about my companies. Even if I think that the risk is minuscule, even if I think the risk is less than 1%, why take it? There’s zero downside in moving the money.

Dickson: I don’t think I’d blame the VCs. The reality is that [SVB] had set up their investment portfolio to be super long dated. Then as [interest] rates shot up super quickly, I mean up 450 basis points in 18 months, a gigantic long-dated investment portfolio shrank in value. At some point the piper was going to come calling. So it maybe hastened that event happening, having the VCs sort of talk it up. But at some point it seems like that was going to come.

2. Irakli Gilauri, is Georgia Capital a chance to invest in quality? – Tilman Versch and Irakli Gilauri

Irakli, let us jump a bit back to the 1990s, the early 1990s. We both were a bit younger then and the world was a bit different then. Georgia just came out of the post-Soviet time, and as I read your biography, I found out that you were studying in the late 1990s in Ireland and in London and learned investing and banking there. But how did you come interested in banking and investing and how was Georgia in the late 1990s or the early 1990s?

[00:02:45] Irakli Gilauri: So, very interesting times. I was very young back in ‘90s when Soviet Union collapsed and it was interesting to observe how the country lost nearly 75% of the GDP. You can imagine that there was no water, no electricity, no roads, roads were collapsing, no eating. Even it was difficult to communicate over the telephone. And I lived as a teenager, I lived in those times and in ’93, I started my university studies at the Georgia Technical University doing business.

Back then, everybody wanted to do business because we didn’t know what the hell the business was. Because everything was owned by the government, you know, nobody knew what the banking was because it was a state-owned bank and state-owned bank was giving loans to the state-owned companies. Retail client base was not existent other than depositing money, which was a small amount of money deposit. You could not even borrow the money from the bank. So, this whole world of doing the private business was really exciting for me and for everybody in Georgia because we didn’t know… I didn’t even know what the marketing was. The word ‘marketing’ or, you know, what kind of action you need to take to sell the product. Because it was, like, all supply driven.

So, when I started my studies in ‘93 in Georgia Technical University, we had the exchange program to study language during the summer. So I applied for the program and I went there for three months and I said that, “Okay, I’m not going back now, you know, I need to stay here and study business”, because, you know, back in Georgia, with all due respect, there was no knowledge of how the business is done, or how economies were working. Because we are in a command economy basically, it was not market economy. So it was really a big eye-opener for me. And so, I applied for the University, I went to the Dean and I had very little English and I persuaded the Dean that I really, really wanted to study here. And the Dean said, “Okay. We usually don’t accept students like this, but you want so much to study that we will accept you.” So, that’s how I ended up, just really wanting to do it.

So for me, it was a game changer basically, because in a way, I was really ahead of my peers in terms of understanding how macroeconomy works, how business works, how accounting works, because there’s different type of accounting as well. But anyway, you know, how the devaluation works, etc. So it was a big difference and after two years studying in the University, I had the choice to choose the banking or management or etc. And I chose the banking because I thought it was something which Georgia really needed to move forward. And I understood that the bank is a big player in the economy. With a good bank, economic can really grow and flourish; without that, it’s very difficult. So it was my choice back then in 1996. In ‘98, I graduated from the University, I had a four year really, really exciting studies there. I’m really grateful to my luck that I ended up there…

…[00:08:01] Tilman Versch: As you had no real private banking or even market economy, so you had to build this banking system from scratch. How much were you involved in this from the beginning or was there already something built when you entered Bank of Georgia?

[00:08:21] Irakli Gilauri: So, when I started with Bank of Georgia in 2004, there were 20,000 debit cards outstanding. Credit card was not existent and debit card cost you a fortune, you had to pay $300 to get debit card. And the bank had eight ATMs. Two handful of ATMs, that was something, yeah. So the strategy was very clear, we wanted to build a retail bank, try to bring people into banking because it was all cash economy. People were using cash, they didn’t even know how to borrow money, banks were not willing to lend to retail client base. So it’s almost all focus was about corporate banks.

When I started with Bank of Georgia, we had a market share of 15%. Market cap of Bank of Georgia was less than 20 million dollars. The balance sheet was 200 million Lari, total assets. I don’t remember the penetration, but it was single digit for sure; could be below five. So total assets to GDP was probably 3% or 4%. And we started consolidating, and we started to open up the branches, we were buying the ATM etc. and we were investing in retail banking heavily. And we went in 2006 in London and we tapped the market. And that was something because we raised $150 million and it was a lot of money back then. It was a huge IPO, it was a big deal. Because we raised the capital and then we issued the Eurobond, that was another big achievement. So we grew very rapidly into retail banking and penetration started to grow, and the assets doubled, tripled in a year. Even more, sometimes we quadrupled in asset size.

So it was a penetration game. It was at very low penetration, consolidation was happening, and this kind of things will never happen now in Georgia in banking, for sure. But there are some sectors you can do where penetrations are low and you have a very fragmented market and that’s where I think that our speciality is, that’s where we can feel the market and grow. And as you know, in this $20 million market cap, we were in the $2 billion market cap in 2018 when we demerged the bank into the investment arm and in the banking. So it was a great ride and I think that fragmented sectors, especially service industry, is a very beautiful thing…

…[00:22:16] Tilman Versch: Georgia has, compared to other markets in the region and also like other developing countries, very pro-business setup. How good or easy is it to do business in Georgia in your eyes?

[00:22:27] Irakli Gilauri: So basically what we have here, is that government understands very well in order to create wealth, we need to bring the investors’ investments in because we don’t have our gas, and we don’t have internal resources, we don’t have internal savings. Investors internally are very limited so we have to be good, friendly to the investors and this is the only way we’re going to grow our GDP. And that government understands very well. That is the primary driver for Georgia to be so business-friendly and investor-friendly. And I think we are very lucky not to have oil and gas, it would be a different country and probably not very well run and managed, to be honest.

It’s my speculation basically, but government knows that we need to have a good governance and they have excellent governance, they have excellent business environment. So we are very happy to be investors here in Georgia. So that’s kind of probably the biggest comfort as well. We as Georgians participate in the building of this country. So that’s a privilege, a lifetime opportunity when you are building the new sectors, you are building new management, you’re building the new companies and you’re doing all of this in your country. These companies are helping Georgia to go forward and you participate in this. You have some small participation in the progress this country is having. It’s a great pleasure to invest here.

[00:24:29] Tilman Versch: Do you see any risk that the pro-business setup changes with political shifts in the near future?

[00:24:37] Irakli Gilauri: I don’t believe because the fall of the Communist 90s, and I’m gonna say probably not very popular thing now, I think we should let all the nations to go fully bankrupt. Because they get their act together and sometimes, we want to help them. We are good people, we don’t want to help them, there is IMF, World Bank, there’s great organizations. But basically, you are not letting the nation to learn it’s lesson and that’s what happened to Georgia in the early 90s when we lost 75% of GDP. Back then, nobody knew what Georgia was. So, we were not even part of the World Bank or IMF probably, in the beginning. 

So what happened that Georgia went bust and people realized that there’s two things why we live so badly. One is the corruption, there was a big corruption during Soviet Union and that was the main thing. And second one is socialism and communism. So the side effect we have now, left wing parties have a very, very low popularity, left-wing parties get less than 5% all together. So basically, you need to be a pro-business, pro-market in order to win the elections in Georgia. So, I do not think that any time soon we are fearing this. That is what another reason why we eradicated corruption etc. We tackled this problem was exactly because of this lesson learned in early ‘90s. That was kind of a big help to the country that we were sorting out the governance and we are actually pro-market. Now our governments are pro-market. Some people want equality and socialists and I would love to invite them to Georgia in ‘90s or late ‘80s to experience it…

...[00:56:16] Tilman Versch: Jumping from mistakes to crisis, I think we already mentioned Covid a bit, but we discussed this in the community with you because you came on to chat with us and people who are interested in this can jump into the application form below via the link to the community. So let’s think a bit about the crisis you currently have like, all the crisis we are both in from the impacts of the Russian attack on Ukraine and all the changes. What does this mean for Georgia and Georgia Capital, like, since the beginning of the year, what has changed through the attack of Russia on Ukraine?

[00:56:55] Irakli Gilauri: So Georgia being attacked before, we know what it is like in 2008. But we were lucky that the whole conflict has been resolved very quickly and we had a very brief war basically. So we, as a nation, knew what Russia is capable of. I think that people forget Georgia as it is too far away from Europe, so it’s not happening with us, it’s happening somewhere else. I think Ukraine was very close to Europe, this war was very close to Europe and that woke up the European countries. And it’s good that they woke up.

But for us, it was obviously a big shock in terms of war, next door neighbours, it’s not very pleasant, it’s not good news. But slowly, we realised and we thought it would be a big economic shock on Georgia and we had the different macro models, and we thought we would have zero growth in 2022, something -2 or -3, but we humans don’t know. We can guess something but it’s difficult to know what would happen. That’s why it’s a good thing to live one day at a time, not to worry too much about the future. Anyway, we realised that a lot of the region has changed, the economy of the region changed. So if you had like Central Asian countries transporting oil and gas and different goods through Russia to Europe, they’re now using alternative routes through Georgia.

We also had a lot of Russians, who we did not expect; they just left Russia and moved to Georgia, especially people working in IT industry. And then now, we are having IT services exporting from Russia to Europe and other countries and the services we never exported. So our labour market changed dramatically, labour structure changed dramatically. The potential GDP growth most likely changed dramatically so a big shift happened there. Then exports, for instance, we brew Heineken beer in Georgia and Heineken stopped producing in Russia. They have ten brewers there and they stopped exporting from Russia. So they needed a destination. Now, we are exporting in seven countries where Heineken Russia was exporting.

So we have a lot of side effects which caused the Georgian Lari to appreciate from 3.3 dollar to 2.85. And against the Euro and against Pound, even greater appreciation because we had a big inflow of foreign currency. Georgia’s business-friendly environment also helps here. So we have investment also coming in. If you look at the foreign investment in Q1 versus the GDP, it’s the highest ever recorded. So you have big investments coming into the country. You have labour market shifting, structure changing, you have the logistics changing, the exports going up. And Georgia did a great thing also to have a free trade agreement with China and EU. There are actually two countries who have a free trade agreement with simultaneously China and EU, it’s Switzerland and Georgia. So basically that also helps because our exports also stepped up.

Tourist recovery was amazing and I think that the government managed to change the tourist structure as well. We managed to attract tourists, after the Covid, from high-earning countries and high spenders. So right now, in terms of numbers, we have 65% of number of tourists recovering. So 65% of 2019 tourists are coming to Georgia in numbers. But in terms of the money spent, it’s more than 100% than what we had back in 2019. So last year, we had 10% GDP growth; this year, we have 10% plus GDP growth. So huge growth, I mean, base was high. So if you look at the recovery in 2019 in Europe, we are number two after Ireland.

So Ireland is ahead of us in terms of the growth of GDP compared 2019 and then it’s Georgia. Because we outperformed 2019 by far. Basically, the nominal GDP in dollar terms now stands at around 25 billion dollars. It’s a small amount, but before Covid, we were around 17, 18. So this huge growth and Lari appreciation together created more attractive investor destination for foreigners.

[01:03:11] Tilman Versch: Besides all the good news, I have to play Dr Doom. So, how do you see that Russia one day attacks Georgia again in this decade?

[01:03:25] Irakli Gilauri: You see, we’ve already been attacked and Russia got what they wanted, these two land plots we used to have. So I think attacking Georgia again, unless there is a Olympic sport of attacking Georgia, I don’t think it would happen. It’s my view, but I think other nations are more under danger than Georgia. We are ahead in that game.

[01:04:07] Tilman Versch: So you don’t see a high likelihood of an attack again?

[01:04:12] Irakli Gilauri: Yeah, I don’t think. If you attack Georgia, what will you gain? You already have what you wanted. So I think that there are more things to do than attacking Georgia.

[01:04:30] Tilman Versch: You already mentioned that many Russians came to Georgia now. Do you have a rough number how many Russians are there? To remember for the audience, it’s 3.7 million people that live in Georgia. So even a smaller migration could make a huge difference.

[01:04:48] Irakli Gilauri: Yeah, especially in the industry which is not present in Georgia. Basically, the numbers are somewhere between 80,000 to 200,000 IT specialists, let’s put that way. And even if you have 50,000, they add 50,000 dollars a year. That’s 2.5 billion, that’s 10% of GDP. It’s a very big number. So if you have 100, that’s 20% of GDP.

3. TSMC’s Turning Point – Gregor Stuart Hunter

TSMC is Asia’s most valuable company and the ninth-biggest worldwide; its $461 billion market capitalization exceeds that of corporate titans like JPMorgan Chase & Co, Visa and Exxon Mobil at the time of writing. For Taiwan, which has just 23 million people, it is a source of considerable local pride, and its billionaire founder Morris Chang is feted as a national hero. The company estimates it accounted for 5.7 percent of the island’s gross domestic product in 2021.

But TSMC offers more than just bragging rights and economic might. The company is the leader in an industry that has long been viewed as a pillar of Taiwan’s defense against an invasion from China, which considers the self-governing island a renegade province and has never renounced force in its quest to take control of the young democracy. In his 2001 book Silicon Shield: Taiwan’s Protection Against Chinese Attack, journalist Craig Addison laid out the case that Taiwan’s electronics sector is so crucial in providing the chips needed for advanced weaponry that it creates significant incentives for allied nations to come to its defense — much as in the Gulf War, when Kuwait’s oil exports drew swift military intervention against Iraq…

…Hence why a new factory in Arizona seemed to cause more panic on the island than passing Chinese warships — many in Taiwan fear it represents cracks in the shield.

After the disruptions of the Covid-19 pandemic, the U.S. government is seeking to promote domestic chipmaking through the CHIPS and Science Act, a $52.7 billion package of subsidies for research, development, manufacturing and workforce development, priming the pump for chipmakers like TSMC as it expands its Arizona fab. Although TSMC has operated fabricators in China, a subsidiary in Washington state and joint ventures in Japan and Singapore, the Arizona plant marks its biggest facility outside of its home market yet. The company has also said it is considering opening additional factories in Japan and Europe…

…It also faces pressure not to stretch itself too thin. With demands around the world, playing defense at home, and the ever-present pressure of the cut-throat chip industry, TSMC — once a master of giving clients what they want — may be giving away too much…

…In between Hsinchu and Tainan, surrounded by little else except strip malls, the rail stops near a patch of mostly-vacant grassland that houses a 351-room dormitory complex made from sustainable construction materials and covered in solar panels. It’s here that the company is housing many of its new American engineers for training before they head to Arizona. Some have been lured by $100,000 starting salaries, subsidized accommodation, and the security of a three-year contract, the first half of which is spent in Taiwan. Others are drawn to the adventure of working for the world’s most advanced chipmaker.

Instead, they find themselves chain smoking to manage the stress or exercising constantly to blow off steam after 12-hour workdays.

On a warm evening in January, many could be found consoling each other at a local dive bar, grousing about the demanding work requirements of their local supervisors. Some of the new recruits hinted that they already want to leave. None of the workers spoke on the record, but experiences matching theirs are easily found online. Posts on Glassdoor, an anonymous company review site, describe a culture of micromanagement and frustrations adapting to Taiwanese work practices, pointing out that the environment is decidedly different to, say, Intel. “Certainly not for everyone,” says one of the more positive comments.

For starters, unlike at many American companies, engineers at TSMC work in shift patterns and overtime is quite common. “That’s why TSMC can operate 24 hours a day without any temporary equipment shutdown,” says Lucy Chen, vice president at Isaiah Research.

Taiwanese engineers, brought up in the TSMC ecosystem and ethos, are often prepared for this lifestyle. But watching their new American counterparts struggle to keep up has prompted a culture clash on the factory floor and in the dormitories. On the anonymous Taiwanese bulletin board PTT, an open-source message board similar to Reddit, some complain that the U.S. engineers are “babies.” And Chen, at Isaiah Research, notes that TSMC is having an easier time recruiting Taiwanese engineers to work in its new Japanese plant than its Arizona one, in part because of “culture adaptability.”…

…The narrative circulating in Taiwan that “TSMC is being hollowed out and extracted from Taiwan,” he says, “fails to mention” significant contextual information, such as the fact that by the time 4-nanometer chips are made in America, smaller 3-nanometer chips will have been rolling off the production lines in Taiwan for some time.

Currently, the company produces all of its most advanced chips in Taiwan — and it will stay this way, the company says, for the foreseeable future…

…“The prospect of seizing the world’s most valuable semiconductor fabs and becoming a silicon hegemon could at some point this decade tip Beijing in favor of an invasion,” says Jared McKinney, assistant professor of international security studies at Air University, the U.S. Air and Space Force’s center for professional military education. As China gets shut out of the semiconductor supply chain, it may become more desperate — and aggressive.

Most analysts agree that as much as China might want to possess TSMC’s capabilities, taking the island by force would immediately leave the company unable to produce chips. The physical devastation, ensuing sanctions, and lack of access to chipmaking equipment sold by the U.S. and its allies would effectively decimate TSMC’s operations. Over time, China could potentially restore some of its manufacturing capacity, but TSMC would likely be one of the first casualties of any conflict.

4. Twitter thread on ‘0 Days to Expiration’ (0DTE) options – Genevieve Roch-Decter

0DTE options are ‘0 Days to Expiration’ Options. Basically, they’re options that expire in less than a day. Since today is Friday, there are a massive amount of options expiring today (most contracts expire on a Friday).

0DTE options are more popular than ever. JPMorgan says that the notional value of 0DTE options trading has grown to about $1 trillion PER DAY. The total market cap of all US stocks is about $20 trillion.

For S&P 500 options, trading in 0DTE contracts accounts for about 44% of the 10-day average daily options volume, up from about 19% a year ago, according to Reuters…

…0DTE options are the ultimate tool for speculating. You can take a position in an option and realize a huge gain (or less) within a few hours. A call option selling for $1 could easily hit $2 by the end of the session if the underlying stock has a good day, or it could hit $0.

So what’s the problem? Massive trading in options introduces volatility risk…

…In other words, share prices don’t always reflect the intrinsic value of the underlying cash flows of the respective companies, like Benjamin Graham originally wrote about. Instead, share prices are being influenced by excessive trading in options.

5. Daniel Ludwig: An Invisible Billionaire – David Senra

But it’s this idea of how he started this business with limited money. It’s called the two-name paper idea. This will come up again later, but this is an overview, I think it’s helpful at the very beginning.

He had persevered during the mid-1930s and developed an ingenious ship financing scheme that would make his first fortune. The idea was to use other people’s credit. First, he’d go to an oil company and persuade it to grant him a long-term charter to haul its petroleum. This done, he would go to a bank, where using the charter as collateral, he would take out a loan to obtain a ship to haul the petroleum. Instead of paying Ludwig, each oil company would make the charter payments directly to the bank, which would then deduct the loan payment and put whatever was left into Ludwig’s account. This allowed Ludwig to build or renovate tankers without having to put up collateral or use his own credit.

As long as he would fulfill his charter contracts, he had a small but steady income, and more importantly, by the time the contract expired, he was the owner of a paid-up ship without having invested any of his own money…

…[00:20:00] And this is really important to understand later on. He does this with very little amount of money down. He’s buying ships from the government after the war, after World War I with only 10% down. So it says the sale of surplus ships at bargain prices much less than it would have cost to build the vessels from scratch, started almost as soon as the Armistice was declared. The result was that hundreds of government-owned vessels built at taxpayer expense were being sold off at well below cost to legitimate shippers and to speculators who did the minimum required renovation work and then sold the ships for a quick profit. That’s what Ludwig is doing right now during his career.

What made the deal attractive was the Shipping Board required investors to put up only 10% of the purchase price and the rest could be paid over time. And so this is what him and his partners were doing. That would mean by investing less than $50,000, right, because the total purchase price is $500,000. They could buy three ships, remodel and sell them. If they manage to sell three vessels for $1 million, they would reap over $0.5 million profit on an investment of only $50,000. And so almost 40 years later, when he’s giving this interview, he’s talking about this time in his early career and he says, he was always in hock at the beginning of his car. What that meant is, he’s always owed the government money and he’s constant, this whole book.

At this stage of his career, especially with that price it — doesn’t help he’s got a bunch of like — he’s got this large fleet and the depression comes, essentially like no one was to haul oil. He’s like his ships — his charters aren’t just valuable. He has a real hard time making payments. So it says during the Fortune interview in 1957, Ludwig said that in early business years, he was “Always in hock.” There may have been a good reason. As long as you’re in hock, it’s hard for a creditor to collect the money from you. So there’s so many times where the government is like, “Okay, the payment is due.” I’m making this day up, January 1. And Ludwig is like, “Oh, give me like a 6-month extension.” And then June comes like, “I need another 9-month extension.” It’s just constant back and forth and he constantly gets in to extend time. But what’s amazing is how fast his fortune is going to change.

[00:21:55] I do want to pull out these things because the book starts, he’s a 80-year-old man, richest man in the world, at this point. Started in the business when he’s 19, but he’d go when he’s 34. I’m going to — I’m going to pull up two things here. So at 34, he’s in debt and he’s barely making his interest payment. This is now into the depression. Ludwig wrote another begging letter to the Shipping Board saying that he was doing his best that he could, but that AM Tankers needed more time to make the payment. 3 years later, 37, and he’s almost going broke during the great Depression. 10 years from now, he’s going to be unbelievable wealthy, so I’m telling this.

Shipping Board auditor reviewing the company’s situation came to an unavoidable conclusion. AM Takers was for all practical purposes insolvent. The firm had no securities left to borrow on and virtually no chance remained that the massive liabilities could be paid off from the ship’s small earnings. But there’s an important point here. Like we have to pause because this is all about to change, right? It’s like, oh, they have massive liabilities. They have all these ships, but the ship is not making any money. Why isn’t it making any money. Because of the demand for shipping during the great depression has plummeted.

But the asset that he owns, the ship is still valuable. You just need something to cause demand to skyrocket. And that is exactly what happens in the late 1930s, when Europe breaks out in war. War makes the demand for Ludwig’s products and services, skyrocket. It’s almost the exact opposite of what was happening to him and his business the previous decade. Wars and rumors of wars pre-stage an upturn in international commerce, which for cargo haulers meant greater demand and higher revenues. A tanker that had been sitting idle at the dock since the start of the depression could now be hired out on a long-term basis at high rates or sold for a handsome price. He is going to make money both ways.

In some cases, now something that was just sitting there, not only there was no charter on it, if you had to sell the ship to try to pay off your loan, maybe get $50,000, $100,000. Those same ships are selling for $800,000 or more. And so this is the most important part in the book. This is what I referenced earlier. The two-name paper idea, plus the fact that he’s going to be shipping oil for Rockefeller equals Ludwig’s wealth, which then in turn causes him to go out and buy and start the hundreds of these businesses. Let’s go into this.

[00:24:05] “Ludwig needed a way to obtain ready money without either taking partners or assuming heavy mortgages. His early experiences with partnerships have been costly and borrowing to finance ship renovations was no better. It was at this time that Ludwig came up with a two-name paper arrangement that he said was a chief reason for his wealth. He would go to an oil company, get it to sign a long-term charter to ship so much oil on a regular basis, take the charter to a bank and using as collateral, obtain a loan to build or renovate a ship to haul the oil to fill the charter. The plan was legal, logical, and ingenious. He was able to start his climb towards being the world’s biggest shipper mainly because he was now hauling oil for the Rockefeller Empire…

…During the depths of the depression, Ludwig was mired deep in debt. He was saddled with do-nothing partners, desperately pressed to keep the Shipping Board and the banks from foreclosing on his few ships, and burden with an unhappy marriage. Now 5 years later, DK was in good financial shape, the owner of a growing fleet of ships and corporations out of debt and enjoying a profitable relationship with the government, the banks, and the oil companies. Moreover, he was building a reliable staff and had a happier marriage…

…This is how he starts the largest salt company in the world. And in the middle of the story is my favorite sentence in this entire book. So it’s this project that’s going to happen. It says it’s on Mexico’s Pacific Coast about halfway down the Baja Peninsula. Located there were huge underground deposits of brine. Concentration of salt in the water were around 30%, nearly 10x that of seawater by the simple process of pumping this brine to the surface and letting it stand in pools where the hot sun could evaporate the water, one could produce millions of tons of salt.

[00:42:04] This is what Ludwig is doing, produce millions of tons of salt, which could be gathered and exported. The economy of the procedure appealed to Ludwig. All he had to do is bring up the brine and nature would do the processing. The main problem was the labor. This part of Mexico was nearly unpopulated, and he would have to import workers and build places for them to live. He has to build essentially a small town for this to happen. The Baja cost was so remote that he would have to build an entire town if he was going to develop the salt deposits. This is my favorite sentence of the book, but he had learned something by now.

“Opportunities exist on the frontiers where most men dare not venture, and it is often the case that the farther the frontier, the greater the opportunity.” I love that line. The majority of businessmen are tied to cities where the ingredients of development already exists, labor, energy, supplies, building, transportation, and so on. Competition also exists there. And the way to escape it is to either do something no one else is doing or do it where no one else is doing it. Much of Ludwig’s success was due to his willingness to venture where more timid entrepreneurs dare not go. This business is unbelievably successful. He winds up selling it a few years later, but the output, the salt output increased to as much as 4 million tons a year, making the largest producer of solar salt in the world.

And that’s just one of these giant projects that he takes on. This is one of my favorite stories in the book too because sometimes you have to do it yourself. He’s already a billionaire at this point in the story. When he’s about to do what I’m going to describe to you now. He was embarking on an ambitious project in Panama, the building of a 55,000 barrel-a-day refinery in an adjoining petrochemical complex. Before starting construction, however, Ludwig had a little tour to perform, one that he intended to do personally. Twice, he had trusted the word of specialists and twice he had been burned. He had believed them when they told him he could bring fully loaded 60,000-ton ore carriers down the Orinoco River, this is in South America without running them aground. They were wrong about that, by the way.

[00:43:59] And his geologists had failed to discover until after considerable work was done that the coral rock underlying Grand Bahama Island was too fragile to support giant supertankers. So these are giant previous projects that he was working on. He got bad information and that cost him a ton of money. So he’s not going let that happen again. These episodes have cost Ludwig considerable time and expense. So before building a refinery in Panama, he decided to check out the site himself. Dressed in baggy workloads, he caught a night flight out of New York to Panama City and arrived just before dawn. He went into a little village store at the Bays Edge and pulled out a quarter out of his pocket.

He paid for his purchases, a heavy bolt and a $0.20 ball of string. He unwound the string, measured it out in 6-foot lengths, and tie a knot at each interval. He went outside and rented a motor boat and spent the rest of the morning. Remember, he is already a billionaire when he was doing this, and spend the rest of the morning and afternoon puttering around the bay checking with his weighted line, the accuracy of every sounding marked on a nautical chart that he had brought along. Only when he had satisfied himself that the water was as deep as the chart said, did he fly back to New York and give the signal to begin construction.

6. Infinite Games – Jack Raines

This idea of finite and infinite games doesn’t just apply to sports such as basketball. In fact, it is much more applicable to the biggest game of them all: life.

We humans have created quite the scoring system, haven’t we?

Are you single? Married? Do you have kids? What do you do for a living? Who do you work for? How much money do you make? Where do you live? How many languages do you speak?

We ask, “Who are you?” but we mean, “Which boxes have you checked?”

And the definition of you, whoever “you” are, is the sum of your boxes. Of course, we don’t consciously analyze our lives in this way. That would seem so vain! But we do it all the same.

Let me give you an example.

What is “prestige?” Well, that’s a broad question. Let’s get more specific. What makes “prestigious” careers prestigious?

You might say exclusivity. Thousands of individuals apply for a limited number of positions, and the ones who successfully land those limited positions become part of the “in-group.” And sure, exclusivity plays a role. But prestige runs deeper than exclusivity alone: prestige represents victory.

You had to beat countless other applicants to land that competitive position. You won, they lost, and your prize includes the prestige that ensues. I’ve won some prestige games myself over the years, like getting accepted to Columbia Business School, for example.

Prestige is a finite game. You play the prestige game for the sake of winning it. But do you know what happens after you win this game? Sure, there’s a moment of dopamine-induced satisfaction as you climb the proverbial mountain, look across the horizon, and scream, “I DID IT! LOOK AT ME!”

But what happens next?

You return from the peak and think, “Damn. I need that rush again. I need to climb another mountain.” But here’s the thing about that next mountain: it has to be greater than the previous one. If not, you’ll only feel underwhelmed as you look out from its peak and see your taller, greater feat from the past taunting you.

And another finite game begins…

…Here’s the problem with treating life as a series of finite games that must be won: No one remains the winner forever…

…Taken to its furthest extreme, the focus on outcome over everything leads to us discounting 99% of our lives for the sake of a few, small, fleeting moments that might provide some sense of satisfaction before the cycle begins anew.

And, I don’t know man, it seems pretty insane to live your life this way, but we do it all the time. You see it among the most successful folks alive, who, despite their billions of dollars and fame and fortune can’t stop chasing that next mountain. That next achievement. Because the last one, which took years to accomplish, lost its luster in minutes.

7. China’s Xi Jinping Shrugs Off Criticism in Push for Even More Control – Chun Han Wong and Keith Zai

Mr. Xi and senior Chinese officials this week agreed to plans to give the party more direct command in an array of areas they see as critical, including security, finance, technology and culture, while further diluting the government’s role in policy-making, according to people familiar with the discussions.

The National People’s Congress is expected to rubber-stamp parts of the plan during its annual session, which starts in Beijing this Sunday. The Chinese legislature will also sign off on senior government appointments, including a new economic team that has already begun trying to rev up growth in the world’s second-largest economy. The new team is expected to try to address concerns among business leaders about the government’s support for the slumping property market and tech industry, which has come under pressure from a regulatory clampdown…

…Mr. Xi faced tough tests of his leadership last year. His insistence on zero-Covid lockdowns in the face of fast-spreading Omicron variants decimated economic activity and eroded trust in the party across many of China’s wealthiest cities. His abrupt and chaotic pivot from zero-Covid in December caught many officials and citizens off-guard. His assertive diplomacy and continued support for Moscow despite Russia’s invasion of Ukraine, meanwhile, damaged China’s standing in the developed world.

Even so, Mr. Xi has continued to exert firm control while pursuing his agenda, seemingly unshaken by what critics describe as some of the biggest policy missteps of his 10-year rule—a demonstration, analysts say, of the practical dynamics of power in China.

“Xi Jinping’s authority has been affected in the eyes of the masses and ordinary cadres, but he still has the gun barrel, the knife handle and the pen shaft in his hands,” said Wang Hsin-hsien, a politics professor at Taiwan’s National Chengchi University, referring to party parlance for the military, security forces and the propaganda apparatus—all key levers of power.

Given the political climate in China, “suffering damage to one’s authority doesn’t represent facing a challenge to one’s power,” but rather suggests that the ruler will seek to tighten his grip on power, Mr. Wang said…

…Mr. Xi seems to believe that policy missteps stem from poor local execution of Beijing’s directives, and thus is trying to ensure that lower-level officials can deliver better governance while the central leadership exerts overall control, said Ryan Manuel, managing director of Bilby, a Hong Kong-based artificial intelligence firm that analyzes Chinese government documents.

The focus on local failings also brings political benefits for Mr. Xi, said Mr. Manuel. “By having local governments take the blame for failing to implement policy adequately, Xi’s not going to take all the heat.”

In recent months, Mr. Xi has reiterated demands for political loyalty, such as by ordering party inspectors to ensure compliance with the central leadership’s edicts, while making efforts to regain public trust. 

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

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

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

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

Here are the articles for the week ending 05 March 2023:

1. Planning for AGI and beyond – Sam Altman

If AGI is successfully created, this technology could help us elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge that changes the limits of possibility.

AGI has the potential to give everyone incredible new capabilities; we can imagine a world where all of us have access to help with almost any cognitive task, providing a great force multiplier for human ingenuity and creativity.

On the other hand, AGI would also come with serious risk of misuse, drastic accidents, and societal disruption. Because the upside of AGI is so great, we do not believe it is possible or desirable for society to stop its development forever; instead, society and the developers of AGI have to figure out how to get it right…

…Although we cannot predict exactly what will happen, and of course our current progress could hit a wall, we can articulate the principles we care about most:

1. We want AGI to empower humanity to maximally flourish in the universe. We don’t expect the future to be an unqualified utopia, but we want to maximize the good and minimize the bad, and for AGI to be an amplifier of humanity.
2. We want the benefits of, access to, and governance of AGI to be widely and fairly shared. We want to successfully navigate massive risks. In confronting these risks, we acknowledge that what seems right in theory often plays out more strangely than expected in practice.
3. We believe we have to continuously learn and adapt by deploying less powerful versions of the technology in order to minimize “one shot to get it right” scenarios…

…As our systems get closer to AGI, we are becoming increasingly cautious with the creation and deployment of our models. Our decisions will require much more caution than society usually applies to new technologies, and more caution than many users would like. Some people in the AI field think the risks of AGI (and successor systems) are fictitious; we would be delighted if they turn out to be right, but we are going to operate as if these risks are existential.

At some point, the balance between the upsides and downsides of deployments (such as empowering malicious actors, creating social and economic disruptions, and accelerating an unsafe race) could shift, in which case we would significantly change our plans around continuous deployment…

…The first AGI will be just a point along the continuum of intelligence. We think it’s likely that progress will continue from there, possibly sustaining the rate of progress we’ve seen over the past decade for a long period of time. If this is true, the world could become extremely different from how it is today, and the risks could be extraordinary. A misaligned superintelligent AGI could cause grievous harm to the world; an autocratic regime with a decisive superintelligence lead could do that too.

AI that can accelerate science is a special case worth thinking about, and perhaps more impactful than everything else. It’s possible that AGI capable enough to accelerate its own progress could cause major changes to happen surprisingly quickly (and even if the transition starts slowly, we expect it to happen pretty quickly in the final stages). We think a slower takeoff is easier to make safe, and coordination among AGI efforts to slow down at critical junctures will likely be important (even in a world where we don’t need to do this to solve technical alignment problems, slowing down may be important to give society enough time to adapt).

Successfully transitioning to a world with superintelligence is perhaps the most important—and hopeful, and scary—project in human history. Success is far from guaranteed, and the stakes (boundless downside and boundless upside) will hopefully unite all of us.

2. Berkshire Hathaway 2022 Shareholder Letter – Warren Buffett

A common belief is that people choose to save when young, expecting thereby to maintain their living standards after retirement. Any assets that remain at death, this theory says, will usually be left to their families or, possibly, to friends and philanthropy.

Our experience has differed. We believe Berkshire’s individual holders largely to be of the once-a-saver, always-a-saver variety. Though these people live well, they eventually dispense most of their funds to philanthropic organizations. These, in turn, redistribute the funds by expenditures intended to improve the lives of a great many people who are unrelated to the original benefactor. Sometimes, the results have been spectacular.

The disposition of money unmasks humans. Charlie and I watch with pleasure the vast flow of Berkshire-generated funds to public needs and, alongside, the infrequency with which our shareholders opt for look-at-me assets and dynasty-building.

Who wouldn’t enjoy working for shareholders like ours?…

…Charlie and I allocate your savings at Berkshire between two related forms of ownership. First, we invest in businesses that we control, usually buying 100% of each. Berkshire directs capital allocation at these subsidiaries and selects the CEOs who make day-by-day operating decisions. When large enterprises are being managed, both trust and rules are essential. Berkshire emphasizes the former to an unusual – some would say extreme – degree. Disappointments are inevitable. We are understanding about business mistakes; our tolerance for personal misconduct is zero.

In our second category of ownership, we buy publicly-traded stocks through which we passively own pieces of businesses. Holding these investments, we have no say in management.

Our goal in both forms of ownership is to make meaningful investments in businesses with both long-lasting favorable economic characteristics and trustworthy managers. Please note particularly that we own publicly-traded stocks based on our expectations about their long-term business performance, not because we view them as vehicles for adroit purchases and sales. That point is crucial: Charlie and I are not stock-pickers; we are business-pickers.

Over the years, I have made many mistakes. Consequently, our extensive collection of businesses currently consists of a few enterprises that have truly extraordinary economics, many that enjoy very good economic characteristics, and a large group that are marginal. Along the way, other businesses in which I have invested have died, their products unwanted by the public. Capitalism has two sides: The system creates an ever-growing pile of losers while concurrently delivering a gusher of improved goods and services. Schumpeter called this phenomenon “creative destruction.”…

…The math isn’t complicated: When the share count goes down, your interest in our many businesses goes up. Every small bit helps if repurchases are made at value-accretive prices. Just as surely, when a company overpays for repurchases, the continuing shareholders lose. At such times, gains flow only to the selling shareholders and to the friendly, but expensive, investment banker who recommended the foolish purchases.

Gains from value-accretive repurchases, it should be emphasized, benefit all owners – in every respect. Imagine, if you will, three fully-informed shareholders of a local auto dealership, one of whom manages the business. Imagine, further, that one of the passive owners wishes to sell his interest back to the company at a price attractive to the two continuing shareholders. When completed, has this transaction harmed anyone? Is the manager somehow favored over the continuing passive owners? Has the public been hurt?

When you are told that all repurchases are harmful to shareholders or to the country, or particularly beneficial to CEOs, you are listening to either an economic illiterate or a silver-tongued demagogue (characters that are not mutually exclusive)…

…I have been investing for 80 years – more than one-third of our country’s lifetime. Despite our citizens’ penchant – almost enthusiasm – for self-criticism and self-doubt, I have yet to see a time when it made sense to make a long-term bet against America. And I doubt very much that any reader of this letter will have a different experience in the future.

3. Does Long-Term Investing Work Outside of the United States? – Ben Carlson

Elroy Dimson, Paul Marsh and Mike Staunton published a book the early-2000s called Triumph of the Optimists: 101 Years of Global Investment Returns that looked at the historical record of equity markets around the globe since the year 1900…

..And lucky for us, the authors update the data on an annual basis for the Credit Suisse Global Investment Returns Yearbook. The latest edition was just released and it’s filled with data and charts about the long-run returns in stock markets around the globe…

…The U.S. is near the top but it’s not like they’re running away with it like Secretariat… Sure, there have been some complete washouts over the years (Russia’s stock market was basically shut down for 75 years following World War I) but returns in other countries have been anywhere from OK to respectable to strong…

…The MSCI World ex-USA dates back to 1970. These were the annual returns1 from 1970 through January 2023:

• S&P 500: 10.5%
• MSCI ex-USA: 8.4%

That’s a pretty good lead for the old US of A but it’s not like the rest of the world has been chopped liver over the past 50+ years. And the majority of the U.S. outperformance has come since the 2008 financial crisis.

These were the annual return through the end of 2007:

• S&P 500: 11.1%
• MSCI ex-USA: 10.9%

It was pretty darn close before the most recent cycle saw U.S. stocks slaughter the rest of the world. And it’s not like U.S. stocks have outperformed always and everywhere.

4. AI-generated comic artwork loses US Copyright protection – Benj Edwards

On Tuesday, the US Copyright Office declared that images created using the AI-powered Midjourney image generator for the comic book Zarya of the Dawn should not have been granted copyright protection, and the images’ copyright protection will be revoked…

…Last September, in a story that first appeared on Ars Technica, Kashtanova publicly announced that Zarya of the Dawn, which includes comic-style illustrations generated from prompts using the latent diffusion AI process, had been granted copyright registration. At the time, we considered it a precedent-setting case for registering artwork created by latent diffusion.

However, as the letter explains, after the Copyright Office learned that the work included AI-generated images through Kashtanova’s social media posts, it issued a notice to Kashtanova in October stating that it intended to cancel the registration unless she provided additional information showing why the registration should not be canceled. Kashtanova’s attorney responded to the letter in November with an argument that Kashtanova authored every aspect of the work, with Midjourney serving merely as an assistive tool.

That argument wasn’t good enough for the Copyright Office, which describes in detail why it believes AI-generated artwork should not be granted copyright protection. In a key excerpt provided below, the Office emphasizes the images’ machine-generated origins:

Based on the record before it, the Office concludes that the images generated by Midjourney contained within the Work are not original works of authorship protected by copyright. See COMPENDIUM (THIRD ) § 313.2 (explaining that “the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author”). Though she claims to have “guided” the structure and content of each image, the process described in the Kashtanova Letter makes clear that it was Midjourney—not Kashtanova—that originated the “traditional elements of authorship” in the images…

…It’s possible that the ruling may eventually be reconsidered as the result of a cultural shift in how society perceives AI-generated art—one that may allow for a new interpretation by different members of the US Copyright Office in the decade ahead. For now, AI-powered artwork is still a novel and poorly understood technology, but it may eventually become the standard way visual arts emerge. Not allowing for copyright protection would potentially preclude its use by large and powerful media conglomerates in the future. So the story of AI and copyrights is not over yet.

5. Even a Brain-Eating Amoeba Can’t Hide From This Cutting-Edge Diagnosis Tech – Ron Winslow

When a middle-aged man who had suffered a seizure was admitted to the University of California San Francisco Medical Center in 2021, doctors seeking the cause for his condition quickly became stumped.

After pathologists spent two weeks peering through microscopes and monitoring petri dishes, doctors knew something serious was harming the patient’s brain; they had no idea what it was or how to treat it.

They turned to an emerging strategy known as unbiased diagnosis. It ultimately confirmed an illness so rare and so deadly, few doctors have ever seen it: brain-eating amoeba disease. The patient’s brain had been invaded by a single-cell critter called Balamuthia mandrillaris, one of at least three types of amoebas known to infect human brains.

The unbiased approach is called metagenomic next-generation sequencing, a powerful technology that analyzes all of the genetic material in a patient’s tissue sample and as a result can screen for a wide range of disease-causing microbes in a single test…

…The conventional search for the cause of an infection involves examining patient tissue under a microscope or culturing samples in a petri dish to see if bacteria or other microbes grow. But doctors have to be looking for a particular bug to find it. Such tests are typically ordered after doctors weigh the details of a case and form a hunch about the cause of the infection—a biased approach…

… Metagenomics is the future of medical diagnostics, said Eric Topol, director of Scripps Research Translational Institute, La Jolla, Calif. “It should be the present,” he said, but not many hospitals are equipped to do it.

A metagenomics test spells out the order of the four letters that make up the genetic code in all the DNA and RNA in a patient sample and compares the result against human and nonhuman genome sequences stored in databases such as the National Institutes of Health’s GenBank. 

A typical sample might yield 100 million snippets of genetic material, Dr. DeRisi said. Some 99% would be human. Those sequences are computationally stripped away and the remaining 1 million pieces are screened against all the sequences in GenBank in an effort to find a match…

…A biased diagnosis can be likened to the card game Go Fish, said Natasha Spottiswoode, an infectious disease physician at UCSF who has overseen care of the Balamuthia patient. A player holding a green fish card asks another, “Do you have any green fish?” If the answer is no, the question on the next turn may be, “Do you have any red fish?” 

For an unbiased query, “What you really want to ask is, ‘Do you have any fish at all?’” Dr. Spottiswoode said. “And then figure out what color they are.” 

In 2014, Dr. DeRisi was among a team of researchers and clinicians at UCSF who reported on one of the first patients to be successfully treated based on metagenomics sequencing—a 14-year-old boy whose treatable, but potentially fatal Leptospirosis bacterial brain infection went undiagnosed for several months until the test was performed.

The case convinced Dr. DeRisi and his colleagues that a metagenomics test should be deployed as a clinical tool for diagnosing brain infections and eventually led UCSF to offer the tests to other hospitals. Innovation in semiconductor technology is helping make the service possible, Dr. DeRisi said. “If we dial back 10 or 12 years ago, we couldn’t do this,” he said. “If we didn’t have increases in computer storage, memory and speed, we’d be sunk.”…

…Metagenomics has limitations. The test can pick up dormant or otherwise clinically irrelevant microbes, making it difficult to interpret results. It can miss pathogens that are detected by conventional means. UCSF’s brain infection test costs about $2,000, far less than the cost of a day in the ICU, but still a potential impediment to regular use. Insurance reimbursement is spotty. Turnaround time can be as long as six or seven days, Dr. DeRisi said.

6. James Revell – Wise: Moving Money Around the World – Zack Fuss and James Revell

James: [00:11:24] Maybe I’ll start with just providing a bit more context and background on the cross-border money transfer market, and that can set up this counter-position Wise has. So if you think about cross-border money transfers, it goes back thousands of years. There are history books written on this. Ultimately, back in the day, it would have been gold bullion or precious metal spices being loaded on to ships and transferred across borders.

Obviously, that is quite impractical, causes security concerns, costs and all sorts. Over time, we’re talking 11th, 12th century now, the bill of exchange was created, whereby you could essentially create an IOU which meant you didn’t need to transfer actual money or currency across borders, it’s more a paper-based exchange of value that could be redeemed at a bank. These were posted at the time.

And then that has grown into telex messaging when cross-Atlantic cables are laid and this electronic means that of communication between bank arrive. And so the history of cross-border transfers, if you think about it, money stopped moving across borders a long time ago. It was a lot of credits and debits of accounts with each banks held with each other, moving numbers around on ledgers as opposed to money actually being sent on a ship or through this kind of pipe. It’s just a series of relationships between banks.

That’s known as correspondent banking, and we can come back to some of the problems with that model. But maybe just to set up then how big this market is. So I think there’s about GBP 100 trillion of volume transferred across borders every year. If you cut out, say, the really big enterprise government or interbank transfers, you’re left with about GBP 2 trillion of personal cross-border transfers and about GBP 9 trillion of small businesses transferring money across borders. So this is a colossal market.

And that flows through to, say, a revenue line of anywhere between GBP 100 million and GBP 200 billion paid in fees by customers. In terms of the split of that market, about 2/3 still sits with banks, about 10% to 20% with money transfer operators like the Western Unions and the MoneyGrams and the rest is split up between the remaining players. The market is growing. So on average, growing about 5% per annum over the past decade. But interestingly, fees are reducing.

There’s some pressure on fee down was largely caused by Wise, but also by regulatory attention. But 2008, 2009, it was about 9% in fees. It’s now like, say, close to 6%, 7%. The tailwinds behind this market is largely been driven by globalization. So international trade and supply chains, global e-commerce, international traveler migration. And it’s received, like I said, a lot of regulatory attention. So the G20 and the FSB are really focused on this right now.

UN have set of goal that by 2030, cross-border transfer fees should be close to 3%. And why that is, is because it’s a very inefficient way of doing things. These are very high. And largely, unfortunately, the people that suffer are normally immigrants, trying to work overseas and transfer money every month back home to support their families. And so that’s one reason I think the regulatory attention has come. The other reason is because financial crime and the proceeds of crime transferring across borders, if that’s in cash, that causes a problem.

So underpinning this market is what’s known as correspondent banking which is the relationship between banks around the world. So if you’re, say, Commonwealth Bank of Australia, you need to transfer money to Barclays in the U.K., you may not have a direct relationship or say, you’re a credit union in Australia, it would very unlikely to have a relationship with the bank in the U.K. And so you’ll contact a bank in Australia who will contact a bank in, say, the U.K. who will then contact the recipients bank.

This chain of communication between banks is called correspondent banking. And what they’re doing is basically transferring the request and the amount of money they need to spend as well as the customer information. Now the way the banks do this communication is reliant upon an organization called SWIFT. So SWIFT is owned by 200 banks. It’s used by over 11,000 of them and it stands for the Society of Worldwide Interbank Financial Telecommunications.

So it’s essentially an electronic communication network that tries to put in place a common language and some standards around how a cross-border transfers work, what format data needs to come in. So you may have heard of SWIFT code or an IBAN number, these are identifiers that banks use globally to help them manage this complex network of communication, which underpins these transfers.

That doesn’t take away the individual effort put on to banks in order to complete transfers. And so maybe just to run through the process quickly. First of all, you’ve got to find a bank so you want to transfer money to Thailand, as a bank, you may need to go through three or four different banks to get to the recipients banking and so you’ve got to find a way of getting to the recipient bank.

Every step of the way you need to validate the data, you needed to complete the regulatory checks, you then need to potentially transform the data for the next bank along in the chain, you need to transmit it. You then need to sort out funding on the back end. So settling crediting and debiting accounts, this could be like a $50 transfer. And these six banks all need to communicate, they all need to settle funds and they all need to reconcile and make sure that all ticks were completed, all data has been transferred accurately and everyone’s got the money they need.

That’s set up, you can see that there’s a ton of problems caused by that. The first and the most obvious one, which has been growing over the past 10 years, particularly since the GFC, the regulatory and compliance burden along that chain is huge. So making sure that there’s no anti-money laundering going on. There’s no counterterrorism financing going on. You’ve got your sanctions checks, you’ve got to protect data.

So you’ve got be mindful of privacy, you’ve got prudential worries about liquidity and bank regulations and consumer protections like if something goes wrong, you got to solve disputes. So there’s an enormous amount of complexity just on the regulation and compliance side. There are interesting things like opening hours. So banks communicating across the world, they have different opening hours.

The way banks transfer money domestically between each other is normally done in a batch process model, whereby it’s only operating five days a week and at certain times of that day. So if you span a weekend, your money is not moving. There’s also a lot of paper and legacy technology involved. There’s liquidity and foreign exchange risks.

So if transfers between banks aren’t done simultaneously, which often they are, that creates liabilities between banks, which is problems and banks charge fees for that. So correspondent banking has a lot of inefficiency built in, and that flows through. So those are supply side problems flow through to problems for customers like our poor Kristo and Taavet back in 2008. It’s incredibly expensive. Every hand off along the chain, they need to be paid. It’s slow.

Each step along the chain, obviously, there’s some waiting time involved and it’s opaque. Often upfront where the transfer starts, you have no idea how much it’s going to cost when it comes out the other end. And ultimately, it’s inconvenient…

…Zack: [01:03:30] And then as you kind of reflect upon what you’ve learned about this business as you studied it and the broader payment space, what is a lesson that you can take from this business and apply to others from an investor’s perspective? And then some of the other early stage or late-stage growth companies that you look at, what are lessons you’d like to see them borrow from Wise and apply to their own business as operators?

James: [01:03:48] The one thing we haven’t really touched upon, which I think Wise has, and being an operator myself is something that I could have learned from. The way they organize their people and prioritize their culture, I think, is relatively unique. I attended a talk with someone from TransferWise back in 2018. You talked about the way they prioritize resource allocation internally.

So what they have is they have small teams, so they have over — I think it’s over 100 teams, small, highly autonomous empowered, cross-functional teams each working on a solution or a feature. And that team is empowered to create its own vision, its own mission, its own objectives. To the extent it can even go and seek its own legal advice. So it’s fully — each team is fully autonomous. And they basically vie for prioritization amongst each other.

So it’s a lot of mini businesses within a business. Why I think that is fascinating. It has some problems, which potentially we can touch on. But why that resonates with me is the thesis around having very highly aligned but very loosely coupled people within your organization to overcome this inertia and this slowdown that occurs as the business gets bigger.

So if you have low alignment and high autonomy, you have very empowered silos, but they’re doing their own thing. They might not be pulling in the same direction. There’s probably some duplication or overlap or potentially even pulling in opposite directions. On the flip side, you can have very high alignment but low autonomy, which is basically a command and control type structure. Both of these can exist and do exist quite regularly, but they really struggle to scale.

Reed Hastings talks about this high alignment loose coupling where you tell people what to do, not how to do it. And I think this is what these small many businesses within the business is what Wise has managed to organize as they’ve grown up. And key to it is having that super clear mission, super clear vision, you’ve got your customer at the center of everything, you know why you exist, and that creates the alignment.

There’s a huge amount of trust and transparency within the business. And I think this culture focusing on one thing, you see in other scale economy shared businesses. There’s been great breakdowns on Floor & Decor or Costco, where it’s a relentless focus on one thing, retaining operational improvements and the benefit of unit cost efficiencies back to the customer. But it is that one thing focus and just doing that one thing really well and organizing a culture behind it. For us, as people and culture investors, that is so powerful, and I think it’s one of the untold stories of this business.

7. Will High Risk-Free Rates Derail the Stock Market? – Ben Carlson

Because of the Fed’s interest rate hikes, investors are being offered a gift right now in the form of relatively high yields on essentially risk-free securities (if such a thing exists). You don’t have to go further out on the risk curve to find yield right now.

Short-term bonds with little-to-no interest rate or duration risk are offering 5% yields.

The big question for asset allocators is this: Will higher risk-free rates impact the demand for stocks and other risk assets which leads to poor returns?

This makes sense in theory. Why take more risk when that 5% guaranteed yield is sitting there for the taking?

The relationship between risk-free rates and stock market returns is not as sound as it would seem in theory…

…The highest average yields occurred in the 1980s, which was also one of the best decades ever for stocks. Yields were similarly elevated in the 1970s and 1990s but one of those decades experienced subpar returns while the other saw lights-out performance…

…I also looked at the performance of the stock market when 3-month T-bill yields averaged 5% for the entirety of a year (which could happen this year). That’s been the case in 25 of the last 89 years.

The annualized return for the S&P 500 in those 25 years was 11%. So in years with above-average risk-free rates, the stock market has actually seen above-average returns.

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

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

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

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

Here are the articles for the week ending 26 February 2023:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ford wrote in his book My Life and Work:

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

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

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

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

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

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

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

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

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

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

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

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

6. From Bing to Sydney – Ben Thompson

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

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

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

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

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

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

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

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

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

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

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

7. Peacetime CEO/Wartime CEO – Ben Horowitz 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 19 February 2023:

1. The AI Bubble of 2023 – Joshua Brown

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

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

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

Let’s get into these items:

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

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

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

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

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

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

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

Jim O’Shaughnessy:

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

David Ha:

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

Jim O’Shaughnessy:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

Jim O’Shaughnessy:

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

Jim O’Shaughnessy:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

Jim O’Shaughnessy:

Lines to Earth, right?

David Ha:

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

3. Brunello Cuccinelli – Om Malik and Brunello Cuccinelli

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

But there’s more to it than that…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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