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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 19 February 2023:

1. The AI Bubble of 2023 – Joshua Brown

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

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

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

Let’s get into these items:

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

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

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

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

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

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

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

Jim O’Shaughnessy:

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

David Ha:

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

Jim O’Shaughnessy:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

Jim O’Shaughnessy:

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

Jim O’Shaughnessy:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

David Ha:

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

Jim O’Shaughnessy:

Lines to Earth, right?

David Ha:

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

3. Brunello Cuccinelli – Om Malik and Brunello Cuccinelli

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

But there’s more to it than that…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2. What’s a Blockchain? – Technically

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

From Wikipedia:

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

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

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

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

This is how a market collapses.

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

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

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

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

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

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

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

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

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

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

Summarizing from Wikipedia:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

You can probably see where we’re going.

7. The Retreat of the Amateur Investors – Gunjan Banerji

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

BOJ Interest Rate hike is a potential catastrophe for Zombie companies

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

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

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

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

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

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

Jim O’Shaughnessy:

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

Mark Nelson:

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

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

Mark Nelson:

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

Mark Nelson:

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

Mark Nelson:

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

…Mark Nelson:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

6. Why America Should Ban Crypto – Charlie Munger

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 29 January 2023:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Germany is I think probably the most challenging ones.

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

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

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

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

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

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

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

Which is basically what happened to Nvidia and AMD.

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

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

GA: 600 gigabytes-per-second.

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

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

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

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

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

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

Two generations behind, yeah.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

They were innovators.

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

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

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

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

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

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

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

But, wait a minute. Why should they?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

7. The Bank that Never Sold – Marc Rubinstein

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 15 January 2023:

1. How Complex Systems Fail – Richard Cook

Complex systems are intrinsically hazardous systems.

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

Complex systems are heavily and successfully defended against failure.

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

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

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

…Human practitioners are the adaptable element of complex systems.

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

…Change introduces new forms of failure.

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

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

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

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

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

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

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

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

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

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

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

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

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

3. Investing lessons for 2023 – Chin Hui Leong

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

6. The Semiconductor Madman – Brent Crane

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

7. No Recession – Michael Batnick

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

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

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

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

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

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