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 January 2025:

1. Mike Alkin – Talking Uranium (Transcript here) – Bill Brewster and Mike Alkin

Alkin: So coming to this market, I did that. I spent a good almost couple of years doing supply/demand on my own. There’s 430 reactors around the world. And understanding the country where they operate, the attitude towards nuclear, understanding the math involved. Often as investors, you look for heuristics. How many reactors are there? How many pounds per reactor would there be? You’re looking for rules of thumb. As you start peeling the onion back, I realize that rules of thumb don’t apply here because the amount of uranium needed for the reactor fleet around the world is not always the same. It depends upon enrichment capacity. We won’t go down that rabbit hole, but there’s a whole other segment you need to learn.

As I was doing that, I would go to these conferences and I would talk to nuclear fuel buyers, people who buy this stuff. It was hard for me at first to really understand what I was dealing with because as somebody at that time having well over 20 years of experience as a hedge fund investor, I talked to people in all industries that were on all sides of the equation. But the people buying it typically were curious as to what we were thinking when we were questioning them. If we were talking to a buyer at a company that was buying a product, they would say “What are you as an investor hearing? What are you hearing from the other side? What are my competitors saying? What are you hearing about inventories?” They were inquisitive. That was not this cohort. As I started speaking to nuclear fuel buyers, I was met with an enormous wall put in front of me telling me, “I’m an outsider, I’m not a nuclear engineer, I don’t know what I’m doing, I should basically stay away and they’ve got it.”

I thought it was that attitude that just said to me, “Something’s not right here because the numbers I’m coming up with, whether I’m looking at inventories or the amount of the cost of the supply, or the actual demand” – for context, at the time the price of uranium was $17, $18, $19 a pound. It would say what it was trading for in the market. As I did the analysis, I realized that the average cost was somewhere in the mid-$50s. I’m not that sharpest tool in the shed but I know that if something costs you mid-$50s to make, you can’t sell it for $17 for very long. So it was then that I had to peel back the onion saying, “Why are they producing it at that price?” Then you start to understand that the uranium market is one driven mostly by long term contracts. Well north of 80% on average will trade in a long-term window with contracts that cover 5, 7, 10, 12, 15 years depending on the contract. But that’s where most of the pounds trade. After the Fukushima event, a lot of these uranium producers, when the spot market had declined precipitously, were still selling into much higher prices. My understanding of that when I was talking to fuel buyers at these nuclear conferences, they were telling me that the price of uranium was $17 and $18, it was going to $10, it was going to $5. There was all this uranium out there.

That’s not what my math was showing me. What my math was showing me was that the model was that the long term contracts that had been signed before Fukushima melted down in 2011 were going to start to expire and rather rapidly. Uranium producers could not sell $17, $18, $20 uranium when it cost him 2.5 times that. At some point, production would have to start to shut down.

So you ask, “Do you think you’re crazy?” Yes, because as I’m talking to people who are obviously very sharp – they’re nuclear engineers – but it’s understanding, as you realize, as an investor, you have to understand incentives and you have to understand market structure. Charlie Munger would always say, “Show me the incentive, I’ll show you the outcome.” It was as I was starting to go and talk to these folks and realizing a couple of things. Number one is, they had no interest in what I was learning on my journey. Even though I’m not a nuclear engineer, I’m still somebody who’s a market participant. I’m still somebody that while I don’t speak their language, sitting at a dinner table or a lunch table or at a bar having a beer with them, I certainly could hold my own in supply/demand conversation. And as I would talk about what I was learning and uncovering, I was shot down at every step. I thought, “Wow, that’s interesting because I’m seeing a recency bias. What is now will always be.” So they were kind of latched onto that.

Then as I started peeling that, I’m thinking, “Why is this?” I’ve been doing this a very long time. Over the years, I’ve been wrong many times. I’ve been right more often than not. But you’re wrong and you try and understand where you’ve been wrong. I was thinking, “What is it? Why are they so uninterested in hearing what an outsider’s view is?” As I started to explore that more, you start to understand the makeup and the cost structure of a nuclear reactor, which I have known, but it really started to come into clear vision for me was the fuel. Uranium is just one part of the fuel cycle that goes in. You have uranium, they convert uranium from a powder into a gas. It then gets enriched, it then gets fabricated into pellets. That takes 18 to 24 months to do this stuff. There’s many different stages of the fuel cycle. As I was starting to think about what are the costs of that, all those stages are probably around 20% to 25%. What’s the cost of the uranium? That depends on the price. But it could be mid-single digits, high-single digits, somewhere around that. As you start talking to them about that, you realize it’s not a meaningful cost.

For comparative purposes, if I’m running a natural gas power plant or a coal power plant, my feedstock, the natural gas and the coal are 80% to 90% of the cost of operating it. Here, the uranium is single digits cost of operating it. The vision that started to come to me was uninterested market participants. They’re in the market very infrequently. Why are they uninterested? Because the cost is de minimis. Not to say it’s meaningless, but it’s de minimis. Then as I started to explore and ask questions, “Why are you not as concerned about this?” I was obviously met with a wall.

But what started to come to me was – and I asked flat out at a particular dinner at a World Nuclear Conference – I asked one, actually there were four fuel buyers at a dinner, I said, “If you all had a really enterprising fuel buyer that did the supply/demand work and said, “I think consensus is wrong. Here we are, $17, $18, $20 a pound. We should be buying uranium because the forecasts going out of the future are for deficits to be forming.” Let me ask you a question. Do you all, if the price were to go parabolic and you had all these great cost savings for your plant, do you participate that in any way, shape or form? Are you rewarded financially? Are you rewarded with a promotion?” The answer was I got laughed at. “What are you talking about? We’re paid to secure fuel.” These were buyers. As you come to a market as an investor, you think buyers are traders – they’re commercial creatures. These aren’t. These are really smart nuclear engineers that happen to buy a product that happens to not be a major cost component. There’s infrequent price discovery on their part and so it’s a lesson in understanding incentives and market structure…

…Alkin: One of the things you see now is you have expert networks who provide hedge funds and mutual funds experts to speak to in any industry. If you’re a hedge fund wanting to get up to speed right now on the nuclear power industry, you’re going to say, “Get me three nuclear fuel buyers. I’d like to speak to them about uranium.” They’re going to get on the phone and they’re going to speak to them. For years – though I’m sure they’ve been doing this – they can get on the phone and speak to three fuel buyers and they say, “Yeah, there’s plenty of uranium out there.” Those are the same folks, when the price was $17 was telling me that, versus here you’re seeing floors and ceilings at $125 and $135. They are the gift that keep on giving. Yet the way the structure of the research process is, they’re going to expert networks. They find these people, and if you don’t understand how the sausage is made, you’re going to be misled. They’re not purposely misleading you. It’s just what their own beliefs are. For me, that’s a beautiful thing. I’ve been doing this a long time now, almost 30 years as a professional investor, and I’ve never seen a cohort of people who are so uninterested in hearing the other side of the story. So far I’ve seen them prices move up 4x in there against them and they still have the same attitude.

Brewster: To your point, it doesn’t sound like they’re very incentivized to care.

Alkin: There’s very little to no incentive to care, other than maybe you would think pride? I don’t know. But it doesn’t matter. It’s just not a thing. We actually chuckle because when we go to these conferences, you talk to them in a hallway or in a bar, it’s as though you’re an adversary. It’s very bizarre. They don’t have an incentive. It doesn’t matter what they pay. So that’s the bizarre thing.

2. Chip Cities Rise in Japan’s Fields of Dreams – Gearoid Reidy

In Chitose, a city of 100,000 in the northernmost main island of Hokkaido, billboards seek recruits for the Self-Defense Forces, which saw a 50% shortfall last year. When I arrived on a fully booked plane from Tokyo packed with salarymen in cheap suits and expensive watches, it was easy to see where the competition was coming from: a half-dozen towering cranes jutting into the sky, a jarring contrast against the surrounding countryside…

…Those cranes are building the first fab for Rapidus Corp., a public-private venture that aims to skip Japan to the head of the chip production queue. Founded just two years ago, it hopes to produce cutting-edge, 2-nanometer chips by 2027, in cooperation with IBM Corp. It’s fraught with risks, and the government’s record in promoting industry is spotty. But this is just the latest and most ambitious example of a series of bets on chips, with Prime Minister Shigeru Ishiba recently pledging an extra ¥10 trillion ($66 billion) on top of ¥3.9 trillion invested since 2021. Near the other end of the Japanese archipelago, 1,500 kilometers (930 miles) to the southwest, is another. In Kumamoto, on the island of Kyushu, mass production is soon set to begin at a $7 billion semiconductor plant.

Here, Taiwan Semiconductor Manufacturing Co., drawn by government subsidies and the region’s supply chain, opened its first Japanese plant in February. A second is in the works, with authorities lobbying for a third. It’s triggered an influx of Taiwanese workers into a city where until recently almost everyone was Japanese…

…As many as 6,000 laborers are employed to build Rapidus. But talk is of the arrival of permanent workers once test production begins. That’ll bring at least 1,000 high-earning jobs, along with their supply chains. On my visit, ASML Holding NV, the Dutch maker of chip-testing tools, had just opened offices, with 50 staff expected. Every second building seems to be being torn down and rebuilt…

…The scale of the ambition creates the risk of spectacular failure, one many in Japan’s media fully expect. Skepticism is warranted, considering previous government-led efforts, from DRAM maker Elpida Memory Inc., sold to Micron Technology Inc. after its 2012 bankruptcy, to troubled Japan Display Inc.

The economy was already doing well even before talk of Rapidus, Mayor Ryuichi Yokota told me, describing the fab as a “Big Bang” that has the city scrambling. Yet at night, when the construction crews leave, the silence is deafening. I couldn’t feel the billions I expected to find flowing, just a cold wind that would soon begin to turn to snow…

…The risk from disaster is unpredictable; but what if these experiments simply don’t work out? Japan has spent billions on subsidies to bring a foreign company in Kumamoto. And when it comes to Rapidus, the risks are immense. Even if the company can find the talent it needs (the country is expected to have a shortfall of 40,000 engineers), the technology succeeds and yields are acceptable, it still has to outcompete rivals — including TSMC — to attract customers with an unproven product.

Chitose mayor Yokota shrugged off these concerns. “I’m convinced it will succeed,” he said, resolute that researchers currently studying with IBM in the US will return, like Meiji-era scholars, with secrets Japan can use to rebuild.

3. Before Berkshire: Warren Buffett’s Tab Card Triumph – Kingswell and Alice Schroeder

He decided that he would come in and invest in this company — Mid-Continent Tab Card Co. — but, interestingly, he did not take Wayne and John’s word for it. The numbers they gave him were really enticing, but again he went through and he acted like a horse handicapper.

Here’s another point of departure from what almost anybody else would do. Everybody that I know — or knew as an analyst — would have created a model for this company and would have projected out its earnings and would have looked at its return on investment in the future. Warren didn’t do that. In fact, in going through hundreds of his files, I’ve never seen anything that resembled a model.

What he did is he did what you would do with a horse. He figured out the one or two factors that could make the horse succeed or fail — and, in this case, it was sales growth and making the cost advantage continue to work. Then, he took all of the historical data, quarter by quarter for every single plant, he got the similar information as best he could from every competitor they had, and he filled pages with little hen scratches of all this information and he studied that information.

And, then, he made a yes/no decision. He looked at it: They were getting 36% margins [and] they were growing over 70% a year on a million of sales. Those were the historic numbers. He looked at them in great detail — just like a horse handicapper studying the tip sheet — and then he said to himself, “I want a 15% return on $2 million of sales.” And then he said, “Yeah, I can get that.” And he came in as an investor.

So what he did is he incorporated his whole earnings model and compounding discounted cash flow into that one sentence. “I want 15% on $2 million of sales.”

Why 15%? Because Warren is not greedy. He always wants a mere 15% day one return on an investment and then it compounds from there. That’s all he has ever wanted. He’s happy with that. It’s a very simple thing. There’s nothing fancy about it…

…The $2 million of sales was pretty simple, too. It had $1 million [and] it was growing 70%. There was a big margin of safety built into these numbers. It had a 36% profit margin and he said, “I’ll take half that.”

He ended up putting $60,000 of his personal non-partnership money into this company, which was about 20% of his net worth at the time. He got 16% of the company’s stock, plus some subordinated notes.

4. China’s Bond Yields Scream the ‘D’ Word – Lingling Wei

Over the past week, just as Chinese leaders tried to get the public—and markets—excited with another round of stimulus talk, China’s 10-year sovereign yield kept falling to fresh lows. Now, the yield is around 1.7%, a full percentage-point plunge from a little over a year ago. The return on the 30-year government bond has also dropped below 2%.

The sovereign-debt yield still has a ways to go before falling to zero, but the speed of the drop is astonishing. The lower the yield falls, the deeper the market is signaling economic stress.…

…In reality, Beijing is sticking to the formula of boosting demand through investment. The official thinking is, investment creates jobs, which would in turn create demand. That means more roads will be built, factories will be expanded and debts will continue to rise. Already, residents in some cities are complaining about the inconvenience from old roads being dredged up as authorities search for ways to invest.

One big irony is the source of bond buying—the force pushing down the yields.

State-owned banks, insurance firms and funds, the very institutions Beijing is counting on to support the economy, are the major purchasers of government bonds. These institutions would rather park their money in the safety of bonds than financing business projects or otherwise putting it to work.

“What’s good to invest in these days when demand is so low?” a Chinese banker told me, referring to weak business and consumer spending.

5. An Interview with Gregory Allen About the State of China Chip Export Controls – Ben Thompson and Gregory Allen

Here’s the question though. China doesn’t generally seem to be operating, and for good reason under the circumstances, under a real stringent return on invested capital calculation. I mean the 7nm chips that are being produced, we know with I think a pretty high degree of certainty, the yields are terrible.

GA: The yields are dreadful.

But they’re doing it anyway just because it needs to be done and this sort of ties into another thing. You referenced Dylan Patel and SemiAnalysis, who have been pretty strident critics of the enforcement of chip controls. But I think a good point he has made is that China, unlike the US, is not necessarily constrained in power or in the ability to build a ton of data centers, and so there’s a bit where they could just sort of — it’s not great, but they could just be way less efficient and accomplish similar things. Is there a bit where these expert controls are fashioned with Western/US constraints and concerns about how you go about building this stuff that might make them less impactful in the long run?

GA: Yeah, the export controls have not achieved their wildest dreams. There was a faction in the Biden administration that says, “Bwahaha, we found the secret weapon, and China’s AI dreams are gone” — that theory is just dead. Where we are now is at more of a cost imposition strategy. “We are going to make this as expensive and complicated as possible for you to do it, we’re going to try and slow you down, we’re going to try and increase your costs, and that is the race that we’re going to run”.

I mean, if you think about it, we’re switching from a mode in which the US AI ecosystem and the Chinese AI ecosystem were largely fused such that if we’re running a race, you can imagine there’s US people giving China Gatorade and those new Nike shoes that make you run faster. Now we’re moving to a moment where we’re trying to trip them in the race, that’s the change in mindset that we’ve experienced, and it’s not working to its most extreme form, but there is real cost imposition takes the form of the fact that SMIC has to operate at these dreadful yields. The economics are terrible, the fact that when they’re building all of these data centers, they’re having to use lousy chips, they’re having to buy more of them, and they’re having to deal with the higher energy costs of all of that.

It’s true that China does have just this extraordinary willingness to spend, but the point is we’re in this race, we’re in this competition, and it gives us an edge, not an infinite edge, but a meaningful edge.

This is a field, maybe you don’t have an answer to this, but there are some that argue that actually the better approach to some of these chips is a much more expensive, a much more high speed memory approach that has much lower latency using SRAM instead of High Bandwidth Memory. Is there a possibility that we actually pushed China down a different route towards developing these chips that maybe ends up being better because we thought HBM was the right way?

GA: I think that’s probably not what’s going to happen. It’s definitely worth saying that that could happen, a version of that kind of happened with YMTC and their NAND memory. There were multiple different approaches they could have taken technologically. All the Western and US allied Asian firms picked one way because it was obviously the best economics, and they held all the intellectual property, they held all the patents and so YMTC basically said, “Okay, we’re going to go down this other road and because we’re so heavily subsidized, it doesn’t really matter that it’s going to be more expensive”, and they did ultimately figure out how to get it work.

I think what you’re describing, the SRAM in massive quantities thing verges on the neuromorphic architecture, and it’s not that that’s impossible, and it’s not that that’s never going to happen, but it’s clearly not the right step for China right now. I think they have a path to domestic HBM production and that’s so much easier for them to chase than a SRAM revolution. I think traditionally they would just wait for somebody else to try and figure out and demonstrate that it’s possible and then they would throw infinite resources at it…

...For all of these chip controls, all this stuff that you’ve covered and written about, does any of it matter, if you add it all up, in comparison to that point that they don’t have EUV?

GA: EUV is the highest return on investment export control that we have had and are likely to have. It’s definitely the case that some of the other stuff hurts. If you talk about SMIC, for example, increasing their yields on their 7nm line and expanding the capacity of their 7nm line, they actually are bottlenecked by US equipment, a lot of US metrology equipment, etc. But if you want to talk about why they can’t—

But they do have the equipment, they just need to figure out how to duplicate it. The challenge with EUV is they don’t even have one, so duplicating it is that much harder.

GA: Yes exactly, it’s a lot harder to reverse engineer something that you don’t have a copy of, it really helps to have a copy of it. So I would say the EUV thing really matters, but there’s areas where China is facing headwinds that aren’t part of the EUV story.

So just to take one example, in DRAM, Micron still doesn’t use EUV in their production of DRAM, and they’re a globally competitive firm. So CXMT, the Chinese domestic champion of DRAM, the reason why they’re not currently globally competitive is not the absence of EUV, but I do think you could make a story that it is the absence of all this other stuff that we’ve been refusing to sell…

…You’re not necessarily like a geopolitical analyst, but the thing that scares me about all this, I think I’ve asked you this every time, it still scares me, is we’re talking and saying the administration needs to do better at enforcing these laws that guarantee a power imbalance in the long run, that is usually very destabilizing. China might think, if we’re going to have a fundamental power imbalance, then how about we take Taiwan off the board because that will screw everyone? Now we’re equal again. Do you worry about this? You’re a strong advocate for doing this better.

GA: So. Number one is, I don’t know that I ever agree that the balance of power is the stable universe. In 1994, the Taiwanese defense budget was half of that of the Chinese defense budget, now the Chinese defense budget is infinity times that of the Taiwanese defense budget. And by contrast, in 1997, I think there was a single U.S aircraft carrier battle group that was more than capable of defeating the entire Chinese Navy and the entire Chinese Air Force, that was a massive power imbalance and it was a very stable relationship. And by the way, it was a relationship in which a lot of people got rich and had productive free trade and all these kinds of happy relationships. So the idea that power parity is the path to peace here, don’t know that I necessarily agree with that, I don’t think the historical record really bears that out.

Now, you could argue if we’re going to make bold moves and try and seize a decisive advantage, could those bold moves be destabilizing? Yeah, I think definitely think so.

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 ASML and TSMC. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 22 December 2024:

1. Meet Willow, our state-of-the-art quantum chip – Hartmut Neven

Errors are one of the greatest challenges in quantum computing, since qubits, the units of computation in quantum computers, have a tendency to rapidly exchange information with their environment, making it difficult to protect the information needed to complete a computation. Typically the more qubits you use, the more errors will occur, and the system becomes classical.

Today in Nature, we published results showing that the more qubits we use in Willow, the more we reduce errors, and the more quantum the system becomes…

…This historic accomplishment is known in the field as “below threshold” — being able to drive errors down while scaling up the number of qubits…

…There are other scientific “firsts” involved in this result as well. For example, it’s also one of the first compelling examples of real-time error correction on a superconducting quantum system — crucial for any useful computation, because if you can’t correct errors fast enough, they ruin your computation before it’s done. And it’s a “beyond breakeven” demonstration, where our arrays of qubits have longer lifetimes than the individual physical qubits do, an unfakable sign that error correction is improving the system overall.

As the first system below threshold, this is the most convincing prototype for a scalable logical qubit built to date. It’s a strong sign that useful, very large quantum computers can indeed be built…

…As a measure of Willow’s performance, we used the random circuit sampling (RCS) benchmark. Pioneered by our team and now widely used as a standard in the field, RCS is the classically hardest benchmark that can be done on a quantum computer today…

…Willow’s performance on this benchmark is astonishing: It performed a computation in under five minutes that would take one of today’s fastest supercomputers 1025 or 10 septillion years. If you want to write it out, it’s 10,000,000,000,000,000,000,000,000 years. This mind-boggling number exceeds known timescales in physics and vastly exceeds the age of the universe. It lends credence to the notion that quantum computation occurs in many parallel universes, in line with the idea that we live in a multiverse, a prediction first made by David Deutsch…

…Willow was fabricated in our new, state-of-the-art fabrication facility in Santa Barbara — one of only a few facilities in the world built from the ground up for this purpose. System engineering is key when designing and fabricating quantum chips: All components of a chip, such as single and two-qubit gates, qubit reset, and readout, have to be simultaneously well engineered and integrated. If any component lags or if two components don’t function well together, it drags down system performance…

…The next challenge for the field is to demonstrate a first “useful, beyond-classical” computation on today’s quantum chips that is relevant to a real-world application. We’re optimistic that the Willow generation of chips can help us achieve this goal. So far, there have been two separate types of experiments. On the one hand, we’ve run the RCS benchmark, which measures performance against classical computers but has no known real-world applications. On the other hand, we’ve done scientifically interesting simulations of quantum systems, which have led to new scientific discoveries but are still within the reach of classical computers. Our goal is to do both at the same time — to step into the realm of algorithms that are beyond the reach of classical computers and that are useful for real-world, commercially relevant problems.

2. X (previously Twitter) thread on quantum computing and Google’s Willow – Jeffrey Scholz

Like a regular computer, a quantum computer keeps bits in groups. So a 64 bit quantum computer would have a vector of 64 2d vectors serving as it’s “word.”

Here is where the speedup happens: in a regular computer, each of the 64 bits don’t know anything about the value of any of the other 64 bits.

If we want one bit to affect another bit, we have to explicilty combine them with a logic gate.

However, in a quantum computer, each of the 64 qbits can “talk to each other” via “quantum entanglement.”

Running a quantum circuit means you plug in a quantum vector, run it through a bunch of matrix multiplications, then collapse the output.

The final vector will be the correct answer. Technically, quantum computers can give wrong answers, but if you run the computation multiple times, then you will get the correct answer on average…

…The current problem with quantum computers is that as the circuit gets bigger, they become less correct on average. All of the “talking to each other” creates so much noise the system stops working.

Once your probability of being correct drops below a certain threshold your quantum computer becomes useless. This is a major blocker for current quantum compute.

Let’s look at a specific (oversimplified but helpful) example. Suppose you shine a laser beam into an ice cube.

Actually simulating what the laser will do when it exits the ice cube is very hard to predict because some quantum phenomena is involved.

To actually compute what the laser will do means you have to explicilty compute quantum entanglement, which is slow for classical computers but “built in” to a quantum computer.

However, you can *estimate* the distribution of how the laser will scatter without a quantum computer, so you can have at least a rough idea if your answer might be correct…

…By analogy, this is what Google was doing. The computation Google was doing was a “pseudo-random quantum circuit” (think pseudoranom ice cube) but we know a quantum circuit is just matrix multiplications (on crack). Therefore, it is a bunch of random matrix multiplications with an output that looks right.

Google’s actual breakthrough was that the output of the circuit “looks correct” — which sounds underwhealming — and compared to the headlines, it definitely is. The academic breakthrough is that Google was able to use a larger circuit and notice an apparent *increase* in accuracy when modeling how a laser shines through an ice cube. That is noteworthy.

You can definitely tell if a computation has failed, and it seemed to be failing less as the circuit got bigger…

…However, note that the problem is “rigged” in favor of quantum computers. The benchmark is explicitly modeling a quantum phenomenon, so *of course* we get a speedup.

In other words, Google created a random distribution on the output that “seems correct.” Why does it “seem correct?” well because by design, the computation cannot be run on a classical computer. But if we can’t run it on a classical computer, how do we know the quantum computer is actually giving the right answer? The answer is we don’t, and this is a serious gap…

…Quantum computing is kind of at the stage right now where some smart teenager wired a few logic gates together in a random fashion and said “hey look, my circuit made a random output and didn’t explode!” Compared to previous attempts, it is an improvement. But he is still a long way from training an LLM.

3. Volatility: A Double-Edged Sword for Long-Term Equity Investors – Daniel Crowley

The ability to measure risk in a portfolio has long been a puzzle for the financial world. When Harry Markowitz introduced Modern Portfolio Theory in 1952, he revolutionized how institutions approached risk and return. His use of standard deviation as a proxy for volatility offered a clean, mathematical way to quantify the unpredictability of markets. It gave investors a seemingly precise tool to compare assets and assess portfolio risk. Over time, this approach became gospel, with concepts like beta and the Sharpe ratio reinforcing volatility as the core measure of risk.

But here’s the problem: volatility tells only part of the story. Financial markets don’t follow the neat patterns of a normal distribution, which is what these models assume. Extreme events occur far more often than traditional models predict. We’ve seen this play out time and again—from the collapse of Long-Term Capital Management to the Great Financial Crisis. The models couldn’t account for the market’s tendency to behave irrationally and with far greater extremes than the math suggested. That’s why I’ve come to view volatility not as risk itself but as a signal, an invitation to investigate further…

…Volatility is often misunderstood because it treats upward and downward price movements as equal. A stock with erratic upward swings may have high volatility but poses little risk if the business fundamentals are sound. Conversely, a stock that steadily declines might appear “safe” on paper but can quietly destroy wealth.

The market’s reliance on volatility as a measure of risk often misses these nuances.

This misunderstanding creates a divide among investors. On one side are those who cling to volatility as the ultimate arbiter of risk, building models that rely on neat equations and assumptions about market behavior. On the other are those who dismiss it entirely, treating volatility as irrelevant noise.

My view lies somewhere in the middle. Volatility is neither good nor bad—it’s just a clue. It’s a signal to dig deeper and assess whether the market’s movements are justified by changes in a business’s intrinsic value.

What I’ve come to appreciate about volatility is its ability to surface opportunity. Markets are emotional, driven by fear, greed, and short-term thinking. Prices frequently diverge from reality, creating moments where high-quality businesses are available at steep discounts. When markets panic, as they did during the COVID-19 pandemic or the Great Financial Crisis, those who can stay calm and look beyond the noise can identify extraordinary opportunities.

Volatility, far from being a risk, is often the price of admission for outsized returns.

4. The AI nuclear renaissance – SMRs role – Rihard Jarc

The global nuclear power market is about 10% of global electricity (about $350-$400B annually) and around 32% of zero-carbon electricity generation.

As of 2023, nuclear energy accounted for about 18.6% of total electricity generation in the United States. The International Energy Agency (IEA) highlights that global nuclear power output must more than double by 2050 to meet net-zero emission targets. Most of the U.S.’s nuclear power plants are over 50 years old and nearing the end of their operational lives. While their lifespans have been extended to support the grid, they will need to be replaced in the coming decades…

…The introduction of ChatGPT and the AI boom that we have experienced in the last 2 years have only accelerated as AI workloads and AI chips consume much more energy than traditional data center workloads. This Nuclear Energy expert gives a good example:

» If you provide a simple search in Google, you consume 0.3 W per hour of electricity. If you do the same with ChatGPT or Alexa or Gemini, any AI that we can imagine, this 0.3 W transforms into 2.9 W, so it means 10X the consumption.«…

…Driven by artificial intelligence (AI), cloud computing, and digital transformation, U.S. data centers consumed an estimated 150 TWh of electricity in 2023, equivalent to around 3% of the nation’s power demand. According to Goldman Sachs estimates, data center demand hovered at 340 TWh in 2023 globally, which is about 1.3% of worldwide electricity use. U.S. data center power use is expected to triple between 2023 and 2030 roughly and will require about 47 gigawatts of new generation capacity…

…Nuclear energy has become very attractive because companies want to be carbon-neutral and have stable power. An additional benefit of nuclear power is that it can provide more stable long-term contracts that are less sensitive to inflation and supply chain problems…

…Interest in nuclear energy, particularly Small Modular Reactors (SMRs), is growing as they have been heralded as a solution to streamline nuclear power production, offering flexibility, lower upfront costs, and modular deployment. The simplest way to imagine SMR is that it is a smaller version of the traditional nuclear reactor. One of their most significant benefits is that they are modular. They are designed to be built in factories, not on-site. Because they are built in factories, they are easier to assemble and control. From quality checks to a more predictable supply chain and quality of workers. When assembled, they are then shipped to the site of the nuclear plant, where they are stacked together to form the whole plant. In terms of energy output, traditional nuclear plants have outputs between 1,000-1,600 megawatts of electric (MWe) per reactor, while SMRs are around 50-300 MWe per module. Some SMRs are also said to be safer due to passive safety features, which rely on natural processes like convection to prevent meltdowns in emergencies. But they also come with cons. The primary one is that they are much smaller than traditional nuclear plants, so they do not have the cost benefits of economy of scale. Because of that, producing the same amount of energy is more expensive than on a traditional nuclear plant…

…Over 25 countries, according to the International Atomic Energy Agency (IAEA), are investing in SMRs. In March, Wood Mackenzie estimated the pipeline of SMR projects was worth more than $176 billion and that SMRs could account for as much as 30% of the global nuclear fleet by 2050…

…We can look at the example of NuScale, which has its Pressurised Water Reactor design. Their levelized cost of electricity ranges from $89-135/MWh, while traditional nuclear plants are in the $110-160/MWh. However, looking at the most traditional alternative in data centers, which is combined solar and gas, gas costs $45-70/MWh, and solar plus storage costs $30-60/MWh…

…State-backed projects in countries like China and Russia have made more progress, leveraging integrated supply chains, controlled costs, and assured revenue streams. But even for them, the costs to build these reactors compared to first estimates are still much bigger…

…We must also face reality, which says that only 2 SMRs are operational right now, one of which is in Russia and the other one in China.

Another important topic when assessing nuclear energy is the problem of nuclear waste and its storage. Most SMR designs produce a similar amount of nuclear waste on a unit production basis than traditional nuclear plants, so the problem of storing nuclear waste stays.

5. How to invest without relying on target prices – Chin Hui Leong

The US stock market is soaring to new heights. But what does that mean for your stock returns in 2025? I would like to give you a definite answer but if I did so, I would be lying to you. In fact, you should view anyone who gives you target prices with suspicion.

Here’s the hard truth: No one can control where the market is headed in the short term. Yet, the allure of target prices persists…

…The answer lies in the inherent difficulty in predicting the future of rapidly evolving technologies.

The best example is Amazon.com. In mid-2010, when I first invested in the company, it had just reported US$24.5 billion in annual revenue, primarily from its online retail business. Here is the twist: it was impossible to know what the business would look like a decade later…

…Fast forward to 2023, and AWS had become a financial cash cow with nearly US$90 billion in annual revenue and an impressive US$24.6 billion in operating income. In other words, AWS, an insignificant division back in 2009, had generated more operating income in 2023 than the entire company’s revenue in 2009…

…I like to go back to the reason why valuation is used in the first place: to reduce your investment risk. The way I see it, valuation is one of the many ways you can employ to manage risk. But valuation is not the only risk in investing.

A weak, shrinking business can pose risks that no amount of stock valuation can solve. Hence, starting with high-quality businesses is my preferred approach.

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) and Amazon. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 08 December 2024:

1. Why China’s Economy Opened Up in the 1970s – Joe Weisenthal, Tracy Alloway, and Odd Arne Westad

Joe (13:32):

What does it mean when you talk about history being “contingent?” You used that word a couple of times and I actually don’t know if I fully understand what that means, but when you’re telling these stories, or this story, and you’re keeping in mind the contingency in history, can you talk a little bit more about this idea?

Odd (13:48):

So you’ll see from the book that we go in and out from the sort of micro to the macro level of telling history. And if you look at the night when the coup against the radicals — the so-called Gang of Four within the party — took place, which we describe in some detail, you know, what happens from hour to hour…

Joe (14:10):

Right, this was the moment in which the left faction, after Mao dies, was arrested, and allowed for a sort of more moderate path to emerge.

Odd (14:21):

That’s right. And it was in effect a military coup. I mean, it was undertaken by the military and the security forces against the people who Mao himself had put in charge of the party, including his widow who was most prominent of all, Jiang Qing. Now that night, and the following few days, things could have ended up very differently. I mean, Shanghai, the biggest city in China by far, was still under control of the radicals. There were military units that supported the radical approach to politics. This could have ended up very differently from what it did.

And as we describe in the book, some of the plotters, some of the coup-makers themselves, in those days that followed the coup itself, were completely surprised by how little resistance there had been from the left. And how little chaos there had been on the streets. So that’s what I mean with it being contingent. I mean, this is something that obviously connects to the larger picture that we see today — going back to your sort of three level version of what happened in China. But it didn’t seem that obvious at the time. And it could have gone in very different directions from what we’re seeing today.

Tracy (15:30):

How important was the fraying of the relationship between China and the Soviet Union in the 1960s, early 1970s to spurring or catalyzing that opening up? Because it does feel like the sudden emergence of the Soviet Union as an external enemy, it feels like that led China in some respects to open up to the US and some other countries.

Odd (15:56):

This is a sort of trajectory that I think it’s really important to get right, because what Mao and his group of leaders did in the late 1960s was to turn to the United States as an ally — a pseudo ally, security ally — against the Soviet Union because they were so deadly afraid that there would be a war with the Soviets — a war that China certainly would have lost, given the state that Chinese communists themselves had pulled China into during the Cultural Revolution. So what Mao did was to turn to the enemy far away, the United States, to help back him against an enemy much closer to home, the Soviet Union, which they had this falling out with mainly for ideological reasons.

From Mao’s perspective, this was always intended to be a strictly security oriented pseudo alliance. It was directed against the Soviet Union. Mao to the end of his days was puzzled that United States would support the real communists, meaning him, against the fake communists, meaning the Soviet Union. But as long as they were willing to do that, he was certainly willing to reap the benefits. But he never intended that this would have any effect in terms of the increasingly radical communist direction that he was taking for China internally, domestically.

So that’s when what happens in 1976, after Mao’s death, becomes so significant, because the people who then took over, they thought, ‘Aha! We have this relationship between United States. They are supporting us for their own reasons in the Cold War against the Soviet Union. We can now also make use of this to supercharge Chinese reform.’ If it hadn’t been for that relationship, strictly security oriented, that already existed between China and the United States, I doubt that that would be possible. So it’s very important when about the longer term US-China relationship to think about that origin and how this actually got started. Very different from the way most people think about it, where the security element and the reform element are sort of conflated into one…

…Odd (36:05):

I think it was both. I mean in the Xi Jinping case, I think he was picked by the party as the, what Chinese would call, the core leader, back in the early twenty-teens, in response to what was seen as a bunch of real problems, from a Chinese Communist Party perspective, over liberalization, decentralization, corruption, strength of private companies that meddled in a lot of things that the communists didn’t want them to meddle in. They wanted to get a strong leader in who could deal with those issues, in a way that his predecessors, Jiang Zemin [and] Hu Jintao, had not been able to do it. So they wanted a strong leader. It’s just that, I think even for many communist leaders of that generation, they got more than they bargained for. So that’s where the personality aspect comes in. They got a leader who really wanted to return, at least on some issues, to the Maoist or even the sort of pre-Mao period, in terms of the CCP’s history and emphasizes the party’s position over what even many party leaders back 10 [or] 15 years ago thought would be good for China.

And it’s a classic example of responding to real world problems — not unknown in this country, right? — by going very far in one direction, hoping that that would resolve the problem that is there, and then getting stuck in a way with the kind of leader that you have in this case, in Xi Jinping. So I think that’s the story, the way we can tell it now. I hope at some point to be able to tell that story based on archives and primary documents, as an historian, we can’t do that yet. But I think at some point, we’ll be able to do that, and then it’ll be fascinating to test that hypothesis about how this happened.

Tracy (37:54):

So just on the revolution from below point, one of the things that you emphasize in the book is a lot of the stuff that happens in this time period is a result of people feeling that they are heading somewhere, that there’s a grander Chinese vision that can be achieved. And so that motivates people to actually do something. I’m curious, just going up to the present day, do you get a sense that people feel that? That there’s like a direction that China is heading in that it’s clear to people what they are trying to do?

Odd (38:33):

At the moment, absolutely not. I think it’s very, very clear that a lot of people in China do not understand where the country is heading and what the reasons are. And you know, you don’t spend much time in Beijing before you realize that these days. I think it was very different in the time period that we are talking about, which was generally a time of uplift, at least in economic and and social terms. And it’s right to say, I mean as many historians have said, that there was an element of a bargain in this. That, at least for some Chinese, not not everyone, but for some Chinese, maybe particularly in business, that would accept a dictatorship for what it was and then went on getting rich and and establishing some of these great or middling fortunes that you find so many of in China today. And that is good. I mean that was positive. It was much, much better than the dark past that we described at the beginning of the book.

It was just that, China wasn’t able to take what, in our view, is a necessary step to improve its political system, its overall attempt at trying to become a more open, more pluralistic country in the period when the going was good, when there was a general sense that China was making advances, domestically and internationally. Now, I think even if people from within the Chinese Communist Party after Xi Jinping would try to move in a direction of increased liberalization — which I think they will have to do at some point because people are just very unhappy with the kind of system that is there at the moment — it would be much more difficult, because the going is not that good. And probably it’s never going to be that good again. I mean, it was a remarkable period of economic transformation, 10% per year growth rates. It would’ve been possible to carry out necessary reform. But these people didn’t want to do it because they had become so preoccupied with holding onto power themselves. And I think, historically, that that might turn out to be the biggest mistake that the Chinese Communist Party has made.

2. Tim Cook Wants Apple to Literally Save Your Life – Steven Levy and Tim Cook

Some companies charge for AI-enhanced services. Did you consider that?

We never talked about charging for it. We view it sort of like multitouch, which enabled the smartphone revolution and the modern tablet.

You’ve personally been using Apple Intelligence for a while. What has been most useful for you?

We’re an email-based company, and I get enormous numbers from users, employees, partners, and so forth. Having it summarize author responses is a game changer, and having it prioritize things for you so you’re not doing your usual triage. Then, of course, there are fun things like the Image Playground.

I’ve heard you say that Apple Intelligence could make you funnier, which seems strange.

I think it can make you friendlier, which, in many ways, can be funnier as well.

Having AI speak for people makes me wonder whether the nature of communication will degrade. If Apple Intelligence writes something funny, who’s being funny, the sender or the AI?

It’s still coming from you. It’s your thoughts and your perspective. You and I both remember the productivity that came from the advent of the personal computer. It was no longer you punching your calculator, you were doing something on a spreadsheet. It was no longer you at the typewriter, you were using a word processor. Logic Pro helps musicians create music, but they’re still the author.

One of your demos involves a fictional recent graduate applying for a job. The cover letter is colloquial and somewhat sophomoric, but with Apple Intelligence a single click changes it to look like a savvy, smart person wrote it. If I’m a recruiter who hired that person, maybe I will feel tricked if they don’t live up to the professionalism of that letter.

I don’t think so. By using the tool, it comes across as more polished. It’s still your decision to use the tool. It’s like you and I collaborating on something—one plus one can equal more than two, right?…

…When you’re thinking about things late at night, don’t you sometimes ask what it would mean if computers had superhuman intelligence?

Oh, of course. Not just for Apple, but for the world. There’s so much extraordinary benefit for humanity. Are there some things you have to have guardrails on? Of course. We’re very deeply considerate about things that we do and don’t do. I hope that others are as well. AGI itself is a ways away, at a minimum. We’ll sort out along the way what the guardrails need to be in such an environment…

…Meta and Snap are leading us to mixed-reality glasses that we’d wear continually. Is the bigger, heavier Vision Pro ultimately headed that way?

Yes, it’s a progression over time in terms of what happens with form factors. AR is a huge deal. With Vision Pro, we’ve progressed to what is clearly the most advanced technology we’ve ever done, and I think the most advanced technology in the world in terms of electronics problems. We’ll see where it goes.

Apple has created a lot of consumer tools for medical technology. What’s the strategy for biological metrics and prosthetics?

It’s clear to me that if you zoom out way into the future, and you look back and ask what Apple’s biggest contribution was, it will be in the health area. That’s what I really believe. When we started pulling that string with the Apple Watch, it was a cascade of events. We started with something simple, like monitoring your heart rate, and then figured out we could pick up heart signals to get to an EKG and an AFib determination. Now we are monitoring sleep apnea. I’ve gotten so many notes over time from people who would have not survived had it not been for the alert on their wrist.

Apple plans to give AirPods the ability to correct for hearing loss. I bet the makers of expensive hearing aids are freaking out.

It’s not about competing against hearing aids on the market. It’s about trying to convince people who have hearing loss to use their AirPods. The vast majority of people with hearing issues have not been diagnosed. For some people, hearing aids have a stigma, and we can counter that with AirPods. And we can have people diagnose themselves. It’s the democratization of health…

…We’re doing this interview at Apple Park, which is now seven years old. Have you been surprised by anything that couldn’t have been anticipated when it was just blueprints?

It’s promoted collaboration even more than I thought. That was a key component of the design, but there are so many places here where you just unexpectedly run into people. In the cafeteria, at the coffee bar, outside when you’re going across the pathway. Also, there’s a connection here to Steve that is incredible and very deep. We have the theater named after him and think about him all the time, but I can feel him in other spaces too.

3. 2024: The State of Generative AI in the Enterprise – Tim Tully, Joff Redfern, Derek Xiao, with Claude Sonnet 3.5

AI spending surged to $13.8 billion this year, more than 6x the $2.3 billion spent in 2023—a clear signal that enterprises are shifting from experimentation to execution, embedding AI at the core of their business strategies…

…Today, 60% of enterprise generative AI investments come from innovation budgets, reflecting the early stages of generative AI adoption. However, with 40% of generative AI spending sourced from more permanent budgets—58% of which is redirected from existing allocations—businesses are demonstrating a growing commitment to AI transformation…

…While foundation model investments still dominate enterprise generative AI spend, the application layer is now growing faster, benefiting from coalescing design patterns at the infrastructure level. Companies are creating substantial value by using these tools to optimize workflows across sectors, paving the way for broader innovation…

…In 2024, much of the action happened at the application layer. With many architectural design patterns established, app layer companies are leveraging LLMs’ capabilities across domains to unlock new efficiencies and capabilities. Enterprise buyers are seizing the moment, pouring $4.6 billion into generative AI applications in 2024, an almost 8x increase from the $600 million reported last year…

…Code copilots lead the charge with 51% adoption, making developers AI’s earliest power users…

…Support chatbots have captured significant usage, with 31% enterprise adoption…

…Enterprise search + retrieval and data extraction + transformation (28% and 27%, respectively) reflect a strong drive to unlock and harness the valuable knowledge hidden within data silos scattered across organizations…

…Meeting summarization ranks fifth in use cases (24% adoption), saving time and boosting productivity by automating note-taking and takeaways…

…When selecting generative AI applications, enterprises have clear priorities: Return on investment and industry-specific customization matter most when selecting new tools. Surprisingly, price isn’t a major issue; just 1% of the enterprise leaders we surveyed mentioned price as a selection concern. Buyers are playing the long game: They are far more focused on tools that can deliver measurable value (30%) and that understand the unique context of their work (26%) over those offering the lowest price tag (1%)…

…When AI pilots stutter or stall, it’s often due to challenges not adequately considered during the selection process. Although buyers aren’t checking price tags, implementation costs, cited in 26% of failed pilots, frequently catch them off guard. Data privacy hurdles (21%) and disappointing return on investment (ROI) (18%) also throw pilots off course. Technical issues, especially around hallucinations (15%), round out the top reasons for failure…

…Traditionally slow to adopt tech, healthcare is now leading generative AI adoption with $500 million in enterprise spend…

…Historically resistant to tech, the legal industry ($350 million in enterprise AI spend) is now embracing generative AI to manage massive amounts of unstructured data and automate complex, pattern-based workflows…

…With its complex data, strict regulations, and critical workflows, financial services ($100 million in enterprise AI spend) are primed for AI transformation…

…From Hollywood screens to creators’ smartphones, generative AI is reshaping media and entertainment ($100 million in enterprise AI spend)…

…Foundation models still dominate. The LLM layer commands $6.5 billion of enterprise investment…

…Rather than relying on a single provider, enterprises have adopted a pragmatic, multi-model approach. Our research shows organizations typically deploy three or more foundation models in their AI stacks, routing to different models depending on the use case or results…

…Among closed-source models, OpenAI’s early mover advantage has eroded somewhat, with enterprise market share dropping from 50% to 34%. The primary beneficiary has been Anthropic,* which doubled its enterprise presence from 12% to 24% as some enterprises switched from GPT-4 to Claude 3.5 Sonnet when the new model became state-of-the-art. When moving to a new LLM, organizations most commonly cite security and safety considerations (46%), price (44%), performance (42%), and expanded capabilities (41%) as motivations…

…To power RAG, enterprises must store and access relevant query knowledge efficiently. While traditional databases like Postgres (15%) and MongoDB (14%) remain common, AI-first solutions continue to gain ground. Pinecone,* an AI-native vector database, has already captured 18% of the market.

4. An Interview with Understanding AI Author Timothy B. Lee – Ben Thompson and Timothy B. Lee

As a side note, just as you sort of referenced it in passing, there is always the question of where are the productivity gains, when it came to, first the PC, and then the Internet? Is your sense that those just take a while to show up? Is there just a massive amount of consumer surplus that is not measured? What’s your big picture take on that question?

TL: There’s a couple of things. One is it takes a while to show up because to really get the big gains from a new general purpose technology, often you need to reorganize a lot of other business processes. There’s a famous analogy economists like to use for when they originally electrified the economy. The first thing they try to do is they tried to take the old steam-powered factories that just had one big crank shaft and put an electric motor in and that didn’t get you much improvement because the electricity was not cheap.

It was arguably worse.

TL: But then ten to twenty years later, people figured out, “Oh, we can have a bunch of small electric motors, one at each workstation, and now factories can be a lot more efficient”, but you had to build new factories and new businesses to do that…

…Believe me, I think we’re around the same age, I know exactly what you mean and feel. That said, I feel like the big company — Wikipedia came out back when I was in college, or around that time and of course everyone, professors or teachers, banned the use of it. But what you quickly realized is that the key way to use Wikipedia is the sources. You go to Wikipedia, and then it has links to all the sources, then you have your original source documentation. I do feel like ChatGPT is just such a better version of that, particularly with the search version, and when it does sources, it’s just like, “What if we make a Wikipedia that just fills all sort of weight and space about knowledge”, and it’s pretty tough to beat in that regard.

TL: Yeah, absolutely. And as with Wikipedia, you have to be smart about it. You can’t assume that everything is accurate, you have to check your work. But I definitely find, anytime I have, if I’m trying to make a list of things and I want to know all the companies in a particular category, it’s a pain in the ass to find that on Google. Whereas if you ask ChatGPT, “Here’s like three companies in this category, give me more on the list”, it’ll know a bunch more of them. There’s so many things like that. So yeah, definitely, I don’t want to say never use it or it’s not useful. It’s definitely useful, but it’s 1% to 2% more productive over the course of a week rather than really transformational…

...Again, to go back to your perspective of looking at it over the last 18, 20 months since you started, do you think we’ve hit a wall with AI? You started wondering this publicly actually last December when Gemini came out and you felt a little underwhelmed, particularly given Google’s advantages. You weren’t sure at the time, was Google underperforming for Google specific reasons, maybe have we gotten as far as we can with GPT-4? What’s your evaluation 11 months on from that article?

TL: The thing I’ve noticed is that we keep hearing about there’s going to be a GPT-5—

It’s not here.

TL: There’s going to be a new big model and it hasn’t been released and I don’t have enough sources in the inside to those companies to know why that’s happening. But it could be they’re just still working on it and it’s going to come out next month and blow my mind, but every month that ticks by makes me a little more skeptical. Especially because the other thing trend we’ve seen is these companies are releasing these smaller models that are almost as good as the big models.

And then even to some extent, I was pretty impressed by o1, but what o1 did is kind of different. It wasn’t like scaling up the model, it’s like we’re going to do more inference time compute. In certain ways, it was much better, but it wasn’t better overall.

So my still pretty rough hypothesis, but my hypothesis is that there’s kind of a limit to what the current LLM architectures can do and we’re sort bumping up against that in various — I mean, another thing, we’ve had multimodal models that are much better, so we can do real-time voice and we can do images, so there’s new things it can do. But in terms of just the increase of overall reasoning capability, it doesn’t seem like we’ve had a big jump, really since March of 2023 when GPT-4 came out, and so I’m not going to make a strong prediction because again, it could come out next month and amaze me, but every month that ticks by I get a little bit more wondering what’s going on.

What do you think is the limitation? Is it data, compute or is it just a fundamental limitation of the transformer architecture?

TL: My guess is it’s a fundamental limitation of the transformer architecture, and I think the main issue is that the transformer architecture requires all of the model state to be in these vectors for individual words, and then it keeps a record of that forever — the whole context, there’s no process where you summarize and abstract a way. If you think about your life, you think about something that happened ten years ago, you don’t remember every single thing you said, everything that others said, you have a abstract memory that, “Oh, in 2014 I remember I lived in this place and I had this job”, and things you learn kind work their way into the brain, but it’s organized in a good way. LLMs just don’t have a way to do that.

So if I think about how people expect that at some point you’re going to have an LLM who’s like a personal assistant who maybe will work with you over your career and know all your habits and make all your appointments stuff and to do that, I just think this architecture where you remember every token exactly and do attention over that whole corpus, I don’t have any way of synthesizing and abstracting and forgetting unimportant things, just as a computer scientist, that doesn’t seem viable to me…

…Do you think there’s a bubble now then?

TL: That’s always a hard question to say. Part of what’s hard about bubbles is that often people start calling a bubble pretty early and then the bubble keeps growing and people keep saying there’s a bubble.

Right. If people think there’s a bubble, there is not a bubble, that’s my heuristic.

TL: Well, there’s that, but also, at some point, the stock or the house price or whatever will peak and then go down, and the people who said it was a bubble right at the top will be right, but some people who called it way at the beginning were probably wrong.

I do expect a period where AI gets overly frothy and then crashes. Whether we’re currently there or just headed for that, is a little hard to say. I do not expect a dot-com bust level expansion, because as you were saying, I do think that this technology has clear benefits, it’s mostly big technology companies, it’s not as venture-funded. In fact, some of the early really crazy-funded companies have already been acquired.

So, yeah, I think the level of hype right now is a little too high and there’ll be some pullback, but I don’t think you’ll see a big crash and I don’t think you’ll see much of a pullback from deployment, because I think there really is enough value here that there’s going to be a big market for a lot of people working on it, and a lot of valuable stuff will come out of it in a pretty direct way.

I saw a new theory this week that actually really resonated with me. So this might be new to you, so I’m going to drop it to you on the spot. I think the big question on if you’re thinking about bubbles, you go back to a Carlota Perez model of the importance of bubbles and driving, you go back to the dot-com era, the really important part was the telecoms build out, which was, at the time, some people called it, and in retrospect, clearly insane. If you’re rolling out all this fiber and everyone’s doing it, the costs are going to go to zero, you’re all going to go bankrupt because it’s all financed by debt, as large infrastructure usually is. But the long-term payoff from that was massive, right? That, basically, booted off the whole Web 2.0 era where now everyone, suddenly, had broadband. Recessions suck, but there was a huge societal benefit that did come from that build out.

You go back to previous ones, whether it be electricity or steam, you had these similar cycles and the big question was, “What’s the societal beneficial output of an AI bubble if there is a bubble?” and chips never quite fit, because chips wear out and chips get better. So, if you buy a bunch of chips, but they’re five-year-old chips, what’s the benefit there? Doug O’Laughlin put this tweet out here, that has been really striking to me. He said, “Internet Bubble:Telecom::AI:Power/DCs”, and to me, that makes sense. If you’re going to actually build more nuclear power, or you’re going to do massive investments in solar and batteries, or whatever it might be to fuel these sorts of things, those are investments that, 1) can definitely make you go bankrupt because you’re taking out a bunch of debt to fund it, but 2) will retain value for many, many, many years to come. What do you think of that analogy? To me, it seems pretty compelling.

TL: Yeah, I one hundred percent agree with that. I mean, I was actually going to say the part of it that seems most bubbly is this stuff about Microsoft leasing out Three Mile Island for 20 years. Again, we were talking before is, “Do I think scaling law thing is going to run out of steam?”, my guess is it probably will. I don’t know if we’re on the verge of that, but, anyway, so I would not be surprised if people look back ten years from now, and say, “Oh, man, all that money companies spent on data centers and power is, that was kind of a waste of money”. But then, like you said, the country needs more power, and at some point, probably, we’ll want to be training really big models and so, if we have a bunch of huge data centers that we can use to train models, probably, we’ll get some value out of that. It’s tech companies spending the money so the social cost is not probably that high.

5. 7% of Book Value; 1x EBITDA; Cash is 2.5x Larger than Market Cap – Dirtcheapstocks

Highlands REIT, Inc. (Ticker HHDS) was created in 2016 when it was spun out of InvenTrust Properties Corp.

HHDS was formed to hold non-core assets of InvenTrust.

Today, HHDS owns 13 apartment houses, 3 retail properties, 1 office property and 1 correctional facility…

…HHDS has:

• $205MM of book value.
• $16.7MM of net operating income (NOI) in 2023.
• $17MM of NOI in 2022.
• $85MM of net debt.
• 57% of NOI generated from multifamily assets

What do you think? Is Highlands worth book value? Is it worth half of book value?

If we want to value the business at an 8 cap, the equity must be worth $124MM.

Within the last two weeks, HHDS has been valued as low as $14.4MM.

That’s less than 1x NOI, and 7% of book value…

…Most companies valued at $14MM might have a few hundred shareholders of record. Apple is valued at $3.5 Trillion, and it has 23,000 record holders.

Highlands has 143,000 record holders…

…Here’s my theory: When Highlands was spun out of InvenTrust, every shareholder was given ownership individually. There are 143,000 separate people/entities that own this stock. And this stock was an afterthought. It was just a few noncore assets being spun out of a $2 billion REIT…

…HHDS, perhaps wanting to ward off future material purchases by Mackenzie, announced a tender offer in October 2023. While Mackenzie was tendering at $0.04/share earlier that summer, HHDS was willing to pay $0.12 – $0.17/share. What’s more, HHDS was committing $20MM to the share buyback.

HHDS would repurchase 13-19% of its shares if fully subscribed.

A few weeks later, HHDS increased the buyback to $25MM!

In the end, $23.7MM was spent to buy in 169MM shares – nearly 20% of the outstanding share count…

…HHDS showed up as an expert market security, even though it’s SEC registered.

But I found that the traditional expert market brokers couldn’t buy shares.

Then I went to alternative market brokers. They’d be happy to take my money, and told me I could get as much volume at $0.10 as my heart desired.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Apple, Meta, Microsoft, and MongoDB. 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 01 December 2024:

1. America, China, and the Death of the International Monetary Non-System – Russell Napier

Something changed in America in the 1990s. The U.S. federal funds rate began a decline from above 5 percent to reach the effective zero bound by 2009. U.S. ten-year Treasury yields declined from above 6 percent to levels not even recorded during the Great Depression. Credit to the U.S. nonfinancial corporate sector rose from 56 percent of GDP to a new all-time high of 87 percent, and U.S. Government debt rose from 60 percent of GDP to a recent high of 106 percent, very near the peak level recorded during World War II. The valuation of U.S. equities rose from a cyclically adjusted price-to-earnings ratio (CAPE) of 15x to the current level of 34x, having reached a new all-time high of 44x in 2000. U.S. tangible investment declined from 7 percent of GDP to as low as just 1 percent of GDP, a level only previously recorded in the Great Depression and briefly in the hiatus of investment after World War II…

…Today, we have an international monetary system that does not have a name…

…It is a non-system to the extent that its terms and conditions were never agreed upon by all the parties involved, but instead it was born from choices made by a few, most notably China, that the other parties accepted and adjusted to. The extremes of interest rates, debt levels, asset price valuation, and investment in tangible assets in the United States are just part of that global adjustment to the new international monetary system that grew from China’s unilateral decision to manage its exchange rate beginning in 1994. This system would never have been agreed to in any negotiation, as it was a system replete with distortions that would lead to dangerously large imbalances with dangerous political ramifications…

…The crucial distortion imposed by China’s decision in 1994 was a decoupling of developed world growth rates from interest rates, the discount rates used in asset valuations, which many assumed to be a new normal. When interest rates appear to be permanently depressed relative to growth rates, asset valuations rise, leverage increases, and investors are incentivized to pursue gain through rising asset prices rather than through investment in new productive capacity. The decoupling of growth and interest rates was driven by the People’s Bank of China’s (PBOC) appearance as a non-price-sensitive buyer of U.S. Treasury securities, and indirectly by the role China’s excessive fixed-asset investment played in reducing global inflation and hence interest rates…

…For developed-world companies facing the cheap resources, cheap finance, and cheap exchange rate of China, there was little incentive to invest in tangible assets at home. In the United States, in particular, where companies are managed to maximize return on equity and returns to shareholders, the corporation was able to benefit from both cheap Chinese production and the low interest rates that allowed balance sheets to be levered to buy back equity. In other countries, with different social contracts and less focus on rewarding management via stock options, closing productive capacity and pursuing financial engi­neering were more difficult. Thus, it was U.S. corporations that most fully adapted to the new international monetary system.

When the Bretton Woods system was established, severe restrictions were placed on the free movement of capital. The architects of that system recognized that maintaining exchange rate stability would not be possible if capital were allowed to move freely. Our current system permits, at least into and within the developed world, the free movement of capital. In this system, the private sector capital that left the developed world for China was transformed, via PBOC exchange rate inter­vention, into an accumulation of developed-world debt securities financed by the creation of renminbi reserves…

…. China’s inability to run sufficient surpluses since 2014 to generate sufficient broad money growth and prevent the escalation of its already high debt-to-GDP ratio is not widely recognized as a similar problem. Yet China’s move to a flexible exchange rate to avoid a debt deflation and create sufficient growth in broad money to reduce its debt burden will end the non-system as surely as President Nixon’s announcement that the U.S. dollar was no longer linked to gold ended Bretton Woods. Few analysts understand the impact that this move will have on the international monetary system and the long-accumulating distortions to credit, money, asset prices and the global economy.

When China moves to a flexible exchange rate, it is difficult to foresee how just one new international monetary system could replace the non-system. Given current geopolitical tensions, the prospect of China and the United States hashing out a new Bretton Woods–style agreement is highly unlikely…

…Predicting how any new U.S.-centric monetary system will develop is not easy, but such a system must allow for excessively high debts, the legacy of the non-system, to be inflated away. While much of the focus is on the high U.S. total nonfinancial debt-to-GDP ratio of 255 percent, there are many countries in the world struggling under even higher debt ratios: Canada, 311 percent; France, 315 percent; Japan, 400 percent; Netherlands, 316 percent; Switzerland, 297 percent, etc.15 The rise and rise of debt-to-GDP levels, a product of the gap between interest rates and growth rates under the non-system, will now have to be addressed.

With austerity, default, hyperinflation, or very high real GDP growth unlikely to be the solution, a new global monetary system will have to be created that offers a path of moderation toward reducing debt‑to-GDP levels. That path of moderation is likely to take the form of financial repression—such as that imposed upon savers in the after­math of World War II, to force their savings to fund the investment needed for postwar reconstruction, but at interest rates that did not reward them for the current and expected levels of inflation. That is a world in which bankers will create more credit and more money and more inflation than they have in recent decades. Higher nominal GDP growth combined with imposed purchases of low-yielding debt securi­ties will, over time, reduce debt-to-GDP levels, just as it did in the decades following World War II. Whatever the new international monetary system looks like, it will have to accommodate the financial repression that will finally begin to reduce debt-to-GDP levels…

…In the long period in which developed-world debts will have to be inflated away, policymakers will have to take a view as to which section of society will bear the heaviest cost. One of the quickest and least painful ways to enforce a deleveraging is through encouraging a rapid re‑equitization of the private sector. The ability of all corporations to deduct interest expense in calculating their taxes has to be reconsidered. In an era when much greater fixed-asset investment is essential, the tax privilege of deducting interest expense should not be available to cor­porations using debt to lever up an existing income stream; rather, the tax code should reward corporations using debt to build new businesses and new income streams. There are of course losers from such a change in taxation, but they are those who have been the winners from the prolonged period of falling interest rates and rising asset prices that have been the key feature of our now failing non-system. A long financial repression is in nobody’s interest, and the longer it prevails, the more likely it will create wealth redistributions that threaten social stability. Proactive intervention to force re-equitization upon a small section of society through the withdrawal of a tax privilege is painful for some but is a more equitable path to reducing high debt-to-GDP levels while facilitating greater investment.

To reduce the high and dangerous debt-to-GDP ratios of the developed world, nominal GDP must grow faster than total credit. This can be achieved by increasing the growth rate in bank credit while limiting the growth in nonbank credit. While the non-system was a key driver of the rise and rise of debt-to-GDP, the disintermediation of credit also played a key role. It is commercial bankers who create money, and if nominal GDP growth is to remain at a high enough level to reduce debt-to-GDP levels, bank balance sheets must grow faster than they have over the past three decades. Commercial banks create money when they expand their balance sheets, and if they do not create enough money, nominal GDP growth will remain low while credit growth, spurred by the growth in nonbank credit, can remain high.18 A combination of faster growth in bank credit combined with the re­striction of the growth in nonbank credit will be at the core of reducing debt-to-GDP ratios. The targeted ending of interest deductibility in the computation of corporate income tax, mentioned earlier, can assist in promoting the growth in bank credit and hence money at the expense of growth in nonbank credit. If it is bankers who are at the vanguard of funding the necessary investment renaissance in the United States, and not credit markets, then the move to lower debt-to-GDP levels will be less painful than if we are forced to take the hard path of austerity, default, hyperinflation, or a very long financial repression. A new focus on the growth of bank credit and therefore money is at the core of any policy to reduce dangerously high debt-to-GDP ratios.

2. Are U.S. Stocks Overvalued? – Ben Carlson

The S&P 500 is up nearly 90% since election day 2020 yet valuations are essentially identical.

How can that be?…

…Stock prices are up a lot but fundamentals2 have kept pace. In fact, the stock market has actually gotten less expensive over the past couple of years because of earnings growth…

…It’s also important to point out that much of the valuation premium on the S&P 500 comes from the largest stocks…

…These stocks have high valuations for good reason — they’re some of the best-run corporations in the world…

…The good news for valuation-conscious investors is there is plenty of value outside of the mega-cap stocks. Valuations for small and mid cap stocks are still pretty cheap. They are far less expensive now than they were before the pandemic. Maybe there’s a reason for that but stocks don’t get cheap for no reason.

3. Amazon’s Moonshot Plan to Rival Nvidia in AI Chips – Matt Day, Ian King, and Dina Bass

Nvidia’s biggest customers — cloud providers like Amazon Web Services, Microsoft Corp.’s Azure and Alphabet Inc.’s Google Cloud Platform — are eager to reduce their reliance on, if not replace, Nvidia chips. All three are cooking up their own silicon, but Amazon, the largest seller of rented computing power, has deployed the most chips to date…

…Fifteen years ago, the company invented the cloud computing business and then, over time, started building the infrastructure that sustains it. Reducing its reliance on one incumbent after another, including Intel Corp., Amazon ripped out many of the servers and network switches in its data centers and replaced them with custom-built hardware. Then, a decade ago, James Hamilton, a senior vice president and distinguished engineer with an uncanny sense of timing, talked Jeff Bezos into making chips…

…After almost four decades in the business, Hamilton knows taking Amazon’s chip ambitions to the next level won’t be easy. Designing reliable AI hardware is hard. Maybe even harder is writing software capable of making the chips useful to a wide range of customers. Nvidia gear can smoothly handle just about any artificial intelligence task. The company is shipping its next-generation chips to customers, including Amazon, and has started to talk up the products that will succeed them a year from now. Industry observers say Amazon isn’t likely to dislodge Nvidia anytime soon…

… The unit’s first chip was designed to power something called inference — when computers trained to recognize patterns in data make a prediction, such as whether a piece of email is spam. That component, called Inferentia, rolled out to Amazon’s data centers by December 2019, and was later used to help the Alexa voice assistant answer commands. Amazon’s second AI chip, Trainium1, was aimed at companies looking to train machine learning models. Engineers also repackaged the chip with components that made it a better fit for inference, as Inferentia2.

Demand for Amazon’s AI chips was slow at first, meaning customers could get access to them immediately rather than waiting weeks for big batches of Nvidia hardware. Japanese firms looking to quickly join the generative AI revolution took advantage of the situation. Electronics maker Ricoh Co., for example, got help converting large language models trained on English-language data to Japanese.

Demand has since picked up, according to Gadi Hutt, an early Annapurna employee who works with companies using Amazon chips. “I don’t have any excess capacity of Trainium sitting around waiting for customers,” he says. “It’s all being used.”

Trainium2 is the company’s third generation of artificial intelligence chip. By industry reckoning, this is a make-or-break moment. Either the third attempt sells in sufficient volume to make the investment worthwhile, or it flops and the company finds a new path. “I have literally never seen a product deviate from the three-generation rule,” says Naveen Rao, a chip industry veteran who oversees AI work at Databricks Inc., a purveyor of data and analytics software.

Databricks in October agreed to use Trainium as part of a broad agreement with AWS. At the moment, the company’s AI tools primarily run on Nvidia. The plan is to displace some of that work with Trainium, which Amazon has said can offer 30% better performance for the price, according to Rao. “It comes down to sheer economics and availability,” Rao says. “That’s where the battleground is.”…

…Amazon’s Trainium2 will likely be deemed a success if it can take on more of the company’s internal AI work, along with the occasional project from big AWS customers. That would help free up Amazon’s precious supply of high-end Nvidia chips for specialized AI outfits. For Trainium2 to become an unqualified hit, engineers will have to get the software right — no small feat. Nvidia derives much of its strength from the comprehensiveness of its suite of tools, which let customers get machine-learning projects online with little customization. Amazon’s software, called Neuron SDK, is in its infancy by comparison.

Even if companies can port their projects to Amazon without much trouble, checking that the switch-over didn’t break anything can eat up hundreds of hours of engineers’ time, according to an Amazon and chip industry veteran, who requested anonymity to speak freely. An executive at an AWS partner that helps customers with AI projects, who also requested anonymity, says that while Amazon had succeeded in making its general-purpose Graviton chips easy to use, prospective users of the AI hardware still face added complexity.

“There’s a reason Nvidia dominates,” says Chirag Dekate, a vice president at Gartner Inc. who tracks artificial intelligence technologies. “You don’t have to worry about those details.”…

…  “We’re particularly impressed by the price-performance of Amazon Trainium chips,” says Tom Brown, Anthropic’s chief compute officer. “We’ve been steadily expanding their use across an increasingly wide range of workloads.”

Hamilton says Anthropic is helping Amazon improve quickly. But he’s clear-eyed about the challenges, saying it’s “mandatory” to create great software that makes it easy for customers to use AWS chips.

4. Key Square Capital 2024 January letter – Scott Bessent and the Key Square team

In essence, a second Trump administration would be expected to embrace a “Peace Through Strength” trade policy. Of course, in the case of recalcitrant trade partners, Trump can always offer them a negotiating session with former US Trade Representative Robert Lighthizer who will likely play a prominent role in his second term.

Our base case is that a re-elected Donald Trump will want to create an economic lollapalooza and engineer what he will likely call “the greatest four years in American history.” Economist Ed Yardeni believes that post-Covid America has the potential to have a boom similar to the “Roaring Twenties” of a century ago. We believe that a returning President Trump would like this to be his legacy. In this scenario, the greatest risk factor, in our opinion, would be a sudden rise in long-end rates.

The talk of revenge will likely be limited to a small group of political enemies, and the wider policies of the administration will be oriented toward de-regulation, energy independence, reviving U.S. manufacturing and extending the tax cuts. We find it unlikely that across-the-board tariffs, as currently reported by the media, would be enacted at the same time as he moves to fix the immigration crisis. The tariff gun will always be loaded and on the table but rarely discharged. Of course, strategic and national security issues around China will remain.

Another differentiated view that we have is that Trump will pursue a weak dollar policy rather than implementing tariffs. Tariffs are inflationary and would strengthen the dollar–hardly a good starting point for a US industrial renaissance. Weakening the dollar early in his second administration would make U.S manufacturing competitive. A weak dollar and plentiful, cheap energy could power a boom. The current Wall Street consensus is for a strong dollar based on the tariffs. We strongly disagree. A strong dollar should emerge by the end of his term if the US reshoring effort is successful.

5. Scott Bessent Sees a Coming ‘Global Economic Reordering.’ He Wants to Be Part of It – Peter Rudegeair and Gregory Zuckerman

In his first interview following his selection, Bessent said his policy priority will be to deliver on Trump’s various tax-cut pledges. Those include making his first-term cuts permanent, and eliminating taxes on tips, social-security benefits and overtime pay…

…Bessent became one of Trump’s closest advisers by adding depth to his economic proposals and defending his plans for more-activist trade policies. He has argued that the president-elect’s plans to extend tax cuts and deregulate parts of the U.S. economy would create an “economic lollapalooza.”…

…Bessent has long been worried about the U.S.’s heavy debt and thinks the main way it can be reduced is by boosting growth, which increases tax revenues.

He has advised Trump to pursue a policy he calls 3-3-3, inspired by former Japanese Prime Minister Shinzo Abe, who revitalized the Japanese economy in the 2010s with his “three-arrow” economic policy. Bessent’s “three arrows” include cutting the budget deficit to 3% of gross domestic product by 2028, spurring GDP growth of 3% through deregulation and producing an additional 3 million barrels of oil or its equivalent a day.

To get government spending under control, Bessent has advocated extending the 2017 Tax Cuts and Jobs Act but with what are called pay-fors to lower its cost. That would involve either reducing spending or increasing revenue elsewhere to offset the impact. He also proposed freezing nondefense discretionary spending and overhauling the subsidies for electric vehicles and other parts of the Inflation Reduction Act.

Earlier this year, Bessent thought about tariffs as a negotiating tool, telling investors in a letter that the “tariff gun will always be loaded and on the table but rarely discharged.” He has since argued for them more forcefully, especially as a source of tax revenue.

In a speech last month titled “Make the International Economic System Great Again,” Bessent argued for increasing tariffs on national-security grounds and for inducing other countries to lower trade barriers with the U.S.  

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, Amazon, and Microsoft. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 24 November 2024:

1. Cash! – The Brooklyn Investor

Over the past few years, people have kept talking about mean reversion to value and whatnot, but I have ignored that for the most part for the reasons I’ve been saying here. The growth / value spread just seems to me so much reflecting values being taken away from the old economy into the new one. Yes, sounds like 1999 bubble, but it just seems true. Retail just seems to be going down the drain, old school marketing / advertising just seems to be losing to online marketing etc…

…The massive transfer of wealth has been going on for decades, or more than a century. Industrialization just sucked the wealth and value out of skilled workers / craftsman and transferred it to large corporations via factories. Formerly skilled workers were transferred into factories that required no skill (therefore, lower income). All the value-added accrued to the owners of the factories (capitalists). Same with national chain restaurants and retail. WMT transferred wealth from the local shops / restaurants to Arkansas; former store-owners end up having to work at WMT for lower pay (as unskilled workers). This is nothing new.

Now, the same thing is happening at so many levels at the same time that it is quite frightening. Just as a simple example, I’ve mentioned this before, but companies like Squarespace and Wix (or free options like WordPress) have sort of wiped out a large part of the web development world. People who knew a little HTML / CSS / Javascript might have been able to make a living not too long ago, but not now. All that ‘wealth’ is transfered to the companies that provide the platform for people to build it themselves.

Photographers are complaining for similar reasons. You no longer need to hire a photographer for low-end projects. You can just buy photos from various photos sites for very low prices, or even have AI generate the exact photo you need. I have used AI to generate artwork, photos and text in various volunteer work, and it is scary. I thought to myself, jeez, I would have paid an art student $300 for this 3 years ago; now I do it for free online via AI…

…This is why when people say the stock market as a percentage of GDP is going up, the concentration of stocks in the market is getting too high etc., I think it is obvious that this is happening because the wealth and value is actually being more and more focused and concentrated, so the market is only reflecting reality…

…A similar group of very rich and smart people are saying that long term rates can’t stay low and they must move substantially higher due to these unsustainably large and growing federal deficits. Do I worry about that? Yes. But, I look to Japan as the model of an aging society and growing government deficits. Sure, there are plenty of differences (Japan is a high savings nation), but I still can’t get around the fact that slowing population growth and maturity of the U.S. economy would make growth harder to achieve going forward. Almost certainly, we can’t get back to the growth of the post-war baby boom generation. So given that, how do interest rates go up? Deficit-driven inflation? We haven’t really seen that in Japan, and even in the U.S. until Covid and Ukraine. So is the recent inflation really deficit-driven inflation? Or exogenous event-driven inflation? Maybe a combination of both.

This is not to say I don’t care about deficits. Of course it’s a problem, and we need to deal with it at some point. My opinion is just seeing things as an investor. I am just telling you why, as an investor, I am not yet concerned too much with the deficit and inflation.

2. Off The Beaten Path Investing – David Katunarić and Lawrence J. Goldstein

Goldstein: I started at Burnham when they had about 22 senior analysts following every industry in America, or so they thought. One day, after discovering the pink sheets, or actually, I found the pink sheets afterwards. I saw a list of trucking companies. It was in the Standard & Poor’s transportation manual, which came out weekly, supplements to put in the looseleaf book. I got a list of every trucking company in the United States and there must have been well over 50, maybe more, and every one of them had lower earnings or losses, except for four companies. Those four were Roadway Express, Denver Chicago Trucking, Merchant Fast Motor Lines and Overnite, spelled N-I-T-E. I called them first, and I ended up making a friend of J. Howard Cochran, the founder and president. At the beginning, he sent me a copy of his monthly financial statement. There were no rules against doing that. I remember they were printed in purple ink on a ditto machine. His first report he sent me was the five months ended May. He had earned in those five months, per share, I remember $1.86. He told me also that in the trucking business, the second half of the year is better than the first half. I said, “Let’s see, five months $1.86, times 2 is over $3.60, and I’m missing a month and the second half is better, so it’s got to be higher than that.” The stock was $1.75 or $1.25 off it. I couldn’t believe it. So I wrote a report, gave it to my boss, head of research.

He said to me, and I can hear it to this day, “Listen, kids, this is an institutional research department. We don’t write or recommend reports on dollar-stocks.” So I knew I was onto something. My boss was crazy. It ended up, by the way, they earned almost $4 a share that year. I got to laugh, it’s funny – I could buy the first share at $1.75, and I did. A number of years later, I think two decades later, or less than, Overnite sold out to, I think it was the Southern Pacific Railway, they sold out for $100 million. This thing was worth $500,000 when I met them. So the pink sheets made sense to look there. Basically, what I came to do was to look left when everybody’s looking right, look down when everybody’s looking up, and find companies that are off the beaten path, overlooked or ignored by otherwise intelligent investors…

…Katunaric: What would you say, Larry, in these 40-some years that you’re managing Santa Monica Partners, how has your investing approach changed since then? What are some lessons that sparked the change?

Goldstein: It’s not changed at all, except that you don’t write to the SEC and ask for the 10-Ks and Qs and the proxy and have it take two weeks if you get it. Now you hit a keyboard and you get it all. That’s changed. The second thing is now there are people like you. There are a lot of people – I don’t mean you personally – who are on top of what’s called microcaps. So everybody’s searching for the goal. Obviously you’ve developed a business and you want to develop a bigger business. But that’s what happened. Competition that didn’t exist. When I did it, there was one firm that got big, Tweedy Browne. You know them? What happened to them was terrible. They got so big they had to buy ordinary stocks…

…Goldstein: When I bought Mastercard, it was not a huge company. When they went public, if I remember right, it was $39, $38, $37. I can’t remember the exact price, and it’s since split 10-for-1. So my cost is, I guess, $3 and change. I forget the exact split. I have to look it up. Let’s say it’s $10, $15 – but I think my cost is less than $15.

Katunaric: I saw somewhere that it was a hundred-bagger since the IPO. Maybe I read it last year. I think it was one of the best performing ones, but I’m not sure also.

Goldstein: I’ll focus on that for a second. The reason I bought it, was in 1971, I went to my boss, Tubby Burnham, and I said, “There’s a business that’s going public, Madison Avenue.” Madison Avenue is where all the advertising agencies were in New York, every one of them. The company that was going public, it was the second company to go, ad company. The first one was a company called Puppet, Koning, and Lois. They had been public for some period of time and the stock did okay. The second one was Batten, Barton, Durstein, and Osborne, which subsequently changed their name to BBD&O, which subsequently changed their name, and it’s the same company to Omnicom, which is the world’s first and second largest advertising agency. Why did I want to buy it? I said to my boss, “Advertising companies are required if you have a consumer product to sell. It’s a royalty company. They get a royalty on every new consumer product that’s marketed to the world.” That’s what I think it was. If you’re going to sell a new widget, you want to advertise it. They get a cut of that. So, a great business. I said, “That’s exactly what Mastercard is.” Everything that anybody buys, they get a cut. By the way, there’s no risk to their business. They don’t make loans. Banks make loans. They get a cut. Banks have risk, but Mastercard, it’s like every time you turn on the water, you get a free glass…

…I tell you, the biggest recommendation to me, and the biggest thing I don’t believe or understand is, Warren Buffett, he has never bought it, except for himself when he was a kid. He bought Oxy. I don’t know that much about Occidental, but there’s nothing better than TPL if you want to be in the oil business. They just own the stuff and you can take it out at your cost and pay them not only for that, but the right to get to the well and leave the well and for the water for fracking. If you run a hose or a pipeline, pay them. What better business is there than that? None.

Katunaric: I agree. You pitched me TPL extensively yesterday and the asset light nature of the business was really attractive.

3. Here’s How Trump Could Lose the Coming Trade War – Paul Krugman

All indications are that China’s era of torrid economic growth is behind it. For decades, Chinese growth was fueled mainly by two things: a rising working-age population and rapid productivity growth driven by borrowed technology. But the working-age population peaked around a decade ago and is now falling. And despite some impressive achievements, the overall rate of technological progress in China, which economists measure by looking at “total factor productivity,” appears to have slowed to a crawl…

…China, however, has built an economic system designed for the high-growth era — a system that suppresses consumer spending and encourages very high rates of investment.

This system was workable as long as supercharged economic growth created the need for ever more factories, office buildings and so on, so that high investment could find productive uses. But while an economy growing at, say, 9 percent a year can productively invest 40 percent of G.D.P., an economy growing at 3 percent can’t.

The answer seems obvious: redistribute income to households and reorient the economy away from investment toward consumption. But for whatever reason, China’s government seems unwilling to move in that direction…

…So what do you do if you have lots of capacity but your consumers can’t or won’t buy what you make? You try to export the problem, keeping the economy humming by running huge trade surpluses…

…China appears to be exporting close to $1 trillion more than it imports, and the trend is upward.

Hence the coming trade war. The rest of the world won’t passively accept Chinese surpluses on that scale…

…That’s why the Biden administration has been quietly pursuing a quite hard line on China, retaining Trump’s tariffs and trying to limit its progress in advanced technologies. It’s why the European Union has imposed high tariffs on electric vehicles made in China, which is probably only the beginning of expanded trade conflict…

…Trump’s insistence that tariffs don’t hurt consumers — even as businesses across America are planning to raise prices when his planned tariffs hit — strongly suggests that neither he nor anyone he listens to understands how global trade works. Not a good thing at a time of trade conflict.

4. Is the United States Going Broke? – Ben Carlson

There seem to be two extreme views when it comes to government debt levels.

One is the view that government debt doesn’t really matter all that much since we have the global reserve currency and the ability to print as much of that currency as we’d like.

The other view is that government debt levels are reaching a tipping point that will lead to calamity…

…It is true that U.S. government debt is enormous…

…Total government debt in the United States was around $23 trillion heading into the pandemic so debt levels are up 50% or so this decade alone.

It’s also true that the interest we pay on government debt has risen considerably because we’ve taken on so much and interest rates are so much higher than they were in the 2010s…

…But you can’t look at debt levels on their own. You have to think of them through the lens of a $30 trillion U.S. economy.

Here is interest expense as a percentage of GDP:..

…It’s shot up considerably in recent years but it’s still below 1990s levels. The Fed cutting interest rates should help on the margins…

…Spending was 45% of GDP during the pandemic. That was obviously unsustainable but things are now back to normal…

…The thing you have to understand is the United States government does not operate like a household when it comes to debt. You pay your mortgage off over time and eventually retire that debt.

The government’s budget is not at all like a household budget. First of all, the government can print its own currency. That helps in a pinch and it’s the main reason our government can’t go broke. Inflation is the true constraint when it comes to politicians spending money.

As long as the economy is growing, debt should be growing too…

…I would be more worried if you told me government and consumer debt were down in the coming decades. That would mean something is seriously wrong with the economy.

Debt grows because assets grow (remember government debt is an asset in the form of bonds for investors). Debt grows because the economy grows. Income grows. Prices grow. So of course debt will rise. 

5. Wall Street’s Elites Are Piling Into a Massive AI Gamble – Neil Callanan, Gillian Tan, Tasos Vossos, Carmen Arroyo, and Immanual John Milton

While much of the speculative hype around AI has played out in the stock market so far, as seen in chipmaker Nvidia Corp.’s share price, the giddiness is spreading to the sober suits of debt finance and private equity.

Analysis by Bloomberg News estimates at least $1 trillion of spending is needed for the data centers, electricity supplies and communications networks that will power the attempt to deliver on AI’s promise to transform everything from medicine to customer service. Others reckon the total cost could be double that…

…Further proof of the “unsatiable demand” for computing horsepower, according to real-estate broker Jones Lang LaSalle Inc., is the more than sevenfold increase over two years in construction work on US co-location centers, which lease out rack space to tech firms. Asking rents in those facilities have jumped as much as 37% in 12 months, the firm estimated in an August report.

All of this unbridled spending is revving up the issuance of both investment-grade debt and riskier leveraged loans, especially in the US, handily for private lenders and fee-starved investment bankers alike. Hedge funds are looking as well to profit from AI hysteria with novel types of debt structures.

It’s also opened up a new corner of the asset-backed securities market, where sales of debt backed by data centers have already jumped to a near-record $7.1 billion this year, according to data compiled by Bloomberg News. Chuck in fiber networks and other bits of kit, and it’ll be much higher. Matt Bissonette, who heads Guggenheim Securities’ business in this area, says the number of buyers for his data-center ABS products has roughly doubled in four years…

…While Blackstone hasn’t risked that kind of capital on construction before, developers of data centers can make stellar returns if all goes well. Property researcher Green Street reckons profit margins on London sites are about 65%.

Financiers are eager to back these grand projects because future occupants have usually pre-signed long leases, making them safer bets. Some banks are offering to lend as much as 70% or 80% of the cost and occasionally more when a lease is already signed, according to a person with knowledge of the matter…

…Lenders are more twitchy, however, about data centers explicitly earmarked for AI rather than more general purposes, according to a banker who works in the sector. Such deals can carry costlier debt and less leverage, he says, because the technology still has to prove its worth.

Separately, a senior partner at a leading private equity firm says he’s troubled by the emergence of speculative development, meaning construction takes place before a tenant has been found, as it’s hard to be sure of final demand. Some lawyers talk of “zombie projects” that may never be finished.

And not everyone believes that the “if you build it, they will come” approach is a surefire winner for those gambling on an era-changing AI breakthrough. Massachusetts Institute of Technology professor Daron Acemoglu says a lot of capital will be wasted.

Despite the misgivings, the appetite for deals from bankers and private lenders — especially for sites with blue-chip, signed-up occupants — is giving most data-center owners and developers a strong hand when pricing debt. A site leased long term by a tech giant can snag bank funding at a margin below two percentage points, says Brookland’s Hussain. Co-locators typically pay 2.5 percentage points or less, he adds.

“Recently, we raised €850 million ($907 million) in nine-year bonds at below 4% and refinanced and upsized our revolving credit facilities to $4.5 billion,” says Jordan Sadler, senior vice president at Digital Realty Trust Inc., a tech property firm that has signed joint ventures with Blackstone and others for almost $9 billion of hyperscale data-center developments…

…Across the Atlantic, one utility told the Federal Reserve Bank of Atlanta that electricity usage by data centers rose 17% in recent months. In Virginia, host to the world’s highest concentration of these sites, records for peak power demand were set six times in July, according to Dominion Energy Inc.

Trying to satisfy energy-devouring data centers means the utility sector’s capital spending is set to exceed $200 billion by next year, about double what it was a decade earlier. That would have stressed utility balance sheets, but a recent easing of how Moody’s Ratings views some of the industry’s riskier hybrid bonds — letting them be treated as half equity — has opened the floodgates to companies raising capital without being downgraded.

Sales of these bonds have risen almost eightfold this year to $15 billion, data compiled by Bloomberg shows. Only issues by bulge-bracket banks match that.

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