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

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

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

Here are the articles for the week ending 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.

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

Here are the articles for the week ending 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.

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 Wix. 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 17 November 2024:

1. OpenAI Shifts Strategy as Rate of ‘GPT’ AI Improvements Slows – Stephanie Palazzolo, Erin Woo and Amir Efrati

In May, OpenAI CEO Sam Altman told staff he expected Orion, which the startup’s researchers were training, would likely be significantly better than the last flagship model, released a year earlier.

Though OpenAI had only completed 20% of the training process for Orion, it was already on par with GPT-4 in terms of intelligence and abilities to fulfill tasks and answer questions, Altman said, according to a person who heard the comment.

While Orion’s performance ended up exceeding that of prior models, the increase in quality was far smaller compared with the jump between GPT-3 and GPT-4, the last two flagship models the company released, according to some OpenAI employees who have used or tested Orion.

Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks, according to the employees. Orion performs better at language tasks but may not outperform previous models at tasks such as coding, according to an OpenAI employee. That could be a problem, as Orion may be more expensive for OpenAI to run in its data centers compared to other models it has recently released, one of those people said.

The Orion situation could test a core assumption of the AI field, known as scaling laws: that LLMs would continue to improve at the same pace as long as they had more data to learn from and additional computing power to facilitate that training process…

…However, OpenAI researcher Noam Brown said at the TEDAI conference last month that more-advanced models could become financially unfeasible to develop.

“After all, are we really going to train models that cost hundreds of billions of dollars or trillions of dollars?” Brown said. “At some point, the scaling paradigm breaks down.”…

…Orion was trained in part on AI-generated data, produced by other OpenAI models, including GPT-4 and recently released reasoning models, according to an OpenAI employee. However, such synthetic data, as it is known, is leading to a new problem in which Orion may end up resembling those older models in certain aspects, the employee said.

2. Prejudice And China – Louis-Vincent Gave

This led us to the comments made in September by Ford chief executive officer Jim Farley. Freshly returned from a visit to China, Farley told The Wall Street Journal that the growth of the Chinese auto sector poses an existential threat to his company, and that “executing to a Chinese standard is now going to be the most important priority.”

By any measure, this is an earth-shattering statement.

Making cars is complicated. Not as complicated as making airliners or nuclear power plants. But making cars is still the hallmark of an advanced industrial economy. So, the idea that China is suddenly setting the standards that others must now strive to meet is a sea-change compared with the world we lived in just five years ago…

…This brings me to the simplest, most obvious, and likeliest explanation why most CEOs and investors missed how China leapfrogged the West in industry after industry over the last five years: during that time, no one from the West bothered to visit China…

…Unlike Japan in the 1990s, China has not seen its banking system go bust and lose its ability to fund new projects. On the contrary, the surge in loans to industry over the past few years lies at the heart of China’s booming industrial productivity…

…This is another key difference between China today and Japan in the 1990s. China today is not only more efficient and more productive than a decade ago, it is probably more efficient and more productive than most other major industrial economies. And it boasts a very attractive cost structure. Until a few years ago, you would need to check your bank balance before going out for dinner in Tokyo. Today, you can stay in the Four Seasons in Beijing or Shanghai for less than US$250 a night. Perhaps the best illustration of how Japan’s past is a very poor guide to China’s present is the difference in their trade balances; a reflection of how different their competitiveness has been…

…This is not to understate the magnitude of the Chinese property bust. The rollover in real estate has been a massive drag on growth and on animal spirits over the past five years. But on this front, there is another key difference between China and Japan: in China, the contraction of real estate was the policy. It was not the unfortunate consequence of policies gone-wrong. Reallocating capital away from real estate and towards industry was a stated goal of the government…

…There seem to be at least three separate visions of China.

The first is the China you read about in much of the Western media: a place of despond and despair. It is permanently on the cusp of social disorder and revolution, or it would be, were it not an Orwellian nightmare of state surveillance, supervision and repression that strangles creativity and stifles progress. This is the place that Westerners who have never visited China typically imagine, because it is the place portrayed by the media…

…The second is the vision of China you get from talking to Chinese millennials in tier-one cities. This version of China recalls the “lost decades” of Japanese deflationary depression…

…This brings me to the third vision of China: that it is only just beginning to leapfrog the West in a whole range of industries. This vision is starting to show up itself in the perception of Western brands in China, and their sales. For example, Apple’s iPhones no longer figure in the five best-selling smartphone models in China. And Audi’s new electric cars made and sold in China will no longer carry the company’s iconic four-circle logo; the branding is now perceived to be more of a hindrance than a benefit.

To put it another way, following years of investment in transport infrastructure, education, industrial robots, the electricity grid and other areas, the Chinese economy today is a coiled spring. So far, the productivity gains engendered by these investments have manifested themselves in record trade surpluses and capital flight—into Sydney and Vancouver real estate, and Singapore and Hong Kong private banking.

This has mostly been because money earners’ confidence in their government has been low. From bursting the real estate bubble, through cracking down on big tech and private education, to the long Covid lockdowns, in recent years the Chinese government has done little to foster trust among China’s wealthy. It’s small surprise, then, that many rich Chinese have lost faith in their government’s ability to deliver a stable and predictable business environment.

This brings me to the recent stimulus announcements and the all-important question whether the measures rolled out will prove sufficient to revitalize domestic confidence in a meaningful way. Will it even be possible to lift confidence as long as the Damocles’ sword of a wider trade conflict with the US and yet more sanctions looms over the head of Chinese businesses?

From this perspective, perhaps the most bullish development for China would be for the new US administration (regardless who sits behind the Resolute desk) to come in and look to repair the damage done to relations by the 2018 semiconductor sanctions and the 2021 Anchorage meeting…

…When it comes to China’s relevance to investors, there are four ways of looking at things.

• China can be uninvestible and unimportant. This is the pool that most investors have been swimming in for the last few years. But this is akin to saying that China is like Africa. It simply doesn’t pass the smell test. Instead of sliding into irrelevance, China’s impact on the global economy only continues to grow.
• China can be uninvestible but important. This is essentially what Jim Farley, fresh back from his China trip, told The Wall Street Journal.
• China can be investible but unimportant. This is the space Japan inhabited for a couple of decades, and into which Europe seems to be gently sliding. However, the idea that China today is where Japan has been for the last three decades is grossly misplaced on many fronts, including the competitiveness of its economy, its overall cost structure, and its weight in global indexes.
• China can be investible and important. This is what David Tepper of Appaloosa Management argued on CNBC following the announcement of China’s stimulus (see Changing Narratives Around The World). For now, this is still a minority view, at least among Western investors. Not that Western investors matter all that much. What truly matters is whether Chinese investors themselves start rallying to this view. If they do, the unfolding bull markets in Chinese equities and the renminbi could really have legs.

3. $2 H100s: How the GPU Rental Bubble Burst – Eugene Cheah

ChatGPT was launched in November 2022, built on the A100 series. The H100s arrived in March 2023. The pitch to investors and founders was simple: Compared to A100s, the new H100s were 3x more powerful, but only 2x the sticker price.

If you were faster to ramp up on H100s, you too, can build a bigger, better model, and maybe even leapfrog OpenAI to Artificial General Intelligence – If you have the capital to match their wallet!

With this desire, $10-100’s billions of dollars were invested into GPU-rich AI startups to build this next revolution. Which lead to ….

The sudden surge in H100 demand

Market prices shot through the roof, the original rental rates of H100 started at approximately $4.70 an hour but were going for over $8. For all the desperate founders rushing to train their models to convince their investors for their next $100 million round…

…For most of 2023, the H100 prices felt like they would forever be above $4.70 (unless you were willing to do a huge upfront downpayment)

At the start of 2024, the H100 prices reached approximately $2.85 across multiple providers…

…In Aug 2024, if you’re willing to auction for a small slice of H100 time (days to weeks), you can start finding H100 GPUs for $1 to $2 an hour.

We are looking at a >= 40% price drop per year, especially for small clusters. NVIDIA’s marketing projection of $4 per GPU hour across 4 years, has evaporated away in under 1.5 years.

And that is horrifying because it means someone out there is potentially left holding the bag – especially so if they just bought it as a new GPUs…

…The average H100 SXM GPU in a data center costs $50k or more to set up, maintain, and operate (aka most of the CAPEX). Excluding electricity and cooling OPEX cost…

…If the price falls below $1.65/hour, you are doomed to make losses on the H100 over the 5 years, as an infra provider. Especially, if you just bought the nodes and cluster this year…

…Many infrastructure providers, especially the older ones – were not naive about this – Because they had been burnt firsthand by GPU massive rental price drops, after a major price pump, from the crypto days – they had seen this cycle before.

So for this cycle, last year, they pushed heavily for a 3-5 year upfront commitment and/or payment at the $4+ price range. (typically with 50% to 100% upfront). Today, they push the $2.85+ price range – locking in their profits…

…When a model creator is done training a model, you have no more use for the cluster. What would they do? – they resell and start recouping some of the costs…

…This ended up creating a triple whammy in reducing the demand for H100s!

1. Finetuning is significantly cheaper than training from scratch.

a. Because the demands for fine-tuning are significantly less in compute requirements (typically 4 nodes or less, usually a single node), compared to training from scratch (from 16 nodes, usually more, for 7B and up models).

b. This industry-wide switch essentially killed a large part of smaller cluster demands.

2. Scaling back on foundation model investment (at small/mid-tier)

a. In 2023, there was a huge wave of small and medium foundation models, within the text and image space.

b. Today, however, unless you are absolutely confident you can surpass llama3, or you are bringing something new to the table (eg. new architecture, 100x lower inference, 100+ languages, etc), there are ~no more foundation model cos being founded from scratch.

c. In general, the small & medium, open models created by the bigger players (Facebook, etc), make it hard for smaller players to justify training foundation models – unless they have a strong differentiator to do so (tech or data) – or have plans to scale to larger models.

d. And this has been reflected lately with investors as well, as there has been a sharp decline in new foundation model creators’ funding. With the vast majority of smaller groups having switched over to finetuning. (this sentiment is combined with the recent less than desired exits for multiple companies).

e. Presently today, there is approximately worldwide by my estimate:

<20 Large model creator teams (aka 70B++, may create small models as well)

<30 Small / Medium model creator teams (7B – 70B)

f. Collectively there are less than <50 teams worldwide who would be in the market for 16 nodes of H100s (or much more), at any point in time, to do foundation model training.

g. There are more than 50 clusters of H100 worldwide with more than 16 nodes.

3. Excess capacity from reserved nodes is coming online

a. For the cluster owners, especially the various foundation model startups and VCs, who made long reservations, in the initial “land grab” of the year 2023.

b. With the switch to finetuning, and the very long wait times of the H100’s
(it peaked at >= 6 months), it is very well possible that many of these groups had already made the upfront payment before they made the change, essentially making their prepaid hardware “obsolete on arrival”.

c. Alternatively, those who had the hardware arrive on time, to train their first few models, had come to the same realization it would be better to fine-tune their next iteration of models. Instead of building on their own.

d. In both cases, they would have unused capacity, which comes online via “Compute Resellers” joining the market supply…. 

…Both AMD and Intel may be late into the game with their MX300, and Gaudi 3 respectively.

This has been tested and verified by us, having used these systems. They are generally:

• Cheaper than a H100 in purchase cost
• Have more memory and compute than a H100, and outperforms on a single node.
• Overall, they are great hardware!

The catch? They have minor driver issues in training and are entirely unproven in large multi-node cluster training.

Which as we covered is largely irrelevant to the current landscape. To anyone but <50 teams. The market for H100 has been moving towards inference and single or small cluster fine-tuning.

All of which these GPUs have been proven to work at. For the use cases, the vast majority of the market is asking for.

These 2 competitors are full drop-in replacements. With working off-the-shelf inference code (eg. VLLM) or finetuning code for most common model architectures (primarily LLaMA3, followed by others)…

…Given that the open-weights model has entered the GPT-4 class arena. Falling H100 prices will be the multiplier unlock for open-weights AI adoption.

It will be more affordable, for hobbyists, AI developers, and engineers, to run, fine-tune, and tinker with these open models.

Especially if there is no major leap for GPT5++, because it will mean that the gap between open-weights and closed-source models will blur.

This is strongly needed, as the market is currently not sustainable. As there lacks the value capture on the application layer for paying users (which trickles down the platform, models, and infra layers)

In a way, if everyone is building shovels (including us), and applications with paying users are not being built (and collecting revenue and value).

But when AI inference and fine-tuning becomes cheaper than ever.

It can potentially kick off the AI application wave. If it has not already slowly started so.

4. Politics, Portfolios & Perspective: Investing in a Crazy Election Year – Alliance Wealth Advisors

How we feel about the economy is directly correlated to if the party we most closely identify with is in power or not. This is regardless of what the economic data actually tells us. In other words, our emotions get the best of us and cloud our ability to stay objective…

…In the past two presidential elections, there were many “expert” predictions claiming that electing both Donald Trump and Joe Biden would cause a significant stock market correction. Yet, both presided over stock market highs at various times. Anyone who made changes to their portfolio based on those election outcomes suffered a serious opportunity cost that will impact them for a long time…

…Politics aside, the stock market is a complex adaptive system, influenced by countless variables interacting with one another in constantly evolving ways. Companies are dynamic and run by smart people who learn to adapt to new environments. History has shown that companies can react to all kinds of changes and have always been able to grow their earnings over time. When they do stock prices tend to follow.

5. Writes and Write-Nots – Paul Graham

I’m usually reluctant to make predictions about technology, but I feel fairly confident about this one: in a couple decades there won’t be many people who can write…

…The reason so many people have trouble writing is that it’s fundamentally difficult. To write well you have to think clearly, and thinking clearly is hard…

…Till recently there was no convenient escape valve for the pressure created by these opposing forces. You could pay someone to write for you, like JFK, or plagiarize, like MLK, but if you couldn’t buy or steal words, you had to write them yourself. And as a result nearly everyone who was expected to write had to learn how.

Not anymore. AI has blown this world open. Almost all pressure to write has dissipated. You can have AI do it for you, both in school and at work.

The result will be a world divided into writes and write-nots…

…Is that so bad? Isn’t it common for skills to disappear when technology makes them obsolete? There aren’t many blacksmiths left, and it doesn’t seem to be a problem.

Yes, it’s bad. The reason is something I mentioned earlier: writing is thinking. In fact there’s a kind of thinking that can only be done by writing.

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 Apple. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 10 November 2024:

1. Why I’m Leaving OpenAI and What I’m Doing Next – Miles Brundage

So how are OpenAI and the world doing on AGI readiness?

In short, neither OpenAI nor any other frontier lab is ready, and the world is also not ready.

To be clear, I don’t think this is a controversial statement among OpenAI’s leadership, and notably, that’s a different question from whether the company and the world are on track to be ready at the relevant time (though I think the gaps remaining are substantial enough that I’ll be working on AI policy for the rest of my career).

Whether the company and the world are on track for AGI readiness is a complex function of how safety and security culture play out over time (for which recent additions to the board are steps in the right direction), how regulation affects organizational incentives, how various facts about AI capabilities and the difficulty of safety play out, and various other factors.

As a sidenote, I think that AGI is an overloaded phrase that implies more of a binary way of thinking than actually makes sense. One of the things my team has been working on lately is fleshing out the “levels of AI” framework referenced here. I hope that OpenAI and I will be able to publish a related paper before long. But for now I’d just note that when I say “ready for AGI,” I am using this as shorthand for something like “readiness to safely, securely, and beneficially develop, deploy, and govern increasingly capable AI systems.”…

…I think the upsides of AI are already big and could be dramatically bigger, as are the downsides. As someone who has worked in this field for longer than most, it has been very sad to see increasing polarization along the lines of whether people focus on one side of the cost/benefit ledger or the other, or have different risk priorities, etc. My view is that there is a lot to worry about and a lot to be excited about, we don’t have to choose one thing to care about, and we should find common ground where it exists.

I think AI and AGI benefiting all of humanity is not automatic and requires deliberate choices to be made by decision-makers in governments, non-profits, civil society, and industry, and this needs to be informed by robust public discussion. Notably, this is true not just for risk mitigation but also for ensuring equitable distribution of the benefits, as is the case with, e.g., electricity and modern medicine as well. This is true for a few reasons, including, non-exhaustively, collective action problems, various unpriced negative externalities, and unequal starting positions of digital infrastructure access, wealth, etc. that affect who benefits and is harmed by default and to what degrees. As with railroads, electricity, etc., corporate and government policies will be critical to ensuring safe and fair outcomes.

I think AI capabilities are improving very quickly and policymakers need to act more urgently…

..I think quantitative evaluations of AI capabilities and extrapolations thereof, in combination with analysis of the impacts of certain policies, will be critical in truthfully and persuasively demonstrating that urgency. There’s great work happening on measuring frontier models from a safety perspective, measuring trends over time in AI, and a growing body of work assessing the labor market implications of AI, but more is definitely needed.

I think we don’t have all the AI policy ideas we need, and many of the ideas floating around are bad or too vague to be confidently judged. This is particularly true of international competition over AI, where I find the existing proposals to be especially bad (e.g. “race against [competing country] as quickly as possible”) and vague (e.g. “CERN for AI”), although it’s encouraging to see a growing trend towards more nuanced discussion of some of these ideas. There are also many aspects of frontier AI safety and security that will require creative solutions…

…I think that improving frontier AI safety and security is quite urgent, given the number of companies (dozens) that will soon (next few years at most) have systems capable of posing catastrophic risks. Given that that is not much time to set up entirely new institutions, I’m particularly interested in opportunities for action under existing legal authorities, as well as shaping the implementation of already-approved legislation such as the EU AI Act.

As noted above, and explained in more detail in this paper and similar work, companies and governments will not necessarily give AI safety and security the attention it deserves by default (this is not a comment specifically about OpenAI, as discussed above). There are many reasons for this, one of which is a misalignment between private and societal interests, which regulation can help reduce. There are also difficulties around credible commitments to and verification of safety levels, which further incentivize corner-cutting: people assume others are going to cut corners to gain an advantage and can’t tell what the ground truth is, or think they will change their minds later. Corner-cutting occurs across a range of areas, including prevention of harmfully biased and hallucinated outputs as well as investment in preventing the catastrophic risks on the horizon. There are, to be clear, some ways in which commercial incentives encourage safety, though I think it would be irresponsible to assume that those incentives will be sufficient, particularly for ambiguous, novel, diffuse, and/or low-probability/high-magnitude safety risks.

I’m excited about understanding how companies can credibly demonstrate safety while protecting valuable and potentially misusable IP. The difficulty of demonstrating compliance without compromising sensitive information is a major barrier to arms control agreements, which requires innovation to address. This issue is also at the core of effective domestic regulation. I’m excited to collaborate with people working on this and other related technical AI governance questions.

While some think that the right approach to the global AI situation is for democratic countries to race against autocratic countries, I think that having and fostering such a zero-sum mentality increases the likelihood of corner-cutting on safety and security, an attack on Taiwan (given its central role in the AI chip supply chain), and other very bad outcomes. I would like to see academics, companies, civil society, and policymakers work collaboratively to find a way to ensure that Western AI development is not seen as a threat to other countries’ safety or regime stability, so that we can work across borders to solve the very thorny safety and security challenges ahead.

Even if, as I think is very likely, Western countries continue to substantially outcompete China on AI, there is more than enough “gas in the tank” of computing hardware and algorithmic progress in autocratic countries for them to build very sophisticated capabilities, so cooperation will be essential. I realize many people think this sounds naive but I think those people haven’t thought through the situation fully or considered how frequently international cooperation (enabled by foresight, dialogue, and innovation) has been essential to managing catastrophic risks…

…I think it’s likely that in the coming years (not decades), AI could enable sufficient economic growth that an early retirement at a high standard of living is easily achievable (assuming appropriate policies to ensure fair distribution of that bounty). Before that, there will likely be a period in which it is easier to automate tasks that can be done remotely. In the near-term, I worry a lot about AI disrupting opportunities for people who desperately want work, but I think it’s simultaneously true that humanity should eventually remove the obligation to work for a living and that doing so is one of the strongest arguments for building AI and AGI in the first place. Likely some will continue to work in the long-term but the incentive to do so might be weaker than before (whether this is true depends on a variety of cultural and policy factors). That is not something we’re prepared for politically, culturally, or otherwise, and needs to be part of the policy conversation. A naive shift towards a post-work world risks civilizational stagnation (see: WALL-E), and much more thought and debate about this is needed…

…Compared to software, data, and talent, computing hardware has unique properties that make it an important focal point for AI policy: “it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain” (quoted from this paper I worked on). This makes it worrying that the part of the US government responsible for overseeing what happens when that compute is shipped overseas is severely understaffed and underfunded, and that more generally there is little serious policy discussion of what the endgame is here (besides occasionally tightening export controls and requiring companies to report their big datacenters and training runs).

To the extent that there is serious analysis of compute governance happening in the academic literature, it generally lags behind developments in industry by a fair amount – e.g., to those within frontier AI companies, it has become increasingly clear in recent years that scaling up inference, not just training, can enable higher performance, but public analysis of the policy implications of this has only begun in earnest relatively recently. Ideas for distributing computing power (and the associated benefits of AI) more widely, such as via the government providing greater compute for academics, are generally too little too late and neglect issues specific to the developing world, which is in a quite different situation.

2. Industry Is Not Destiny – Greg Obenshain

We’d go as far as to argue that industry analysis generally is much less valuable than fundamental investors or strategy consultants might hope.

Mauboussin’s new study, Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation, grapples with this issue. Mauboussin’s study includes a chart that is difficult to unsee once you’ve seen it (h/t Edward Conard’s Macro Roundup for highlighting this)…

…This chart shows that profitability varies more within industry (the vertical bars) than across industries (the dots). Over the long run, the fate of a company is not primarily determined by its industry—a finding consistent with Chicago school research from the 1980s that dealt a death blow to structure-conduct-performance theory in antitrust law.

Mauboussin notes that while industry analysis matters when it comes to deciding where to compete, ultimately the right unit of analysis is not the industry level but the company level…

…Industries with higher overall profitability have more companies that are profitable, but even within industries with low profitability, there are still companies that have returns well above the cost of capital and some companies that have profitability substantially above.

Industry is not destiny. Great companies can emerge from mediocre industries.

3. Watch Out: Wall Street Is Finding New Ways to Slice and Dice Loans – Matt Wirz

Goldman Sachs GS 2.14%increase; green up pointing triangle this month sold $475 million of public asset-backed securitization, or ABS, bonds backed by loans the bank makes to fund managers that tide them over until cash from investors comes in. The first-of-its-kind deal is a lucrative byproduct of the New York bank’s push into loans to investment firms, such as these so-called capital-call lines.

Goldman’s new deal reflects two trends transforming financial markets. Increasingly large managers of private-debt and private-equity funds are moving up in the Wall Street pecking order, but they often need money fast. Banks, once again, are reinventing themselves to adapt…

…The transactions are relatively small for now. Still, they are intertwining banks (in Wall Street parlance, the sell side) with investors (the buy side) in ways that are new and difficult to parse for analysts, regulators and others…

…Capital-call loans function like credit cards for private-fund managers. The funds borrow money to invest quickly in private debt, private equity, real estate and infrastructure. They then “call up” cash commitments from clients in the funds, mostly institutions such as pensions and insurers, and repay the loans when the clients deliver.

Defaults on capital-call commitments from large institutions “have been historically close to 0%,” according to a marketing document for Goldman’s bond viewed by The Wall Street Journal. That makes the bonds extremely safe, said debt fund managers to whom Goldman offered the deal.

Even so, the shiny new products that banks are inventing have yet to be tested through market cycles…

…As Goldman and other banks make more capital-call loans to private-fund managers, they are also buying insurance from many of the same investment firms to protect against potential losses from corporate, consumer and real-estate loans. The so-called synthetic risk transfers, or SRTs, help banks reduce risk to meet new regulatory requirements and give fund managers investments to put into their wildly popular private-credit funds.

Some private-credit funds are developing another product that is similar to capital-call lines called net-asset-value, or NAV loans, made to private-equity fund managers. Rising interest rates have made it harder for private-equity funds to sell companies they own to repay their limited partners. NAV loans help them to start returning cash to clients until they can dispose of the companies. Many of the firms that manage private-equity funds also manage private-credit funds…

…The International Monetary Fund published a report in April warning that “interconnections and potential contagion risks many large financial institutions face from exposures to the asset class are poorly understood and highly opaque.”

4. Big Banks Cook Up New Way to Unload Risk – Matt Wirz

U.S. banks have found a new way to unload risk as they scramble to adapt to tighter regulations and rising interest rates…

…These so-called synthetic risk transfers are expensive for banks but less costly than taking the full capital charges on the underlying assets. They are lucrative for the investors, who can typically get returns of around 15% or more, according to the people familiar with the transactions.

U.S. banks mostly stayed out of the market until this autumn, when they issued a record quantity as a way to ease their mounting regulatory burden…

…In most of these risk transfers, investors pay cash for credit-linked notes or credit derivatives issued by the banks. The notes and derivatives amount to roughly 10% of the loan portfolios being de-risked. Investors collect interest in exchange for shouldering losses if borrowers of up to about 10% of the pooled loans default…

…The deals function somewhat like an insurance policy, with the banks paying interest instead of premiums. By lowering potential loss exposure, the transfers reduce the amount of capital banks are required to hold against their loans.

Banks globally will likely transfer risk tied to about $200 billion of loans this year, up from about $160 billion in 2022, according to a Wall Street Journal analysis of estimates by ArrowMark Partners, a Denver-based firm that invests in risk transfers…

…Banks started using synthetic risk transfers about 20 years ago, but they were rarely used in the U.S. after the 2008-09 financial crisis. Complex credit transactions became harder to get past U.S. bank regulators, in part because similar instruments called credit-default swaps amplified contagion when Lehman Brothers failed.

Regulators in Europe and Canada set clear guidelines for the use of synthetic risk transfers after the crisis. They also set higher capital charges in rules known as Basel III, prompting European and Canadian banks to start using synthetic risk transfers regularly.

U.S. regulations have been more conservative. Around 2020, the Federal Reserve declined requests for capital relief from U.S. banks that wanted to use a type of synthetic risk transfer commonly used in Europe. The Fed determined they didn’t meet the letter of its rules…

…The pressure began to ease this year when the Fed signaled a new stance. The regulator said it would review requests to approve the type of risk transfer on a case-by-case basis but stopped short of adopting the European approach.

5. Xi Stimulus Clues Found in Protest Data Showing Economic Stress – Rebecca Choong Wilkins

From a basement in Calgary, often accompanied by his pet cat, Lu Yuyu spends 10 hours a day scouring the internet to compile stats on social instability before they are scrubbed by China’s censors. The 47-year-old exile won’t reveal his exact method because it risks jeopardizing the overall goal of the project called “Yesterday,” which documents cases of group protests.

“These records provide an important basis for people to understand the truth of this period of history,” said Lu, who started the effort in January 2023 but didn’t make it public until he arrived in Canada a year ago. “I didn’t want to go to jail again,” he explained.

While Lu’s interests are political, his database — available for free — is among a growing number of metrics tracking dissent in China that investors are watching to figure out when Xi will open up the spigots to bolster growth. And some banks are now starting to develop similar products.

Morgan Stanley in September debuted a new gauge of distress that could be used to predict policy swings in China. Robin Xing, the bank’s chief China economist, says it’s nearing the low levels reached two other times in the past decade: in 2015, when Beijing took drastic steps to arrest a $7 trillion stock market rout, and in 2022 — the point at which the Communist Party abruptly dropped its strict Covid controls after simultaneous street protests in major cities…

…While China’s opaque political system makes it difficult to attribute policy moves to any single factor, investors and analysts who track instances of unrest say authorities may be especially sensitive to them when deciding on whether to roll out stimulus and how much to deploy. Economic protests have become more frequent in recent years as China’s youth unemployment rate soared and its housing crisis worsened…

…Getting a read on what’s happening on the ground is a challenge for academic researchers and finance professionals alike. Widespread censorship, heavy surveillance and suppression of dissent have made it hard to assess the depth of economic malaise in the country of 1.4 billion people…

…The rising prominence of dissent metrics is part of a blossoming industry of so-called alternative data aimed at decoding the state of the world’s second-biggest economy…

…Life has become tougher for many in recent years as pandemic lockdowns, a real estate crisis and trade tensions have slowed growth in China.

Incomes are still rising, but gains under Xi have been the weakest since the late 1980s. Faith in the country’s meritocracy also appears to be waning, leaving white-collar workers feeling increasingly disillusioned. Companies mired in fierce price wars are laying off employers, while college graduates are struggling to find work.

China Dissent Monitor’s data shows that cases of dissent rose 18% in the second quarter compared to same period last year, with the majority of events linked to financial issues.

“If you look at everything regarding social well-being — be it wage growth, urban unemployment rate, consumer confidence and even tracking labor incidents — I think it’s deteriorating,” Morgan Stanley’s Xing said.

Although protests aren’t particularly rare in China, they’re typically small scale, uncoordinated with other places and lacking in overt criticism of Beijing. Still, political criticism can bubble up, usually in cases linked to rural land actions where the local governments find themselves the target of discontent, according to China Dissent Monitor research…

…Even so, there are few signs that the unrest is coalescing around a particular instance of perceived injustice or a single issue. Unlike the Tiananmen Square protests and unrest in the late 1980s, current dissent doesn’t present an existential threat to the regime. A more likely response is therefore a dose of economic medicine that will keep the market guessing.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 27 October 2024:

1. China’s Fiscal Policy Update – Leonid Mironov

Ministry of Finance top brass spoke at a press briefing, and outlined the extent of the Fiscal policy support they can offer *within the confines of the current budget*. However, while the steps laid out suggest a cautious and structured approach, notable gaps in specific figures leave room for market speculation…

…China is set to enhance its strategy for managing local government debt, which remains a critical issue. The central government will issue large-scale debt swaps, a move aimed at addressing the opaque “hidden debt” local authorities have accumulated off the books. Local governments still hold 2.3 trillion yuan in available funds, providing some breathing room to manage obligations in the final quarter of 2024. These steps aim to steady the debt situation, though the path forward will undoubtedly be closely watched…

…There is commentary out there to say that this is not new spending, I would counter with that yes, its not new per se, but its spending that would go in to this gap (authorised/unspent) but won’t anymore. So this is stimulative…

…With property markets showing persistent weakness, local governments now have the authority to deploy funds from special bonds to purchase unsold homes. These homes will be converted into subsidized housing—a dual-purpose measure to both alleviate property inventory and address housing affordability. It signals a nuanced, albeit gradual, approach to propping up the beleaguered real estate sector.

This is likely where most of that 2.3trn RMB mentioned in (1) will go. Again since the the property market is such a significant drag on the economy, this is reasonable…

…In line with recent People’s Bank of China (PBOC) directives, four major state-owned banks announced forthcoming cuts to existing mortgage rates. These rate reductions, effective from October 25, are part of broader efforts to ease financial pressures on households and further stimulate economic activity. Again given the sheer amount of total mortgages outstanding (38 trn RMB at the end of ‘23, see chart), this is significant. PBOC expects an effective cut of about 50pbs on average…

…Perhaps the most telling aspect of the press conference was what remained unsaid. There were no specifics on the magnitude of additional fiscal stimulus or further bond issuances. Additionally, there was no precise indication of how much the fiscal deficit might increase—a critical piece of information many market participants were hoping for…

…The Ministry of Finance’s approach at this juncture reflects a cautious yet deliberate strategy. While existing resources are being leveraged, and flexibility is maintained, major new initiatives have not yet been unveiled. All eyes now turn to the late October NPC meeting, where the prospect of more significant fiscal interventions could reshape the economic landscape for the year ahead.

2. The Bitter Lesson – Rich Sutton

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore’s law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation…

…We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.

The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.

3. Austan Goolsbee Explains the Fed’s Big Rate Cut – Tracy Alloway, Joe Weisenthal, and Austan Goolsbee

Joe (12:30):

You mentioned lags. I want to ask you a question about that. When the Fed started jacking up rates aggressively, one of the theories for why it didn’t have a sharper impact on the economy is that so many households and corporations had in, say, 2020, first half of 2021, termed out their debt and so there was not a lot of sensitivity to debt.

The flip side of that now — and people have been writing about this — is that even though the Fed has now commenced a cutting cycle, that the weighted average cost of debt is probably going to rise in 2025 basically just mathematically, right? Because eventually that’ll have to be refi-ed at higher rates and so forth. How do you think about that dynamic now when you’re thinking about these lags? You’re starting a cutting cycle, but at the same time probably cost of debt is actually going to rise for a fair number of economic actors in this economy.

Austan (13:19):

You have remarked on this subject and thought it through. In my world that goes into the economic conditions and there are many things that have made this a hairy, strange time for central banks because the business cycle, both down and up, looked almost nothing like historical precedents. This is one aspect of that.

We’ve analyzed this specifically thinking about mortgages. Okay, so if I had told you, the premise of your question, I a hundred percent agree with, six years ago, if you said ‘The Fed is going to raise 500 basis points in a single year, what is going to happen?’ I think most all economists would say ‘Yikes, there’s going to be a major, major contraction and it’s going to be concentrated. Autos down the tubes. Consumer durables, bye-bye. Business fixed investment construction, all going to collapse because they’re very interest rate sensitive.’

We didn’t really see the economy go into the steepness of collapse that you would’ve expected. And so that brings us back to this question. It’s kind of a twofold. Is there something about this unusual business cycle that makes economic activity less sensitive to the interest rate? Or is there something strange about this moment that the lag effect is longer. And it can be both and they can run together, but in the case of mortgages, one of the things that has made monetary policy transmission less direct, is the fact that a vastly higher share of mortgages are 30-year fixed mortgages now, than they were in 2005, 2009, whenever you want to look at.

And so when they change the interest rate — in some countries virtually all mortgages are adjustable rate mortgages. So when their central bank raises rates, they bring out parents onto TV, ‘The central bank is killing us. You know, our mortgage payment went up.’

In the US, if everybody’s on a 30-year fixed, in a way that’s just a delay, but it’s a 30-year delay. So I do think that notion that there are companies that don’t have a lot of debt so they aren’t as especially sensitive to the interest rate, that the term structure of their debt may be such that the average rates they’re paying might even be higher as the Fed cuts. I think that’s not a problem, that’s just a fact and we just need to understand it and see what the magnitude is…

…Tracy (16:29):

Yeah, but my question is going to be ultra simplistic. Can you explain to us in excruciating detail what exactly you expect happens in the economy now, as you cut interest rates? How does that cut get transmitted?

Austan (16:45):

Oof. Okay, as a general matter, the Fed has only one tool really, which is a screwdriver that can tighten or can loosen and I always say if your problem is, you know, a loose fender, that’s great. If your problem is can you make breakfast? No, you kind of can’t do that with a screwdriver.

So the main channels of monetary policy impact on the economy, I think are on the real economy side and they are on interest rate sensitive parts of the economy — like consumer durables, business fixed, investment construction, things like that.

Now there are other channels of monetary transmission where there’s a lot of argument. How important are they and they are, well if you change the value of assets, like the value of housing, the value of stocks, etc., is there a wealth effect so that consumer spending might go up as the asset values go up. Or if you contract and asset values go down, would that limit spending?

There’s a dollar channel that if rates in the US are moving relative to how rates are moving in other places, can affect the currency and that could affect imports and exports.

Those are probably a lot of the main channels and it’s always in the counterfactual. What would be happening if we didn’t do this? So to the extent that there’s already a debt structure or to the extent that we went through a business cycle that for the first time ever was not driven by cyclical industries, but was driven by services because nobody could spend money on that, and services aren’t especially interest rates sensitive, that’s another reason why you might think the monetary transmission mechanism, which is actually a whole bunch of different transmission mechanisms, just looks different this time than before.

Now everything that looks different is not bad. Okay, in a way this is frustrating that monetary policy doesn’t have the same impact, but at the same time in 2023 we hit what I called the golden path. Inflation came down almost as much as it ever came down in a single year, and there was no recession. And that never happened before. And so the unusualness of this thing, sometimes it’s good!…

…Austan (21:27):

Yes, does not necessarily. I agree with [that]. So let me finish two thoughts. One, did the Fed have anything to do with it? That’s kind of the question. If it was all supply shocks, then the Fed didn’t really, yes, the Fed can’t be blamed for the inflation going up, but then the Fed shouldn’t take credit for it coming down.

There is some component that as supply shocks heal, you get immaculate disinflation. I do think that the fundamentally different thing that happened this time than the last time we were getting supply shocks, like at the end of the 70s, is that the market expectations of inflation basically never went up. In the 70s, as actual inflation went up, the expectations went up. And part of what made the Volcker experience so hard is you didn’t have to just slay the inflation dragon. You had to go convince everyone that we will hold this thing underwater for as long as it takes until it surrenders and that’s a brutal process.

I do think that expectations stayed — even as actual inflation was almost double digits — stayed exactly at PCE 2% as the inflation target said, was fundamentally the Fed making a promise it may look bad but we’re going to get it back, and that the market de facto believed it. And that is to the Fed, is about Fed credibility, and I do think it made a big difference…

…Tracy (38:43):

That was perfect. Can I ask one more serious question before we wind it down? But you talked about restrictiveness earlier in this conversation and I get where that comes from and people look at things like real yields and stuff.

But if you look at stock market prices, we’re recording this on October 9th, I think stock indices are at records again. If you look at credit spreads, those are at multi-year lows. Where’s the restrictiveness? Because I don’t see it in parts of the financial market, let me put it that way.

Austan (39:14):

I’d say two things. I told you, my focus is primarily on the real side of the economy. I think those are the biggest, most impactful parts of the monetary policy transmission mechanism, historically.

So I’m less of a fan of interpreting financial conditions indices as a measure of monetary restrictiveness or what monetary policy should do because, in my view, it’s got a major reflection problem that, let’s say the market, which is forward-looking, decides they think it’s going to work, that there will be a soft landing, that rates are going to come down because inflation has been tamed and is at 2%. Then equity markets go up, long rates would come down and that would then be interpreted as a loosening of financial conditions and it would be like ‘Oh, you better stop cutting, you better raise.’ But that’s just self-referential. So I think that’s a little problematic.

And the inverted yield curve, for two years, which everybody has been saying is an indication that there’s about to be a recession, that’s not normal. If we go back to a regularly-shaped yield curve like we’re in more normal conditions, that’s not the end of the world.

My view of restrictiveness is we set the Fed funds rate, we set it high and held it there for more than a year and as inflation came down, the real Fed Funds rate just kept going up, passive tightening. That’s the highest the real Fed Funds rate had been in decades. And so to me that’s where the restrictiveness is.

4. Investing lessons from a mini-Berkshire Hathaway – Chin Hui Leong

Gayner believes mistakes of omission are far more costly than mistakes of commission.

He shared a personal example of passing on investing in Berkshire Hathaway (A shares) in 1984 when he first discovered the company.

At the start of 1984, shares were trading at around US$1,300. By the time he got around to buying some shares, the stock price had risen to US$5,750. Hence, he missed out on a gain of over 340 per cent.

I’ll add a second lesson to his point.

Shares of Berkshire Hathaway (A shares) closed at nearly US$694,000 per share last Friday. In other words, even though Gayner did not invest earlier, his shares are worth about 120 times more than what he paid.

While his returns could have been over 530 times if he invested earlier, I don’t think anyone would lose a smile with a 120-fold return.

So, here’s my take: if you find a great company with a promising future, it may not be too late to invest, even if the stock has already appreciated…

…When selecting stocks to invest, Gayner looks for four key factors.

The first is about finding a profitable business with minimal to no debt and a good return on capital. The reason is clear; starting with this pool of stocks increases your chances of finding a winner.

Secondly, he wants to have a talented management team with integrity.

Gayner may have taken a leaf out of Buffett’s playbook here. As Buffett once said, without integrity, the other positive management qualities, will work against you.

Interestingly, Gayner also connected the use of debt with management’s character.

For him, debt is a character marker.

In a podcast recorded earlier this year, Gayner recalled the advice of Shelby Davis, another legendary investor and mentor. Davis pointed out that in the absence of knowledge about a new business, the use of debt can be telltale sign.

Simply said, if a business is entirely equity-financed, the management team will have no incentive to steal from their own funds.

To be sure, this does not mean that a debt-laden company is fraudulent.

However, Gayner argued that leverage creates conditions for a dishonest management team to exploit since the money does not belong to them.

5. A Message From the Past (Thoughts on Nostalgia) – Morgan Housel

I was recently asked at a conference how investors should feel about the stock market given that it’s basically gone straight up over the last 15 years.

My first thought was: you’re right. If you started investing 15 years ago and checked your account for the first time, you would gasp. You’ve made a fortune.

Then I thought, wait a minute. Straight up for the last 15 years? To echo my wife: What are you talking about?

Are we going to pretend like the 22% crash in the summer of 2011 never happened?

Are we supposed to forget that stocks plunged more than 20% in 2016, and again in 2018?

Are we – hello? – now pretending that the worst economic calamity since the Great Depression didn’t happen in 2020?

That Europe’s banking system nearly collapsed?

That wages were stagnant?

That America’s national debt was downgraded?

Are we now forgetting that at virtually every moment of the last 15 years, smart people argued that the market was overvalued, recession was near, hyperinflation was around the corner, the country was bankrupt, the numbers were manipulated, the dollar was worthless, on and on?

I think we forget these things because we now know how the story ends: the stock market went up a lot. If you held on tight, none of those past events mattered. So it’s easy to discount – even ignore – how they felt at the time. You think back and say, “That was so easy, money was free, the market went straight up.” Even if few people actually felt that way during the last 15 years.

So much of what matters in investing – this is true for a lot of things in life – is how you manage the psychology of uncertainty. The problem with looking back with hindsight is that nothing is uncertain. You think no one had anything to worry about, because most of what they were worrying about eventually came to pass.

“You should have been happy and calm, given where things ended up,” you say to your past self. But your past self had no idea where things would end up. Uncertainty dictates nearly everything in the current moment, but looking back we pretend it never existed.

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 Markel (Tom Gayner is the CEO of Markel). 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 20 October 2024:

1. Actual Reform has materialised – Leonid Mironov

The Ministry of Justice and the NDRC have put out the draft of the Law on Private companies, or to give it’s full name, People’s Republic of China Private Economy Promotion Law. And it’s really good one…

…It emphasizes innovation, technological advancement, and participation in strategic industries, while also providing improved legal protections and equal treatment to address longstanding concerns. In return, private businesses are expected to follow Party leadership, contribute to national development, and operate in compliance with laws and regulations…

…The takeaway is that the private enterprises are now not discriminated against in the key project deployment. They will have similar cost of capital to the SOEs and will be able to supply most major national projects.

Chinese SOEs have been told to get more competitive earlier in the year, now the playing field is being somewhat levelled. The government, to my mind, is taking onboard the idea that employment is employment, whether SOE or not, and if a private enterprise can provide it, its fine.

I honestly think that this is the most consequential announcement, as it’s an example of a long-term reform that the government has committed to, and it is carrying out. This gives us hope for land and hukou reforms, as well as pension reform eventually. But also, this is a sign that there no decision to increase direct state participation in the economy but rather, assuming that companies follow guidance form the CCP, the more efficient actors, whether private or SOE, will drive the new policies.

2. Becoming Berkshire: 1969 – Illinois National Bank – The Weekend Investor

Around this time, Buffett and Munger sought a bank to purchase and found a candidate in Rockford, Illinois.

On April 3, 1969, Berskhire Hathaway, Inc. acquired 81,989 shares, out of a total of 100,000 shares outstanding, of the common stock of the Illinois National Bank and Trust Co. of Rockford, Illinois, at a cash price of $190.00 per share. They also have made a tender offer to acquire the remaining outstanding shares at the same cash price.

Buffett considered Rockford Bank one of the most well-run and profitable he had ever seen. It was managed by Eugene Abegg, who was 71 years old.

Abegg, who owned one-quarter of the shares, had been negotiating to sell the business to someone else before Buffett came along. The potential buyer had started criticizing the deal and wanted an audit. This affected Abegg, and he decided not to go ahead with the deal. Meanwhile, Buffett worked out what he was willing to pay, which turned out to be about $1 million less than the other buyers.

Abegg was so fed up with the other bidders that he pressured his fellow shareholders to accept Buffett’s offer, threatening to resign if they did not.

The crusty Abegg was just the type of fellow that Buffett liked.

In 1931, Eugene Abegg, a young man with only $250,000 of capital, formed a bank in Rockford, Illinois… It had $400,000 of deposits. Since then, no new capital had been added to the bank by its owners. Nevertheless, by 1969, Abegg had built, piece by piece, a bank with a net worth of $17m and $100m of deposits.

He carried thousands of dollars of cash in his pocket and cashed checks for people on the weekends. He carried a list of the number of unrented safe deposit boxes with him everywhere and would try to rent you a safe deposit box at a cocktail party. Mind you, this is the biggest bank in the second-largest city in Illinois at that time…

…The Illinois National Bank, which Buffett soon came to refer to by its colloquial name of Rockford Bank, had been chartered in the days before the U.S. Treasury assumed the exclusive right to coin money. Buffett was fascinated to discover that it still issued its own currency. The ten-dollar bills featured Abegg’s picture. Buffett, whose net worth was now more than $26 million, could have bought almost anything he wanted, but not this. Gene Abegg had done him one better. He and the United States Treasury had the privilege of issuing their own currency, but not the Buffett Partnership or Berkshire Hathaway. The idea of legal tender with your own picture on it captivated him. He began carrying a Rockford bill in his wallet…

…Berkshire paid $190 per share to acquire Illinois National, plus $2 per share to an investment bank for services rendered in the transaction…

…With total Assets of $117.3 million, shareholder equity of $16.8 million, and a net profit of $1.7 million in 1968, the bank had an ROE of 10% and ROA of 1.4%.

3. Jigar Shah on the Nuclear Power Revival in the US – Tracy Alloway, Joe Weisenthal, and Jigar Shah

Joe (04:30):

Thank you so much. So I’m going to start off with this question, which is: Okay, we went for a long time basically without building new nuclear power plants. It’s starting to pick up again. How much is it because something has changed policy-wise with subsidies and tax credits, et cetera, versus demand is back, therefore the economics of nuclear makes sense? Or would you say it’s not binary?

Jigar (04:53):

Well, look, I think that when you think about what happened through a historical context in the 1970s, we had high inflation and nuclear power was subject to high inflation. And so part of this is people were already worried about building new nuclear plants before the incident occurred because things were just getting more expensive. And when you think about the utility bankruptcies that occurred way back when, it was because they had cost overruns on nuclear power. And so I think that in general, it goes to when America stopped believing in itself and its ability to do big things and infrastructure. And I think this moment, with load growth and with the president saying we are going to build big things here, has gotten people thinking again, “Hey, what would it take to actually figure this out this time around?”

Tracy (05:48):

This is actually exactly what I wanted to ask you about because I was reading that the initial construction cost for Unit One of Three Mile was about $400 million. And I guess today the cost of building a nuclear plant would be like $5 billion, $10 billion. Obviously the $400 million isn’t adjusted for 1960s prices, but it does seem in general like it’s more expensive to build nuclear plants, certainly since the 1960s. Where did that additional cost increase actually come from?

Jigar (06:23):

So when you think about building things… like if you were to build multifamily housing and you would build one multifamily housing building versus building 12 throughout the city. You can imagine if you’re using the same design, it would be cheaper. The workers would get better. The first one would cost more, the second one would cost less, the third one would be even less.

You get faster. I mean, you see that when you go into a new home construction place. The first home takes it seems a lot longer and then suddenly the homes start popping up every week. This is the same with nuclear power. We trained 13,000 people to build the Vogtle nuclear plant in Georgia, and then we were done. And where did all those workers go? To other jobs. So now if we wanted to build Units Five and Six — we wanted to rebuild V.C. Summer [Nuclear Station] in South Carolina, which is like a hundred miles away — we’d have to go out and find another 13,000 workers. And so one of the things that we have to figure out how to do is to figure out how to build 10, right? And have those same workers that we trained, all those same EPC [engineering, procurement, and construction] contractors, all of those same suppliers, not have to stop and start, but we continue to do these one-off things…

…Jigar (09:42):

So when you restart a nuclear plant, the nuclear plant is viewed as new additional capacity, right? Because it was shut down. And so as a result, this technology agnostic credit that was created by Senator Wyden, right?

Because remember we always had the solar tax credit and the wind tax credit and all these other things. So over time the IRA moves us to a technology-neutral tax credit so that everything that is clean gets this technology-neutral tax credit. It’s a pretty lucrative tax credit. Depends on the technology, but let’s say 3 cents a kilowatt hour. And so now you’re in this place where you actually have a bonus production credit. Now you separately can choose to get an investment tax credit, but it happens to be that the production tax credit is more lucrative for these restarts of nuclear plants. But if you decide to do the investment tax credit, then you get the 30% tax credit, then there’s bonus tax credit.

So if it’s part of an energy community, you get an extra 10%, right? If you have a lot of domestic content, you get another 10%, right? So you could imagine that some of the folks who are building brand new nuclear plants might go that direction, but as a result of these incentives, nuclear power is now very cost effective.

Then the question becomes who actually wants to buy this power? Because wholesale market prices have been low. And so then the question becomes who wants to buy it? And it happened to be that two different utility groups in Michigan competed over wanting to buy all the output out of the Palisades restart. And so he picked one of the groups to buy that power and then that led to the project becoming financeable, right? And so once that succeeded, then Constellation was like, hell, maybe we could do this…

…Jigar (11:49):

So for a restart, you generally choose a production tax credit, not the investment tax credit. And that’s because the cost of restarting a reactor is a lot lower than the cost of building a brand new reactor. So you make more money by getting that extra 3 cents a kilowatt hour for the next 20 years. So the math there is you put up, it depends on where the final cost runs out, but let’s call it $1 [billion] to $2 billion to do the restart. And then you get this 3 cents a kilowatt hour multiplied by the number of kilowatt hours that plant creates. And remember, a nuclear power plant runs on average, in the United States, 92% of the time. So that’s a lot of kilowatt hours that comes out of that plant. Whereas with a solar farm, you might get 25% of the time production, with a tracking system. The math means that you could get almost all of your money back on the $1-to-$2 billion from the tax credits.

Then you’ve got the sale of the power that you’re signing a long-term contract for, and that’s where you make your return…

…Jigar (14:42):

Into the PJM [Interconnection.] And Microsoft says, depending on what happens with this power, we will make you whole on the payment. So if we said that we’re going to pay 9 cents a kilowatt hour and you end up getting 7 cents a kilowatt hour, we’ll pay that 2-cent difference. And that includes not just the kilowatt hour price, but also includes the capacity payment. So you may have heard that the PJM had a very large increase in the price that the capacity payment cleared and the capacity payment is essential, because it convinces the coal plants or the natural gas plants or others who are sort of at the end of life to make investments to last a little longer because they got paid a capacity payment to stay open. So the pieces that come here are both a capacity payment and the energy payment, and Microsoft is saying that we get all the attributes, so we get to call our usage green, but separately, if for whatever reason the wholesale market value for what the nuclear power plant is creating is less than the strike price that we agreed to, then we will make you whole…

…Jigar (19:34):

It really is an extraordinary thing. I think that most people view electricity like water. So you just put a bigger pump in, you put in more pipe, it gets to your house, you got hot water, that’s great. It’s not like that at all. There is this complex physics equation that you have to solve for.

Joe (19:53):

Because the grid has to be in perfect balance all the time, right?

Jigar (19:56):

Well, so there’s the perfect balance between supply and demand. But then there’s also figuring out what the constraints are of each individual segment on the transmission line.

So if you’re using power in New York City and you’re creating a lot of extra power out of the nuclear plants in Illinois, then that power has to go via Indiana and Ohio and then through Pennsylvania to New York City, and they may or may not be able to carry that much. And so they have to do these studies. So every time you try to add something to the grid, they have to do a study and they have to figure out whether that capacity is there, how often it’s there, whether it would continue to be balanced or whether it would be imbalanced.

And so the big fight there is that… so in Texas what they do is they just look at the safety part of it, but they don’t look at the capacity part of it. They just say, “You connect at your own risk and if we’re clogged, we’re just going to tell you to shut down, and that’s on you.” That’s why they’re approving people super fast. Whereas with the PJM and others, they’re saying, “Not only are we’re going to do a safety study, we’re also going to do a capacity study and we’re not going to let you connect until this other generator shuts down and frees up capacity for your generator.” And so that then makes the wait time much longer.

4. Invest Local? – Victaurs

Well, a community bank in the U.S. is generally defined as a depository or lending institution that primarily serves businesses and individuals in a small geographic area. These banks emphasize personal relationships with their customers and often have specialized knowledge of their local community and customers. They tend to base credit decisions on local knowledge and nonstandard data obtained through long-term relationships, rather than relying solely on models-based underwriting used by larger banks…

…As of a year or two ago there were roughly $25 trillion in assets in the entire U.S. Banking system.

And the entire amount of assets in the Community Banking system is … drumroll please … $4.8 trillion for a grand total of 19.2%. Only 1/5 of all the assets in the system are controlled and managed by these smaller banks…

...When people don’t bank locally, they inadvertently contribute to a cycle that can harm their local economies:

Capital Drain: Deposits in non-local banks are often invested in national or international ventures, rather than being reinvested in the local community

Reduced Access to Credit: As community banks disappear, so does their deep understanding of local economic conditions and business opportunities.

Loss of Personalized Service: Large banks often use standardized lending criteria that may not account for local economic conditions or individual circumstances.

Economic Homogenization: As local banks disappear, communities lose a key institution that helps maintain their unique economic character.

Decreased Local Decision-Making: When banking decisions are made in distant headquarters, local economic needs and opportunities may be overlooked.

I don’t want to over dramatize the situation, but do any of these things sound good to you? And given lots of us grew up in small towns, love where we came from, owe our position in life to the kindness of a HS coach or the first job at a local restaurant, do you want capital to move away from these people? I don’t think I do.

Banking is numbers, so here are some numbers because they paint a stark picture:

• For every $100 deposited in a local bank, $58 is reinvested locally. For large banks, that number drops to just $36. This isn’t to demonize big banks, only to point out the facts.
• Community banks make 60% of small business loans, despite holding only 12% of all banking assets. (I know their 12% doesn’t jive with my 19%).
• When a community bank closes, the local area experiences an average 33% reduction in small business lending for several years. I highly recommend checking out this study. This is an awful second level impact of losing community banks…

…As of 2023, the United States is home to a staggering 33.2 million small businesses. These enterprises employ 61.7 million people – that’s 46.4% of all U.S. employees. To put it in perspective, if small business employees formed a country, it would be the 23rd most populous nation on Earth, just behind Italy. That was pretty crazy to me. Imagine if all of the small businesses went away?

But it doesn’t stop there. Small businesses are the dynamos of American innovation and economic activity:

• They generate 44% of U.S. economic activity.
• They create 1.5 million jobs annually (64% of new jobs created) – that’s like creating a new city the size of Philadelphia every year, filled entirely with new job holders. And even for those of us who aren’t Eagles or Phillies fans, we can agree this is a massive deal.
• They contribute to 33.6% of known export value and represent 97.5% of all exporters in the United States.

“Small businesses are more than just economic units,” says Dr. Emily Chen, economist at the Small Business Administration. “They’re the innovation labs of America, constantly adapting and evolving to meet new challenges and opportunities.”…

…This is a repeat stat, but worth mentioning again. Community banks provide 60% of all small business loans, despite holding only 12% of all banking industry assets. It’s as if the local high school football team was outscoring all the pro teams combined!

They make 80% of agricultural loans, forming the financial backbone of rural America.

During the COVID-19 pandemic, community banks processed 57.5% of all Paycheck Protection Program (PPP) loans, saving countless small businesses.

Community banks have over 50,000 locations nationwide, compared to about 18,000 locations for the largest banks. That’s like having a friendly neighbor on every block, compared to a distant acquaintance every few neighborhoods…

…Community banks have consistently demonstrated resilience in the face of economic challenges:

During the 2008 financial crisis, community banks continued to lend when larger banks pulled back, increasing their small business lending by 5.2%.

In the recent COVID-19 pandemic, community banks were often the first to step up, offering forbearance and emergency loans to struggling local businesses.

66% of small businesses that received PPP loans from community banks said the process was “easy,” compared to 51% for large banks…

…In 2005, Hamdi Ulukaya bought a defunct yogurt factory in New Berlin, New York, with the help of a Small Business Administration loan backed by a local bank. From this modest beginning, Chobani has grown into a billion-dollar company, employing thousands and revolutionizing the yogurt industry.

“Without that initial loan and the trust of our local bank, Chobani might never have existed,” Ulukaya has said. “They believed in us when no one else would.”

5. The next tectonic shift in AI: Inference – Rihard Jarc

To simplify it, the o1 model has a backtracking ability. The model predicts something, realizes it did something wrong, goes back, erases that, and comes back and predicts again from that point.

The most significant implication of this kind of model is that inference workloads should grow substantially more than we were expecting in the pre-o1 period.

The calculation for Inference is now not just the number of users using it multiplied by the number of times they use it. The model can now take 10x or even more time on inference compute to come up with an answer. So inference also becomes part of the accuracy process.

The second big implication for investors is that inference computing is now becoming a new scaling paradigm. So, you not only scale the model with what is now known as data and training compute, but you can also scale them with more inference.

Noam Brown, an OpenAI researcher, has said that a study on the board game Hex using AI found that if you have 15x the inference compute, it equals 10x the training compute.

The fact that you can now scale LLMs via inference means that:

A. You can have smaller models that you dedicate more inference compute that can be as good as bigger parameter models with less inference compute

B. Inference computing is much cheaper than training computing, but the market for inference will be vastly bigger than training computing. In my discussion with Sunny, I asked Sunny how big he thinks, as an industry insider, the Inference market will be; Sunny revealed that he had the chance to preview an interview with Jensen Huang, the CEO of Nvidia, where Jensen said that Inference will be 1 billion times larger than Training. Sunny added that it makes sense to think that a model is going to be used billions of times before it is updated (trained) again.

It is also important to note that the Inference chip market has much more competition than the training market, where Nvidia dominates. From an industry expert:

»Training also is notoriously hard because you need special architectures and special cards and interconnects between the clusterand RDUs and stuff like that. It’s mostly dominated by NVIDIA because they’ve done the best work there. Inference is interesting because inference can be done anywhere. Inference is very, very easy to do on any hardware. Training is harder.«

This means that other companies will be able to reap the benefits of inference chips besides Nvidia. It also means margins on inference chips are not going close to Nvidia’s margins on its training GPUs, where it basically has a monopoly.

It also opens a path for some companies to lower some of their costs, and instead of going heavy on training GPUs and scaling there, they can split some of that on inference chips and still scale the models. Inference for customers is vastly cheaper than training…

…The thing that I also didn’t mention, but because of the o1 model release and the fact that we are coming to the start of Big Tech reporting earnings, I believe there is a high chance that the hyperscalers and companies like Meta, who are building these LLMs will increase their CapEx expectations now even more in the short-term than what they did before and much higher than analysts expect. The reason is that they now have to account for spending on Inference compute to improve these models. Inference was before a cost that they could gradually introduce and control more with users getting limited access to AI features, etc. This has changed, and you can use inference to scale the model. This might not be what investors will like in the short term. Still, it is something that, in the long run, brings us even more capable models, possibilities of easier agentic AI use cases, and SLMs that have a good enough accuracy to be used more often compared to bigger LLMs. There are already estimates on how much the inference costs are more expensive with an o1 model than with »pre o1 models«. This industry expert quantifies how much more expensive it is:

» Analyst: Strawberry o1, I’ve been told it’s 4X-5X more expensive than ChatGPT?

Industry Expert: Yeah. That’s the right level. That adds up given that it will essentially use 4X-5X more tokens on average. In the worst case, it will be 10X possibly. The 4X-5X is an average number of how much more expensive it is.«

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Meta Platforms, 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 13 October 2024:

1. An Interview with Meta CTO Andrew Bosworth About Orion and Reality Labs – Ben Thompson and Andrew Bosworth

Orion, Meta’s AR glasses, is spectacular. I must start with the caveat that this is not a shipping product; the glasses that I tried felt like a consumer-ready product, but they reportedly cost $10,000 each, and Meta has decided to hold off on shipping a consumer version until they can bring the price down. That will be a tall order, and that challenge should be kept in mind with everything that follows.

What follows is unadulterated praise. Orion makes every other VR or AR device I have tried feel like a mistake — including the Apple Vision Pro. It is incredibly comfortable to wear, for one. What was the most striking to me, however, is that the obvious limitations — particularly low resolution — felt immaterial. The difference from the Quest or Vision Pro is that actually looking at reality is so dramatically different from even the best-in-class pass-through capabilities of the Vision Pro, that the holographic video quality doesn’t really matter. Even the highest quality presentation layer will pale in comparison to reality; this, counter-intuitively, gives a lot more freedom of movement in terms of what constitutes “good enough”. Orion’s image quality — thanks in part to its shockingly large 70 degree field of view — is good enough. It’s awesome, actually. In fact — and I don’t say this lightly — it is good enough that, for the first time ever, I felt like I could envision a world where I don’t carry a smartphone.

Orion is a standalone product, at least in terms of needing a phone; instead there is a “puck”, an oblong unit that holds the compute for the operating system and connectivity, and which connects to the glasses wirelessly. The glasses themselves contain the compute necessary for low-latency calculations that pertain to the actual display. One challenge I see in this model is input: voice works well, and the wristband that detects the electrical signals in your arm worked flawlessly for me — you can control your glasses with your hand without anyone knowing — but I wouldn’t mind if that “puck” contained a Blackberry-style keyboard for extended text entry…

…Was there any aspect of the Vision Pro going so high end that also made you re-anchor on the low end, or do you think you would’ve ended up here regardless?

AB: No, I think we would’ve ended up here regardless. I mean, listen, I love that the Vision Pro — people won’t believe me — I love that they went maximalist. Just like, “What if we just take this dial and turn it to 11, and let the rest of the system fall where it does?”, and you see why we haven’t done that, just in terms of weight and cost. Like yep, that’s what it takes to take this dial and turn it to 11.

And this is why I think you and Mark right away seemed almost relieved by the Vision Pro.

AB: Your only real fear when a competitor launches a product is that they’ve had a breakthrough that you haven’t had. That there’s something that they’ve figured out, some technical thing that you haven’t figured out, because then they have a sustaining advantage potentially for some period of time until you can beat them on it. So I think whenever a device comes out, it’s like, “Oh good, this is all made with materials that we are aware of, this is all made with technologies that we have access to”.

“We understand why it costs this much, why it weighs this much.”

AB: We could have built this, we chose not to build this. It is both great for the world that there’s people exploring different quadrants of the space. By the way, if the Vision Pro had sold really well, of course we’d be changing our strategy. We’d be like, “Oh, okay, cool, there’s actually a bigger market than we’ve realized up there, let’s go do it”. And I think, by the way, I do actually think there will be a market there, when there’s software.

Are you surprised at how little content Apple’s released with Vision Pro?

AB: It’s how do you get the content? Before you have the devices and it’s a chicken-and-egg problem where it’s like, “Hey, okay cool, you have these devices out there, but there’s not enough for me to build my content for”.

Is part of the low cost a bet that if the egg is the end market, that’s the most important part?

AB: One hundred percent. We’ve talked about this all the time, you almost always hear me talk about the Quest ecosystem. I’m not talking about the Quest line of devices, I’m talking about building as big an audience as I can for developers to target so they can sell their software, so there’s more developers, that brings more consumers, and you have flywheel that way. Then at some point, that’s how you power your way up market. That’s how you power your way to, “Hey, we can now sell higher margin, higher end devices because there’s plenty of stuff”.

Well, to that point, Mark talks about, “In every market there’s the integrated version and the modular mass market one”, but if you go back to the PCs, Microsoft swept the market. Now one thing that’s important about that era that’s different from the smartphone era is in the smartphone era, Apple was first. In the PC era, DOS was first, so Microsoft was actually first, so they actually had developers first. At this point, seeing the Vision Pro, seeing what’s happened over the last six to nine months, are you shifting from a, “Yeah, we can both be winners here”, to, “We’re going to win the whole thing”?

AB: Man, I feel good about our position, if that’s what you’re asking.

I want to pretend I’m turning off the mic and getting your honest thought.

AB: With me, you’ll always get an honest thought, I have to make sure I’m phrasing this in a clever way.

The only reason I’m being careful here is I think — I don’t really want to be antagonistic with anybody, including Apple, I think it’s great that they’re investing, I want them to continue invest. Actually the Vision Pro has caused a surge of interest in investing in the entire space, including in us. I’ve gotten calls in the last couple of months especially that I would not have gotten, had Apple not launched the Vision Pro, and if they weren’t courting people to consider that there’s going to be a follow-on version. So it’s really, really healthy to have that competition. Good for consumers, good for us.

I also think that right now, if you’re a developer, you’d be an idiot not to build for us first, we have an audience that can actually go buy your software. It’s big enough to sustain you, and then yeah, no problem, bring it over to Apple Vision Pro after that.

Is your bigger concern losing to Apple, or that a market never materializes for these devices?

AB: Oh, good question. Yeah, my biggest concern is that the market gets capped somehow, like it doesn’t take off. The thing I worry about with Apple specifically is that they have their phones and devices so locked down that they can self-preference a ton. So they can easily, you look what our Orion glasses, these full AR glasses, incredible. We’ve got custom silicon in the glasses, we’ve got custom silicon in the puck, but Apple could build all that and just be like, “Oh, it only works with us,” which they’ve already done with the AirPods.

They don’t need a puck because they have a phone.

AB: They already have a phone, and they did this with Airpods.

Or the Apple Watch.

AB: Apple Watch. Those aren’t the best possible things you could build, but no one else is allowed to build those things, so it’s like, “Oh cool”, so if I have a concern about Apple, it’s not the competitiveness or non-competitiveness of their headsets, it’s that they’re going to bundle into their ecosystem in a way that really makes it hard for us to compete…

…This is the first device I’ve ever used that — I know you guys have been saying it — that genuinely feels post-phone.

AB: It could do it, right?…

…I’ll be totally honest, after using Orion, I’m excusing you all your billions of dollars a year spent, that’s how incredible it is, but I do think one of the critiques, and you talked about it when you went in, this was an entity that had two completely different camps that want to go in totally different directions. Then even a few years after you were there, you’re having an operating system bake-off for years instead of months and then it’s, “Should we do processors? Should we partner with someone doing processors?”. What is the forcing function that is getting you into, “Okay, we’re going to stop experimenting and actually start building”? What got you to that point? Was it the Year of Efficiency? Was there a bit where, “Look, we have to lay off half the team, so we’ve got to decide which half”?

AB: I love this question. It was before the Year of Efficiency hit. I think it’s not uncommon, you have these expansionary periods where you’re like, “We don’t know what matters yet, we truly don’t know what technology is the right technology, we don’t know what operating system is the right operating system, we don’t know what trade-offs matter yet”. So if you want to be successful with high confidence in a certain timeframe, it pays to parallel path a ton of stuff.

But how long do you parallel path it?

AB: We honestly turned the corner with Quest 2, especially when we had mixed reality in sight. That started the process, and now you’ve got to a point with a mixed reality with our metaverse division where it’s extremely focused, have a very clear vision of what good looks like, have a very clear ability to discern this is the path, this is not the path. As a consequence, you can be really, really much more efficient with your resourcing, your parallel pathing list, you’re just blitzing the things that matter more.

With augmented reality, Orion, a year ago, we actually hit this point where we’re like, “Okay, we believe in this, we see it, we have a really clear sense of where we’re going with this”, and you know what really helped a lot with that was the Ray-Ban Meta glasses as well. Cool, it’s not just that we have this distant AR thing, we actually have an entire family of devices coming before that that also matter.

Did AI save Reality Labs?

AB: Oh my gosh. So AI, because FAIR, the Fundamental AI Research group reported to me until this year. We just moved it over to join the rest of the AI stuff with Chris, and I don’t know if it saved us, but it’s a wonderful tailwind, it’s the first tailwind I can remember having. For us, it’s mostly just headwind after headwind after headwinds like, “Oh, guess what? This thermal performance is worse than you thought, this battery life is worse than you thought, the efficiency is worse than you thought”, and so we finally got a tailwind. We finally got a thing that showed up before it was expected, which was AI.

So I think to answer your first question, each of these devices has gone through an expansionary period and a contractionary period where it expands until you feel like you have a good understanding and intuition of what good looks like, and then you can start to prune and then you can get really good about pruning. Today our architecture is really tight, hand tracking, eye tracking, face tracking, Codec Avatars, these are shared technologies, they work in both VR and AR, and we have a single shared team building those technologies. Separately, the operating system for AR has to be its own operating system because it turns out the use cases, what you actually do, the interaction paradigm, completely different.

2. Xi Jinping is worried about the economy – what do Chinese people think? – Kelly Ng and Yi Ma

What is less clear is how the slowdown has affected ordinary Chinese people, whose expectations and frustrations are often heavily censored.

But two new pieces of research offer some insight. The first, a survey of Chinese attitudes towards the economy, found that people were growing pessimistic and disillusioned about their prospects. The second is a record of protests, both physical and online, that noted a rise in incidents driven by economic grievances.

Although far from complete, the picture neverthless provides a rare glimpse into the current economic climate, and how Chinese people feel about their future…

…The slowdown hit as the pandemic ended, partly driven by three years of sudden and complete lockdowns, which strangled economic activity.

And that contrast between the years before and after the pandemic is evident in the research by American professors Martin Whyte of Harvard University, Scott Rozelle of Stanford University’s Center on China’s Economy and Stanford masters student Michael Alisky.

They conducted their surveys in 2004 and 2009, before Xi Jinping became China’s leader, and during his rule in 2014 and 2023. The sample sizes varied, ranging between 3,000 and 7,500.

In 2004, nearly 60% of the respondents said their families’ economic situation had improved over the past five years – and just as many of them felt optimistic about the next five years.

The figures jumped in 2009 and 2014 – with 72.4% and 76.5% respectively saying things had improved, while 68.8% and 73% were hopeful about the future.

However in 2023, only 38.8% felt life had got better for their families. And less than half – about 47% – believed things would improve over the next five years.

Meanwhile, the proportion of those who felt pessimistic about the future rose, from just 2.3% in 2004 to 16% in 2023.

While the surveys were of a nationally representative sample aged 20 to 60, getting access to a broad range of opinions is a challenge in authoritarian China.

Respondents were from 26 Chinese provinces and administrative regions. The 2023 surveys excluded Xinjiang and parts of Tibet – Mr Whyte said it was “a combination of extra costs due to remote locations and political sensitivity”…

…In 2004, 2009 and 2014, more than six in 10 respondents agreed that “effort is always rewarded” in China. Those who disagreed hovered around 15%.

Come 2023, the sentiment flipped. Only 28.3% believed that their hard work would pay off, while a third of them disagreed. The disagreement was strongest among lower-income families, who earned less than 50,000 yuan ($6,989; £5,442) a year…

…There are other indicators of discontent, such as an 18% rise in protests in the second quarter of 2024, compared with the same period last year, according to the China Dissent Monitor (CDM).

The study defines protests as any instance when people voice grievances or advance their interests in ways that are in contention with authority – this could happen physically or online. Such episodes, however small, are still telling in China, where even lone protesters are swiftly tracked down and detained.

A least three in four cases are due to economic grievances, said Kevin Slaten, one of the CDM study’s four editors.

Starting in June 2022, the group has documented nearly 6,400 such events so far.

They saw a rise in protests led by rural residents and blue-collar workers over land grabs and low wages, but also noted middle-class citizens organising because of the real estate crisis. Protests by homeowners and construction workers made up 44% of the cases across more than 370 cities…

…Between August 2023 and Janaury 2024, Beijing stopped releasing youth unemployment figures after they hit a record high. At one point, officials coined the term “slow employment” to describe those who were taking time to find a job – a separate category, they said, from the jobless.

Censors have been cracking down on any source of financial frustration – vocal online posts are promptly scrubbed, while influencers have been blocked on social media for flaunting luxurious tastes. State media has defended the bans as part of the effort to create a “civilised, healthy and harmonious” environment. More alarming perhaps are reports last week that a top economist, Zhu Hengpeng, has been detained for criticising Xi’s handling of the economy.

3. Will Hurricane Helene Cause a Chip Shortage? What the Major Chipmakers Are Saying – Tae Kim

Hurricane Helene flooded and damaged the local infrastructure in Spruce Pine, making some roadways impassable, according to local news reports. Sibelco and The Quartz Corp., the two companies that manage the quartz mines in the town, have both temporarily shut down mining operations.

High-purity quartz found in Spruce Pine is a key material used in the production of silicon wafers that are used to make semiconductors. Quartz’s ability to withstand extreme temperatures is useful for making crucibles or containers that hold the melted polysilicon material used to produce wafers and solar cells.

According to Vince Beiser, author of The World in a Grain, the two companies’ Spruce Pine mines provide 70% to 90% of the world’s production of high-purity quartz used for the semiconductor industry…

…Ed Conway, author of Material World, a book about raw materials, posted that Spruce Pine quartz mines are unique in terms of purity and consistency, and finding another high-purity source would take months or possibly years.

But in a bad scenario, where the mines are offline for months, the chip industry may be insulated. “The significance of supply disruptions from the [Spruce Pine] mines is exaggerated,” Dylan Patel, chief analyst at SemiAnalysis said.

Patel added that the raw wafer companies had months of inventory, there are other countries that have high-purity quartz mines, and there are methods to purify lower-quality quartz or create synthetic quartz crucibles.

4. The Truth Behind the Highlight Reels – Thomas Chua

People are often shocked when seemingly perfect couples announce a divorce or breakup, even though their social media showed nothing but happiness just days earlier. What’s hidden from view are the realities that unfold behind the scenes—disagreements, financial pressures, or emotional distance.

The same kind of comparison happens in investing. We look at people like Warren Buffett, who delivered an incredible 30.4% annualized return during his partnership years (1957–1969), or Peter Lynch, who achieved 29.2% annualized returns at Fidelity’s Magellan Fund (1977–1990). Their results are awe-inspiring, but we rarely consider the personal price they paid to achieve them.

Warren Buffett’s biography, The Snowball, talks about how he spent his days working and his nights poring over Moody’s Manual. While his wife, Susie, took care of him wholeheartedly and assumed the responsibility of managing their household and raising the children, Buffett’s mind remained elsewhere. Even during family trips—like a visit to Disneyland—he would sit alone, engrossed in reading.

This single-minded focus on work created a widening distance between Buffett and his family. His children longed for his attention, and Susie craved a deeper connection. The strain eventually became too much, leading to Susie’s departure…

…Buffett and Lynch’s legendary results required intense focus and commitment, often at the expense of their relationships. This is not unlike the sacrifices elite athletes make, dedicating everything to training, diet, and recovery to reach the pinnacle of their sport…

…When it comes to investing, we need to ask ourselves: What’s the price we’re willing to pay? How much time, energy, and money are we truly prepared to invest? 

5. X (or Twitter) thread on China’s stimulus – Adam Wolfe

Why is China’s stock market booming if most economists, including me, don’t think the stimulus measures announced or reported go far enough to solve China’s economic problems or even its cyclical slump? I think it’s how the stimulus has been designed. 1/

The measures aimed at the real economy are mostly incremental, small, and inconsequential. But the measures that the PBoC announced to support the stock market are new, unlimited, and significant. 2/…

…Start with the 20-bps policy interest rate cut. 3/

PBoC Governor Pan was at pains to say this is as much as he could do. Rate cuts pass through to loan rates faster than deposit rates, so to keep banks profitable, he couldn’t offer any more. That also implies he won’t be cutting rates further soon. 4/…

…Existing mortgage rates were cut, too, saving some CNY150bn in interest payments per-year, according to Pan. But they would have adjusted in January anyway, so the actual savings are ¼ of that. They did the same thing last year. It had no macro impact. 7/

Lower down payment requirements for second home purchases? Done that before, too. It didn’t lead to higher sales of new homes. 8/…

…Another CNY1tn will be used to support consumption. About half of that would go toward extending the cash for clunkers programs. Those have helped specific industries but have had little macro impact. And the impact is getting smaller the longer these programs run. 11/

The only new thing for the real economy is the reported program that would give a monthly allowance to families with two more kids. I estimate that a bit over 10% of families would qualify, so it would cost about CNY500bn/year. 12/

This could have a big multiplier effect on growth, but CNY500bn is small beans. Plus, temporary support measures like this tend to be saved…

…But what about the stock market? The PBoC will set up a CNY500bn facility for institutional investors to higher risk assets for safe assets from the PBoC. This higher-quality collateral would then allow the investors to take on more leverage to buy more stocks. 14/

The PBoC will also open a CNY300bn re-lending window to encourage banks to finance the repurchase of stocks by listed companies. Pan made a point to say that both programs could be doubled or tripled if they work. The sky is the limit! 15/…

…Inflating a bubble in stock prices without doing much to boost earnings could end in tears. Alternatively, sucking liquidity out of safer assets like bonds could lead to another “redemption crisis” for WMPs/bond funds and losses for households. 19/

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 Apple, Meta Platforms, and Microsoft. Holdings are subject to change at any time.