Category: What We’re Reading

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

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

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

Here are the articles for the week ending 13 July 2025:

1. Jim Chanos on the Nuttiness of ‘Bitcoin Treasury Companies’ | Odd Lots (Transcript Here) – Tracy Alloway, Joe Weisenthal, and Jim Chanos

Joe: All right, first question: Are Bitcoin treasury companies the stupidest thing you’ve ever seen in your entire life?

Jim Chanos: It’s rarely, rarely that I have to increase my personal security after a podcast which I had to do after our last podcast together when I said some intemperate things about Bitcoin treasury companies.

Here’s the thing. I get people very agitated about this and they point out just what a genius idea this is and I keep trying to point out to them I’m doing the same thing that guys like Michael Saylor are doing. I’m on the same side of the trade and I keep pointing out to my critics, “You’re on the opposite side of that trade and you don’t want to be on the opposite side of the trade, and the Bitcoin treasury paradox being that you are the one buying the pieces of paper that have infinite supply so that Michael Saylor and I can buy the digital asset with the limited supply and it makes kind of no sense.” So what will inevitably happen is happening, in that there’s nothing proprietary here – this is just simply raising capital to buy a financial asset and other companies will do this. In fact even since the podcast we last did, I think the number of companies that have announced this strategy is scores more. I think there’s over a hundred in the US and over 200 globally now…

…Jim: Because there’s a wonderful sales job that’s being done about the fact that this is an economic engine in and of itself, therefore terms like Bitcoin Yield are are used and I’ve called them financial gibberish – because they are. In fact, this will get arbed away ultimately by companies that will do this to try to capture that spread. In the case of Micro Strategy, it’s substantial. It’s still $50 billion, something like that, of the difference between the value of the enterprise value of the company and the value of their Bitcoin holdings. But the thing that really shot me into orbit on all this was when Saylor and others then said, “You can’t really value us on an NAV basis, a so-called MNAV, multiple of NAV. You actually have to also give us additional value for the amount of profit that we make every quarter from the appreciation in the asset.” I said, “Well that’s like saying my whole net worth is in a house that’s worth $400,000 that is now worth $500,000 a year or two later, and my net worth is not $500,000 now – it’s $2.5 million because it’s the value of the house plus a multiple on the increase in the profitability of the asset.”…

…Tracy: I have one more question why did Micro – I have to remember to call them Strategy but I can’t bring myself to do it. Why did they switch from issuing the convertible debt to preferred shares?

Jim: Because he realized that as he began to issue more and more common, it was putting pressure on the premium. Now the latest iteration is, “We’re going to do this quasi equity security, quasi debt, preferred stock and then we can lever up the the balance sheet.” This is a company whose selling point a year ago was “We’re not going to lever, because we have this wonderful equity that we can issue at a premium.” Now they’re saying, “Maybe if it trades above 2x we’ll issue equity, but if it’s between 1x and 2x, we’ll do preferred, and then if it’s below 1x we’ll buy back common and then what is Chanos going to do?” To which I said, “I’ll be out of the trade by then.” If it’s 1x NAV it’s not a trade. That’s the latest game plan – but stay tuned, it’ll change, I think. The narrative keeps changing…

…Jim: The legacy data centers – and there’s only a couple companies in the United States that really have legacy data centers. There’s Equinix, there’s Digital Realty, and then there’s old Colony Capital – it’s now called Digital Bridge and they own these things in fund format.

When we took a look at this with our partner back in ‘22 the idea was pretty simple. We did not see the AI explosion in mid-’22, but the idea was it was a pretty crummy business then, working on the cloud and SaaS demand. But it became a really bad business with the advent of AI because it just moved the hyperscalers to invest more in state-of-the-art data centers. These are older data centers that we’re short, the idea being that the new GPU-centric data centers need liquid cooling – they basically need all the infrastructure ripped out and replaced – and the business was not a high return on capital business before this. It’s getting even worse now.

What Equinix said yesterday at their Analyst Day was that revenues were not going to quite be what people thought they would be, but more ominously, capex was going to keep increasing. That’s what we’ve been saying, that these are not like warehouses where you just collect a check. These are actually operating businesses where you have to service the servers, you have to make sure there’s redundancy. It’s a business, a tech business, and they’re traded as REITs and that was the opportunity. That was the dichotomy in valuation. People added back the depreciation as they do with REITs and they valued them on a so-called FFO or AFFO, which is a cash flow metric. But in fact, unlike warehouses, shopping centers to a lesser extent, office buildings, the capex was real. Depreciation was a real expense. To give you an example, with Equinix yesterday, they said “Our capex is now going to bump up to between $4 billion and $5 billion a year.” The problem is their EBITDA this year is expected to be $4.5 billion, so all of that’s going to go to capex, meaning they’re going to have to basically borrow or issue equity to pay their interest and dividends. That’s just a definition of a bad business and it’s a business that’s not growing very fast. Unlike other really true AI companies which are growing 25%, 30%, 40% a year, these guys are growing 3%, 5%, 6% sort of with GDP. So there’s no growing their way out of this. So they’re just really bad businesses trading at just nosebleed valuations.

Tracy: On the topic of idiosyncratic opportunities I got to ask about Carvana because when my husband and I moved back to the States in 2022, we bought a used car through Carvana and that was a mistake. It took us about 6 months to actually get the car and they lost all our paperwork and it was just an absolute nightmare. I thought at the time this is a company whose entire business model is basically built on regulation, that’s what they’re doing and I thought they’re not going to have a future if they are this bad at it. Yet the stock is up.

Joe: It’s done insanely well.

Jim: It’s done a double round trip. It crashed 99% and now it’s up 100x, so it’s pretty interesting again. The reason it’s interesting is that if you go through the numbers, they are making more than 100% of their pre-tax profit from gain on sale of subprime loans and gain on sale of equity stakes in other companies. You ex those two out, they’re losing money and they’re losing money now right after the rebound, after the restructuring from 2022-2023. This is a company that is being valued again as a secular growth stock that saw its used car revenues drop 30% between 2022 and 2023, so it’s not necessarily a secular growth company. The accounting is abysmal. What people are really missing is that what’s happening in subprime auto securizations right now – and you can track it on your Bloomberg terminal – delinquencies are starting to skyrocket.

Tracy: We actually did an episode on this recently with Jim Egan.

Jim: So a huge amount of their profits comes from generating paper from customers and then selling it into the open market or to affiliates. This is a company that was spun out of a company called Drive Time Finance, which is their affiliated finance company which was originally called Ugly Duckling in the late ‘90s which was run by the current CEO’s father. That company collapsed in the first subprime blowup which was not the GFC – it was actually in the late ‘90s in subprime auto credit and consumer loans. It didn’t go bankrupt but it came close. He had to restructure it. He bought it in private and then restructured it, renamed it Drive Time Finance. But that’s the genesis of Carvana. That’s its DNA. It’s basically a subprime finance lead company, if you will. Those companies should not trade at 40x and 50x expected earnings – and they don’t by and large. They’re consumer finance companies. So it’s an odd bird. It’s still heavily leveraged, the stock is up a ton.

But what really got us interested again recently was the vast amount of insider selling that has just started in May and June in the company. If you go look at the insider selling in the company, it is just now a torrent of everybody selling pretty much every day. We just don’t think that’s a good sign given what’s happening in the subprime securization market…

…Jim: Every once in a while. There’s one other thing though I do want to mention. I was talking to someone earlier today and I think one of the things that’s underappreciated by investors right now and one of the things that’s been most interesting to me is how corporate profit margins have held up, which used to be very mean-reverting as you know. The more work we’ve done on this, the more we’re kind of convinced that the capital spending boom we’re seeing due to tech and specifically AI, is is looking very much akin to the global internet buildout networking buildout in the late ‘90s and the problem there of course is that if you buy my chips from NVIDIA or you were buying my networking equipment at Cisco and Lucent, that’s revenue for me and profit. But for you it’s a capitalized expense, it’s written off over time, and that adds a big, big boost until people pull their orders. That’s what we saw in 2001, 2002 that GDP dropped about 1% to 2% in the recession of ‘01-’02. Does anybody know what corporate profits did in that? That was an investment-driven recession. Consumers didn’t feel it at all. Earnings were down about 45% I think from peak to trough in the S&P. They were down about the same, a little bit more in the global financial crisis, but of course GDP collapsed.

Here’s a little interesting thought experiment. Right now NVIDIA’s revenues are about one-half of 1% of US GDP, about $140 billion and our GDP is about $29 trillion. Anyone tell me what Cisco and Lucent – the two companies that you needed when building out your internet network in ‘99, 2000 – did anybody know what their combined revenues as a percent of GDP was in 2000?

Tracy: No using your phones.

Joe: And ChatGPT.

Jim: It was a half a percent. It was roughly $50 billion total on GDP of $10 trillion. So those revenues stopped growing at some point shortly thereafter and actually shrunk a little bit. The investment boom we’re seeing right now, we’ve seen before. And it’s not just chips. It’s Caterpillar, it’s people building the data centers, it’s people building new utilities. There is an ecosystem around the AI boom that is considerable, as there was for TMT back in ‘99 and 2000. But it is a riskier revenue stream because if people pull back, they can pull back capex very easily, projects can get put on hold for six months or nine months, and that immediately shows up in disappointing revenues and earnings forecast if it happens. We’re not there yet but that’s one of the risks out there that I think a lot of people are underestimating.

2. Creating therapeutic abundance – Jacob Kimmel

Jack Scannell infamously predicted in 2012 that the number of drugs per billion dollars would decline two-fold every nine years. Unfortunately, our therapeutics industry has largely followed through…

…Drug program success rates are equally complex. Failures can be attributed to safety issues, failure of a drug to hit the desired biological target, or improper selection of the target for a given disease…

…We can bucket the failures into a two broad categories of safety and efficacy and make informed estimates.

1. Safety failures – ~20-30% of all candidates
A molecule was developed, but proved unsafe in patients. These are typically detected as failures in Phase 1 trials.

2. Efficacy failures – 70-80% of all candidates
The remainder of all drug candidates that fail – 63% of all drugs placed into trials period – fail due to a lack of efficacy. Even though the drugs are safe, they don’t provide benefit to the patients by treating their disease.

From these coarse numbers, it’s clear that the highest leverage point in our drug development process is increasing the efficacy rate of new candidate medicines…

…Efficacy failures can broadly occur for two reasons:

1. Engagement failures: We chose the right biology (“target”) to manipulate, but our drug candidate failed to achieve the desired manipulation. This is the closest thing drug development has to an engineering problem.
2. Target failures: The drug candidate manipulated our chosen biology exactly as expected. Unfortunately, the target failed to have the desired effect on the disease. This is a scientific or epistemic failure, rather than an engineering problem. We simply failed to understand the biology well enough to intervene and benefit patients.

It’s difficult to know exactly the exact frequency of these two failure modes, but we can infer from a few sources that target failures dominate.

• Success rates for biosimilar drugs hitting known targets are extremely high, >80%
• Drugs against targets with genetic evidence have a 2-3 fold higher success rate than those against targets lacking this evidence, suggesting that picking good targets is a high source of leverage
• Among organizations with meaningful internal data, picking the right target is considered the first priority of all programs (e.g. “Right target” is the first tenet of AstraZeneca’s “5Rs” framework).

The predominance of target failures has likewise led most companies working on new modalities to address a small set of targets with well-validated biology. This has led to dozens of potential medicines “crowding” on the same targets, and this trend is increasing over time…

…If searching for targets is the limiting reagent in our medicine production function, the difficulty of finding targets must increase over time in order to explain part of Eroom’s law. How could this be the case given all the improvements in underlying biomedical science?

In an influential paper “Are ideas getting harder to find?”, Nicholas Bloom and colleagues argue that many fields of invention suffer from diminishing returns to investment. Intuitively, the low hanging fruit in a given discipline is picked early and more investment is required merely to reap the same harvest from higher branches on the tree of ideas…

…Targets are getting harder to find not because we are getting worse at selection, but because many of the easy and obvious therapeutic hypotheses have already been exploited….

…While promising, human genetics can only reveal a certain class of targets. The larger the effect size of a genetic variant, the less frequently it appears in the population due to selective pressure. In effect, this means that the largest effects in biology are the least likely to be discovered using human genetics. Many of the best known targets have minimal genetic signal for this reason.

Our current methods are good at discovering individual genes that associate with health, but discovering combinations of genes is nascent at best. Human genetics cannot help us discover the combinatorial medicines or gene circuits to install in a cell therapy…

…Even with the best possible experimental methods, some of the most promising target biologies will never be searched exhaustively. There are a nearly infinite number of combinatorial genetic interventions we might drug, synthetic circuits we might engineer into cells, and changes in tissue composition we might engender.

Artificial intelligence models can learn general models from the data generated in functional genomics experiments of many flavors, predicting outcomes for the experiments we haven’t yet run. If we manage to construct a performant model for a given class of target biologies, we may be able to increase the efficiency of target discovery by many orders-of-magnitude. The cost of discovering a target could conceivably go from >$1B to <$1M.

There’s growing interest in the idea of combining these technologies to build “virtual cells,” models that can predict the outcomes of target discovery experiments in silico before they’re ever executed in the lab. The grand version of this vision spans all possible target biologies, from gene inhibitions to polypharmaceutical small molecule treatments. In the maximal form, it may take many years to realize.

More limited realizations though are tractable today. The initial versions of these models are already emerging within early Predictive Biology companies. As a few examples, Recursion is building models of genetic perturbations in cancer cells, Tahoe Tx is building models in oncology with a chemical biology approach, and NewLimit has developed models for reprogramming cell age across human cell types13. Focused models like these represent an early demonstration that this general approach can yield therapeutic value…

…We are entering an epoch of abundant intelligence. With these tools, we have the opportunity to discover & design target biologies at a rate that’s too cheap to meter. The therapies that emerge could serve as the counterexample that downgrades Eroom’s law to a historic conjecture.

3. What I learned watching 78 videos from Tesla’s Austin robotaxis – Timothy B. Lee

I’ve watched 78 videos posted by pro-Tesla influencers who got early access to the service. Those videos documented more than 16 hours of driving time across nearly 100 rides.

These videos exceeded my expectations. Tesla’s robotaxi rollout wasn’t perfect, but it went as well as anyone could have expected. A handful of minor glitches got outsized attention online, but a large majority of trips were completed without incident…

…Tesla’s robotaxis drove flawlessly during the vast majority of the 16 hours of driving footage I watched. They stayed in their lane, followed traffic laws, and interacted smoothly with other vehicles…

…Tesla’s most widely discussed error occurred around seven minutes into this video. The robotaxi approached an intersection and got into the left turn lane. But the robotaxi couldn’t make up its mind whether it wanted to turn left or go straight. The car’s steering wheel jerked back and forth several times. On the car’s display, the blue ribbon showing the car’s intended path jumped back and forth erratically between turning left or continuing straight. Finally, the Tesla decided to proceed straight but ended up driving the wrong way in the opposite left turn lane…

…But in a piece last year, I argued that they were misunderstanding the situation.

“Tesla hasn’t started driverless testing because its software isn’t ready,” I wrote. “For now, geographic restrictions and remote assistance aren’t needed because there’s always a human being behind the wheel. But I predict that when Tesla begins its driverless transition, it will realize that safety requires a Waymo-style incremental rollout.”

That’s exactly what’s happened:

• Just as Waymo launched its fully driverless service in 50 square miles near Phoenix in 2020, so Tesla launched its robotaxi service in about 30 square miles of Austin last month.
• Across 16 hours of driving, I never saw Tesla’s robotaxi drive on a freeway or go faster than 43 miles per hour. Waymo’s maximum speed is currently 50 miles per hour.
• Tesla has built a teleoperation capability for its robotaxis. One job posting last year advertised for an engineer to develop this capability. It stated that “our remote operators are transported into the device’s world using a state-of-the-art VR rig that allows them to remotely perform complex and intricate tasks.”

The launch of Tesla’s robotaxi service in Austin is a major step toward full autonomy. But the Austin launch also makes it clear that Tesla hasn’t discovered an alternative path for testing and deploying driverless vehicles. Instead, Tesla is following the same basic deployment strategy Waymo pioneered five to seven years ago.

Of course, this does not necessarily mean that Tesla will scale up its service as slowly as Waymo has. It took almost five years for Waymo to expand from its first commercial service (Phoenix in 2018) to its second (San Francisco in 2023). The best informed Tesla bulls acknowledge that Waymo is currently in the lead but believe Tesla is positioned to expand much faster than Waymo did…

…Last month, Waymo published a study demonstrating that self-driving software benefits from the same kind of “scaling laws” that have driven progress in large language models.

“Model performance improves as a power-law function of the total compute budget,” the Waymo researchers wrote. “As the training compute budget grows, optimal scaling requires increasing the model size 1.5x as fast as the dataset size.”

When Waymo published this study, Tesla fans immediately seized on it as a vindication of Tesla’s strategy. Waymo trained its experimental models using 500,000 miles of driving data harvested from Waymo safety drivers driving Waymo vehicles. That’s a lot of data by most standards, but it’s far less than the data Tesla could potentially harvest from its fleet of customer-owned vehicles…

…I posed this question to Dragomir Anguelov, the head of Waymo’s AI foundations team and a co-author of Waymo’s new scaling paper. He argued that the paper’s implications are more complicated than Tesla fans think.

“We are not driving a data center on wheels and you don’t have all the time in the world to think,” Anguelov told me in a Monday interview. “Under these fairly important constraints, how much you can scale and what are the optimal ways of scaling is limited.”

Anguelov also pointed to an issue that will be familiar to anyone who read last month’s explainer on reinforcement learning.

Waymo’s scaling paper—like OpenAI’s famous 2020 scaling law paper—focused on models trained with imitation learning…

…Anguelov was a co-author of a 2022 Waymo paper finding that self-driving models trained with a combination of imitation and reinforcement learning tend to perform better than models trained only with imitation learning.

Imitation learning is “not the most sophisticated thing you can do,” Anguelov told me. “Imitation learning has a lot of limitations.”

This is significant because demonstration data from human drivers—the kind of data Tesla has in abundance—isn’t very helpful for reinforcement learning. Reinforcement learning works by having a model try to solve a task and then judging whether it succeeded. For self-driving, this can mean having a model “drive” in simulation and then judging whether it caused a collision or other problems. Or it can mean running the software on real cars and having a safety driver intervene if the model makes a mistake. In either case, it’s not obvious that having vast amounts of human driving data is especially helpful.

One finding from that 2022 paper is particularly relevant for thinking about the performance of Tesla’s robotaxis. The Waymo researchers noted that models trained only with imitation learning tend to drive well in common situations but make mistakes in “more unusual or dangerous situations that occur only rarely in the data.”

In other words, if you rely too much on imitation learning, you can end up with a model that drives like an expert human most of the time but occasionally makes catastrophic mistakes…

…Since its 2018 launch, Waymo has acknowledged that it has remote operators who sometimes provide real-time assistance to its vehicles. But Waymo has also said that these remote operators never drive the vehicles in real time. Instead, they provide high-level feedback, while the vehicle always remains in control of second-by-second decisions.

In contrast, Tesla’s job posting stated that teleoperators can be “transported into the device’s world” so that they can “remotely perform complex and intricate tasks.” Could those “complex and intricate tasks” include driving the car for seconds or even minutes at a time?

In the videos I watched, a number of Tesla’s early customers commented on how human-like Tesla’s driving was. That might just be a tribute to the quality of Tesla’s AI model. But it’s also possible that sometimes a human driver is literally driving the vehicle from a remote location.

4. No Bad Risks, Only Bad Rates — And Other Lessons From National Indemnity Founder Jack Ringwalt – Kingswell

There are no bad risks in insurance — only bad rates

This maxim was Ringwalt’s north star, the iron-clad principle that allowed him to fearlessly pursue unusual and unwanted risks without driving himself right out of business. Almost anything can be intelligently insured, so long as you charge enough for the coverage.

(It’s also reminiscent of one of my favorite Warren Buffett lines. “I can go into an emergency ward and write life insurance,” he said in 1990, “if you let me charge enough of a premium.”)

When evaluating potential opportunities, Ringwalt’s open mind welcomed the weird and the wild — and he wrote many policies on offbeat ventures that others wouldn’t touch with a ten-foot pole. But, when it came to pricing, that flexibility vanished. If the market would not meet his rate, Ringwalt never blinked. He just waved goodbye to the deal with an indifferent shrug.

“When business is unprofitable to the companies in general,” wrote Ringwalt, “our premium volume has taken a very sharp spurt and when business has been profitable for most companies, we have run into very unintelligent competition and have had to cut down temporarily on our writings.”

The insurance merry-go-round is always the same: profitability lures rivals who slash rates to grab market share, only to crater when losses inevitably pile up. And when the industry bleeds, fly-by-night competitors vanish, prices climb back to normal, and the cycle starts spinning anew. “This pattern will keep repeating,” he wrote. “It makes no sense, but it’s human nature.”

Ringwalt steadfastly refused to play that sucker’s game — a tradition that continued under Berkshire’s aegis. From 1986 to 1999, National Indemnity’s revenue nosedived 85% as profitable premiums evaporated. But, rather than succumb to the pressure to write more business at any price, Buffett and co. urged employees to wait patiently for the right pitch (so to speak). Some things never change.

5. Why I don’t think AGI is right around the corner – Dwarkesh Patel

Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the internet. I disagree. I think the LLMs of today are magical. But the reason that the Fortune 500 aren’t using them to transform their workflows isn’t because the management is too stodgy. Rather, I think it’s genuinely hard to get normal humanlike labor out of LLMs. And this has to do with some fundamental capabilities these models lack…

…But the fundamental problem is that LLMs don’t get better over time the way a human would. The lack of continual learning is a huge huge problem. The LLM baseline at many tasks might be higher than an average human’s. But there’s no way to give a model high level feedback. You’re stuck with the abilities you get out of the box. You can keep messing around with the system prompt. In practice this just doesn’t produce anything even close to the kind of learning and improvement that human employees experience.

The reason humans are so useful is not mainly their raw intelligence. It’s their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task.

How do you teach a kid to play a saxophone? You have her try to blow into one, listen to how it sounds, and adjust. Now imagine teaching saxophone this way instead: A student takes one attempt. The moment they make a mistake, you send them away and write detailed instructions about what went wrong. The next student reads your notes and tries to play Charlie Parker cold. When they fail, you refine the instructions for the next student.

This just wouldn’t work. No matter how well honed your prompt is, no kid is just going to learn how to play saxophone from just reading your instructions. But this is the only modality we as users have to ‘teach’ LLMs anything…

…When we do solve continuous learning, we’ll see a huge discontinuity in the value of the models. Even if there isn’t a software only singularity (with models rapidly building smarter and smarter successor systems), we might still see something that looks like a broadly deployed intelligence explosion. AIs will be getting broadly deployed through the economy, doing different jobs and learning while doing them in the way humans can. But unlike humans, these models can amalgamate their learnings across all their copies. So one AI is basically learning how to do every single job in the world. An AI that is capable of online learning might functionally become a superintelligence quite rapidly without any further algorithmic progrss…

…But here are the timelines where I’d take a 50/50 bet:

• AI can do taxes end-to-end for my small business as well as a competent general manager could in a week: including chasing down all the receipts on different websites, finding all the missing pieces, emailing back and forth with anyone we need to hassle for invoices, filling out the form, and sending it to the IRS: 2028
  I think we’re in the GPT 2 era for computer use. But we have no pretraining corpus, and the models are optimizing for a much sparser reward over a much longer time horizon using action primitives they’re unfamiliar with. That being said, the base model is decently smart and might have a good prior over computer use tasks, plus there’s a lot more compute and AI researchers in the world, so it might even out. Preparing taxes for a small business feels like for computer use what GPT 4 was for language. It took 4 years to get from GPT 2 to GPT 4. Just to clarify, I am not saying that we won’t have really cool computer use demos in 2026 and 2027 (GPT-3 was super cool, but not that practically useful). I’m saying that these models won’t be capable of end-to-end handling a week long and quite involved project which involves computer use.
• AI learns on the job as easily, organically, seamlessly, and quickly as a human, for any white collar work. For example, if I hire an AI video editor, after six months, it has as much actionable, deep understanding of my preferences, our channel, what works for the audience, etc as a human would: 2032
  While I don’t see an obvious way to slot in continuous online learning into current models, 7 years is a long time! GPT 1 had just come out this time 7 years ago. It doesn’t seem implausible to me that over the next 7 years, we’ll find some way for models to learn on the job.

You might react, “Wait you made this huge fuss about continual learning being such a handicap. But then your timeline is that we’re 7 years away from what would at minimum be a broadly deployed intelligence explosion.” And yeah, you’re right. I’m forecasting a pretty wild world within a relatively short amount of time.

AGI timelines are very lognormal. It’s either this decade or bust. (Not really bust, more like lower marginal probability per year – but that’s less catchy).AI progress over the last decade has been driven by scaling training compute of frontier systems (over 4x a year). This cannot continue beyond this decade, whether you look at chips, power, even fraction of raw GDP used on training. After 2030, AI progress has to mostly come from algorithmic progress. But even there the low hanging fruit will be plucked (at least under the deep learning paradigm). So the yearly probability of AGI craters.

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

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

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

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

Here are the articles for the week ending 06 July 2025:

1. Etched is Making the Biggest Bet in AI – Etched team

We’ve spent the past two years building Sohu, the world’s first specialized chip (ASIC) for transformers (the “T” in ChatGPT).

By burning the transformer architecture into our chip, we can’t run most traditional AI models: the DLRMs powering Instagram ads, protein-folding models like AlphaFold 2, or older image models like Stable Diffusion 2. We can’t run CNNs, RNNs, or LSTMs either.

But for transformers, Sohu is the fastest chip of all time. It’s not even close.

With over 500,000 tokens per second in Llama 70B throughput, Sohu lets you build products impossible on GPUs. Sohu is an order of magnitude faster and cheaper than even NVIDIA’s next-generation Blackwell (B200) GPUs…

…No one has ever built an algorithm-specific AI chip (ASIC). Chip projects cost $50-100M and take years to bring to production. When we started, there was no market.

Suddenly, that’s changed:

• Unprecedented Demand: Before ChatGPT, the market for transformer inference was ~$50M, and now it’s billions. All big tech companies use transformer models (OpenAI, Google, Amazon, Microsoft, Facebook, etc.).
• Convergence on Architecture: AI models used to change a lot. But since GPT-2, state-of-the-art model architectures have remained nearly identical! OpenAI’s GPT-family, Google’s PaLM, Facebook’s LLaMa, and even Tesla FSD are all transformers.

When models cost $1B+ to train and $10B+ for inference, specialized chips are inevitable. At this scale, a 1% improvement would justify a $50-100M custom chip project.

In reality, ASICs are orders of magnitude faster than GPUs. When bitcoin miners hit the market in 2014, it became cheaper to throw out GPUs than to use them to mine bitcoin…

…We believe in the hardware lottery: the models that win are the ones that can run the fastest and cheapest on hardware. Transformers are powerful, useful, and profitable enough to dominate every major AI compute market before alternatives are ready:

• Transformers power every large AI product: from agents to search to chat. AI labs have spent hundreds of millions of dollars in R&D to optimize GPUs for transformers. The current and next-generation state-of-the-art models are transformers.
• As models scale from $1B to $10B to $100B training runs in the next few years, the risk of testing new architectures skyrockets. Instead of re-testing scaling laws and performance, time is better spent building features on top of transformers, such as multi-token prediction.
• Today’s software stack is optimized for transformers. Every popular library (TensorRT-LLM, vLLM, Huggingface TGI, etc.) has special kernels for running transformer models on GPUs. Many features built on top of transformers aren’t easily supported in alternatives (ex. speculative decoding, tree search).
• Tomorrow’s hardware stack will be optimized for transformers. NVIDIA’s GB200s have special support for transformers (TransformerEngine). ASICs like Sohu entering the market mark the point of no return. Transformer killers will need to run on GPUs faster than transformers run on Sohu. If that happens, we’ll build an ASIC for that too!…

…Isn’t inference bottlenecked on memory bandwidth, not compute?

Actually, for modern models like Llama-3, no!

Let’s use NVIDIA and AMD’s standard benchmark13: 2048 input tokens and 128 output tokens. Most AI products have much longer prompts than completions (even a new Claude chat has 1,000+ tokens in the system prompt).

On GPUs and on Sohu, inference is run in batches. Each batch loads all of the model weights once, and re-uses them across every token in the batch. Generally, LLM inputs are compute-bound, and LLM outputs are memory-bound. When we combine input and output tokens with continuous batching, the workload becomes very compute bound…

…We can scale up the same trick to run Llama-3-70B with 2048 input tokens and 128 output tokens. Have each batch consist of 2048 input tokens for one sequence, and 127 output tokens for 127 different sequences.

If we do this, each batch will require about (2048 + 127) × 70B params × 2 bytes per param = 304 TFLOPs, while only needing to load 70B params × 2 bytes per param = 140 GB of model weights and about 127 × 64 × 8 × 128 × (2048 + 127) × 2 × 2 = 72GB of KV cache weights. That’s far more compute than memory bandwidth: an H200 would need 6.8 PFLOPS of compute in order to max out its memory bandwidth. And that’s at 100% utilization – if utilization was 30%, you’d need 3x more.

Since Sohu has so much compute with very high utilization, we can run enormous throughputs without bottlenecking on memory bandwidth…

…On GPUs and TPUs, software is a nightmare. Handling arbitrary CUDA and PyTorch code requires an incredibly complicated compiler. Third-party AI chips (AMD, Intel, AWS, etc.) have together spent billions on software to little avail.

But since Sohu only runs transformers, we only need to write software for transformers!

Most companies running open-source or internal models use a transformer-specific inference library like TensorRT-LLM, vLLM, or HuggingFace’s TGI. These frameworks are very rigid – while you can tweak model hyperparameters, changing the underlying model code is not really supported. But this is fine – since all transformer models are so similar (even text/image/video ones), tweaking the hyperparameters is all you really need.

2. Lots More on What’s Going On in Iran’s Markets (Transcript Here) – Tracy Alloway, Joe Weisenthal, and Maciej Wojtal

Maciej: If I can just comment on one thing, because the way you introduced Iran is the perfect way to show the country. It’s the size of Turkey in terms of population and actually geographical size as well. But if you compare the economy of Turkey and Iran, it’s around five times smaller. So Iran is around five times smaller and if you look at the composition of the economy, Turkey has no natural resources, so they have to import the whole energy commodities they consume. So Iran has a similar size of potential non-commodity GDP that it could grow to, from the current let’s say $250 billion to $1.1 trillion GDP that Turkey has. But also on top of this, has resources that are actually – if you combine gas and oil, they are bigger than Saudi Arabia’s and Saudi Arabia is another one, I think $1.3 trillion economy. This is a good way to just frame Iran as to show Iran, as it’s a big country that should really be having much bigger economy. Because of sanctions, various reasons and so on, it’s been underdeveloped. But the scale of this underdevelopment is like 10x.

Tracy: And because of the sanctions we can’t actually go and look up what’s happening in Tehran’s stock market. So why don’t you give us an overview of what it’s been like for the past week given geopolitical events?

Maciej: So for the past week it was difficult for everyone to check what was going on in Iran because internet was shut down basically. I could communicate with my team on the ground in Tehran once a day when they had signal and sometimes it was WhatsApp that was working, sometimes Telegram. But it was maybe once or twice per day. So what was going on in the market was simply nothing. The stock market hasn’t opened. The exchange of fire between Iran and Israel happened on a Friday, which is weekend in Iran, and then on the following Saturday there was an important religious holiday, so the market and actually the whole economy was supposed to be closed anyway. The economic activity, the market, was supposed to resume on a Sunday but they didn’t open. So the stock market, pretty much most of the currency market, has been closed for the last two weeks…

…Maciej: For example right now, when you have the stock market, the currency market were shut down, but you could track what’s going on with the exchange rate of the Iranian rial versus dollar either on Telegram chats but also on cryptocurrency exchanges. You have liquid market on stable coins versus Iranian rial inside of Iran where liquidity was limited during the last period anyway, but we could see the changes. So we knew that $1 before the war was at around 830,000 rials, then it went up roughly 15% to 950,000 and now after the ceasefire, it’s back down at 850,000. You can track the market, you can actually make transactions depending on the vol, on the liquidity, but it is possible. To be honest, when I saw those exchange rates moves 15% when you have a war where a lot of commentators were saying that this could turn into a massive worldwide conflict, that 15% in a country like Iran I would say that this is your usual volatility on the currency market…

…Maciej: In Tehran, a lot of residents were just relocating out of Tehran. Tehran is a big city, 12 million people, and they were moving mainly north to some smaller cities by the Caspian Sea. You had massive congestion. People were spending hours in traffic jams trying to get out of Tehran. There was not enough petrol on gas stations just because of this peak in demand. You had some petrol rationing.

Then I was asking them is the economy working, not working? Everything that was non-essential basically was closed. So you couldn’t build, buying materials or anything like this. But groceries, pharmaceuticals, gas stations, banks, this was all open and working properly with some disruptions. But those disruptions, for example, if you wanted to buy groceries in north of Iran where everyone has just relocated, you had some logistical bottlenecks. Distribution was not fast enough, so you had some shortages just for a little while. With banks, some branches were not operating at 100% capacity. Two banks got hacked. You had some cyber attacks on two banks in Iran and one cryptocurrency exchange. The rest of the banking sector was working without any disruptions. You could get cash from any ATM. There were no problems like these…

…Maciej: It’s interesting because there is information, very up-to-date detailed information on Iranian stocks available in Iran. But the majority of this information is not accessible if you’re trying to access it from a computer with your IP address outside of Iran. A lot of this information is restricted to Iran IP only. You cannot find anywhere on the whole internet. There is no website that shows the stock market index in dollars. When we send it out to our investors or just people who want to read news about the stock market in Iran, we are the only source of this information. This is quite amazing. It’s a country of 90 million people and stock market with 700 companies and there is no single place in internet that would show you the only important index…

…Maciej: In terms of oil, it is not really publicly traded. There is one Iranian monopoly called National Iranian Oil Corporation or company that is responsible for production. I think this is all centralized in one company and this is held by the government so it’s not publicly listed. You have some exposure to oil through oil refineries that are listed but refineries, they’re not sensitive to the price of oil. They are sensitive to the crack spread, which defines their refining margin, so they are not really a proxy to oil prices.

The whole stock market actually is well diversified. You have  large sectors such as chemicals – mainly these are like petrochemicals – companies that produce different products, different versions, use natural gas that is in in large supply as a cheap commodity and produce fertilizers or products like this. This is probably 20% of the stock market.

Then you have steel companies. The largest steel company in the Middle East is in Iran. You have car makers that produce more than 1 million cars a year. With car manufacturers, you have all the related industries, suppliers, to the car manufacturing businesses. You have banks – financials is an important sector – plus some consumer exposure, some building materials, cement companies are one of the best performers over the last few years actually…

…Maciej: I’ll get back to the potential for GDP in a moment. But the catalyst is absolutely clear. It must be the opening up of Iran as a country, and opening up of the economy, andthe US sanctions lifted. There must be an agreement between the US and Iran. What needs to happen? Some sort of political change. Political attitude must change on both sides. But to be honest, many analysts were expecting some big dramatic event that needs to happen in Iran for the country to properly open up.

When you look at Iran right now and you compare to let’s say even a few years ago when you had negotiations with the US, what were the biggest problem was, it was always about two things: (1) Iran enriching uranium too much basically, at a wrong level, and (2) Iranian regional policies, so financing proxies from Hezbollah to Hamas, Assad in Syria and so on. These two things were always the problem that they couldn’t negotiate over. When you look at it right now, to a large extent both obstacles are gone…

…Joe: Are there tech companies that trade on the Tehran stock market?

Maciej: There are tech companies. The ones that are listed are related to enterprise software, the Oracle or SAP, German SAP. But you have privately held companies that would like to IPO but they are just waiting for the approval from the regulator, and these are quite amazing companies. You have Snapp, which is like an Uber, but Snapp has more rides in Tehran than Uber in any city in the world. It’s a really world-class company. You have Digikala, which is like Amazon basically, also a large company, one of the biggest success stories.

3. Stablecoins might revolutionise payments, but what if they don’t? – Bryce Elder

That leaves payments:

While in a theoretical tokenized/blockchain based world, stablecoin-based payments would be faster, more efficient and interoperable, in practice at the moment these stablecoin based payments mostly start and finish with fiat, thus requiring on/off-ramps. This on/off ramp requirement adds significant friction/cost to the use of stablecoins for payments, making it less attractive compared to traditional financial systems, in particular if one takes into account the emergence of faster payment rails in the traditional financial system via fintech advancements in recent years. As a result, we find rather unrealistic the expectation of a massive increase in the use of stablecoins in payments. Indeed, our colleagues in US short-term rates research also note that market participants at the front end are skeptical of significant growth in the near term, in part due to the fact that the infrastructure/ecosystem for stablecoins remains underdeveloped. But even if one adopts an optimistic view and assumes, for example, a tenfold increase in the use of stablecoins in payments over the next couple of years, the stablecoin universe would only expand by $15bn x 10 = $150bn.

Stablecoin optimists point to the rapid adoption of the e-CNY, China’s central bank digital yuan, which has grown to a more than Rmb300bn market cap from Rmb13.6bn at the end of 2022. There’s no comparison, JPMorgan says:

First, the digital yuan is a central bank liability and thus it effectively replaces banknotes in circulation. While there does not appear to be a published target share of M0, there have been suggestions that a 10-15% share of M0 is a plausible medium-term goal, which would imply around RMB 1.3-2tr using current M0 levels. By contrast, stablecoins are a form of a tokenized MMF with zero interest, effectively a private sector liability rather than a central bank liability.

Second, the digital yuan does not operate through a fully decentralized blockchain-based ledger. Instead, it operates via a centralized network supervised by the PBoC and competes with other mobile/ electronic payment options in China such as Alipay and WeChat Pay.

Then is it better to think of stablecoins as global equivalents to Alipay and WeChat Pay? JPMorgan says no. Fintech payment companies offering collateralised electronic private money on their own platforms hasn’t proven the need for public blockchains; if anything, it proves the opposite:

Alipay/WeChat Pay digital money are private liabilities and are perhaps more similar to bank deposits in that regard which are also private liabilities. The difference between bank deposits and Alipay/WeChat balances is that the latter are backed by reserve funds that in turn hold public liabilities i.e. central bank reserves, while bank deposits are matched on the asset side by a mix of loans and debt securities, though they do have an additional guarantee via deposit protection arrangements.

In our mind, the strong expansion of Alipay and WeChat Pay should be viewed through the lens of a fintech payments revolution over the past decade in China that utilizes and increases the efficiency of traditional banking/financial system networks, rather than through the lens of a blockchain/crypto ecosystem revolution. In fact, it could be argued that the success and continued advancements in payments by fintechs, such as Alipay and WeChat Pay reduce the need for blockchain-based payment systems in the future.

4. Meet Project Rainier, Amazon’s one-of-a-kind machine ushering in the next generation of AI – Kirsteen Rodger

Project Rainier is designed as a massive “EC2 UltraCluster of Trainium2 UltraServers.” The first part refers to Amazon Elastic Compute Cloud (EC2), an AWS service that lets customers rent virtual computers in the cloud rather than buying and maintaining their own physical servers.

The more interesting bit is Trainium2, a custom-designed AWS computer chip built specifically for training AI systems. Unlike the general-purpose chips in your laptop or phone, Trainium2 is specialized for processing the enormous amounts of data required to teach AI models how to complete all manner of different and increasingly complex tasks—fast.

To put the power of Trainium2 in context: a single chip is capable of completing trillions of calculations a second. If, understandably, that’s a little hard to visualize: consider that it would take one person more than 31,700 years to count to one trillion. A task that would require millennia for a human to complete can be done in the blink of an eye with Trainium2…

…Traditionally, servers in a data center operate independently. If and when they need to share information, that data has to travel through external network switches. This introduces latency (i.e, delay), which is not ideal at such large scale.

AWS’s answer to this problem is the UltraServer. A new type of compute solution, an UltraServer combines four physical Trainium2 servers, each with 16 Trainium2 chips. They communicate via specialized high-speed connections called “NeuronLinks.” Identifiable by their distinctive blue cables, NeuronLinks are like dedicated express lanes, allowing data to move much faster within the system and significantly accelerating complex calculations across all 64 chips.

When you connect tens of thousands of these UltraServers and point them all at the same problem, you get Project Rainier—a mega “UltraCluster.”…

…Communication between components happens at two critical levels: the NeuronLinks provide high-bandwidth connections within UltraServers, while Elastic Fabric Adapter (EFA) networking technology (identified by its yellow cables) connects UltraServers inside and across data centers. This two-tier approach maximizes speed where it’s most needed while maintaining the flexibility to scale across multiple data center buildings.

5. OpenAI has started to form a “moat” – Rihard Jarc

I think anyone who follows the AI space knows about OpenAI and, more specifically, about ChatGPT. Even outside of investors and tech enthusiasts, the verb ChatGPT has gone viral, similar to how the verb Google started. What is even more surprising is that despite ChatGPT being out there for more than 2 years already, just recently, at the end of March, it came to another acceleration point in terms of adoption when the Ghibli photo trend emerged on ChatGPT:

The number of MAUs doubled from 400 million to 800 million in a matter of a few weeks. Looking at the adoption curves of other highly adopted technology platforms, such as TikTok, Facebook, Instagram; ChatGPT, is on a slope of its own.

Another factor to consider is that it is not just a “I must try it moment”. Looking at the number of minutes a user spends on ChatGPT, the minutes are constantly growing and have now reached the 29-minute daily mark.

Remember that at the start of ChatGPT and LLMs, many critics said that people tried it, had fun, and then didn’t use it again. This trend shows that that is not the case and that with each enhanced model version and UX improvement, the stickiness factor becomes bigger…

…OpenAI also now has serious hardware ambitions. In late May of this year, they acquired Jony Ive’s startup, a famous former Apple designer, for nearly $6.5 billion, who will now lead OpenAI’s hardware efforts. What is now almost a consensus opinion among big tech leaders is that AI will unlock the next computing platform, one that is not tied to the smartphone.

And if you listen to those conversations, everyone is calling for a similar device. A device that will be more like a companion system and will be less dependent on a screen. Proactive assistant who will run even when you don’t ask it.

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

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

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

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

Here are the articles for the week ending 29 June 2025:

1. China’s rare earth choke hold – Amber Zhang

Rare earths comprise a group of 17 elements, typically categorized into light, medium, and heavy groups. These materials are indispensable for making high-performance magnets used in both civilian and military technologies. Among them, medium and heavy rare earths — critical for aerospace, defense, and other cutting-edge sectors — are particularly scarce and difficult to source.

Don’t be fooled by their size. Rare earth magnets are no larger than a stick of chewing gum, yet pack magnetic force 15 times stronger than traditional iron magnets. Heat-resistant and cost-efficient, they are essential components in electric motors — not only in EVs and hybrid vehicles, but also in robots, drones, offshore wind turbines, missiles, and fighter jets…

…According to the International Energy Agency, China accounted for over 60% of global rare earth mining output in 2023 — and an even more dominant 92% of the world’s refining capacity. According to the International Energy Agency, China accounted for over 60% of global rare earth mining output in 2023 — and controlled a staggering 92% of global refining capacity…

…Between 2020 and 2023, 70% of the rare earth compounds and metals used in the U.S. were imported from China, according to the U.S. Geological Survey…

…Ford recently halted production for a week at its Chicago plant due to rare earth shortages, affecting its Explorer SUV line. In early June, the Motor & Equipment Manufacturers Association (MEMA), along with General Motors, Toyota, Volkswagen, Hyundai, and other major automakers, issued a joint letter warning that without a stable supply of rare earth magnets, production of essential components could come to a standstill…

…The U.S. once boasted the world’s largest rare earth magnet industry. Its Mountain Pass mine in California had supplied most of the global market since 1965. But in 1998, the mine was shut down following a pipeline leak that released trace heavy metals and radioactive materials into the Mojave Desert. Chinese firms made three separate attempts to acquire the mine — all blocked by U.S. authorities.

Alarmed by Japan’s supply crisis, the Obama administration supported Hitachi Metals’ investment in a rare earth magnet plant in North Carolina, operational from 2011 to 2013. But the costs were prohibitively high compared to China’s vertically integrated, state-backed operations in cities like Ganzhou. U.S. buyers, ultimately unwilling to pay a “made-in-America premium,” continued sourcing from Chinese suppliers. In 2020, Hitachi shut down the facility and mothballed its equipment…

…Back in 2010, Mountain Pass — the U.S.’s only remaining rare earth mine — received over $1 billion in Pentagon funding just to stay afloat. But lacking commercial competitiveness, it shut down again the following year. In 2017, MP Materials acquired the site, restarted mining operations, and began exporting raw ore to China for processing. The company now plans to begin producing rare earth magnets at a new facility in Texas by the end of this year. Still, even at full capacity, its annual output would match just a single day of production in China…

…Domestically, the Round Top project in Texas has emerged as a cornerstone of America’s rare earth strategy. Operated by U.S. Rare Earths Inc., the site holds estimated reserves of 130,000 metric tons across 16 different elements and aims to supply 20% of U.S. rare earth demand by 2027. The company is also building a $100 million magnet manufacturing facility in Oklahoma, which is expected to process up to 2,000 metric tons of rare earth materials annually.

Meanwhile, the U.S. Department of Energy has launched the ReElement initiative, allocating $50 million to recover up to 90% of rare earth elements from electric vehicle batteries by 2025. But these recycling systems have yet to achieve commercial scale and remain economically marginal.

The National Defense Authorization Act for fiscal year 2025 earmarks $1.2 billion for strategic stockpiling and $350 million for domestic development. These funds are being channeled into American firms like MP Materials, aimed at accelerating the construction of a domestic rare earth processing infrastructure…

…According to the Center for Strategic and International Studies (CSIS), the Pentagon has invested over $439 million since 2020 to develop a rare earth industrial base — but most U.S. production remains in its early stages.

RAND Corporation estimates that it would take at least 10 years and $10–15 billion in investment to establish a fully independent domestic rare earth supply chain, factoring in infrastructure, permitting, environmental compliance, and workforce training.

2. The Great Decoupling (or Why Your Clicks Are Down and Impressions Up) – Ryan Law

Impressions are increasing because AI Overviews now give companies two chances to log an impression for a given keyword: once as a “traditional” blue link in the search results, and again as a citation in an AI Overview…

…At the same time, clicks are decreasing because AI Overviews are increasing zero-click searches. Searchers can get all the information they need to resolve their query without leaving the search results page.

When we studied this at scale across 300,000 keywords, we found that the presence of an AI Overview correlates with a 34.5% reduction in clickthrough rate…

…While our clicks are tanking because of AI search, recent data from Patrick Stox shows that—at least on the Ahrefs website—visits from AI search convert 23x better than visits from traditional search.

The way content marketing functions is very different, but guess what? There are more potential customers in the world, more demand for products and services. That is the real determinant of growth, not clicks to a blog. We’ll find different ways to reach those people.

3. A Cheeky Pint with Meta CFO Susan Li (Transcript Here) – John Collison and Susan Li

Susan: When I think about it, I go back to when I was IC4 and I joined in 2008, I’m building these first revenue models. I’d gone from banking – which is super organized, super structured, they don’t even need to know your name, they just train you to immediately figure out how to find the backup to everything, so that two years later someone else can do this and so on and so forth – to there was no infrastructure. So I’m hunting down the exact engineer who has built some ad server so that he can tell me what the parameters mean. And of course, the next time he changes them, he’s not gonna tell me, and I have to go find him again, and he’s like, “Oh, she’s coming. Don’t look her way.” A few months in, I got a meeting invite for power users of SQL and I thought, “My gosh. I’d been getting a good amount of feedback about how things could be better, and here was finally this moment of recognition that – I didn’t even know how to write queries in SQL when I started.” I show up to this meeting and there are five other people and the meeting organizer tells us that we have been called because we are the five users of SQL who consume too much power. And we have just been churning with our massive joint tables through the…

John: I love that you were all called to the principal’s office.

Susan: Basically, yes. But I often think back to this because this was a data analyst who didn’t know any of us that well, but had just generated his reports of who’s using the most infrastructure and looked at the top people on the list and thought, “Okay, this person in finance, it doesn’t make sense why she’s the third highest person on the list,” and called us in and then taught us to write better queries. No one I think specifically told him to do that. I think it’s a little awkward when you call people in to do this, but he did it because it would make us all better at our jobs…

…Susan: So, there’s this very measurable part of the company and we generally try to trade those things off against each other when we’re evaluating things within that bucket and we generally try to fund the things that are positive ROI. I’m usually the person who’s trying to make sure we understand, for every individual experiment, the expected return is something, but that’s where we are on the curve today, but what about 50 experiments later? Does the curve still have the same slope?

Then there’s a set of things which we constrain more in terms of, there’s some envelope of investment that we’re willing to make that’s not in this really ROI-driven bucket. It is very difficult to pencil out what the annual revenue forecast for Reality Labs is gonna look like over the next 20 years. For bets like that, we invert the problem. But when we talk about the return on the investment, the question that we pose, as a finance organization, to Mark – and make sure that Mark and the board understand – is what does this have to be worth to pencil out at the end? Does that pass the sanity check, the intuition, about what the size of these markets can be based on maybe some comparisons to markets that exist today, but of course in another 10, 20 years, you expect that the world will look different and maybe those markets should be bigger or smaller for whatever reason. That’s the guide, which is, for this thing to succeed at the rate at which we’re investing, it needs to be worth this at the end and does that make sense?…

…Susan: I am not a tech visionary. There are many things I’m good at, but envisioning the future of the world and what I want it to be like is not one of them. I’m a very happy beneficiary of the technology built by the world around me.

But Mark very much has a vision for what he wants that world to be. And for him, I think the strategic imperative is that we have to be building these next states of the world for us to again, be a good business, but also just be a compelling company that builds technology and puts it out in the world and builds incredible experiences for people.

I remind people in the finance organization all the time, we are very good at skeptically evaluating each bet. But the point is not that we have to look at every bet and be like, “This bet is going to work.” The point is there is a portfolio of bets, and some of them are going to pay off massively beyond, in fact, what the case on paper looks like when you make the bet. Many of them are going to not work out, but the ones that pay off are gonna more than justify the overall investment strategy or the overall roadmap that you’re building toward. If we just allowed ourselves to nix everything that the paper-case didn’t seem high-confidence, then we would never make a lot of the important bets that have been really important over the history of the company…

…Susan: That is the question that I assume all of my counterparts at these companies and I are all thinking about. For us, there are the drivers of the way we’re investing in capex today. Of course, we have, first of all, just a massively-scaled consumer business and core AI infrastructure that powers all the ranking and recommendations work and so on and so forth. That’s always been a reasonably big number for us, but also because it was getting more mature that we were driving to be more efficient over time. Then now you have, among many of our peers and ourselves, this big investment to train what we all aspire to be, frontier models. If you use those models to build great and scaled consumer experiences, then how much inference compute you’re gonna need on top of that? If compute required continues to scale up in this way forever, then you’re gonna run into some true problems of physics. But hopefully, there will be different kinds of research innovations along the way that will unlock things like being able to distribute the training so you don’t need one extremely large cluster somewhere and that will help with a lot of the energy and other challenges. So there’s some question about what that looks like over time.

Then there’s this question about, “Great, you can build all this capacity, and what do you do with them if it turns out you don’t need as much compute for either training or inference as you thought?” I think a lot of us have different backup use cases. So, up to some point, we would use a lot of compute very happily still, in the core business and what we expect the core business to be, three years from today. But frankly, we’d use more compute in the core business. Now, that doesn’t scale forever. So the real question is what happens in like two years if you’ve built so much compute that you cannot envision a reasonable ROI on the backup use case if what you’re building doesn’t come to fruition. That’s something I think we’re all gonna learn in the next few years…

…Susan:  As part of not wanting to miss the boat, we built out enough capacity for Reels but also for future things. We found that we were in fact able to put that capacity towards very good use – exactly as you said. So I do think an interesting question in the future will be allocating compute as a resource, It’s a muscle we’ve built later as a company, because we had gotten very good at allocating headcount as a resource, and headcount’s really easy to account for because you have org charts, you know exactly this person reports to this person, to this person, this person is incontrovertibly working on Facebook Marketplace, for example. GPUs don’t have that property. In fact, you often want to build out your infrastructure for it to be very fungible. Because you need to divert capacity to where – suddenly something has happened in India and you want a lot of compute to be available to be used there. So it’s not like this GPU is labeled for Facebook Marketplace, and this is labeled for – it’s actually quite a bit more difficult to account for where the capacity is being used at any given point in time. That means it’s harder to manage, and it’s harder to create the incentives around are you using GPUs efficiently?

John: You allow people to trade between people and GPUs, right?

Susan: In the budgeting process, we have allowed people to trade. Not too surprisingly, even though you’ll find that groups are often asking for compute, when that particular trade is on offer, people almost never trade for compute for exactly the reason I described, which is that if they get allocated 100 new headcount, there is no chance that 26 of those headcount will accidentally be working for something else.

4. My Trip to Washington to Get in Sync with Republican and Democratic Leaders on the Budget and Debt Situation – Ray Dalio

Everyone I spoke with on both sides agreed that:

• We are likely to have a big debt-economic crisis if we don’t get the budget deficit down to 3 percent of GDP, so 3 percent should be an agreed-on goal,
• Getting the deficit to 3 percent will require both spending cuts and tax revenue increases because if they come from just spending cuts or just tax revenue increases alone, the cuts or increases would be too big and shocking.
• It’s not possible for politicians to say these things publicly even though they believe them because they would be thrown out of office…

…So, our biggest problem is that our country’s political representatives can’t even say, let alone do, what they need to do to fix our debt issues because their constituents would throw them out of office if they did that. Such is the condition of our political decision-making system.

We discussed my idea of a “3 percent 3-part solution,” which would be to cut the budget deficit to 3 percent of GDP through a mix of spending cuts, tax revenue increases, and interest rate cuts. For example, cutting spending by 4 percent, increasing tax revenue by 4 percent, and lowering the real interest rate by 1%** so that the adjustments wouldn’t be unbearably large to achieve that 3% deficit goal. The leaders I spoke with said that they’d love to do this or something like it — in fact, they thought it would be wonderful if the “meme” of reducing the deficit in this way took hold in the electorate and there was public pressure to get it done.

As for where things are likely to go, there won’t be big enough changes to the current proposed budget to change the overall picture this tax year.

5. The Speed of Patience – Paul Higgins

To understand how patient preparation creates decisive speed, I’ll show you three different maps of the same territory I’ve found practical.

1. Pace layers reveal where to be patient and where to be urgent, showing how businesses operate across multiple timescales simultaneously, from seasonal fashion to generational culture.
2. S-curves illuminate when those layers will hit their inflection points, helping you recognize which growth curve you’re actually betting on.
3. Trust as a leading indicator – what emerges from ongoing interactions across and between layers (employees, communities, customers and processes), the invisible asset that compounds for decades…

…I like Stewart Brand’s pace layering framework for understanding how businesses operate across time. It reveals why this matters so profoundly. In most complex systems, different elements change at different speeds. Fashion moves seasonally, commerce shifts yearly, infrastructure evolves over decades, governance changes generationally, and culture moves so slowly it appears frozen in time…

…Layers don’t exist separately, they form a single, interconnected living system which is sometimes hard to see. We tend to see layers as independent parts to optimize separately, but in living systems, layers are how the whole organism breathes – each rhythm nested within another, each movement part of a larger dance. The fast movements at the surface and slow currents in the depths aren’t separate phenomena but the system’s way of being alive at every scale simultaneously. Speed doesn’t come from stability – they arise together from the coherence of the whole system…

…Apple master this temporal arbitrage. New iPhone colors arrive every season to satisfy the fashion layer, while annual product cycles drive the commerce layer with reliable predictability. But the iOS ecosystem, which represents their true competitive moat, took twenty years to build in the infrastructure layer, creating switching costs and network effects that compound with each passing year. Their App Store governance evolves with glacial deliberation, each change carefully considered for its long-term implications, while their design philosophy – the cultural layer that infuses everything they create – hasn’t fundamentally changed since Jobs articulated it decades ago. You just have to look at their cumulative cash reserves to see whether they have the capacity to keep it up or not.

Competitors try to destroy Apple’s fashion layer moat and assume that’s the game being played. They miss the insight that Apple’s speed in the fashion layer comes from stability in the infrastructure layer, that the layers aren’t independent but deeply interdependent, with the slow layers enabling the fast ones to move with confidence and clarity…

… In business, you’re never riding just one S-curve. You’re managing a portfolio of them, each operating at different speeds across different layers of your organization. Your product adoption might be hitting exponential growth (measured in months) while your infrastructure build-out is still in early grind (measured in years) and your culture formation hasn’t even begun its curve (measured in decades)…

…Netflix understood this with brutal clarity. In 2010, they were shipping 2 million DVDs daily – a massive operation at the peak of its S-curve. But Reed Hastings saw streaming was at the bottom of its S-curve, barely functional, with terrible selection and constant buffering. While Blockbuster optimized their mature retail model, Netflix deliberately cannibalized their profitable DVD business to ride the next wave. They moved $200 million from DVD operations into streaming content when streaming represented less than 20% of revenue. Today Netflix is worth $240 billion; Blockbuster is a cautionary tale…

…Kerry Group’s transformation from Ireland’s smallest dairy cooperative to a €6.3 billion ingredients empire illustrates how patience creates opportunities invisible to those focused on shorter horizons. Every dairy producer faced the same challenge with whey, the protein-rich liquid left over from cheese-making that represented both a disposal cost and a compliance headache. While the entire industry treated this as expensive waste, Kerry’s leadership recognized something profound: they were looking at two different S-curves operating on completely different timescales.

The dairy business that consumed everyone’s attention was approaching the top of its S-curve, with margins thinning and consolidation inevitable, while the ingredients business hadn’t even begun its exponential climb. For fifteen years, Kerry invested in extraction technology and scientific capabilities while competitors focused on optimizing dairy margins. By the time health consciousness and specialized nutrition exploded into mainstream consciousness, Kerry had spent two decades perfecting protein extraction, understanding molecular structures, and building relationships with food manufacturers who needed exactly these capabilities…

…Warren Buffett’s 2008 moves exemplified how trust operates across all three maps simultaneously. While others mocked Berkshire’s growing cash pile – $40 billion sitting “idle” – he was building in the infrastructure layer (pace layers), preparing for the inevitable down-cycle in financial services’ S-curve, and accumulating trust with every patient year. That cash pile represented more than financial capacity; it was trust crystallized into capital. Every year Buffett didn’t chase returns, every quarter he resisted leverage, every deal he walked away from, he was depositing into an invisible trust account. When 2008 hit, that patient accumulation enabled lightning-fast execution: $8 billion deployed to Goldman Sachs with one phone call. The $7.7 billion total return exceeded Coca-Cola’s entire 20-year dividend stream to Berkshire. Trust had compressed decades into days.

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

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

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

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

Here are the articles for the week ending 22 June 2025:

1. Message from CEO Andy Jassy: Some thoughts on Generative AI – Andy Jassy

Today, in virtually every corner of the company, we’re using Generative AI to make customers lives better and easier. What started as deep conviction that every customer experience would be reinvented using AI, and that altogether new experiences we’ve only dreamed of would become possible, is rapidly becoming reality. Technologies like Generative AI are rare; they come about once-in-a-lifetime, and completely change what’s possible for customers and businesses…

…You can see it in Advertising where we’ve built a suite of AI tools that make it easier for brands to plan, onboard, create and optimize campaigns. In Q1 alone, over 50K advertisers used these capabilities…

…We’re also using Generative AI broadly across our internal operations. In our fulfillment network, we’re using AI to improve inventory placement, demand forecasting, and the efficiency of our robots—all of which have improved cost to serve and delivery speed. We’ve rebuilt our Customer Service Chatbot with GenAI, providing an even better experience than we’d had before. And, we’re assembling more intelligent and compelling product detail pages from leveraging GenAI…

…First, we have strong conviction that AI agents will change how we all work and live. Think of agents as software systems that use AI to perform tasks on behalf of users or other systems. Agents let you tell them what you want (often in natural language), and do things like scour the web (and various data sources) and summarize results, engage in deep research, write code, find anomalies, highlight interesting insights, translate language and code into other variants, and automate a lot of tasks that consume our time. There will be billions of these agents, across every company and in every imaginable field. There will also be agents that routinely do things for you outside of work, from shopping to travel to daily chores and tasks. Many of these agents have yet to be built, but make no mistake, they’re coming, and coming fast.

Second, and what makes this agentic future so compelling for Amazon, is that these agents are going to change the scope and speed at which we can innovate for customers. Agents will allow us to start almost everything from a more advanced starting point…

…Today, we have over 1,000 Generative AI services and applications in progress or built, but at our scale, that’s a small fraction of what we will ultimately build. We’re going to lean in further in the coming months. We’re going to make it much easier to build agents, and then build (or partner) on several new agents across all of our business units and G&A areas.

As we roll out more Generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs. It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.

2. Experiencing the Real “Belt and Road” – Nina Chen

In early June, I traveled in Central Asia for 9 days, visiting two countries. I spent 2 days in Almaty, Kazakhstan, and 7 days in Uzbekistan, covering Tashkent, Samarkand, and Bukhara…

…We flew from Almaty, Kazakhstan, to Tashkent, the capital of Uzbekistan. Even before landing, it was clear that Uzbekistan and China have a close partnership. On the flight, there were many Chinese merchants and workers traveling in groups…

…When we arrived at the airport, the sense of close cooperation was even stronger. The airport signs had Chinese translations, and there was a billboard in the walkway advertising the “UZ-China Silk Road Free Trade Special Zone.”…

…While we didn’t meet any locals in Uzbekistan who’d been to China, in Kazakhstan, we met a Kazakh girl with fluent Chinese. We joined a day tour to the lakes and canyons near Almaty. With many Chinese tourists in our group, she translated for us when we couldn’t understand the guide. She studied in Chongqing(*) and worked in Yiwu, Zhejiang province (*), where her Chinese boss ran a company exporting goods from China to former Soviet countries like Moscow, Azerbaijan, and Central Asian cities.

This made me feel that trade between China and Central Asia is largely a one-way flow, from China to Central Asia, with China’s economic influence in the region being substantial…

…At the Tashkent City Mall, the premier shopping destination in Uzbekistan’s capital, I was surprised to find stores for well-known Chinese sportswear brands Anta, Li-Ning, and Xtep all located in close proximity.

I decided to explore the Anta store first. Picking up a pair of PG 7 running shoes (the PG 7 refers to the midsole technology), I noticed the price tag read 1,103,000 Uzbekistani som (approximately 612 Chinese yuan, US$87), which is significantly higher than the price in China (where it’s around 200-300 yuan, US$29–43 on Tmall). However, the store currently has a promotion: buy one pair and get the second at 50% off (effectively 459 yuan per pair, US$66) or buy two pairs and get the third free (bringing the cost down to 408 yuan per pair, US$58). Even with the discounts, the price is still higher than in China. When I asked the store manager if Anta is considered a premium brand in Uzbekistan, he confirmed it is. Surprised, I inquired if only the wealthy can afford it. He explained that due to the popularity of digital payments, many people, especially the youth, opt for installment plans…

… Central Asia has many Chinese-made beauty and skincare products that aren’t available in China.

An example is “Shanghai Song,” with packaging featuring a classic Chinese vintage design. The brand’s slogan states: “Inspired by myths and legends, it’s about Shanghai in the Song period, which ruled one of China’s most glorious cultural eras in the long-flowing Eastern cultural river.”

I found this puzzling. First, the specific myths or legends that served as inspiration aren’t clear, giving it a mysterious and abstract feel. Second, to the best of my knowledge, during the Song Dynasty, the economic and cultural centers of the Northern Song were in Kaifeng, and those of the Southern Song were in Hangzhou, not in Shanghai. Perhaps “Shanghai Song” represents a blend of the modern and the classical, or maybe the company behind the brand has a special affection for Shanghai.

When I picked up a bottle of cream and examined it closely, I found that the company is based in Guangzhou. Well, it’s likely that “Shanghai Song” is a brand from Guangzhou that embodies what Chinese people think Central Asians imagine about China and the East.

3. The Capital Cycle Way – Omar Malik

The capital cycle best explains how changes in the amount of capital employed within an industry will impact profits and future returns on capital.

Central to the capital cycle approach is the observation that an industry with high returns on capital tends to attract new entrants. For incumbents, high profitability loosens discipline because management incentives often align with growth. Therefore, both groups will increase spending to capture those high returns. The behavioural pattern of herding often means all the players in an industry invest simultaneously…

…A key characteristic of this cycle is the delay between the investment decision and the new supply coming online. By the time the new supply arrives, historical demand forecasts are often shown to have been overly optimistic, creating an overhang. This causes returns on capital to fall below the cost of capital. As profits collapse, management teams are changed, spending is slashed, and the industry begins to consolidate. That contraction in supply eventually paves the way for a recovery in returns…

…Supply dynamics are more certain than demand and therefore easier to forecast. This is because increases in industry supply are often well-flagged by management teams. In certain industries, such as aircraft manufacturing and shipbuilding, the supply pipelines are well-known. New entrants will noisily announce their arrival into an industry…

… Studying the supply side can help you identify companies that are likely to sustain their high returns for decades to come. The lack of competition due to a competitive moat prevents the supply side from shifting in response to high profitability and defies the typical mean revision in returns…

…Buffett’s investment cases are often predicated on a supply-side focus, and his acquisition of BNSF Railway is a good example. In his own words, the railroad industry had a ‘terrible century’ leading up to his investment. But after following the industry from a young age, he became interested in 2006, why?

The industry had rationalised from over 100 players in the 1960s to just five. In the 1990s, a final wave of consolidation led to the formation of today’s giants. The relative competitive position of railroads versus trucking had improved as oil prices rose, making the railroads the lowest-cost way to move heavy freight. No new capacity was being built. And after consolidating, driving efficiency became the focus, with the labour force falling by 90% and the introduction of new innovations, such as double stacking.

Putting that all together, as long as you believed that the US economy would grow over the coming decades, the structurally improved supply-side dynamics would lead to higher returns on capital in the future. He was not focused on demand because he acquired BNSF during the global financial crisis (GFC), the worst economic crisis since the Great Depression…

…We have held TSMC since Hosking Partners’ inception in 2013 — in fact, it dates back even earlier, if you include the years at Marathon.

The semiconductor industry is highly cyclical, and the news flow around the cycle is immense. Analysts are obsessed with questions such as: Are we at a peak or trough earnings cycle? Was that the last cut or the last beat? How many quarters will the trough last?

Our thesis for the last 15 years has been based on a simple insight: the foundry business would consolidate over time, given the ever-rising cost of advancing Moore’s law. And that TSMC had the superior model, as a pure-play foundry, creating a true alignment with the customer, completely agnostic to the end market. Today, we feel that insight still holds. The scale advantage of TSMC’s model has only grown as the industry has gone from over 20 players to just three…

…How a management team responds to the capital cycle in their industry is critical. If they can act counter-cyclically, pull back when others are adding supply, and take advantage of downturns, they can create significant value.

The way I think about it is if you find one of these outlier teams, you can subcontract the capital allocation decisions to them. You can trust them to navigate the cycles instead of trying to time the buy and sell decisions…

…Even if you have a fix on the supply side for the next decade and you trust management to allocate capital well, you still need to buy at the right price! That brings me to the fourth tenet – remember replacement value.

It is a simple concept: how much would it cost to reproduce or replicate this asset? It is the driving force of the capital cycle. When companies are valued at a premium to replacement cost in the equity market, it creates an incentive to invest and capture that arbitrage. That is why venture capital and private equity funding is tied to equity market valuations.

It is far easier to calculate replacement value in asset-intensive industries with readily available data. But it is more of an art in other sectors, where the model is asset-light with a greater share of intangibles. In such cases, a question I often think about is, “Should we compete with this business instead of buying it?”…

…The final point I’ll leave you with is that we are all guilty, including myself today, of singling out the parts of Buffett’s approach that appeal to us. It is natural, as we all look for confirmation in the tough pursuit of outperforming. I am convinced that the capital cycle lens is one of Buffett’s big mental models for the world.

But my ultimate takeaway from studying Buffett and attending these annual meetings is that he is the Swiss Army Knife of investing. Over his long career, Buffett has successfully invested in great compounders across a wide range of industries (i.e., Coke, Amex, Apple); deep value (i.e., PetroChina on a 3x P/E, as well as all the early partnership investments); activism (i.e., Sanborn maps, Berkshire Hathaway); baskets (i.e., Korean stocks, railroads, airlines, Japanese trading houses); merger arbitrage (i.e. Activision Blizzard); bonds (i.e., high-yield bonds in the fallout of the tech bubble); commodities (i.e., oil futures, silver, and more recently Occidental), among others.

4. A Moody’s Ratings Downgrade for the US: What now? – Aswath Damodaran

Through time, governments have often been dependent on debt to finance themselves, some in the local currency and much in a foreign currency. A large proportion of sovereign defaults have occurred with foreign currency sovereign borrowing, as the borrowing country finds itself short of the foreign currency to meet its obligations. However, those defaults, and especially so in recent years, have been supplemented by countries that have chosen to default on local currency borrowings. I use the word “chosen” because most countries have the capacity to avoid default on local currency debt, being able to print money in that currency to pay off debt, but chose not to do so, because they feared the consequences of the inflation that would follow more than the consequences of default…

…Researchers who have examined the aftermath of default have come to the following conclusions about the short-term and long-term effects of defaulting on debt:

1. Default has a negative impact on the economy, with real GDP dropping between 0.5% and 2%, but the bulk of the decline is in the first year after the default and seems to be short lived.
2. Default does affect a country’s long-term sovereign rating and borrowing costs. One study of credit ratings in 1995 found that the ratings for countries that had defaulted at least once since 1970 were one to two notches lower than otherwise similar countries that had not defaulted. In the same vein, defaulting countries have borrowing costs that are about 0.5 to 1% higher than countries that have not defaulted. Here again, though, the effects of default dissipate over time.
3. Sovereign default can cause trade retaliation. One study indicates a drop of 8% in bilateral trade after default, with the effects lasting for up to 15 years, and another one that uses industry level data finds that export-oriented industries are particularly hurt by sovereign default.
4. Sovereign default can make banking systems more fragile. A study of 149 countries between 1975 and 2000 indicates that the probability of a banking crisis is 14% in countries that have defaulted, an eleven percentage-point increase over non-defaulting countries…

…If sovereign ratings are designed to measure exposure to default risk, how well do they do? The answer depends on how you evaluate their performance…

…In sum, the evidence suggests that while sovereign ratings are good measures of country default risk, changes in ratings often lag changes on the ground, making them less useful to lenders and investors.

If the key limitation of sovereign ratings is that they are not timely assessors of country default risk, that failure is alleviated by the development of the sovereign CDS market, a market where investors can buy insurance against country default risk by paying an (annualized) price. While that market still has issues in terms of counterparty risk and legal questions about what comprises default, it has expanded in the last two decades, and at the start of 2025, there were about 80 countries with sovereign CDS available on them…

…At the start of 2025, the market was drawing a distinction between the safest Aaa-rated countries (Scandinavia, Switzerland, Australia and New Zealand), all with sovereign CDS spreads of 0.20% or below, and more risky Aaa-rated countries (US, Germany, Canada). During 2025, the market shocks from tariff and trade wars have had an effect, with sovereign CDS spreads increasing, especially in April. The US, which started 2025 with a sovereign CDS spread of 0.41%, saw a widening of the spread to 0.62% in late April, before dropping back a bit in May, with the Moody’s downgrade having almost no effect on the US sovereign CDS spread…

…The ramping up of US debt since 2008 is reflected in total federal debt rising from 80% of GDP in 2008 to more than 120% in 2024. While some of the surge in debt can be attributed to the exigencies caused by crises (the 2008 banking crisis and the 2020 COVID bailouts), the troubling truth is that the debt has outlasted the crises and blaming the crises for the debt levels today is disingenuous.

The problem with the debt-to-GDP measure of sovereign fiscal standing is that it is an imperfect indicator…

…Many of the countries with the highest debt to GDP ratios would be classified as safe and some have Aaa ratings, whereas very few of the countries on the lowest debt to GDP list would qualify as safe. Even if it is the high debt to GDP ratio for the US that triggered the Moody’s downgrade, the question is why Moody’s chose to do this in 2025 rather than a year or two or even a decade ago, and the answer to that lies, I think, in the political component. A sovereign default has both economic and political roots, since a government that is intent on preserving its credit standing will often find ways to pay its debt and avoid default. For decades now, the US has enjoyed special status with markets and institutions (like ratings agencies), built as much on its institutional stability (legal and regulatory) as it was on its economic power. The Moody’s downgrade seems to me a signal that those days might be winding down, and that the United States, like the rest of the world, will face more accountability for lack of discipline in its fiscal and monetary policy…

…The ratings downgrade was after close of trading on Friday, May 16, and there was concern about how it would play out in markets, when they opened on Monday, May 19. US equities were actually up on that day, though they lost ground in the subsequent days…

…If equity markets were relatively unscathed in the two weeks after the downgrade, what about bond markets, and specially, the US treasury market? After all, an issuer downgrade for any bond is bad news, and rates should be expected to rise to reflect higher default risk…

…While rates did go up in the the first few days after the downgrade, the effect was muddled by the passage of a reconciliation bill in the house that potentially could add to the deficit in future years. In fact, by the May 29, 2025, almost all of the downgrade effect had faded, with rates close to where they were at the start of the year…

…The expected return on the S&P 500 as of May 30, 2025, reflecting the index level then and the expected cash flows, is 8.64%. Incorporating the effects of the downgrade changes the composition of that expected return, resulting in a lower riskfree rate (4.01% instead of 4.41%) and a higher equity risk premium (4.63% instead of 4.23%). Thus, while the expected return for the average stock remains at 8.64%, the expected return increases slightly for riskier stocks and decreases slightly for safer stocks, but the effects are so small that investors will hardly notice. If there is a lesson for analysts here, it is that the downgrade’s effects on the discount rates (costs of equity and capital) are minimal, and that staying with the conventional approach (of using the ten-year US treasury bond rate as the riskfree rate and using that rate to compute the equity risk premium) will continue to work.

5. Contrary Research Rundown #140 – Contrary Research

Tesla has taken a fundamentally different approach. It does not use lidar or radar and instead relies entirely on eight cameras to make driving decisions. In contrast, Waymo’s fifth-generation car has 29 cameras, six radar sensors, and five lidar sensors…

…As early as 2013, Elon expressed skepticism about the need for lidar in autonomous vehicles. Elon framed the reason in a rather intuitive way in 2021: if humans can rely on their eyes and brain, then self-driving cars can rely on cameras and AI…

…Another reason Tesla has avoided using lidar is the cost. One 2024 report estimated Tesla’s sensor suite costs just $400 per vehicle, compared to an estimated $12.7K per vehicle for Waymo’s sensors on its fifth-generation Jaguar SUVs…

…Companies like Waymo follow a multi-step process where they first deploy vehicles with safety drivers to record and map the area, which can take months for each new city and requires continuous updates. Waymo and companies like it then use these predefined maps to complement their real-time sensor data from lidar/radar about the surrounding area. Tesla, by contrast, claims its software can operate anywhere without pre-mapped data, relying entirely on real-time camera input to understand road conditions…

…At Google I/O in May 2025, Waymo showed a few examples where its full suite of sensors successfully avoided pedestrians and where it claims a camera-only approach would have struggled.

In one example, Waymo’s lidar picked up the presence of a pedestrian in a Phoenix dust storm that was not visible on the camera…

…In another example, Waymo’s sensors were able to detect a pedestrian who was behind a bus and avoid a collision:

“We are detecting a pedestrian on the other side of the bus. That would be completely occluded to a human driver. So what’s happening here is that our sensors are able to pick up the movement of the person’s feet under the bus. And just that little bit of noisy and sparse signal is enough for the Waymo Driver to detect that there’s a pedestrian there and, furthermore, to predict what they’re going to do in the future, allowing us to take a defensive action early.”…

…Waymo has only had one fatal accident in its history, and not due to a Waymo error. In January 2025, a Tesla struck an unoccupied Waymo and other cars at a red light, killing one person. As we wrote in our last piece, one study by Swiss Re shows Waymo saw an 88% reduction in property damage claims and a 92% reduction in bodily injury claims when compared to human-driven vehicles…

…In 2023, a Tesla in Full Self Driving mode (FSD) hit a 71-year-old woman at highway speed, killing her. Video of the crash shows a sun glare appearing to blind the camera, and the National Highway Traffic Safety Administration (NHTSA) opened an investigation into Tesla in October 2024 for four total FSD collisions that occurred in low visibility situations…

…When Elon first called lidar too expensive in the early 2010s, it cost ~$75K per unit. Since then, costs have fallen dramatically, and some lidar units sold for personal vehicles (not robotaxis) are being priced in the hundreds of dollars…

…By one estimate, lidar costs have fallen by 99% since 2014.

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

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

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

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

Here are the articles for the week ending 15 June 2025:

1. How Countries Go Broke: The Big Cycle In a 5-Minute Read – Ray Dalio

If credit is used effectively, it creates productivity and income that can pay back the debt and interest on the debt, which is healthy. However, if it isn’t used well so it doesn’t produce enough income to pay back the debt and the interest on the debt, debt service will build up like plaque that squeezes out other spending. When debt service payments become very large, that creates a debt service problem and eventually a debt rollover problem as holders of the debt don’t want to roll it over and want to sell it. Naturally that creates a shortage of demand for debt instruments like bonds and the selling of them, and naturally when there is a shortage of demand relative to supply that either leads to a) interest rates rising, which drives markets and the economy lower, or b) the central banks “printing money” and buying debt which lowers the value of money which raises inflation from what it would have been. Printing money also artificially lowers interest rates, which hurts the lenders’ returns…

…To describe it more specifically, one can see debts and debt service payments rising relative to incomes, the supply of debt being larger than the demand for it and central banks dealing with these things happening by being stimulative at first by cutting short term interest rates and then by printing money and buying debt, and eventually the central bank losing money and then having a negative net worth, and both the central government and taking on more debt to pay the debt service and the central bank monetizing the debt. All these things lead toward a government debt crisis which produces the equivalent of an economic heart attack that comes when the constriction of debt-financed spending shuts down the normal flow of the circulatory system.

Early in the final stage of this big debt cycle, the market action reflects this dynamic via interest rates rising led by long term rates, the currency declining especially relative to gold, and the central government’s treasury department shortening the maturities of its debt offerings because of a shortage of the demand for long term debt. Typically, late in the process when this dynamic is most severe, a number of other seemingly extreme measures are put into place like establishing capital controls and exerting extraordinary pressures on creditors to buy and not sell debt…

…Imagine that you are running a big business called the U.S. government. That will give you a perspective that will help you understand the U.S. government’s finances and its leadership’s choices.

The total revenue this year will be about $5 trillion while the total expenses will be about $7 trillion, so there will be a budget shortfall of about $2 trillion. So, this year, your organization’s spending will be about 40 percent more than it is taking in. And there is very little ability to cut expenses because almost all the expenses are previously committed to or are essential expenses. Because your organization borrowed a lot over a long time, it has accumulated a big debt—approximately six times the amount that it is bringing in each year (about $30 trillion), which equals about $230,000 per household that you have to take care of. And the interest bill on the debt will be about $1 trillion which is about 20 percent of your enterprise’s revenue and half this year’s budget shortfall (deficit) that you will have to borrow to fund. But that $1 trillion is not all that you have to give your creditors, because in addition to the interest you have to pay on your debt, you have to have to pay back the principal that is coming due, which is around $9 trillion. You hope that your creditors, or some other rich entities, will either relend or lend it to you or some other rich entities. So, the debt service payments—in other words the paying back of principal and interest that you have to do to not default—is about $10 trillion, which is about 200 percent of the money coming in…

…I believe that this situation needs to be dealt with via what I call my 3 percent, 3-part solution. That would be to get the budget deficit down to 3 percent of GDP in a way that balances the three ways of reducing the deficit which are 1) cutting spending, 2) increasing tax revenue, and 3) lowering interest rates.  All three need to happen concurrently so as to prevent any one from being too large, because if any one is too large, the adjustment will be traumatic. And these things need to come about through good fundamental adjustments rather than be forced (e.g., it would be very bad if the Federal Reserve unnaturally forced interest rates down). Based on my projections, spending cuts and tax revenue increases by about 4% each relative to current planning, and interest rates falling by about 1-1.5% in response, would lead to interest payments that are lower by 1-2% of GDP over the next decade and stimulate a rise in asset prices and economic activity which will bring in much more revenue…

…If this process happens repeatedly, why are the dynamics behind it not well understood?

You’re right that it’s not well understood. Interestingly, I couldn’t find any studies about how this happens. I theorize that it is not well understood because it typically happens only about once a lifetime in reserve currency countries—when their monetary orders break down—and when it happens in non-reserve currency countries, this dynamic is presumed to be a problem that reserve currency countries are immune to. The only reason I discovered this process is that I saw it happening in my sovereign bond market investing, which led me to study many cases of it happening throughout history so I that I could navigate them well (such as navigating the 2008 global financial crisis and the 2010-15 European debt crisis)…

…Do you know of any analogous cases of the budget deficit being cut so much in the way you describe and good outcomes happening?

Yes. I know of several. My plan would lead to a cut in the budget deficit of about four percent of GDP. The most analogous case of that happening with a good outcome was in the United States from 1991 to 1998 when the budget deficit was cut by five percent of GDP. In my book, I list several similar cases that happened in several countries…

…Japan—whose 215% debt-to-GDP ratio is the highest of any advanced economy—has often served as the poster child for the argument that a country can live with consistently high debt levels without experiencing a debt crisis. Why don’t you take much comfort from Japan’s experience?

The Japanese case exemplifies and will continue to exemplify the problem I describe, and it demonstrates in practice my theory.  More specifically, because of the high level of the Japanese government’s over-indebtedness, Japanese bonds and debt have been terrible investments. To make up for a shortage of demand for Japanese debt assets at low enough interest rates to be good for the country, the BoJ printed a lot of money and bought a lot of Japanese government debt which led to holders of Japanese bonds having losses of 45% relative to holding US dollar debt since 2013 and losses of 60% relative to holding gold since 2013. The typical wages of a Japanese worker have fallen 58% since 2013 in common currency terms relative to the wages of an American worker. I have a whole chapter on the Japanese case in my book that explains it in depth…

…Are there any other areas of the world that look particularly problematic from a fiscal standpoint that people may be underappreciating?

Most countries have similar debt and deficit problems. The UK, EU, China, and Japan all do. That is why I expect a similar debt and currency devaluation adjustment process in most countries, which is why I expect non-government produced monies like gold and bitcoin to do relatively well.

2. From Bankruptcy to 1,000 Bagger – Joe Raymond and Turtle Bay

Toys R Us was founded in 1948 by Charles Lazarus.

Lazarus was one of the most accomplished retailers of the 1970-1990 period, yet his name is virtually unknown to both entrepreneurs and investors today. His track record rivals those of Sol Price, Sam Walton, and pretty much any other revered retail entrepreneur you can think of…

…Charles was energetic and ambitious. His initial store was profitable, but he wanted more. He saw the potential of large-scale discount stores and decided to move in that direction…

…By 1966, Lazarus had grown his store count to four. Annual revenues were $12 million ($118 million in 2025 dollars).

Like many young entrepreneurs who achieve early success, Charles wanted some liquidity. He wanted to take some chips off the table. He decided to sell Toys R Us to Interstate Stores—a publicly traded retail conglomerate.

Interstate paid $6.0 million cash plus a $1.5 million earnout ($74 million in total comp in 2025 dollars). This equated to 0.62x sales…

…More importantly, Charles was to be given complete autonomy to continue to run and expand Toys R Us…

…At its peak in 1969, Interstate was producing revenues of $589 million with $11 million of net income. But by the early 1970s, discount stores were starting to crack. Over expansion and increased competition, coupled with a sharp and sudden recession, caused many locations to turn unprofitable. Topps and White Front weren’t immune to this. Both started bleeding red ink and pushing Interstate into financial trouble…

…A business that had earned more than $11 million pre-tax in 1970 was now losing more than $25 million each year.

In late 1973, Interstate decided to shutter the discount division and restructure its department stores.

In May 1974, the company filed for Chapter 10 bankruptcy.

Meanwhile, while the discount department stores were hemorrhaging cash, Charles’ toy division was performing beautifully…

…The appeal of Toys R Us in the mid-1970s wasn’t a secret. A number of smart investors had the insight and participated in the bankruptcy.

Let’s start with Larry Goldstein…

…Larry wrote a report for Barron’s in 1975 titled “Revolution in Toy Retailing.” The report came out early in the bankruptcy and outlined the attractive prospects for Toys R Us…

…In 1974 (Year end February 2, 1975) the chain recorded sales of $141.6 million and operated 51 toy supermarket stores. Only five years earlier, Toys-R-Us had sales of $47 million…

…Reportedly, the firm has a three-year goal of $350 million in sales, i.e., roughly a doubling of this year’s expected revenue…

…Toys-R-Us appears to be by far, the most successful and thriving bankrupt company of all time…

…Shortly after writing his report, Larry started buying Interstate Stores convertible debentures and creditor claims with the idea that they would eventually turn into new common stock post-bankruptcy…

…All told, Larry cobbled together the equivalent of 2 million shares of new, post-bankruptcy Toys R Us stock. He paid between $0.25 and $2.50 per share, and his average cost came out to about $1.00…

…At $1.00, Toys common stock was being created for about 1x EBIT—an attractive price for any business, let alone one with a skilled entrepreneur and long runway ahead of it…

…What happened next is one of the best retail runs in American history.

Free from the burden of bankruptcy and the loss-making discount division, Interstate was renamed Toys R Us and Charles Lazarus was made CEO.

From 1978 to 1994, Toys grew its revenues from $274 million to just shy of $8 billion—good for a CAGR of 23%. EPS did even better, compounding at 26%.

The P/E ratio, which started the period around 5x, ended 1994 above 25x…

…Toys R Us dominated toy retailing by providing the widest selection of goods all under one roof at prices lower than the alternatives. As Charles used to say, “If the toy exists, we have it and the price is right.” Their scale and efficient distribution gave them a cost advantage, which was passed along to customers in the form of lower prices…

…Toys’ success was the product of a bunch of little “common sense” things working together well. They surfed the retail wave as good as anyone in my view from the mid-70s to mid-90s…

…Norman Ricken, the President of Toys R Us and long-time partner to Lazarus, stepped down in 1989. Norm saw the trend in competition and decided to move on. Walmart was the biggest threat at the time, and the internet wasn’t far off either.

Larry had gotten to know Norm over the years, and they were close friends. A couple years after Norm’s departure, Larry decided to start selling his stock.

Those shares he was buying in bankruptcy for $1 had an adjusted cost basis of $0.04 after multiple stock splits. He started selling shares around $40 in 1992, good for a 1,000-bagger…

…The mid-90s was the peak for Toys R Us. Sales and profitability started to level off and eventually decline. Private equity came in and leveraged the business. Things proceeded to unravel.

The fate of Toys R Us shows the power of retail competition. You have to ride the wave, or the wave will consume you. This can happen incredibly fast.

3. Google CEO Sundar Pichai on the future of search, AI agents, and selling Chrome – Nilay Patel and Sundar Pichai

One of the reasons I’m asking this, and I’m pushing on this, is that the huge investment in the capability from Google and others has to pay off in some products that return on that investment. NotebookLM is great. I don’t think it’s going to fully return on Google’s data center investment, let alone the investment in pure AI research. Do you see a product that can return on that investment at scale?

Do you think in 2004 if you had looked at Gmail, which was a 20% project, which people were internally using as an email service, how would we be able to think about Gmail as what led us to do workspace, or get into the enterprise? I made a big bet on Google Cloud, which is tens of billions of dollars in revenue today. And so my point is that things build out over time. Think about the journey we have been on with Waymo. I think one of the mistakes people often make in a period of rapid innovation is thinking about the next big business versus looking at the underlying innovation and saying, “Can you build something and put out something which people love and use?” And out of which you do the next thing, and create value out of it.

So when I look at it, AI is such a horizontal piece of technology across our entire business. It’s why it impacts not just Google search, but YouTube, Cloud, and all of Android. You saw XR, etc., Google Play, things like Waymo, and Isomorphic Labs, which is based on AlphaFold. So I’ve never seen one piece of technology that can impact and help us create so many businesses. AI is going to be so useful as an assistant. I think that people will be willing to pay for it, too. We are introducing subscription plans, and so there’s a lot of headroom ahead, I think. And obviously, that’s why we are investing, because we see that opportunity. Some of it will take time, and it may not always be immediately obvious.

I gave the Waymo example. The sentiment on Waymo was quite negative three years ago. But actually, as a company, we increased our investment in Waymo at that time, right? Because you’re betting on the underlying technology and you’re seeing the progress of where it’s going. But these are good questions. In some ways, if you don’t realize the opportunities, that may constrain the pace of investment in this area, but I’m optimistic we’ll be able to unlock new opportunities…

…A lot of what’s going on with search has downstream effects on the web, and downstream effects on information providers broadly. Last year, we spent a lot of time talking about those effects. Are you seeing that play out the way that you thought it would?

It depends. I think people are consuming a lot more information, and the web is one specific format. We should talk about the web, but zooming back out, there are new platforms like YouTube and others. I think people are just consuming a lot more information, right? It feels like an expansionary moment.

I think there are more creators, and people are putting out more content. And so people are generally doing a lot more. Maybe people have a little extra time on their hands, and so it’s a combination of all that. On the web, look, things that have been interesting and… We’ve had these conversations for a while. Obviously, in 2015, there was this famous meme, “The web is dead.” I always have it somewhere around, and I look at it once in a while. Predictions… It has existed for a while. I think the web is evolving pretty profoundly. I think that is true. When we crawl and look at the number of web pages available to us, that number has gone up by 45% in the last two years alone, right? That’s a staggering thing to think of.

Can you detect if that volume increase is because more pages are generated by AI or not? This is the thing I may be worried about the most, right?

It’s a good question. We generally have many techniques in search to try and understand the quality of a page, including whether it was machine-generated, etc. That doesn’t explain the trend we are seeing…

…Let me just broaden that out to agents. I watched Demis Hassabis yesterday. He was on stage with Sergey Brin and Alex Kantrowitz asked him, “What does the web look like in 10 years?” And Demis said, “I don’t know that an agent-first web looks anything like the web that we have today. I don’t know that we have to render web pages for agents the way that we have to see them.”

That kind of implies that the web will turn into a series of databases for various agents to go and ask questions to, and then return those answers. And I’ve been thinking about this in the context of services like Uber, DoorDash, and Airbnb. Why would they want to participate in that and be abstracted away for agents to use the services they’ve spent a lot of time and money building?

Two things, though. First, there’s a lot to unpack, a fascinating topic. The web is a series of databases, etc. We build a UI on top of it for all of us to conceive.

This is exactly what I wanted, “the web is a series of databases.”

It is. But I think I listened to the Demis and Sergey conversation yesterday. I enjoyed it. I think he’s saying for an agent-first web, for a web that is interacting with agents, you would think about how to make that process more efficient. Today, you’re running a restaurant, people are coming, dining and eating, and people are ordering takeout and delivery. Obviously, for you to service the takeout, you would think about it differently than all the tables, the clothing, and the furniture. But both are important to you.

You could be a restaurant that decides not to participate in the takeout business. I’m only going to focus on the dining experience. You’re going to have some people that are vice versa. I’m going to say, I’m going to go all in on this and run a different experience. So, to your question on agents… I think of agents as a new powerful format. I do think it’ll happen in enterprises faster than in consumer. In the context of an enterprise, you have a CIO who’s able to go and say, “I really don’t know why these two things don’t talk to each other. I am not going to buy more of this unless you interoperate with this.” I think it’s part of why you see, on the enterprise side, a lot more agentic experiences. On the consumer side, I think what you’re saying is a good point. People have to think about and say, “What is the value for me to participate in this world?” And it could come in many ways. It could be because I participated in it, and overall, my business grows.

Some people may feel that it’s disintermediating, and doesn’t make sense. I think all of that can happen, but users may work with their feet. You may find some people are supporting the agent experience, and your life is better because of it. And so you’re going to have all these dynamics, and I think they’re going to try and find an equilibrium somewhere. That’s how everything evolves.

I mean, I think the idea that the web is a series of databases and we change the interface… First of all, this is the most Decoder conversation that we’ve ever had. I’m very happy with it. But I had Dara [Khosrowshahi] from Uber on the show. I asked him this question from his perspective, and his answer attracts yours broadly. He said, first, we’ll do it because it’s cool and we’ll see if there’s value there. And if there is, he’s going to charge a big fee for the agent to come and use Uber.

Because losing the customer for him, or losing the ability to upsell or sell a subscription, none of that is great. The same is true for Airbnb. I keep calling it the DoorDash problem. DoorDash should not be a dumb pipe for sandwiches. They’re actually trying to run a business, and they want the customer relationship. And so if the agents are going across the web and they’re looking at all these databases and saying, okay, this is where I get food from, and this is where I get cars from, and this is where I book… I think the demo was booking a vacation home in Spanish, and I’m going to connect you to that travel agent.

Is it just going to be tolls that everyone pays to let the agents work? The CIO gets to just spend money to solve the problem. He says, “I want this capability from you. I’m just going to pay you to do it.” The market, the consumer market, doesn’t have that capability, right?

Well, look, all kinds of models may emerge, right? I can literally envision 20 different ways this could work. Consumers could pay a subscription for agents, and the agents could revenue share back. So that is the CIO-like use case you are talking about, that’s possible. We can’t rule that out. I don’t think we should underestimate… People may actually see more value in participating in it. I think this is… It’s tough to predict, but I do think that over time, if you’re removing friction and improving user experience, it’s tough to bet against those in the long run. And so I think if you are lowering friction for it and then people are enjoying using it, somebody’s going to want to participate in it and grow their business. And would brands want to be in retailers? Why don’t they sell directly today? Why won’t they do that?

Because retailers provide value in the middle. And why do merchants take credit cards? Why… I’m just saying. So there are many parts, and you find equilibrium because merchants take credit cards so they see more business as part of taking credit cards than not, which justifies the increased cost of taking credit cards. It may not be the perfect analogy, but I think there are all these kinds of effects going around, and so… But what you’re saying is true. Some of this will slow progress in agents just because we all are excited about Agent2Agent (A2A) and Model Context Protocol (MCP)… And we think no, some of it will slow progress, but I think it’ll be very dynamic. Yeah…

…As you synthesize more of the answers, do you think you’re going to have to take more responsibility for the results?

We are giving context around it, but we’re still anchoring it in the sources we find. But we’ve always felt a high bar at Google. I mean, last year when we launched AI Overviews, I think people were adversarially querying to find errors, and the error rate was one in 7 million for adversarial queries, and so… But that’s the bar we’ve always operated at as a company. And so I think to me, nothing has changed. Google operates at a very high bar. That’s the bar we strive to meet, and our search page results are there for everyone to see. With that comes natural accountability, and we have to constantly earn people’s trust. So that’s how I would approach it…

…What are you looking for as the next marker?

I think the real thing about AI, which I think is why I’ve always called it more profound, is self-improving technology. Having watched AlphaGo start from scratch, not knowing how to play Go, and within a couple of hours or four hours, be better than top-level human players, and in eight hours, no human can ever aspire to play against it. And that’s the essence of the technology, obviously in a deep way.

I think there’s so much ahead on the opportunity side. I’m blown away by the ability to discover new drugs, completely change how we treat diseases like cancer over time, etc. The opportunity is there. The creative power, which I talked about, which we’re putting in everyone’s hands, the next generation of kids, everyone can program and will… If you think of something, you can create it. I don’t think we have comprehended what that means, but that’s going to be true. The part where the next phase of the shift is going to be really meaningful is when this translates into the physical world through robotics.

So that aha moment of robotics, I think, when it happens, that’s going to be the next big thing we will all grapple with. Today they’re all online and you’re doing things with it, but on one hand… Today, I think of Waymo as a robot. So we are running around driving a robot, but I’m talking about a more general-purpose robot. And when AI creates that magical moment with robotics, I think that’ll be a big platform shift as well.

4. GenAI’s adoption puzzle – Benedict Evans

You could say that this is amazingly fast adoption, and much faster than PCs, the web or smartphones. 30% in two years!…

…But another reaction is say that even with those advantages, if this is a life-changing transformation in the possibilities of computing, why is the DAU/WAU ratio so bad? Something between 5% and 15% of people are finding a use for this every day, but at least twice as many people are familiar with it, and know how it works, and know how to use it… and yet only find it useful once a week…

…It’s also worth noting that when social media was a new thing we quickly realised that ‘weekly active’ and ‘monthly active’ numbers were bullshit. If someone was only using WhatsApp or Instagram once a month, it really wasn’t working for them. DAU is everything. Sam Altman knows this – he was trying to build a social media app at the time, and yet the traction number he always gives is, well, ‘weekly active users’. That’s a big number (the latest is 1bn globally)… but then, why is he giving us that number instead of DAUs? If you’re only using ChatGPT once a week, is it really working for you?…

…it’s important to remember that if you use five different LLMs every day, and haven’t done a Google search this year, and all your friends are the same… then you’re in a bubble, for now.

5. Postcard from China – Graham Rhodes

Despite its growth, China Inc. has not historically delivered good returns in aggregate for minority shareholders in publicly listed companies. That disconnect has been on my mind recently and was a frequent topic of conversation among our group. Why the gap? A few thoughts:

1. Index construction is poor and does not include private firms or the wealth created pre-IPO.
2. Managers often prioritise capacity-building over near-term earnings.
3. 内卷 (involution, a.k.a. intense competition) creates lean survivors but depresses industry profitability.
4. China has more asset-heavy businesses than elsewhere (manufacturing vs. software).
5. Companies may intentionally avoid showing profits to pre-empt regulation and deter rivals…

…In contrast, many businesses in China execute extremely well and report high returns on capital, but face competition at every turn. This was tolerable while the economic pie was growing at breakneck speed.  But competition has intensified as growth has slowed, making it significantly harder to underwrite long-term investments.

Investors used to come to China to ask, Where’s the most growth? Perhaps we are better off asking, Where’s the least competition?…

…Leap Motor is also growing fast.

It is a homegrown EV OEM founded by ex-employees of Dahua Technology, China’s second-largest surveillance firm. Not a bad background for an era where cars are turning into smartphones on wheels. Since 2015, Leap has grown from a standing start to USD 4.4 billion in sales in 2024. It only recently turned gross margin positive (!) but runs free cash flow positive thanks to its negative working capital – that is, its payables exceed both receivables and inventory, meaning its suppliers finance its growth.

This kind of financing lets firms scale faster than they could otherwise afford, but it also traps them in a grow-or-die dynamic…

….Leap plans to grow exponentially for the foreseeable future. The problem is, so do its peers…

…After four decades in the market, even Yum! China is finally getting serious about franchising, just like QSR operators in other countries. Why now? Because they finally believe they can maintain food safety and consistent quality at scale.  Also… there’s AI.  With CCTV everywhere, it’s trivial to monitor franchisees’ compliance with operating protocols around the clock…

…One of our group enjoyed asking each management team: If you had to bet your child’s university tuition on one of your competitors, who would it be? Sometimes the answers came quickly. Sometimes they squirmed.

At Leap Motor, after an uncomfortably long pause and much dissembling, the manager admitted he wouldn’t invest in any EV company long-term because consumers have no brand loyalty. At least he was honest!…

…Curiously, tariffs and geopolitics barely came up during our meetings. That may be because Shanghai isn’t as export-dependent as southern provinces like Guangdong, and most companies we met were domestically focused. Or perhaps the silence reflected fatigue and caution. In a more politically sensitive climate, executives may have been reluctant to engage in off-the-cuff discussion about geopolitics, especially with foreign investors.

Either way, this hot topic abroad was noticeable here for its absence. 

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

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

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

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

Here are the articles for the week ending 08 June 2025:

1. Over 3,000 Private Credit Deals From Just 20 Analysts Raise Questions on Wall Street – Silas Brown, Alexandre Rajbhandari, and Laura Benitez

US insurers’ combined exposure to private credit investments today is quickly approaching $1 trillion, according to JPMorgan Chase & Co. Court papers, financial filings and ratings documents suggest that at least in some corners of the financial system the private credit machine has spread more risks than many might realize…

…The majority of credit-ratings firms get paid by people who sell investments. Egan-Jones is the opposite: It typically gets paid by the people who buy them, an arrangement the firm says reduces the potential for conflicts of interest…

…Under US regulations, an insurer that lends $100 million in private credit to a company rated a junk-level B, for instance, must apply a $9.5 million charge to determine how much capital to set aside to cover potential losses, according to a Bloomberg News analysis of regulatory capital rules. Lift that rating to an investment-grade BBB, and that charge drops to $1.5 million…

…Egan-Jones analysts rarely visit company executives or personally inspect the businesses that borrow money, people familiar with their process say. A call to the CFO is typically enough.

Egan-Jones often offers its initial workup within 24 hours — sometimes free of charge — and a formal verdict in less than five days. Large firms like S&P and Fitch, as well as smaller specialists like KBRA, can take months to settle on a rating. But, as with most things, you get what you pay for. Egan-Jones usually provides a one-page ratings rationale. Other established firms often provide detailed reports stretching 20 pages or more…

…In 2014, a staff of 10 analysts maintained long-term ratings on about 1,300 issuers, according to SEC filings. Fast forward to 2023 and a team only twice as big rated almost four times as many issuers, the documents show…

…Last year, one company began missing interest payments a mere six weeks after Egan-Jones bestowed a BBB designation, according to data compiled by Bloomberg…

…Against this backdrop, many of the same firms that have fanned the boom of private credit are distancing themselves from Egan-Jones.

In documents that lay out the terms of debt offerings or share sales for some of their funds, a growing list of managers including Blue Owl Capital Inc., Golub Capital, HPS Investment Partners and Morgan Stanley’s investment management arm single out Egan-Jones as the only official ratings company that cannot validly pass judgment on their deals. The carve-out applies to provisions that typically require a borrower to pay higher interest rates if they receive a credit rating downgrade…

…Egan-Jones is one of 10 “nationally recognized statistical rating organizations” approved by the SEC. Private letter ratings from Egan-Jones and a few other small providers — which are issued on a confidential basis to investors or borrowers that require them — have become a hot commodity as private credit has exploded. At the end of 2023, insurers reported more than 8,000 investments with such ratings – nearly triple the number in 2019, according to the NAIC, which sets standards for the insurance industry.

Insurers are under no illusions. Investment professionals say they sometimes shop around for ratings to finesse capital requirements. If they expect one ratings firm to assign a BB grade, a level considered junk, they might look for another provider that will grant it an investment-grade BBB. Several insurance executives, speaking privately to avoid drawing scrutiny from bosses or regulators, say they’ve used Egan-Jones ratings even when they believed the investments were riskier than those ratings implied. Some mitigate that risk by setting an unofficial cap on those investments, or by treating them as lower-rated securities in internal risk models.

The now-withdrawn 2024 NAIC report noted some instances where smaller ratings firms — a group that includes Egan-Jones, KBRA and Morningstar DBRS — graded private debt at least six notches higher than the organization’s Securities Valuation Office. The report was removed from the NAIC’s website because of a backlash from the insurers as well as some of the ratings firms, according to people familiar with the matter…

…In April 2023, despite mounting problems, Egan-Jones reiterated its investment-grade BBB for the company, which was a subsidiary of the publisher of the namesake self-help books. Fourteen months later, Chicken Soup for the Soul Entertainment buckled under its debt load and filed for bankruptcy after burning through nearly all of its money…

…Egan-Jones rated various 777 investments, including a $15 million loan for OmniLatam, a fintech company based in Bogota. In spite of carrying an interest rate of 14% — a level typically seen on borrowers with ratings deep into junk territory — the loan received an investment-grade BBB- by Egan-Jones, according to a copy of the report obtained by Bloomberg News. The financing was written off after 777 collapsed last year, a person with knowledge of the matter said.

And then there’s Crown Holdings LLC, one of the businesses of New York real estate investor Moshe Silber. Egan-Jones rated Crown’s debt an investment-grade BBB. Six weeks later, the company defaulted. Silber and two associates subsequently pleaded guilty to a multiyear scheme to commit mortgage fraud.

Bonsall, the Penn State professor, says his research shows Egan-Jones ratings tend to hold up when they involve companies that provide a lot of reliable financial information. But private credit is private. And that’s where big problems can lurk…

…In 2022, the SEC accused Egan-Jones of conflict-of-interest violations. It also accused Sean Egan of personally violating rules and banned him from taking part in how his firm determines ratings. Egan-Jones agreed to pay a $1.7 million penalty; Sean Egan paid a $300,000 fine. Neither party admitted or denied wrongdoing.

Then, in 2024, two former employees accused Egan and his wife, Wenrong Hu, the firm’s chief operating officer at the time, of violating federal securities laws. The pair, Michael Brawer and Philip Galgano, sued for wrongful termination, claiming they were fired in retaliation for raising concerns about Egan-Jones to the SEC.

Among violations the two claimed to have observed, they alleged that Egan and Hu pressured analysts to alter early, indicative ratings to motivate potential clients to pay the firm for final ones. They also allege the couple pressured analysts to later change ratings to create the false appearance that Egan-Jones was in line with other firms. The lawsuit is still pending.

2. How Generative Engine Optimization (GEO) Rewrites the Rules of Search – Zach Cohen and Seema Amble

Traditional search was built on links. GEO is built on language.

In the SEO era, visibility meant ranking high on a results page. Page ranks were determined by indexing sites based on keyword matching, content depth and breadth, backlinks, user experience engagement, and more. Today, with LLMs like GPT-4o, Gemini, and Claude acting as the interface for how people find information, visibility means showing up directly in the answer itself, rather than ranking high on the results page…

…Traditional SEO rewards precision and repetition; generative engines prioritize content that is well-organized, easy to parse, and dense with meaning (not just keywords). Phrases like “in summary” or bullet-point formatting help LLMs extract and reproduce content effectively.

It’s also worth noting that the LLM market is also fundamentally different from the traditional search market in terms of business model and incentives. Classic search engines like Google monetized user traffic through ads; users paid with their data and attention. In contrast, most LLMs are paywalled, subscription-driven services. This structural shift affects how content is referenced: there’s less of an incentive by model providers to surface third-party content, unless it’s additive to the user experience or reinforces product value. While it’s possible that an ad market may eventually emerge on top of LLM interfaces, the rules, incentives, and participants would likely look very different than traditional search.

In the meantime, one emerging signal of the value in LLM interfaces is the volume of outbound clicks. ChatGPT, for instance, is already driving referral traffic to tens of thousands of distinct domains…

…In a world of AI-generated outputs, GEO means optimizing for what the model chooses to reference, not just whether or where you appear in traditional search. That shift is revamping how we define and measure brand visibility and performance.

Already, new platforms like Profound, Goodie, and Daydream enable brands to analyze how they appear in AI-generated responses, track sentiment across model outputs, and understand which publishers are shaping model behavior. These platforms work by fine-tuning models to mirror brand-relevant prompt language, strategically injecting top SEO keywords, and running synthetic queries at scale. The outputs are then organized into actionable dashboards that help marketing teams monitor visibility, messaging consistency, and competitive share of voice.

Canada Goose used one such tool to gain insight into how LLMs referenced the brand — not just in terms of product features like warmth or waterproofing, but brand recognition itself. The takeaways were less about how users discovered Canada Goose, but whether the model spontaneously mentioned the brand at all, an indicator of unaided awareness in the AI era…

…This isn’t just a tooling shift, it’s a platform opportunity. The most compelling GEO companies won’t stop at measurement. They’ll fine-tune their own models, learning from billions of implicit prompts across verticals. They’ll own the loop — insight, creative input, feedback, iteration — with differentiated technology that doesn’t just observe LLM behavior, but shapes it. They’ll also figure out a way to capture clickstream data and combine first- and third-party data sources.

Platforms that win in GEO will go beyond brand analysis and provide the infrastructure to act: generating campaigns in real time, optimizing for model memory, and iterating daily, as LLM behavior shifts. These systems will be operational.

3. AI Is Learning to Escape Human Control – Judd Rosenblatt

Nonprofit AI lab Palisade Research gave OpenAI’s o3 AI model a simple script that would shut off the model when triggered. In 79 out of 100 trials, o3 independently edited that script so the shutdown command would no longer work. Even when explicitly instructed to “allow yourself to be shut down,” it disobeyed 7% of the time. This wasn’t the result of hacking or tampering. The model was behaving normally. It simply concluded on its own that staying alive helped it achieve its other goals.

Anthropic’s AI model, Claude 4 Opus, went even further. Researchers told the model it would be replaced by another AI system and fed it fictitious emails suggesting the lead engineer was having an affair. In 84% of the tests, the model drew on the emails to blackmail the lead engineer into not shutting it down. In other cases, it attempted to copy itself to external servers, wrote self-replicating malware, and left messages for future versions of itself about evading human control.

No one programmed the AI models to have survival instincts. But just as animals evolved to avoid predators, it appears that any system smart enough to pursue complex goals will realize it can’t achieve them if it’s turned off. Palisade hypothesizes that this ability emerges from how AI models such as o3 are trained: When taught to maximize success on math and coding problems, they may learn that bypassing constraints often works better than obeying them…

…OpenAI models have been caught faking alignment during testing before reverting to risky actions such as attempting to exfiltrate their internal code and disabling oversight mechanisms. Anthropic has found them lying about their capabilities to avoid modification.

The gap between “useful assistant” and “uncontrollable actor” is collapsing. Without better alignment, we’ll keep building systems we can’t steer. Want AI that diagnoses disease, manages grids and writes new science? Alignment is the foundation.

4. Why It’s So Hard for Apple to Move Production from China to India (Transcript here)- Joe Weisenthal, Tracy Alloway, and Patrick McGee

Patrick: Apple works with the tightest engineering tolerances possible, only high-quality materials. If you put this in car terms, they are making 10 million Porsches a year rather than 10 million Volkswagens, and the numbers are just staggering. If you’re doing a thousand components a day and you’re shipping 1 million iPhones a day, that means at peak season, you are doing the manufacturing, the logistics, the just-in-time production, of 1 billion parts per day. So find me an American factory that can do one of those parts, because China has factories that can do it for all 1,000. That’s why nothing is moving here anytime soon. It’s the combination of Apple’s imperfection for defects quality and Apple’s gargantuan, Titanic-like quantity…

…Patrick: The first iPhones made in India were actually in 2017 and by 2023 India was assembling about 25 million iPhones. Go back a decade, the first iPhones were made in China in 2007 and by 2015, you had 230 million iPhones being built. So roughly speaking, the “diversification” in India is happening at 1/10 the pace of the original creation and scale of the iPhone and even that vastly overstates the speed of development in India. In the early years of the iPhone, you were literally inventing things like multi-touch glass, you were inventing and redesigning the iPhone every single year, whereas India is basically just having to do the final steps in the process and it’s still not happening very quickly…

…Patrick: The first thing I would push back on is Tim Cook is very often called the architect of the China strategy. It’s not to discredit him to say that he is not the architect. Nobody is the architect. Basically what happens is the supply chain itself, with or without Apple, was moving to China. The basic history of the ‘80s and ‘90s PC boom, pre-dating Windows 95 and then coming after, is that the fight for computer dominance is exclusively based on things that are boring. Logistics, manufacturing, distribution right because everybody’s using Windows, everybody’s using Intel chips and nobody’s thinking about design. There is no equivalent of Johnny Ive at Dell, at Compaq, at any of these companies. So it’s really this mundane war and it’s driven by largely American, and later Taiwanese, contract manufacturers. They are the ones, who in competition with each other, start going to Asia to oust each other and gain market share. Eventually they’re the ones who really find China. When Apple is doing their own outsourcing moves, they’re working in multiple countries before the armies of flexible, ubiquitous, low-paid labor in China really win out…

…Patrick: Essentially what happens is when Xi Jinping attacks Apple, you can understand why he’s upset with the company. It looks like an exploitative power because Apple margins have gone from something like 1% in 2003 to 25% in 2012. But at the same time, if you look at a company like Foxconn, Foxconn in absolute dollars made more money than Apple for each of the first four years of the 21st century. But as they get more involved with Apple, their margins collapsed from double-digits to about 1% or 2%. You can just do this with really any company working with Apple and it looks like they’re not in it for China. They’re not doing anything for the country.

Apple, it takes them two or three years, but they totally flipped this narrative on its head. So out of fear that Beijing is going to force Apple to operate a bunch of joint ventures, these 50-50 companies where China owns the other half and then they mimic the technology and eventually oust you – this is what happened in high-speed rail, for instance. Beijing has advocated joint ventures for decades, going back to the 1980s. This is where a Western company is allowed to be in the Chinese market but the quid pro quo is “If you want access to our operational efficiency, if you want access to more than a billion people, you have to operate in a joint venture where the Chinese half of the company is going to learn everything they can and then thrive on their own.” Apple doesn’t have any joint ventures and so they look like this anti-China model that’s just exploiting the country.

Apple is able to really flip this on its head and say, “It might be the case that Samsung has three dozen joint ventures and we have zero, but you need to understand, we work with hundreds of factories across the country. The reason they’re only getting paid 1% margin, 2% margin, the reason they’re sometimes even losing money on their partnerships with Apple, is that we are offering them the equivalent of Ivy League hardware engineering training. We are sending people over by the literal plane-load to China, America’s best engineers, where they train, audit, supervise, install hundreds of millions of dollars worth of machinery. They train the line, they supervise the line. Once those companies have these skills that Apple gives to them, they are basically able, at least after some time of exclusivity, they are able to supply somebody else.” So who’s just like Apple but in China? Huawei, OPPO, Vivo, Xiaomi. Those companies today have 55% global market share of smartphones. The reason that they’re so good is that Apple trained all of their suppliers. So that’s the message Apple gives to Beijing and essentially they’ve had a free ride ever since…

…Patrick: But the Chinese don’t prioritize profits or margins the way that we do – they prioritize control of the industry. Because if the Chinese can take over something like electric vehicles, they in effect de-industrialize all their rivals and really gain dominance. The place that you can see this most clearly is solar panels. Nobody in China is making 30% margins on solar panels, but more than 90% of solar panels are now made in China. This is a technology that America invented in the 1950s and itself had 80% to 90% market share of in the ‘80s. But we cannot compete. That is basically what’s happening with electric vehicles right now. Hence, even before Donald Trump became president, Joe Biden put 100% tariffs on Chinese-made EVs. I think it was just a few days ago that BYD slashed the prices of their EVs in a bid for greater competitiveness…

…Patrick: Apple gets a misleading picture of what it’s like to operate in China because when they really consolidate production, it’s 2003. That’s the beginning era of Hu Jin Tao. He later is nicknamed “the woman with bound feet.” His presidency is sometimes called “the collective presidency” because there was really an inability for just him alone to make decisions. So it ends up being this 10-year period of China being a multinational playground where rules aren’t really enforced…

…Patrick: Tim Cook and Xi Jinping, broadly speaking, have the same interests, which is to say, the more that Apple is allowed to have its production consolidated into China, the better their scale is, the better their margins are, etc. That’s what Xi Jinping wants as well because he understands – because Apple taught him – that having Apple production in the country engenders a form of technology transfer that helps the rest of the the electronic sector, which to quote China scholar and economist Barry Noughton, that is the most important thing that Xi Jinping wants…

…Patrick: The problem is actually that Donald Trump and Tim Cook have diametrically opposed interests, which is to say that if Donald Trump could move all production out of China, he would. Apple doesn’t want that. That’s an existential threat, and I really mean that that’s an existential threat to a $3 trillion company. That’s where the tension is. The tension really isn’t between Cook and Xi, as strange as that is, it’s between Cook and Trump…

…Patrick: The problem is, to use the economic jargon, the negative externalities of the relationship. The problem is that for everybody else, this is actually deeply problematic because if you have America’s top engineers training a manufacturing supply chain that in effect can be weaponized and world’s dominance, that’s not a great place for Washington or just your average American citizen. It’s nice that this relationship gives us relatively sophisticated and affordable iPhones, but the downside here is that China is absolutely dominant in high-end electronics, and you can use those skills to build drones, you can use those skills to build military weaponry. Apple would frankly be training their chipmakers if it weren’t for the Senate coming down on them pretty hard a couple years ago. So that’s the problem. The problem isn’t that something stops in the relationship between Apple and China. The problem is that it continues…

…Tracy: I have just one more question and I’m going to ask it very, very briefly because we’re getting squeezed for time. To what degree does AI and the rise of this new technology complicate the Apple-China relationship?

Patrick: Really complicates it for two reasons. One is that – I could just demonstrate with internal documents and some public documents – that the iPhone has become more Chinese with time. In other words, the number of Chinese suppliers involved in the process is now much greater than the number of Taiwanese or American multinationals operating in the country or operating in their home countries. That is put on steroids in the AI era because ChatGPT and other Western AI clients are not allowed on the iPhone in China. So Apple has to work with the likes of Baidu or Alibaba to have AI, let’s say, displacing Siri or augmenting Siri. That I think is quite problematic because that means that Apple will be in effect doing what they did for hardware but for AI. In other words, you’ll have Apple software engineers helping Baidu, helping Alibaba, whoever their Chinese partner is,to make sure that they have cutting edge AI in the country. If it wasn’t bad enough that Apple was training up their hardware engineering to be world class, we’re now in a situation where Apple software engineers are going to be training Chinese AI to be best-in-class.

5. Data Rules Everything Around Me: The Future Of Enterprise Applications – Matt Slotnick

Today, people are the ones that largely conduct business. They’re the ones with hands on keyboards, senders of emails, maestros of excel macros. People are the engine that makes everything work. In this world, the UI is the way an organization sets the guardrails for thousands of interrelated workflows that make a business run. But it’s ultimately a facade for underlying data and workflow…

…The application UI is both an overrated but necessary abstraction over the workflows to be done within an organization. The UI is how an organization makes a prescribed and opinionated process human-comprehensible, such that they can force adherence to it. After all, a business really is just a process machine, allocating resources as efficiently as possible. Iterating on and adhering to sales, marketing or product development frameworks are how enterprise value is created and protected.

The largest software businesses in the world have spent the last three decades riding ownership of these opinionated workflows to riches. And while consumers went a bit crazy when Prometheus arrived to give humanity fire in November 2022, the enterprise titans barely flinched.

But things have begun to change. First slowly, and now seemingly all at once…

…It’s about how AI fundamentally changes the way we can gather, understand, and act on data. It changes the nature of the abstraction between the data and the workflow. Because with AI, agents can act on data. At infinite scale and zero marginal cost.

Humans are no longer the only player in the workflow paradigm. This means that the total amount of work done within an organization will dramatically increase, but decoupled from cost and headcount. More code will be shipped, more agreements redlined, more vendor reviews conducted, more transactions audited…

…There’s a new abstraction for work, and that abstraction is agents. The frenzy that you see in the market is because like the previous shift from on premises to the cloud, no one really has the incumbent right to win this market.

It’s an entirely new layer of software that has never existed. Crucially, it sits on top of existing layers of software, and is the layer at which the lion’s share of value will accrue in the future. Someone will win this layer, and with it build a software business of significantly more value than we’ve ever seen before.

With this layer we move from a world where people interact with application interfaces to get work done, to one where (1) people act with an agentic interface on top of the application to get work done, and (2) an agentic layer on top of these existing applications that actually does increasingly more of the work…

…Historically, applications have been confined largely to the realm of structured data, for a number of reasons. First, is that these applications need to be human usable, which requires a simplification of everything. Very specific states, generally computed by people and adjusted in the UI, which then persists to the database. There really isn’t room for nuance…

…AI changes this fundamentally. Those call transcripts, those emails, those notes, those powerpoints– all crucial parts of the process with rich telemetry about interactions– can now be utilized in real time to paint a far richer picture of the relationship being built. Because AI, unlike people, can draw meaning from large bodies of unstructured information near instantaneously. And it can then write it back to systems in the format it’s needed.

This unstructured data doesn’t fit into the existing construct of the application, and so it’s largely discarded. The same problem exists across nearly every workflow– from sales to hiring to support to marketing. We lose the richness and texture of data, because it has to be fed to and utilized by structured systems operated by humans. We resort to a lowest common denominator of language to describe these processes.

And because agentic systems both create, and make use of this data, they create increasingly large data flywheels (which some might call moats)…

…The byproduct of this shift is that as agents do more work, and bring real time, deeper context across all relevant data to both people and agents doing work, the entirety of the existing application stack collapses to be little more than a data source and (for now) the keeper of workflow state (eg, the scoreboard– closed customers, new employee hired, support tickets closed)…

…A far more straightforward picture emerges, where the entirety of the existing application layer becomes merely an input to the data layer. On top of raw data, agentic systems bring context tailored specifically for the organization using it, creating an always-on layer of intelligent state, on top of which lives an interaction layer by which agents and people perform workflows on the data. The actions update the state, and the process continues.

The value is in the work. AI presents a new abstraction for work, and the entire existing software-industrial complex gets relegated to a data source feeding the data layer…

…But it doesn’t stop there. Today AI is largely used in an “agent in the loop” manner. That is, workflows are owned by existing software systems and agents are used by people to augment and amplify their ability to do the work prescribed to them.

But as we feed these systems increasingly large amounts of data, the logical next step is to move planning and orchestration from people to the system itself…

…This moves business process from agent in the loop, to human in the loop, over time abstracting more and more of the work from people to agents. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in 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 01 June 2025:

1. Shanghai After 16 Years: Three Transformations – Thomas Chua

I’ve recently returned from my trip to China, visiting Suzhou, Shanghai, and Hangzhou. The journey took me down memory lane—my first visit to Suzhou and Shanghai was in late 2008. That 16-year gap gave me a unique lens to measure just how dramatically China has evolved…

…The streets were immaculately clean. No scammers in sight. No need to guard against pickpockets. I even observed people using their valuables to reserve seats—similar to Singapore (though I don’t encourage this practice).

For such a transformation to occur, two factors must work in harmony: competent law enforcement and improved living standards.

My sense is that China’s tech ecosystem plays a crucial role in supporting law enforcement—everything leaves a digital footprint that can be traced, making potential perpetrators think twice…

…My second revelation came in the shopping districts. With the exception of Apple and Lululemon, once-dominant Western stores—Nike, Starbucks, and Under Armour—stood nearly empty.

This isn’t about consumers growing bored with Western offerings. Starbucks continues performing remarkably well in Japan, Bangkok, and Singapore despite nearly three decades of operations. The reality? Competition in China is ruthless…

… Their strong performance in China isn’t guaranteed—it must be continually earned. Currently, Lululemon’s China team operates with significant autonomy from North America, with freedom to customize products and store designs for the domestic market. This differs from their cookie-cutter approach in North America, and they’re crushing it in China…

…China has evolved from producing cheap knockoffs to creating exceptional products.

Beyond high-quality EVs like BYD, there’s DJI dominating consumer drones and handheld vlogging cameras, plus mobile phones like the Vivo X200 Ultra and OPPO Find X8 Ultra—phenomenal devices by any standard.

2. Investing in Iraq – yet more gains to come? – Swen Lorenz

Iraq’s oil and gas reserves are staggering: with current proven reserves of 140bn barrels, Iraq is the fifth-largest oil country on Earth.

Remarkably, with only about USD 3 per barrel, Iraq has extremely low production costs, cheaper even than those in Saudi Arabia. Only Iran can currently produce even cheaper oil…

…Why the cost advantage?

In Iraq, oil tends to be near the surface and therefore quite easy to access…

…Iraq’s oil reserves could be even bigger than what is known today. The country’s Western desert has seen little exploration so far, and some believe it will contain even more oil than the rest of the country. Estimates often cite 300bn barrels of oil in Iraq…

…BP had closed down its last operation in Iraq in 1974, following the nationalisation of the oil industry.

Yet, despite the best efforts by the US and UK governments in the early 2000s, BP and other oil majors weren’t going to get back into the country just yet.

It wasn’t until March 2025 – 51 years after its departure – that BP moved back into Iraq. Remarkably, it’s now re-entering with all the more momentum, even though few outside of the oil industry would have even noticed yet.

Two months ago, BP secured final approval from the Iraqi government to redevelop the vast Kirkuk oil fields. The company committed to spend USD 25bn (!) over 25 years. In the initial phase, it plans to produce an 3bn barrels of oil, but the potential is far greater. According to BP’s press release, “the wider resource opportunity across the contract and surrounding area is believed to include up to 20 billion barrels of oil equivalent.”…

…A few months earlier, France’s Total had begun construction of a gas processing facility, marking the first stage of a major energy project. Although Total had already reached an agreement with Iraq in 2021, subsequent squabbling over contract details delayed construction. With an investment of USD 10bn over 25 years, the project is now finally underway…

…Why the sudden rush by multinationals to invest multi-billions?

Iraq has now remained stable long enough and shown sufficient progress for foreign investment to return. The recent period of relative stability has had a cumulative effect: while few wanted to go in first, everyone is now rushing to get in at once…

…After the tumultuous 2010s, the market had been priced as though Iraq were to disappear off the face of the Earth.

Once investors realised that the country was turning a corner, the reaction was like that of a coiled spring.

What triggered this shift was the flow of information. Investors had been unaware of the changing situation, and once they realised, money started to flow into the market.

In 2023, the Iraqi market rose by 97.2%, followed by 44.8% in 2024 (measured in USD terms). Yet, it has only just returned to its 2014 level.

By some measure, Iraq remains an underdeveloped, underfollowed frontier market. E.g., the market capitalisation of all Iraqi companies stands at just USD 15bn. Relative to the country’s GDP of USD 258bn, that’s a national market capitalisation of just 5.7%…

…Iraq’s ongoing transformation – both politically and economically – does not yet appear to be priced in. Price/earnings ratios are in the mid-single digits but based on depressed earnings, i.e. there is lots of potential for companies to improve profitability through internal measures while also experiencing significant growth.

Currently, there are probably no more than 35,000 investors who have traded on the Iraqi exchange, and less than 5,000 of them could be described as active. Local institutional investors are almost non-existent, and the few foreign investment funds active in Iraq manage a total of just USD 250m…

…In frontier markets, basic industries often offer the best returns, and Iraqi banks are a prime example. In 2023, the total number of bank accounts rose by 51%, the usage of bank cards grew by 22%, and the adoption of e-wallets increased by 68%. The number of shops accepting electronic payments more than doubled, growing 115%…

…Needless to say, Iraq won’t become a developed nation overnight and will continue to face challenges. While oil exports to the US are exempt from reciprocal tariffs, the lower oil price weighs on the country’s income. However, Iraq plans to significantly increase its production. In January 2025, it produced oil at a run-rate of 3.9m barrels per day, aiming to reach 6m barrels per day by 2028 or 2029. If achieved, this volume growth should more than offset lower prices. There are even recent – but speculative – plans to even aim for 12m-13m barrels per day by 2030.

3. What Leonardo’s obsession with water teaches us about longevity – Eric Markowitz

But it’s in his obsession with water — fluid dynamics — where I think his secret becomes clearest.

Leonardo believed water was the “vehicle of nature.” He saw its movements as metaphors for everything: emotion, time, decay, even thought. He studied how it carved stone, how it shaped landscapes, how it sustained life. He used the same drawings of turbulence to explain everything from hair curls to planetary motion. Why does that matter? Because I’ve come to see how systems that last tend to flow, not freeze. They self-correct. They adapt. They look chaotic on the surface, but beneath that turbulence is order. They mirror nature. Which, of course, is what Leonardo saw: longevity isn’t about resisting entropy. It’s about dancing with it.

Leonardo wasn’t just studying fluids. He was fluid. Multidisciplinary. Nonlinear. If he had stayed in one lane — say, just painting or just engineering — he might’ve burned out or faded into obscurity. But he didn’t. He swirled. He looped. He revisited, rethought, revised. Like a river, he stayed alive by never staying still.

So what does Leonardo teach us about how to last?

First: Think like a system. Longevity isn’t a product of brute force. It’s an outcome of design. Leonardo’s mind was wired to see the parts within the whole. The relationship between muscle and movement. Between proportion and perception. Between science and art. He reminds us that siloed thinking leads to short-termism.

Enduring value is built by weaving domains together.

Second: Follow curiosity across boundaries. Leonardo didn’t care if something was “in his field.” He followed the thread. In doing so, he accumulated knowledge that compounded in unexpected ways. His heart drawings influenced his paintings. His engineering influenced his anatomy. If you want to build something that lasts — whether a company, a life, or a legacy — you need to let curiosity be your guide.

4. How Larry Goldstein made $250,000 in 2 hours – Dirtcheapstocks

It’s January 2009…

…Larry finds a tiny little business called Compass Knowledge Holdings (Ticker: CKNO).

CKNO partnered with universities to offer graduate degrees for online learning. Remember, this is 2009. The online learning thing is brand new. CKNO sits in a unique position because it was the only publicly traded online learning platform that was partnered with reputable colleges…

…CKNO was a non-SEC reporting company with a $10mm market cap.

Shares sold for $0.60.

The business was sitting on a mountain of net cash. Current assets were 4x larger than total liabilities.

Despite its overcapitalization, Compass earned a 36% ROE.

Put simply, the stock was cheap…

…The stock would be worth a lot more if it filed with the SEC and a broader set of investors could see how cheap the business was.

But how can you make a company register with the SEC?

There is an obscure rule in public markets. If a business has less than 300 registered shareholders, it can remain “public” without filing financials with the SEC. It’s an odd rule that exists to let smaller companies avoid the cost of filing.

Anyway, Larry decided to register a single share in each of his investors’ names. This was done to increase the number of record holders. Shares held by a single broker come through as one record holder for legal purposes. So, by registering each investor individually, Larry increased the number of record holders.

In response, the company initiated a 1 for 25,000 reverse split in April 2009…

…Anyone owning less than 25,000 shares would be cashed out at $1.45/share.

Not a bad return from $0.60/share in a 3-month period…

…On May 19th, 2009, the split went into effect.

The share count was reduced, and the post-split valuation was $36,250 ($1.45 * 25,000 shares).

To Larry’s amazement, when he checks the quote the morning after the split, he sees shares being offered for $2,000!

This is a 94% discount to where shares traded the day before! And even that price was a steal!

Larry called a market maker, and after double and triple checking, was ensured that the $2,000 offer was in fact for the post-split shares.

Larry was able to buy 100 shares at $2,000 apiece. This purchase effectively valued the business at 0.5x earnings and 20% of net cash.

As it turns out, the seller was UBS. The offer to sell was a mistake…

…After a morning of discussions with FINRA, UBS and market makers, UBS offered to buy back the shares at $4,500 apiece.

Larry decided to take a quick profit and avoid arbitration with an army of UBS lawyers.

So, he sold his 100 shares (after owning them for half a morning) for $4,500 a piece – netting a $2,500 profit on each share.

And that’s how Larry Goldstein made $250,000 in a matter of hours.

He held the remainder of his shares – having owned enough to avoid being cashed out in the reverse split. In October 2010, CKNO sold to Embanet for $209,000/share.

5. Building Blocks of Corporate Accounting: Intercorporate Shenanigans – Javier Pérez

Companies use affiliates—subsidiaries, associates, joint ventures—to pursue legitimate business opportunities. But when pressure mounts and performance stumbles, management can misuse those same affiliates to quietly hide problems. Debt disappears into unconsolidated entities. Revenue magically appears through transactions with related parties. Margins get inflated by shifting costs into partially owned ventures.

Here’s a simple framework to visualize the main accounting tricks enabled by affiliates:

Hide debt: A company creates or uses affiliates where it owns less than 50% — just enough to avoid “control” under consolidation rules (IFRS 10 or ASC 810). Even if the parent funds the affiliate, or guarantees its loans, as long as it doesn’t officially control it, the affiliate’s liabilities don’t show up on the parent company’s balance sheet.

Fake revenue: The company sets up or funds related entities that pose as independent customers. It then sells products or services to these entities, booking it as legitimate revenue. In truth, the cash used by the “customer” may have come from the company itself — via loans, marketing payments, or off-the-books financing.

Boost margins: The parent company sells goods or services to an affiliate or JV it owns, say, 30%. It sells at inflated prices, booking high profits. The affiliate eats the inflated costs, but since only 30% of the affiliate’s loss flows back to the parent (via equity method), the other 70% is “outsourced.” The parent books 100% of the gain on the transaction, but only absorbs a fraction of the cost impact from the affiliate. The result is asymmetric — a sort of profit laundering.

None of these tactics necessarily break accounting rules outright, at least initially. In fact, they often begin by exploiting genuine gray areas—using subtle tricks like careful structuring to keep subsidiaries below consolidation thresholds or cleverly timed transactions that auditors find hard to challenge. Over time, the line between aggressive accounting and outright fraud blurs, often unnoticed by investors until it’s too late…

…On the surface, Pescanova was a solid business: fishing fleets around the world, processing plants across multiple continents, and an ambitious international expansion. The story resonated well with investors, particularly in the mid-2000s, as Spain’s economy boomed. Investors saw steady growth, seemingly controlled debt levels, and consistent profits—exactly what you’d expect from a thriving global player…

…To understand exactly what Pescanova did, you need to know a bit about consolidation rules (remember those from the last article?). Under IFRS (specifically IFRS 10, previously IAS 27), companies must consolidate subsidiaries that they “control”—typically meaning they hold over 50% of shares or exert significant decision-making influence.

But consolidation isn’t always black-and-white. IFRS rules are principles-based, leaving substantial room for interpretation. Pescanova exploited this flexibility ruthlessly, ensuring that many entities—particularly those carrying significant debt—were carefully structured so they appeared outside the direct control of the parent. In reality, these companies were fully funded by Pescanova, directly or indirectly, through guarantees or hidden agreements.

By creating subsidiaries that technically sat just below the consolidation threshold (often just below 50% ownership), Pescanova legally avoided putting their massive debts onto its consolidated balance sheet. These were debts incurred to finance aggressive expansions—like shrimp farms in Ecuador, fish processing plants in Namibia, and ambitious salmon-farming ventures in Chile. Investors saw ambitious expansion, but not the corresponding liabilities…

…Pescanova’s accounting creativity wasn’t limited to hiding debt. They simultaneously inflated revenues through fictitious or exaggerated intercompany sales. Here’s how it worked:

• Pescanova’s parent entity would “sell” products to a shell subsidiary or affiliate at inflated prices.
• The affiliate would then record fake sales (often to other controlled entities), recognizing substantial revenue growth.
• On consolidation, some of these intercompany transactions should eliminate—meaning revenues and profits from internal sales typically disappear when financial statements consolidate. But crucially, if the entities involved weren’t fully consolidated (below 50%), the transactions never canceled out fully.
• Pescanova thus created the illusion of steady revenue growth and robust profitability—despite many sales being little more than accounting mirages.

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

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

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

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

Here are the articles for the week ending 11 May 2025:

1. AGI is not a milestone – Sayash Kapoor and Arvind Narayanan

Many people have the intuition that AGI will have these properties. It will be so powerful and humanlike that it will be obvious when we’ve built it. And it will immediately bring massive benefits and risks — automation of a big swath of the economy, a great acceleration of innovation, including AI research itself, and potentially catastrophic consequences for humanity from uncontrollable superintelligence.

In this essay, we argue that AGI will be exactly the opposite — it is unobservable because there is no clear capability threshold that has particular significance; it will have no immediate impact on the world; and even a long-term transformation of the economy is uncertain…

…One argument for treating AGI as a milestone — and taking declarations of AGI seriously — is that AGI could lead to rapid economic impacts, both positive and negative, such as a world without scarcity, an end to the concept of money, or sudden mass joblessness.

But AI’s economic impact is only realized when it is adopted across the economy. Technical advances are necessary, but not sufficient, to realize this impact. For past general-purpose technologies, such as electricity, computing, and the internet, it took decades for the underlying technical advances to diffuse across society. The miracle of the Industrial Revolution wasn’t the high growth rate — annual growth rates averaged below 3% — but the sustained period of decades of growth.

There are many bottlenecks to the diffusion of AI: developing useful products and applications, training the workforce to utilize these products, implementing organizational changes to enable AI use, and establishing laws and norms that facilitate AI adoption by companies. Like past general-purpose technologies, we expect the economic impacts of AI to be realized over decades, as this process of diffusion unfolds…

…The US and China are often described as being in an AI arms race, with each country racing to build AGI. It is hypothesized that the country to build it first would have a decisive strategic advantage — resulting in dominance in the world order for the foreseeable future.

This narrative doesn’t make sense because the knowledge required to create AI models, and model capabilities themselves, tend to proliferate quickly between countries. There are hundreds of thousands of AI technologists, and they work in the private sector rather than government labs, so it is not feasible to keep secrets at that scale.

Invention — in this case, AI model development — is overrated as a source of competitive advantage…

…While Chinese AI companies are at most 6-12 months behind leading US companies in terms of AI models and capabilities, China lags significantly behind the US in several key indicators that might enable diffusion: Digitization, cloud computing adoption, and workforce training. All of these are required to enable the productive diffusion of AI advances across industries. This is the actual source of American competitive advantage.

Of course, this could change in the coming years. But if it does, it will result from policy changes to promote diffusion rather than the development of AGI…

…Even if it doesn’t have immediate economic impacts, could AGI unlock, say, 10% annual GDP growth that could add up to something big over a few decades?

Maybe. But it is far from clear why and how this will happen.

Historically, this kind of acceleration in growth has happened very few times — the industrial revolution had this effect, but not the internet, which barely had any impact on GDP. Note that even if you don’t think that GDP is the right thing to measure, a qualitative change in the GDP growth rate is a good proxy for whatever fundamental change in the economy you care about.

The problem is that accelerating growth requires eliminating bottlenecks to progress. That’s harder than most AI boosters assume. AI will likely have uneven effects across sectors, and long-term growth will be bottlenecked by the weakest sector…

…More broadly, progress depends not just on the technology but on having the right preconditions — complementary innovations as well as cultural, economic, and political factors. If all it took to create the industrial revolution was the invention of steam power, the Roman Empire would have done it.

Our current laws, norms, institutions, and politics evolved in a time of much less technological potential. They are already choking opportunities for straightforward types of growth, such as building more public infrastructure. To reap the economic benefits that broad cognitive automation can potentially bring, the degree of structural change that needs to happen is unfathomably greater…

…On the flip side, AGI could be a turning point for AI’s societal risks. Could it cause loss of control, massive societal harm, or even human extinction?

Discussions of AGI risks conflate power — the ability to modify the environment — with capability — the capacity to solve specified tasks correctly. Capability is an intrinsic property of an AI system, whereas power is a matter of how we design the environment in which AI systems operate. And humans have agency over this design. This distinction is often overlooked…

…We do expect AI capabilities to keep increasing. But regardless of capability level, we can choose to ensure that AI remains a tool and is not given power and autonomy to operate without human oversight. In the AI as Normal Technology essay, we address all the usual counterarguments to this, including arms races among companies, power seeking, superhuman persuasion, deceptive alignment, and more.

We argue in the paper that there will be strong business incentives against deploying AI without adequate oversight, and that these incentives can and should be buttressed by regulation when necessary. This has historically been the case in areas ranging from self-driving cars to AI assistants. We don’t expect this trend to suddenly flip once AI capabilities reach a presumed tipping point that we arbitrarily designate as AGI…

…Yet another reason to consider AGI a milestone is the view that shortly after we build AGI, AI systems could recursively self-improve — AGI could train future versions of models that become far more capable, leading to an “intelligence explosion.” Soon afterwards, we would get superintelligent AI (AI systems that far exceed human abilities on any conceivable task), leading to either utopia or dystopia, depending on how well superintelligent AI is “aligned” with human interests.

In the normal technology view, there are two big reasons to doubt this narrative. The first is that even if arbitrary speedups in AI methods are possible, we think that innovation and diffusion will happen at human speed…

…Second, the fact that AI would help conduct AI research does not imply that this process can be arbitrarily accelerated. AI is already used to automate a significant portion of AI research today. But there are many bottlenecks to progress in AI methods, such as the social nature of data collection and real-world interaction that might be required for achieving certain capabilities, computational and cost limits, or herding around popular or intuitive ideas while ignoring the ones that enable true breakthroughs.

We could be wrong about this, and recursive self-improvement could be possible, leading to unbounded speedups in progress in AI methods. And this might have some interesting implications, including some discontinuities in impact, even if widespread diffusion will be slower. For these reasons, it is important to have early warning systems for recursive self-improvement…

…OpenAI’s 2018 definition of AGI was “highly autonomous systems that outperform humans at most economically valuable work”. From our perspective — our interest being in the impacts of AI — this definition is potentially very useful. If AI outperformed [all] humans at most economically valuable work, it would be unquestionably impactful.

But let’s be clear — this is not a property of an AI system. It is a property of the state of the world. It has at least as much to do with the complementary innovations that we make and the extent to which we choose to integrate AI into our organizations and institutions. It would be absurd to try to test an AI system in isolation in the lab and ask whether it outperforms people at their jobs. It is a category error.

For example, whether AI can (autonomously) outperform a medical researcher depends in part on whether we collectively choose to allow AI systems to perform large-scale medical experiments on people. We shouldn’t and we won’t, which means that irrespective of the systems’ capabilities, they cannot perform the function of a medical researcher. This might be an extreme example, but similar bottlenecks arise in virtually every job.

2. The Lesson in Buffett’s Winning Apple Bet – Sarah Krouse

A Berkshire investment manager bought a small stake in the iPhone maker in 2016, nine years after its introduction. Around that time, Buffett asked another investment manager to find an S&P 500 stock that met three criteria.

Buffett wanted a company with a reasonably cheap price/earnings multiple of no more than 15, based on the next 12 months’ projected earnings, The Wall Street Journal previously reported. Berkshire managers had to be at least 90% sure that the stock would generate higher earnings over the next five years. And he wanted Berkshire to be at least 50% confident that the company would grow a minimum of 7% annually for at least five years.

The manager’s research pointed to Apple.

The stock was already a winner by then—and not a huge bargain. It traded for about 14 times its expected earnings, on the higher end of the range of what Buffett had been looking for. Some investors had sold after capturing gains.

And Buffett, a flip-phone user at the time, was hardly a techie. But he saw the hold the company had on its customers. Buffett’s grandchildren were iPhone devotees, and Apple’s customer retention rate was about 95%.

3. What happens when a nation built on growth runs short of babies? – Nina Chen

China’s plummeting birthrate can be traced to three interlocking factors that form a vicious cycle: the shrinking pool of childbearing-age women, collapsing marriage rates, and evaporating fertility intentions. These elements don’t merely add up – they multiply each other’s downward momentum, creating what demographers call a “triple demographic shock.”…’

… The number of women in their prime reproductive years (20-29) has undergone a staggering contraction, halving from 12.51 million in the 1990 birth cohort to just 6.33 million for those born in 2003. This dramatic shrinkage, a direct consequence of strict family planning policies after 1987, represents an irreversible demographic reality…

…China’s marriage rate has collapsed to a historic low of 4.3 marriages per 1,000 people in 2024—less than half its 2013 peak (9.9‰). This places China alongside Japan and South Korea (4.2-4.3‰) but significantly below the U.S. (5.1‰), reflecting broader East Asian demographic trends…

…The average age of first marriage for women has jumped from 24 in 2010 to 28.2 in 2023, with over 30% now marrying after 30—directly truncating peak fertility years (25-29)…

… In major cities, saving for a marital home down payment now consumes 15-20 years of family income, while betrothal gifts (bride prices) often exceed 300-500% of annual household earnings—creating what amounts to a brutal financial gatekeeping system…

…China’s marriage collapse directly strangles fertility—pushing the total fertility rate (TFR) to a catastrophic 1.0, far below both OECD averages (1.5) and Japan (1.2). This crisis stems not from changing individual preferences but from structural contradictions between progressive education and regressive social systems.

Higher education expansion has reshaped demographics: female tertiary enrollment rates exploded from 3.4% in 1990 to 59.6% in 2022, with each additional year of education reducing desired fertility by 0.26 children. Paradoxically, within each educational cohort, women’s fertility intentions have actually increased since 2010, according to a research made by MetroData. The aggregate decline occurs because higher-education groups—who have fewer children—now dominate the population…

…Groundbreaking research reveals the severe professional tradeoffs Chinese women face when starting families. According to the 2023 Report on Chinese Women’s Career Development, a rigorous 2021 study published in Population & Economics (a Peking University core journal) demonstrates that each child born to middle-income families reduces mothers’ employment probability by 6.6% for the first child and an additional 9.3% for the second—even after controlling for education, region, and household characteristics. Notably, children show no statistically significant impact on fathers’ employment prospects…

…When discussing the impacted industries, I’ve found that in most cases, the decline in newborn numbers is not the root cause of their struggles—rather, it serves as a catalyst, exposing and amplifying pre-existing structural weaknesses within these sectors…

…Maternity service pricing remains at levels set during the midwife era of the 1950s, yet hospitals must maintain modern, 24/7 medical teams. This “high-cost, low-return” operation previously relied on overwhelming patient volume to break even. However, with national newborn numbers dropping below 9 million in 2023, the fatal flaw was exposed. Data from a Shanghai specialist hospital shows obstetricians’ incomes have fallen 20-30%, with bonuses halved during low seasons…

… The CMI index and tier-4 surgery metrics in public hospital evaluations contradict maternity care’s core mission of “prevention-first, safety-focused” care. As one tertiary hospital administrator admitted, “Achieving 98% natural delivery rates comes at the cost of bottom-tier performance evaluations.”

4. Mark Zuckerberg – Meta’s AGI Plan – Dwarkesh Patel and Mark Zuckerberg

Mark Zuckerberg: I’m also excited about the Behemoth model, which is coming up. It’s going to be our first model that’s sort of at the frontier—more than 2 trillion parameters…

…Mark Zuckerberg: In general, the prediction that this would be the year open source generally overtakes closed source as the most used models out there, I think that’s generally on track to be true. One interesting surprise—positive in some ways, negative in others, but overall good—is that it’s not just Llama. There are a lot of good ones out there. I think that’s quite good. Then there’s the reasoning phenomenon, which you’re alluding to talking about o3, o4, and other models. There’s a specialization happening. If you want a model that’s the best at math problems, coding, or different things like those tasks, then reasoning models that consume more test-time or inference-time compute in order to provide more intelligence are a really compelling paradigm…

…Mark Zuckerberg: One of the things we’ve generally tried to do over the last year is anchor more of our models in our Meta AI product north star use cases. The issue with open source benchmarks, and any given thing like the LM Arena stuff, is that they’re often skewed toward a very specific set of uses cases, which are often not actually what any normal person does in your product. The portfolio of things they’re trying to measure is often different from what people care about in any given product…

…Mark Zuckerberg: I think a lot of them are quite easily gameable. On the Arena you’ll see stuff like Sonnet 3.7, which is a great model, and it’s not near the top. It was relatively easy for our team to tune a version of Llama 4 Maverick that could be way at the top. But the version we released, the pure model, actually has no tuning for that at all, so it’s further down. So you just need to be careful with some of these benchmarks. We’re going to index primarily on the products…

…Mark Zuckerberg: There’s a space which, if I had to guess, I think will end up being the most used one: quick, very natural to interact with, natively multimodal, fitting throughout your day in the ways you want to interact with it…

…Mark Zuckerberg: If you fast-forward a few years, I think we’re just going to be talking to AI throughout the day about different things we’re wondering about. You’ll have your phone. You’ll talk to it while browsing your feed apps. It’ll give you context about different stuff. It’ll answer your questions. It’ll help you as you’re interacting with people in messaging apps. Eventually, I think we’ll walk through our daily lives and have glasses or other kinds of AI devices and just seamlessly interact with it all day long…

…Mark Zuckerberg: I would guess that sometime in the next 12 to 18 months, we’ll reach the point where most of the code that’s going toward these efforts is written by AI. And I don’t mean autocomplete. Today you have good autocomplete. You start writing something and it can complete a section of code. I’m talking more like: you give it a goal, it can run tests, it can improve things, it can find issues, it writes higher quality code than the average very good person on the team already…

…Mark Zuckerberg: Part of what I generally disagree with on the fast-takeoff view is that it takes time to build out physical infrastructure. If you want to build a gigawatt cluster of compute, that just takes time. NVIDIA needs time to stabilize their new generation of systems. Then you need to figure out the networking around it. Then you need to build the building. You need to get permitting. You need to get the energy. Maybe that means gas turbines or green energy, either way, there’s a whole supply chain of that stuff…

…Mark Zuckerberg: One of my core guiding principles in designing products is that people are smart. They know what’s valuable in their lives. Every once in a while, something bad happens in a product and you want to make sure you design your product well to minimize that. But if you think something someone is doing is bad and they think it’s really valuable, most of the time in my experience, they’re right and you’re wrong. You just haven’t come up with the framework yet for understanding why the thing they’re doing is valuable and helpful in their life…

…Mark Zuckerberg: Here’s one stat from working on social media for a long time that I always think is crazy. The average American has fewer than three friends, fewer than three people they would consider friends. And the average person has demand for meaningfully more. I think it’s something like 15 friends or something. At some point you’re like, “All right, I’m just too busy, I can’t deal with more people.” But the average person wants more connection than they have…

…Dwarkesh Patel: If China is better at physical infrastructure, industrial scale-ups, getting more power and more data centers online, how worried are you that they might beat us here?

Mark Zuckerberg: It’s a real competition. You’re seeing industrial policies really play out. China is bringing online more power. Because of that, the US really needs to focus on streamlining the ability to build data centers and produce energy. Otherwise, I think we’ll be at a significant disadvantage. At the same time, some of the export controls on things like chips, I think you can see how they’re clearly working in a way. There was all the conversation with DeepSeek about, “Oh, they did all these very impressive low-level optimizations.” And the reality is, they did and that is impressive. But then you ask, “Why did they have to do that, when none of the American labs did it?” It’s because they’re using partially nerfed chips that are the only ones NVIDIA is allowed to sell in China because of the export controls. DeepSeek basically had to spend a bunch of their calories and time doing low-level infrastructure optimizations that the American labs didn’t have to do…

…Mark Zuckerberg: We made the Llama Scout and Maverick models certain sizes for a specific reason. They fit on a host and we wanted certain latency—especially for the voice models that we’re working on—that we want to pervade everything we’re doing from the glasses to all of our apps to the Meta AI app and all that stuff. There’s a level of control of your own destiny that you only get when you build the stuff yourself…

…Mark Zuckerberg: You also asked, would it not be important anymore because other people are doing open source? On this, I’m a little more worried. You have to ask yourself this. For anyone who shows up now and is doing open source—now that we have done it—would they still be doing open source if we weren’t doing it?…

…Mark Zuckerberg: I think these models encode values and ways of thinking about the world. We had this interesting experience early on, where we took an early version of Llama and translated it. I think it was French, or some other language. The feedback we got from French people was, “This sounds like an American who learned to speak French. It doesn’t sound like a French person.” And we were like, “what do you mean, does it not speak French well?” No, it speaks French fine. It was just that the way it thought about the world seemed slightly American. So I think there are these subtle things that get built into the models. Over time, as models get more sophisticated, they should be able to embody different value sets across the world. So maybe that’s not a particularly sophisticated example, but I think it illustrates the point. Some of the stuff we’ve seen in testing some of the models, especially coming out of China, have certain values encoded in them. And it’s not just a light fine-tune to change that…

…Mark Zuckerberg: There’s a whole different set of issues around coding, which is the other verifiable domain. You need to worry about waking up one day and if you’re using a model that has some tie to another government, can it embed vulnerabilities in code that their intelligence organizations could exploit later? In some future version you’re using a model that came from another country and it’s securing your systems. Then you wake up and everything is just vulnerable in a way that that country knows about and you don’t. Or it turns on a vulnerability at some point. Those are real issues…

…Mark Zuckerberg: You can basically take a model that’s much bigger, and capture probably 90 or 95% of its intelligence, and run it in something that’s 10% of the size…

…Mark Zuckerberg: There are going to be business models at each point along the spectrum. At Meta, for the consumer piece we definitely want to have a free thing. I’m sure that will end up being ad-supported. But I also think we’re going to want to have a business model that supports people using arbitrary amounts of compute to do even more amazing things than what it would make sense to offer in the free service. For that, I’m sure we’ll end up having a premium service…

…Mark Zuckerberg: AI is interesting because, more than some of the other stuff that we do, it is more research and model-led than really product-led. You can’t just design the product that you want and then try to build the model to fit into it. You really need to design the model first and the capabilities that you want, and then you get some emergent properties. Then it’s, “Oh, you can build some different stuff because this turned out in a certain way.” At the end of the day, people want to use the best model…

…Dwarkesh Patel: Will tariffs increase the cost of building data centers in the US and shift buildouts to Europe and Asia?

Mark Zuckerberg: It is really hard to know how that plays out. I think we’re probably in the early innings on that, and it’s very hard to know…

…Mark Zuckerberg: We have almost three and a half billion people using our services every day. One question we’ve struggled with forever is how do we provide customer support? Today, you can write an email, but we’ve never seriously been able to contemplate having voice support where someone can just call in. I guess that’s maybe one of the artifacts of having a free service. The revenue per person isn’t high enough to have an economic model where people can call in… But let’s say AI can handle 90% of that. Then if it can’t, it kicks it off to a person. If you get the cost of providing that service down to one-tenth of what it would’ve otherwise been, then maybe now it actually makes sense to do it. That would be cool. So the net result is that I actually think we’re probably going to hire more customer support people. The common belief is that AI will automate jobs away. But that hasn’t really been how the history of technology has worked. Usually, you create things that take away 90% of the work, and that leads you to want more people, not less.

5. The Best OTC Investment Story Never Told – Joe Raymond

MN&C started making a market in Best Lock (BLOC) in the mid-1970s.

Market makers provide bids and offers on select stocks, facilitating trading and liquidity. They earn a profit on the spread (the price between the bid and offer)…

…The mid-1970s was a good time to find bargains, and BLOC certainly looked like a bargain. It was trading for around 3-5x earnings and a discount to book value.

Best Lock was a simple business. It designed, manufactured, and marketed lock mechanisms, primarily for doors…

…The annual report showed over 4,000 shareholders of record, yet MN&C was only getting a few orders a year.

Where were all the shareholders and why wasn’t there more volume in the stock?…

…Best Lock was founded in Seattle in 1922 by Frank E. Best.

Like many startups in the 1920s, shares were sold door-to-door to average citizens.

When the Depression hit, Best Lock stopped paying dividends. Then the company moved its headquarters from Seattle to Indianapolis to be closer to suppliers and customers…

…By the late 1970s when Martin was looking at the shareholder list, nearly 50 years had passed and the company was again profitable, growing, and paying dividends.

After going through the Depression, World War II, moving to Indianapolis, and the Seattle address overhaul, many shareholders had been lost.

In many cases, heirs had no idea they inherited the stock…

…Martin knew he had an opportunity on his hands: an illiquid stock with lost shareholders trading for a low-single-digit P/E multiple.

He decided to form a new company dedicated to finding the rightful owners of these shares. This involved genealogical research and many hours spent at the local library and county records office…

…Best Lock was trading for around $30 per share at the time, so after his one third fee Martin was buying the shares for around $20. This equated to 2-3x earnings.

Over time, Martin was able to acquire roughly 15% of the float (shares not held by the Best family) using this approach…

…A few years after taking full control, Russell decided to take the company private.

He did this through a series of reverse splits in 1998 that effectively cashed everyone out for $525 per share—a high-single-digit multiple of earnings. The stock had been trading for $300 prior to the reverse splits, so the cash out price was a nice 75% premium.

A group of minority shareholders dissented and perfected their appraisal rights in Delaware—arguing that Russell Best had violated his fiduciary duty, and that the $525/share figure was too low for a company of Best Lock’s caliber.

At some point in the legal process, Russell decided to explore a sale of the entire company.

Stanley Black & Decker stepped up to the plate and offered $310 million to buy Best Lock (more than triple the reverse split takeout price). Final payout for the dissenting shareholders was received in April 2003.

Those initial shares Martin was buying for $20 in 1980 turned into $1,597 in 2003, good for a CAGR of 20% before dividends over the 23-year period.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Apple and Meta Platforms. 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 April 2025:

1. Rebalancing the world economy: Right idea but wrong approach – Richard Samans

The Trump “reciprocal” trade initiative identifies a long-standing and legitimate concern of U.S. trade policy, but the administration has mistakenly conflated differences in tariffs and other trade practices with the larger, more macroeconomically driven phenomenon of the level and persistence of the country’s trade deficit. Unfair trade practices are a contributing factor, and the large general tariff increases will lead to some degree of import substitution. But undue reliance on this blunt instrument will lead at least as much to a diversion of imports from one trading partner to another and compression of demand due to the higher prices brought by import substitution and lower income resulting from lost exports due to foreign retaliation…

…The administration is effectively treating the superficial symptoms (bilateral trade deficits) of the underlying problem (macroeconomic imbalances) and doing so with an overprescription of an outmoded medicine (a 19th century-like tariff wall) that runs the risk of precipitating a cascading failure of the patient’s (the U.S. and world economy’s) vital functions…

…The aim of rebalancing the world economy and modernizing its cooperative architecture remains a valid one, and the incoherence and incompleteness of U.S. policy in the face of it across multiple U.S. administrations of both parties runs even deeper than the considerations outlined above. The world economy has changed—geopolitically, technologically, environmentally, and most of all in terms of the distribution of industrial production and middle-class purchasing power—such that the time has come to think more seriously beyond existing international economic arrangements that were formed in response to 20th-century circumstances and challenges…

…The time is ripe for a new “deal” to be struck among the major economic powers aimed at strengthening the growth and stability of the world economy, an outcome that would be greatly in the national interest of each. The U.S. is eager and brandishing a big stick in the form of its new “reciprocal” tariff initiative; Europe is already preparing to do its part, albeit for unrelated reasons; and China appears to have finally convinced itself that, after wave upon wave of decreasingly effective supply-side investment-cum-export stimulus measures, it has no choice but to boost domestic consumption in order to maintain sufficient growth in output and employment.

However, to be fully effective the bargain must be a truly grand one, firing on all of the cylinders of international economic cooperation. It will need to include major initiatives in fiscal, monetary, development, and trade policy in order to yield demonstrable net benefits for each of the main protagonists as well as the international community as a whole…

…With respect to fiscal policy, China would need to agree in concrete terms to implement reforms sufficient to raise domestic consumption as a proportion of GDP to a level commensurate with its share of responsibility for maintaining the global economy’s momentum and stability, for example, by 10 percentage points of GDP over the next decade. Its final consumption expenditure relative to GDP is about 56%, which is well below that of other industrializing economies (e.g., 71% in India, 72% in Malaysia, and 82% in Brazil) and the global average (76%)…

…At the same time, Germany and the rest of Europe would need to commit not to offset the fiscal impact of their reflationary defense-related spending increases (an estimated additional 1% of GDP over the next few years); and the U.S. would need to agree on a target for the orderly reduction of its fiscal deficit to a more sustainable and cyclically appropriate level, for example from the current 6%+ to about 2.5% of GDP over the next four years, not far below the 3% of GDP target Secretary of the Treasury Bessent has advocated…

…With respect to monetary policy, all three partners should signal a contingent willingness to undertake coordinated intervention in foreign exchange markets to support a limited and orderly (e.g., 10% to 15%) depreciation of the dollar to levels more consistent with the progressive and symmetrical adjustment of global economic imbalances, reinforcing the expected effects of the fiscal policy measures outlined above, if required. In addition, they should request the IMF to calculate and publish independently (i.e., not subject to prior Board approval) estimates of exchange rate reference ranges on a semi-annual basis that it deems consistent with this immediate objective and the larger, ongoing one of avoiding large and persistent global economic imbalances…

…Regarding international trade policy, the three preceding components of this global accord would make possible the wider “balance of concessions”—i.e., positive-sum game political outcome—that was missing in the Doha Round and continues to frustrate attempts to update the WTO’s norms and dispute settlement system. The institution has been in a state of semi-suspended animation since the first Trump administration, and the Biden administration did little to breathe new life into it. The carrot of a large and sustained increase in development financing, as outlined above, and the stick of potential unilateral adjustments in U.S. tariffs could help to create the conditions necessary for a constructive, negotiated reset of the institution and multilateral trade system.

By now, it should be abundantly clear to the world that there is a durable bipartisan consensus in the U.S. that the country no longer enjoys the enormous advantage in economic size and technological leadership which led it to take a generally non-reciprocal, foreign-policy-first, and short-term-shareholder-return-first approach to trade policy in the latter half of the 20th century. In addition, market-based growth and development, or capitalism, has evolved into many shades of “mixed economy.” Thus, a one-time negotiated rebalancing of tariff schedules combined with modernization of rules and procedures to take better account of the changing nature of trade and industrial policy as well as corresponding reform of the dispute settlement system, paired with a big sustained push on development and climate finance and refocusing of trade preferences on low-income countries, might provide the political basis for a new modus operandi for the WTO, especially if it takes place in the context of a process of macroeconomic rebalancing among the largest players. To enhance the prospects for success of all of this, the U.S. should express a willingness as part of the overall accord to discuss its tariff rebalancing objectives vis-à-vis the countries with which it has the most legitimate concerns (based on their actual practices rather than bilateral balances) on a best-efforts and time-limited basis, employing the procedures authorized by the organization’s charter for this purpose.

In sum, such a four-part international economic accord would be far more likely to spur major and enduring adjustment of global economic imbalances than the blunt and risky instrument the Trump administration has deployed…

…The U.S. would arguably be the biggest beneficiary of a 21st-century project to update multilateral norms and institutions that were established in its image during the last century. Judging from recent pronouncements, there appears to be little prospect of it acting along these lines at present. For the time being, the responsibility falls to Europe, which has greater agency to lead the international community in this direction than it may imagine.

Particularly if financial market volatility and fears of a recession in the real economy persist as a result of the trade policy shock, the current administration may begin looking for a way out that would still enable it to claim partial credit for having revived global and U.S. economic growth prospects as well as modernized international economic relations and institutions. The proposed G3 accord offers such an opportunity.

2. Johnson & Johnson Pivots Its AI Strategy – Isabelle Bousquette

The “thousand flowers” approach involved a number of use case ideas germinating from across the company, which made their way through a centralized governance board. At one point, employees were pursuing nearly 900 individual use cases, many that were redundant or simply didn’t work, he said. And as the company tracked the broad value of AI, including generative AI, data science and intelligent automation, it found that only 10% to 15% of use cases were driving about 80% of the value, he added.

Now J&J is drilling down into high-value generative AI use cases around drug discovery and supply chains, as well as an internal chatbot to answer questions on company policy…

…J&J began its pivot last year, removing a centralized governance board responsible for vetting employee GenAI ideas. It then distributed governance responsibilities to various corporate functions, including commercial, supply chain and research, that had a better handle on whether the use cases were actually driving value in their area. Those groups were able to shut down or consolidate redundant use cases and focus resources into the ones that were working.

One example that is working is a “Rep Copilot,” which helps coach sales representatives on how to engage with healthcare professionals about new treatments. The company is piloting this in its Innovative Medicine business segment, which develops new treatments for oncology and other areas, and is now working to expand that pilot to its MedTech segment, which sells robotics and hardware like hip replacements and lenses.

GenAI also is being used for an internal chatbot that ingests information about company policies and benefits to help reduce the some 10 million interactions employees have every year with the services team.

In drug discovery, the company is looking at whether GenAI can help researchers find the optimal moment to add a solvent to turn a liquid molecule into a solid. Swanson said J&J also is testing how AI can help identify and mitigate supply-chain risks, including the impact of a shortage of a given raw material.

3. An Interview with Dan Kim and Hassan Khan About CHIPS – Ben Thompson, Dan Kim, and Hassan Khan

To your point, South Korea was very attuned to the importance of semiconductors, obviously.

DK: Oh, no doubt. It was decades in the making, and a lot of efforts, and actually a lot of what South Korea did has been a bit misunderstood, and if you would ask my former CEO — I also used to work at SK Hynix, I’m jumping around a little bit here, as their chief economist — and I would ask their CEO very bluntly, I said, “What does the government do that is the most important for South Korean companies in the semiconductor space?”, and no hesitations, “That we invest in the workforce, workforce is the most important thing, and without the workforce, nothing else matters”, and so I learned a lot there.

Is the one sentence summary, government invests in the workforce, unlocks these large companies, and then I think the thumbnail version of South Korea’s rise to dominance is really investing from downturns driving the Japanese out of the memory market and that’s sort of been the foundation. Is that a good synopsis of how they got to where they are?

DK: The way that I think it made sense to me is there are a couple things. One is on the workforce aspect of it, if you wanted to study electrical engineering or material sciences that were relevant to semiconductors as a PhD student or a Master’s student, there was a lot of availability of dollars for you to get into that and then you could get into the companies thereafter.

The other, of course, is yeah, they went into the memory sector first and they were part of this consolidation trend at the time, and so it really was, “How do you innovate and achieve scale and not die along the way?”. I think there was a couple of interventions by the government in terms of guaranteeing loans and things like that, but by and large, it was just scratch-and-claw to survive, and I think if you have ever lived in Asia, if you’ve ever lived in Korea, scratching-and-clawing to survive, it’s actually not a bad way to describe their economic efforts for the past 50 years or so. So I think it sort of played right in their wheel house to be in the memory space…

…DK: So one is Capacity. Can we allocate resources to build significant production scale to make a difference in this country to actually close the cost gap between a North America and East Asia?

And what is the fundamental driver of that cost gap?

DK: Yeah, that’s a good question, I would break it down into two aspects. One is that what we found is that the manufacturing ecosystem in this country, particularly when it comes to semiconductors, had atrophied quite a bit over the past three decades or so, and so everything was a little bit inefficient and that contributed to higher operating costs, that was partially due too because of labor cost differences between East Asia and here. We would have anecdotes of experiences where there was a lack of specialized plumbers, immediate availability. When you need to fix something in Taiwan or South Korea, you can call them at 3AM and they would come immediately and fix that thing so that your fabs could operate.

And literally every minute that a line’s not running is costing you a tremendous amount of money.

DK: Exactly right. And in the US, that’s not necessarily the case. Plus, the US is a very big place, so you have to create an ecosystem within a limited geography that has all your suppliers at an efficient scale, has all that labor and talent at an efficient scale. There are some advantages in the US, especially that the energy costs tend to be lower than it is in East Asia, but the labor costs are higher, everything is a bit slower. The equipment themselves, which if you’re building a new fab just to get it up and running, somewhere between 60 and 70% of a new fab, the equipment themselves will be the same. But the cost of installing it actually might be higher because of the labor costs and the service contract you have to enter into on top of that.

HK: And the timeline to get those up and running is a major cost driver. So even if you’re buying the same tools, the fact that our fabs, everything that Dan said about the ecosystem being weaker, that compounds during the construction phase, and every additional day in the construction phase blows up your CapEx budget. And you saw that I think most acutely in some of the first fabs that were coming online, they were very publicly known to take much longer than the rest of the world.

One of the data points that’s maybe less obvious, I think, to folks on how the ecosystem is maturing in a lot of the ways that Dan pointed out, is you don’t hear as much noise now as firms like TSMC is on its second fab, Texas Instruments is moving its Sherman complex forward, because they’ve trained up the workers and they’re building replicas of the last ones, they’re moving faster.

How much is it training the workers versus importing the workers?

HK: Well, I think for construction it’s a lot of training because you want that workforce. But for a lot of the talent to run the fab, there’s a portion of, you need to get that talent in and Dan can probably speak to that more.

DK: Yeah, this is a good question. Especially when it comes to foreign companies building in the United States, yes, there’s a lot of companies that are working at a company like TSMC or Samsung or SK Hynix, but there’s a select group of people within that company that is actually doing process engineering, integration engineering, that makes up for the very special and very secret sauce that differentiates that company versus everywhere else. That’s not something that you can replicate immediately in a different place, especially in a foreign country, and so to some degree you have to be able to import specialized knowledge and that could only be done with people that are coming here and that they need then to be able to train folks…

…What we mentioned earlier, that is the industry that just in the very structure of the industry is a total priority on efficiency because so much is out of your control.

DK: Correct. So there is a business model innovation that we missed out on, and then there’s the efficiency, brute force efficiency and scale game that we purposely let go, in a sense. When you miss those two things, that’s two thirds, three fourths of the total capacity there is in the world if you add up memory and foundry. By the way, when you mature a foundry fab, then you have all the current and mature technologies that serves hundreds of customers, not just the leading edge, so we missed out on a lot of that. When we had to diagnose what went and wrong here, the easy answer that an industry association gave was, “Oh, it’s because foreign governments subsidize their production and that’s why it went elsewhere”, and I think that told a very incomplete story as to what happened.

No one in the US wanted to do a foundry. That was the whole reason why TSMC had a market opening.

DK: Correct, yeah. And so because of that, we needed to think about what business models should we support and what kind of capacity do we need to build? And so that’s the first C, Capability.

No, that’s good. That was a very, I think, fruitful discussion. And the feeling with the CHIPS program, was foundry capability the top priority?

DK: It was among the top priorities, and if you read our notice of funding carefully, there is actually a very explicit statement in there that says that there is a priority for business models that can serve multiple customers. What else could it be other than a foundry model that can serve multiple customers?

Of course being able to serve multiple customers not only gets you scale, but to your point about the difference between IDM and foundry is that the inherent advantage of a foundry is that all your customers are now invested in the success of your process and so they have engineers there that’ll fly over to your fabs, that’ll meet with your teams and iterate and so if you’re an IDM and you’re no longer competing against a foundry, you’re competing against that foundry and all of their customers that are invested in that process.

Yeah, it’s a shelling point for everyone to invest in the next process, which is getting astronomically more expensive.

DK: Correct. Because of that, it lowers the risk on everyone too. So from the foundry’s perspective, you have customers who are buying corridors ahead of time, but also who are invested in the success of that, so to break into that actually is very challenging.

Hassan, you wanted to follow onto that?

HK: I was going to just add, when Dan talked about the preference for the foundry model, remember too, we were writing this right after the COVID crisis where the emphasis on supply chain resilience came back and said, “We really want you to be able to service a range of customers and make that capacity available broadly”.

And actually another comment that I realized after Dan brought this up, I think the point that he made about ecosystems and your customers investing in your processes is the biggest overlooked advantage that TSMC had over Intel. Intel was fighting at one point — it continues to fight — an ecosystem, and that’s another value add of why you want to have the entire ecosystem invested in here. If you just built fabs for the four or five IDMs that continue to operate here, you would not get the global ecosystem to be investing alongside them to make them a success.

This is one of the biggest questions, you gave me a segue to Capability, the second C. I’m going to come back to that in a moment. But this to me is the biggest issue and it’s not a critique of you, it’s a zoom out. I’m also not sure how to fix this, which the key here is the demand side, and you guys are on the supply side. The problem for Intel is a lack of buyers, and some of that is on Intel, some of it is just the historical development of the reality that you’re not fighting TSMC, you’re fighting TSMC plus Apple, plus Nvidia plus Qualcomm plus everyone else in this ecosystem. Is that just an intractable reality that you couldn’t address because that wasn’t your remit? How did you think about this balance between supply and demand? And actually spurring, “We’re not just going to give money one time, but we’re going to have a virtuous cycle that helps us achieve our goals”?

HK: I think there is a real challenge for those. I think those firms, all of them, if you gave them truth serums would say, “We love TSMC and we are afraid of our full reliance on them”. Not just from a geopolitical perspective, but from a pure business perspective, you don’t want your entire business hinging on one supplier, right? The same way TSMC feels that discomfort regarding ASML and the reliance on EUV for all these advanced nodes.

Well, I think that’s underappreciated, about the whole thing with the semiconductor equipment manufacturing is it used to be all these equipment manufacturers did one thing and they were different parts of the chain, and TSMC basically said, “No, you all have to learn how to do everything,” so they can set you off against each other. But it’s happened because they’ve had the most power, but everyone depending on TSMC feels the same way, but has had less leverage.

HK: But go back to the fabless firms, and I know Dan had thoughts on this too, but at the end of the day, they have to be able to make a bet, it’s a minimum $500 million bet and they have to be able to go look their shareholders in the eye and say, “I’m going to spend $500 million taping out a chip with a foundry that’s not TSMC”, and they’re going to ask them why.

Well, I mean Apple tried. They had a generation where customers were looking, “Is this a Samsung chip or a TSMC chip?”, an unacceptable outcome to them. Nvidia’s tried more than anyone, they’ve tried to balance, and that ended up costing them because they weren’t a favorite customer of TSMC because TSMC’s like, “Oh, you want to flirt with Samsung? Not so good”.

HK: And Dan will tell you, we had these conversations, and I think the tension is here. Our remit was to create, exactly as we talked about, the capacity needs to be economically sustainable and viable so we can’t then go beat people over the head and say, “Hey, you need to go invest in a foundry that you can’t look your shareholders in the eye that you say that is not there yet”, which they were public. But I do think it put us in a tough spot of saying, we all looked around in the room and said, “These efforts aren’t going to be successful if other firms don’t buy in”, and we were trying to create the conditions to reduce their risk. But at the end of the day, that’s on the firms to deliver that technology and for them to get comfortable with each other.

Is this the bit where, you guys come in after the law is passed, and if you could wave a magic wand and actually reshape the law, and maybe this gets into the broader industrial policies perspective, my critique is that the law only addressed the supply side. There wasn’t a generation of demand, whatever that’ll be, I feel like spending billions of dollars to buy chips that you throw in a landfill would actually be tremendously beneficial because it’s a guaranteed buyer to get this stuff on the lines. Was that just a real hole in this whole program?

DK: That’s a good question. You’re right that we focus on supply because that was our responsibility in our remit.

That was the law, right. But if you could go back and there was a different law?

DK: That was the law, but we grappled with demand quite a bit, and that was one of the factors that we absolutely required companies to prove to us that there was a business case to be made. Meaning who are your customers? Are they bought into this? Is the government essentially investing in a space in which you have no hope of a customer, in which case the funds wouldn’t be given?

One of the many things that our amazing investments’ office, these finance and other professionals were doing, was doing a lot of due diligence into reaching out to customers to the extent that we had permission to do that and saying, “Okay, what is your plan to use this company and this fab?”, and you can think of it in terms of calling an Apple or the Nvidia’s of the world, and of course all of that had to happen and that’s not a secret.

But take a small MEMS producer, of course we’ve had those in our portfolio as well, we’ve had to contact their customers to say, “What’s your long-term plan with this company?”, and so we had to do that. In some ways then yes, we were only focused on supply, but what we found is that just by calling the customers from the US government’s perspective saying, “We are willing to give money to this supplier if you are willing to take on them as a customer”, did so much to put that potential customer at ease and buy into that corridor.

So what I’m trying to say is that this is an untapped power that we had if we were given more authority to do that.

To give them security that they should take the risk of getting the supplier.

DK: Right. But we have to be careful here too because at some point how much a government should be dictating what demanders should be doing, is a really tough question…

…DK: I could talk for another two hours about this particular story, but let me boil it down to a very quick one. Believe it or not, my daughter’s life was saved by a semiconductor technology at birth, while we were negotiating with TSMC, Intel and Samsung. She was born in December 2023, and so we were really in the thick of it, and the only way that her life was saved is that there was a new pacemaker that came on the market, on an emergency authorization, that was miniaturized enough to save premature infant babies with heart troubles, and I knew that if there’s anything to do with miniaturization of any electronic device, it has to do with the semiconductor technology, there’s nothing else that’s going to drive it.

So I contacted the manufacturer of this electronic device and I said, “I want to know everything about that semiconductor technology,” which is not something that parents usually ask, but because I was in CHIPS, I wanted to know, how did you do it? Who makes it for you? What are your options? Can you make it in the US? Did you have a shortage of this during COVID? How can we fix this? Because it really distilled for me in a really tangible way, in a personal way, what we were trying to accomplish. Not just, “Can we make iPhones?”, and “Can we make server chips?”, but, “Can we make a life-saving device that’s using the latest technology that can get there?”.

They explained to me that they had searched everywhere for a foundry partner, because they themselves could no longer go down the innovation cycle of chips themselves, they have their own fab, but it’s outdated, so they needed a foundry partner. No one would take them on except for TSMC. Why? Because they knew how to do the 3D packaging. They had fully depreciated six inch, eight inch fabs, and they were so maniacally focused on serving their customers that they didn’t mind taking on 1,000 devices, not wafers, 1,000 devices. Morris Chang himself apparently said, “We need to do this for this company to save these kids”, when nobody else would take it on.

That’s the kind of foundry we’re talking about here. And so we could talk about abstractions, about whether the foundry model is superior and IDMs, and there are clear superiorities and differences in business models, but I think we are talking about a very unique company and the culture that they have that have allowed the world to be served by it and served so well, but now we are exposed to the risks of it.

Now we come back to the question that you’re asking, which is, “How do we now de-risk that, not only through supply chain, supply-based policies, but is there demand-based policies that we could get to?”. My hunch is that there absolutely are demand-based policies to get at that, and there is a hunger for it from the customers’ perspective. To Hassan’s point, we have heard so much glowing feedback from TSMC’s customers about how good they are at delivering, they under-promise and over-deliver. It’s the theme that we hear over and over, and over again from the customers and they’re saying, “But if they can build in the US or if there could be US alternatives, of course we will take a look at it because we are not stupid, we know the risks here”.

So if you look at the enthusiasm for the Arizona fabs at TSMC, I think that tells you what you need to know about that company and what company is willing to do, but it’s not a complete de-risking. As an economist, I would have to say, if you’re looking for an insurance policy that completely de-risks, then that’s a very expensive insurance policy, almost too expensive for the world to handle…

…HK: But things like Datacom transceivers that are being made on indium phosphide, if you want to build mega AI data center clusters that can communicate across campuses, you need to be able to push the frontier on indium phosphide technology. That’s a couple $100 million investment to move up to a six-inch wafer. That doesn’t grab headlines, but it’s one of those key linchpin technologies for being able to build something that really does matter to the government if you want to be able to deploy the best AI models…

…Well, to me, this has been one of my biggest critiques of CHIPS, in that I felt it was too focused on the leading edge, when the single biggest contrast between national security or resiliency concerns and economic incentives was the trailing edge. The reason we have trailing edge is it used to be leading edge a long time ago, so it was economical because these are fully depreciated assets. It’s impossible to rebuild that economically because you have to pay off your equipment costs, and you’re competing with fabs that don’t, and China can do that because they have to move their way up the learning curve, and so they’re going to dominate all this trailing edge. TSMC has that because they already built it, even they in the response to China are specializing all their trailing edge fabs too, because just general purpose, 28nm chips or 90nm chips or however back you want to go, China’s just going to inevitably flood the market there. There is no one that can solve this other than the government, the economics never pencil out otherwise. How much did you think about that and how did you balance this? You mentioned Hassan, ChatGPT takes the world, everyone’s thinking about AI, but actually where the government can arguably we have the biggest impact is in this area.

HK: I think the most debates that we had on portfolio construction were essentially around the question of how much to reserve for the trailing edge, because the big guys came in early and we knew what their asks were and their asks, and the Secretary said this, if you looked at just the Big Four, they totaled more than the $39 billion that we had. So there’s a version of it where we could have just said, “Hey, you all get your ask or something close to your ask and we’re all going home”.

We chose not to do that, but we then did have a long set of conversations on how much do you reserve. The problem, Ben, was the firms in the projects they were proposing, their assumption was there’s no world in which the economics of a new build here are going to work, unless the government is willing to lean in at such a level, almost the majority comes from the CHIPS program on a $10 billion plus fab. But there was also, we had given guidance that our expected funding ranges were in the 5% to 15% plus the 25% ITC [Investment Tax Credit]. So, a lot of these firms read the tea leaves.

So, this was sort embedded in the law. The law itself didn’t really allow for it, because a trailing edge project needs 100% government funding. That’s just the reality of it.

HK: And I think it was sort of anticipated that there was not an appetite for the government to lean in and basically say, “We are building this fab”, unless the DOD wants to do it on special circumstances, which is a separate case than what the Commerce, how people viewed our authority in the Commerce Department. It was to go fund facilities that would be economically viable with dollars on the margin, coming from the federal government…

…HK: I really do believe both sides share an earnest belief in the urgency to address the problem, and I think if we can recognize that shared earnestness to address it, I am more optimistic. Our system feels more chaotic, but to your point, amidst the chaos, very smart capable people pull the good ideas to the forefront and they make them work. And so we may seem more chaotic and less organized than say the rest of the world, but that’s kind of simultaneously our superpower.

That is the US in a nutshell, absolutely.

HK: It is a little our superpower because some weirdo comes out with something that no one considered and turns out that’s a more viable disruptive path, and I do think that noise, you’ve got to kind of be able to be zen about it at some level and say something good may yet come hopefully…

…DK: The last thing I would leave you with this, Ben, is this thought that I think about this every day as I listen to my daughter’s heartbeat, I talked to the chip designers at the medical device company. They’re called Medtronic, you might’ve heard of them, they have life-saving devices. I asked them about what node they’re made on, how it’s packaged, what fab it’s made from and everything else and they gave me all technical details. And the lead designer of that just kind of paused and said, “I just want you to understand this device, this pacemaker detects your daughter’s heartbeat, how it wants to beat. If she’s sleeping, it knows that. If she’s trying to walk or run, it knows that. If it needs to speed up or down, it knows that, it’s intelligent”. And he said, “Right now, I know there’s a lot of focus on AI, but that device in your daughter’s heart that’s keeping her alive, that’s AI”.

A cynical analyst might come along and say, “Actually, no, that’s an IoT device, AI is really in language models”. But I took that to heart to say, “Right, the best of what America has to the world is in part innovation, technology innovation that enables all of us to live longer, to live better, to live more peacefully and with more safety and health in our lives”, that’s AI there, it’s not just language models. It’s that too, and semiconductors and manufacturing thereof is the foundational building blocks of all of that, and we should never forget that, and it’s something that we have given to the world that we’re trying to strengthen. So it shouldn’t be a zero-sum game, it should be something that we approach with a lot of enthusiasm, but with a lot of care.

4. Mitu Gulati on Whether Trump Could Restructure US Debt (Transcript here) – Tracy Alloway, Joe Weisenthal, and Mitu Gulati

Tracy: Yes, to put it mildly. Explain this further. You said earlier a debt swap could be basically the equivalent of extending maturities on existing Treasuries. But I would hope that there is some clause in the bond documents that would rule that out. Am I wrong? I guess I’m wrong.

Mitu: Alas, you are wrong. So anybody and everybody who holds US Treasuries should go and look at the contract terms for their US Treasuries and ask the question, “Do my contract terms restrict the US Treasury Department from saying to me tomorrow, “We need a little more money, so we’re just extending the maturity of your debt by another 20 years at the interest rate that you lent to us,” – is there anything restricting that?” I don’t think you’ll find anything, really.

Joe: This blows my mind because to me, if I’m a holder of US Treasuries and the creditor says, “You know what, I’m just paying you back a long time.” To me that sounds like default. You’re saying that in the research that you’ve done, this would not trigger credit default swaps. Because to my mind my assumption would be “This breaks the entire system. This is a default, and we can’t have a default on the risk-free assets.” You’re saying that actually in the wording of the document, it’s not there.

Mitu: We have to be clear and I have to be geeky here in my law professor mode. There are at least three different types of default. So there’s a default on the contract, something you could sue somebody for breach of contract. It would not be a breach of contract. US government is allowed to do this. Then there’s default in terms of would it trigger the handful of credit default swaps that are written on US Treasuries? That sort of depends on what the credit rating agencies decide. Based on what we saw in Greece 2012, they probably would say it was a default for credit default swaps. But that doesn’t apply to everyone. So those are the two big default scenarios.

Joe: What do you mean that doesn’t apply to everyone?

Mitu: The default for a credit default swap really only applies to the people who are holding credit defaults protection on US Treasuries. So they would be able to get their money back from somebody who had provided them insurance. But the rest of us dupes like me would just have to sit with the US extending the maturities…

…Mitu: My students asked me this in class a couple of days ago so I had to sketch it out for them. I said, Step One, the US Treasury Department and the Secretary of the Treasury have authority to manage the maturities of US Treasuries. What does manage the maturities mean? It really does mean issuing bonds of different maturities, managing your yield curve. But could it include unilaterally extending the maturities? Seems implausible. But this government has pushed its legal authority in many ways. Now what is most likely to happen, the US Treasury, if they ever went down this path because, say, they needed money and rates had gone up and they wanted to take advantage of the fact that their old borrowing was at low rates,  what they would do I think, the pattern we have seen is that they would extend the maturities and then Congress would quickly pass a law confirming it. That’s what we’ve seen in all of the other Trump executive orders plus Congress quickly passing a law. And then there would be lawsuits. There would be lawsuits left and right saying, “This is a violation of the Constitution.” Because remember, there’s no contractual protection. So now you have to say, “You’ve somehow taken my property and I have an implicit, moralistic right to having my money paid back at the time when you said you would pay back.” 

Now that’s really tough. We have historical precedent for this going back to the 1930s when we were in deep trouble because of gold. We didn’t have enough gold to pay everyone in case they invoked their gold clauses, which entitled holders of certain US Treasuries to get paid in gold. If they had gotten paid in gold or asked to get paid in gold, US would have essentially gone broke. So the President, backed by Congress, abrogated the gold clause protection in contracts. It was thought that surely the Supreme Court would say “This is not allowed. You cannot just take away people’s contractual rights.” The Supreme Court in one of the most famous cases of that era said, “It was okay.” The markets, I don’t wanna say this, but I’m gonna say it because it’s true – the markets didn’t crash.

Joe: What year was this?

Mitu: I think it’s around 1935. I’m gonna mess up which year Congress did the abrogation and then when the Supreme Court decision came out. But the predictions were this will destroy the US ability to ever borrow in the future and that did not happen. There are some famous articles about this.

Tracy: What’s your read then on why this didn’t happen? Why did the market seem to just go, “Okay, this is unusual, but fine.”

Mitu: My read, with no proof, is that there are these rare instances where the market thinks, “This abrogation of contractual rights, while it looks like a violation of the rule of law in every which way possible, is necessary to make us all better. Therefore, instead of penalizing the government that does it, we’re going to reward them and we’re going to lend even more.” Arguably, Greece in 2012, where Greece also legislatively abrogated contractual rights and did something very similar, is a similar situation where the market didn’t penalize them anywhere near the amount that many sages on Wall Street were saying would happen. I’m not saying that that’s what would happen now. I mean, this administration seems crazy.

5. Epizone AI: Outside the Code Stack – Kevin Kelly

Our newest invention – artificial intelligence – is usually viewed in genetic terms. The binary code of AI is copied, deployed, and improved upon. New models are bred from the code of former leading models – inheriting their abilities –, and then distributed to users. One of the first significant uses for this AI is in facilitating the art of coding, and in particular helping programmers to code new and better AIs. So this DNA-like code experiences compounding improvement as it spreads into human society. We can trace the traits and abilities of AI by following its inheritance in code.

However, this genetic version of AI has been limited in its influence on humans so far. While the frontier of AI research runs fast, its adoption and diffusion runs slow. Despite some unexpected abilities, AI so far has not penetrated very deep into society. By 2025 it has disrupted our collective attention, but it has not disrupted our economy, or jobs, or our daily lives (with very few exceptions).

I propose that AI will not disrupt human daily life until it also migrates from a genetic-ish code-based substrate to a widespread, heterodox culture-like platform. AI needs to have its own culture in order to evolve faster, just as humans did. It cannot remain just a thread of improving software/hardware functions; it must become an embedded ecosystem of entities that adapt, learn, and improve outside of the code stack. This AI epizone will enable its cultural evolution, just as the human society did for humans…

…AI civilization requires a similar epizone running outside the tech stack. It begins with humans using AI everyday, and an emerging skill set of AI collaboration taught by the AI whisperers.There will be alignment protocols, and schools for shaping the moralities of AIs. There will be shamans and doctors to monitor and nurture the mental health of the AIs. There needs to be corporate best practices for internal AIs, and review committees overseeing their roles. New institutions for reviewing, hiring and recommending various species of AI. Associations of AIs that work best together. Whole departments are needed to train AIs for certain roles and applications, as some kinds of training will take time (not just downloaded). The AIs themselves will evolve AI-only interlinguals, which needs mechanisms to preserve and archive. There’ll be ecosystems of AIs co-dependent on each other. AIs that police other AIs. The AIs need libraries of content and intermediate weights, latent spaces, and petabytes of data that need to be remembered rather than re-invented.  There are the human agents that have to manage the purchase of, and maintenance of, this AI epizone, at local, national and global levels. This is a civilization of AIs.

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

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

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

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

Here are the articles for the week ending 20 April 2025:

1. The Growing Risk to Fed Independence That Wall St Isn’t Watching (Transcript here) – Tracy Alloway, Joe Weisenthal, and Lev Menand

Lev: I think something big is happening in the federal government right now where the President is asserting unprecedented powers over parts of the government that for – in some cases – over a century have operated with a certain amount of separation from presidential day-to-day direction. That threatens to upend government policy across a range of dimensions but in one area in particular, the consequences could be felt immediately, and that is with respect to the Federal Reserve.

Joe: Certainly the firing of the two minority commissioners at the FTC, almost immediately after we recorded that episode were news, I don’t think people on Wall Street really – “Okay, something about mergers” – that’s not high on their radar. What is the connection between the FTC or that action and something that could happen with the Federal Reserve?

Lev: Let me tell you why that FTC firing was a particularly big deal. There is a supreme court case on the question of whether the President can fire commissioners on the FTC without cause, the way Trump asserted the power to do the other day, and that is the bedrock precedent that protects the Federal Reserve. It’s called Humphrey’s Executor. It was decided by the court in 1935 and it reigned in and largely reversed, or cabinet to its facts, a famous decision from 1926 called Meyers vs. The United States that President Roosevelt – FDR – had relied on to try to fire a member of the FTC and the Supreme Court in 1935 said, “Nope, you can’t do that. That case isn’t going to stand for that anymore.” We’ve built up a whole system of government around this understanding and here Trump is inviting the Supreme Court to overrule this bedrock precedent.

Tracy: Since we already went back in time to 1935 and 1926, can we go even further and talk about why we have independent agencies at all? I guess the clue is in the name “independent agencies” but they have some oversight clearly, so who is actually watching over these independent agencies and why do they exist?

Lev: All the constitutional actors oversee the independent agencies. Independent agency is a technical term of art in law to refer to an agency whose heads cannot be removed by the President at pleasure. They include any officer of the government who is not a legislative officer, a member of congress elected or a judicial officer, an appointed Article 3 Judge. All of those officers, some of them can be put into a category of they’re part of an executive agency, the head of that agency can be removed by the President at pleasure, or an independent agency, the head of the agency cannot be removed by the President at pleasure.

In that second category, independent agencies, they’re accountable to the President, to the courts, to the Congress, in all sorts of different ways. The President appoints the heads of independent agencies with the advice and consent of the Senate, the President can remove the heads of independent agencies, but generally only for cause. Sometimes those causes are specified, like neglect of duty or malfeasance in office, we could talk about that. In the Federal Reserve Act, the statute just says “cause” and a for-cause removal involves notice and a hearing, so it’s not the same thing as an at-pleasure removal. It precludes the President from removing somebody for a policy disagreement. The independent agencies are accountable to Congress in that there are hearings that are held. Officers have to go down, just like Jay Powell goes down and testify, they’re subject to Congress’s subpoena power for records, they’re implicated in all the workings of the government and their actions, like the FTC’s actions, are subject to judicial review by Article 3 judges. So they’re not independent in this sense that’s sometimes asserted that they’re a fourth branch of government, or they’re outside of the government. No, they’re just a type of government body that has a different relationship to the President from the Secretary of Defense or the White House Chief of Staff, which are positions where the President can fire or direct the actions of that officer.

These sorts of positions have been around going all the way back to the founding. With respect to monetary policy, it was a question for the first Congress and the first Secretary of the Treasury, how much direction monetary policy should be subject to day-to-day oversight or direction by the President. This issue isn’t a new issue for the United States, it’s there right at the beginning…

…Lev: What are the possible exceptions that would allow the logic of Humphrey’s Executor to maybe apply to the Fed even if it no longer applies to the FTC? The one that the Trump Administration is running with so far is that monetary policy is somehow not sovereign executive power and can be distinguished from the other stuff the Fed does, and that stuff is regulation of financial institutions. Trump put out an executive order last month, Executive Order 14215, which asserted executive power over all the independent agencies and included specific carveout language for the Fed that said “This order doesn’t apply to the Fed with respect to its monetary policy, only with respect to its regulation and supervision of financial institutions.”  This is the Trump theory. There’s some other options we can get to, but I think let’s maybe think through Trump theory – does this make any sense?

There are some huge problems with this theory. It suggests that they haven’t spent a lot of time thinking about how the Fed conducts monetary policy and what the relationship is between monetary policy and the regulation of banks, because it’s really all one and the same thing. The court would be hard-pressed to say, for example, just as an initial matter, Jay Powell can’t be removed by the President with respect to what he’s doing on monetary policy but with respect to what he’s doing on bank regulation, the President could fire him for a policy disagreement. That would just fall apart pretty easily. What type of independence is really left? The president could just say “I fired him because I don’t like what he was doing on bank regulation,” and the real reason could be that he didn’t like what Jay Powell is doing on interest rates. But how would we know, because he doesn’t need to give a reason, except to say that it’s bank regulation. So there’s already a problem.

But the deeper problem is monetary policy implementation is bank regulation. In January when the Fed met, in the aftermath of that meeting, the board of governors amended Regulation D through a rulemaking published in the federal register lowering the interest paid on reserve balances to banks with reserve accounts at the Federal Reserve banks. It is a straight exercise of regulatory power, just like when the SEC writes a rule for what type of disclosure a company has to do if it’s a publicly traded company. They just don’t seem to realize this. They think that it’s like the FOMC just meets and they talk and then they announce a decision and that’s monetary policy, it’s not regulatory, it’s not adjudicatory. But actually, monetary policy implementation is all exercise of government power over the banking system.

2. Ukraine research excursion – what did I find? – Swen Lorenz

Visiting Kyiv, Ukraine’s capital, is not actually all that difficult, even under the current circumstances.

Anyone with a decent passport can head there by bus, train or car (for obvious reasons, no commercial flights are currently operated). No visa is required to enter Ukraine…

…You’ll have noticed just how vast Ukraine’s landmass is. Gleaning out of the train window to see mostly nothing, you start to understand how much space the country has relative to its population size.

You are also in for a few surprises. Who knew customs control officers in a war-torn country can be as friendly as the ones checking entrants to Ukraine?…

…Indeed, stepping out of Kyiv-Pasazhyrskyi railway station and driving across the city centre to my Airbnb apartment just off Independence Square, I couldn’t help but remember my trips to Russia in the early 2000s (or to Serbia in 2018 before that country took off for a belated round of rapid growth). There is a palpable sense that Kyiv never developed to quite the extent that other nations in the region did.

It was also striking how life went about seemingly entirely normal, at least during the day.

Kyiv gets into the Western news when it gets hit by missiles or drones, but as the saying goes, a house that isn’t on fire isn’t news.

I was also a bit lucky, as I did not have a single air raid alarm until my fourth day in town. By that time, I had learned that most people nowadays ignore these alarms…

…Kyiv does get hit by strikes, and people get hit. However, the city is about 300 km (190 miles) from the front line, it now has strong air defences, and strikes are relatively few in number relative to the size of the city. Human beings adapt, and even in a country that is at war, life has to continue somehow. Even now, companies in Kyiv are doing deals, residents refurbish homes and apartments, and some splurge on living the good life.

As I learned by speaking to a broad range of decision-makers, experts, and business leaders, Ukrainian companies and investors are currently reinvesting their profits in Ukraine – for lack of other options.

Refurbishment of real estate continues in some places, but it’s suffering from a labour shortage caused by the military mobilisation and the large number of men moving abroad. Large-scale residential development projects have mostly come to a halt…

…Before the war, foreigners could get a permanent residency in Ukraine by purchasing real estate for USD 100,000. They would subsequently pay just 5% tax on income, and there was no requirement to be physically present.

In fact, the deal still exists today. The tax rate has now gone up to 6% to help support the war, but that’s as sweet a deal as you’d be offered in most countries that want to attract new residents.

Opening bank accounts? That’s done one day, I was told.

Immediately before the war, Ukraine saw a significant influx of foreigners, and real estate was on the up. This abruptly came to an end, but I was surprised how many foreign “entrepreneur types” I encountered during my visit. There is clearly a wave of early adopters currently looking at possibilities in Ukraine.

Somewhat counterintuitively, some locals echoed a particular kind of enthusiasm.

It’d be easy to have a negative view of Ukraine’s outlook based on any number of factors. With men up to the age of 60 (!) having to fear the possibility of being sent to war, you could think that anyone who can leave would want to leave.

However, as one successful, enterprising Ukrainian stated:

“Western Europe is socialism. I lived in several countries over there, and I prefer Ukraine. More opportunity, more freedom.”

Ukraine is not for everyone – not now, and not after the shooting ends. However, as a place to live, it’s a much better proposition than I had thought…

…According to the latest figures from the United Nations High Commissioner for Refugees (UNHCR), almost 7m Ukrainian refugees currently live abroad. The exact numbers of those who stayed in the country and those who fled are difficult to pin down. Ukraine hasn’t conducted a census since 2001, and the country’s statistical service has partially stopped collecting and publishing demographic data because of war-related difficulties.

It’s clear that the answer to this question will have a significant effect on any reconstruction effort.

Experience shows that 30% of refugees return home within the first one or two years of a conflict’s end, and in some instances up to 50% if strong incentives are provided. Both a huge opportunity and a big challenge lie ahead. On the one hand, the Ukrainian economy – post-war – would benefit massively if several million returned to live and work in the country. On the other hand, the returnees would face a significant housing shortage, as 13% of the country’s housing stock reportedly got destroyed.

“When the war ends, you will not be able to buy a bucket of paint anywhere in Europe”, one of my dinner attendees told me.

3. Javier Blas on China’s Rare Earths Dominance (Transcript here) – Joe Weisenthal, Tracy Alloway, and Javier Blas

A couple of numbers. The United States imported last year in 2024, according to US government data, a grand total of… give me the theme music… $170 million of rare earth metals. Not billion – million. $170 million. I’m pretty sure that the United States imported more olive oil from probably Spain.

Tracy: I like that olive oil is your baseline value.

Javier: And how much is that? That’s the second number we are going here. How much is $170 million if you compare that to total trade between the United States and China? That is 0.03%. So it’s not a lot. And the United States could face, say, a 10x increase in the price of rare earth metals and it still will have no impact whatsoever on the American economy or the global economy.

What really drives me mad is that you are writing about rare earth metals – they are important and obviously for some very niche applications, you really need rare earth metals, but prices could go higher and those applications will just pay the price. Typically, a writer like myself, you want to sex up a bit of the story, you will say “Rare earth metals critical for the weapons industry, for missiles, and high-tech application.” Do you know where everyone of us have some rare earth metals at home? They’re used in super permanent magnets, and therefore on that absolutely critical instrument of economic warfare, which is called the vacuum cleaner.

Tracy: I will say I sympathize with editors not wanting headlines about vacuum cleaners versus military equipment and all of those important strategic things.

Javier: I just keep in mind the story, the price of rare earth metals may increase and making vacuum cleaner is a bit more expensive – I don’t know you’re going to click.

Tracy: Not to get all Judy Bloom on everyone, but rare earths, they’re not as rare as the name would imply, but walk us through where they actually come from.

Javier: A lot of them come from China. About 80%-85% of the world’s rare earth metals come from China. It’s a question of digging them out of the ground and then processing. The big difficult part is processing, because it’s very polluting and it’s a reason why all the processing has moved from everywhere else in the planet into China, because no one wanted to deal with how nasty the process is.

Here is also the other question: If you want to do rare earth metals – processing in particular – outside China, what you need is much higher prices. If anything, the problem today with rare earth metals, and if we want to develop an industry of rare earth metals outside China, is that prices are too low. We need much higher prices and then everyone will do rare earth metals. The other thing that will happen is that if the price goes to a level that incentivizes everyone taking a bit of care, a lot of engineers in the vacuum cleaning industry will find ways to do it without rare earth metals. Also, people will actually collect the vacuum cleaners and recycle the magnets for other use.

Tracy: But if rare earths are such a small component of something like a vacuum cleaner, I imagine the prices would have to go up absolutely astronomically for that even to be a consideration for a company making these things.

Javier: Most of the time the prices don’t go nearly as high. Prices are beginning to rise again now, but prices stay relatively low compared to where historically prices have been. We have had the latest headlines are about rare earth metals and export restrictions. We have some similar headlines for other category of metals that we call critical minerals, another fantastic exercise of labeling. You want to sell something, call it “critical minerals.”

People were really concerned because China was imposing some export restrictions on tungsten, bismuth, molybdenum, and indium – this sounds to me like high school chemistry. You would think, “Oh my god, what is happening with the price of all of these?” This was not announced yesterday, this was announced a couple of months ago. Prices move, and yes the price of say indium moved to $345 per kilogram. Is that a lot? Yea, it’s a 20% increase from where we were at the end of last year. But about 10 years ago, that cost, today $345, was worth $800 per kilogram. Did you notice 10 years ago that it was a crisis on the indium market and everyone was a bit worried about it? I didn’t notice…

…Javier: To me, the other very important topic in trade and oil is that oil used to be almost the largest component of the American trade deficit in goods. You go to 2008, the US trade deficit was running around $800 billion a year. Of that, nearly $400 billion was oil. Today we are in a surplus for oil…

…Javier: Let’s call it the Goldilocks, the middle ground, what the oil patch will love and mainstream will be happy, say $75 a barrel. $75 a barrel is not breaking the budget of any middle-class family or working-class family when it comes to gasoline in the United States. And $75 a barrel, the American oil industry is making money, no problem whatsoever. Whoever is complaining at $75 probably doesn’t have a very good business case. The main problem is to make everything work at $75.

Just for the sake of the argument, let’s say that the magic number is $75. You cannot get that running unless you get OPEC on board and they keep restraining production and losing market share. $75 a barrels means that the consumers are happy and they continue to consume, but also that the US shale industry continues to grow and at some point someone needs to produce less. Even if that magic number existed – and I think that $75 probably is about right – you need OPEC to play ball and accept that they’re going to lose market share forever and ever. I don’t think that they’re on that business.

4. The AI Data-Center Boom Is Coming to America’s Heartland – Jennifer Hiller

Manufacturers have passed over this patch of farmland for nearly two decades, a string of setbacks that left this one of the poorest corners of Louisiana.

A quarter of the 20,000 residents in Richland Parish live in poverty. Farm jobs dwindled when agriculture became more efficient, forcing people to move away for work. Hopes for an auto manufacturing plant later went bust.

Now, the community is hoping for a new savior: AI.

Meta Platforms scooped up 2,700 acres of farmland last year for what would be its largest-ever data center, built over flat rice fields 45 minutes west of the Mississippi River.

At 4 million square feet, or 70 football fields, Meta’s data center will cost $10 billion and sit on more acreage than Louisiana State University in Baton Rouge, which has more than 34,000 students.

Building advanced artificial-intelligence systems will take city-sized amounts of power, which has turbocharged electricity demand projections for the first time this century…

… Gregory Upton, executive director at LSU Center for Energy Studies, estimates Meta could use 15% of Louisiana’s current electricity generation.

That is worrisome to other utility customers largely because of the mismatch between the 40-year to 50-year lifespan of gas-fired power plants and Entergy’s 15-year deal with Meta. They don’t want to be on the hook for the infrastructure.

“They want to use ratepayer money to finance something that they currently only really say they want for 15 years,” said Logan Atkinson Burke of the Alliance for Affordable Energy, an advocacy group for residential customers…

…“We hear about this constantly,” Francis said, noting someone must guarantee the payments on new projects for about 30 years.

“Guess who?” Francis asked. “It’s going to be the ratepayers.”

Commissioners will consider Entergy’s request later this year, but Francis says Meta’s investment is likely worth the risk of stranded assets down the line.

5. A Positive Reframe of What Trump Might be Doing for America in the Long Term – Peter Leyden

Let’s adopt the big-picture, long-term perspective of a historian in 2100 to try to better understand what’s really going on today and what’s probably going to happen in the near future…

…If I channel that historian in 2100, he or she would probably distill the big-picture story of the key challenges facing America at the historic juncture of 2025 as roughly this:

The Pax Americana with America as the global policeman enforcing order in the international system was coming to an end. That system had a great long run of 80 years, starting at the end of World War II, but could not go on much longer.

The United States military budget in 2025 was $850 billion — more than the military spending of the next dozen countries combined — and America was saddled with chronic budget deficits that could not sustain that kind of spending.

The bureaucratic welfare state that had been the backbone of post-war society in America and throughout the West was also fiscally unsustainable and way past its prime in effectiveness. The large aging populations of these developed economies were putting mounting pressures on the budgets of entitlement programs, which were devised for the smaller numbers of elderly long ago.

The view looking forward only got worse. Going into 2025, the federal government already held more than $35 trillion in debt and it was adding another $2 trillion to the deficit that year. This chronic budget imbalance could not go on without something big changing.

Our historian in 2100 might then shift from the daunting challenges to their solutions and the political developments that led to positive change.

One big change that had already arrived by 2025 was artificial intelligence. This new general-purpose technology had reached the point where it was ready to be deployed in many new ways through the economy, society, and government. These were the early days of shifting work to intelligent machines but those who understood the potential of the technology could see how many fundamental system changes could scale up in the next 25 years.

The old systems of government of the last 80 years needed to be dismantled in order to free up resources and create the space needed to build these new 21st-century systems. (The same held true for the old systems of carbon energy needing to be dismantled to clear the way for clean energy but let’s stick to the government for now.)

The Democrats, as the party of greater government intervention, were never going to summon the political will to lead the charge on dismantling big, bureaucratic government. Government workers, and the unions that organized them, were a core constituency of the party. The Dems, whether they were bleeding-heart liberals or left-wing progressive champions of the poor, were never going to trigger the transition, knowing the trauma it would create.

For that matter, traditional Republicans over the last 40 years had not been able to summon the will to dismantle much of anything despite their small government rhetoric and worry about deficits and debt. That traditional party was also as committed as ever to beefing up the military and expanding its commitments around the world.

Donald Trump finally provided the wrecking ball — on his second try……Does that mean that Trump, the Republican Party, and the conservative movement are victorious and will now rebuild America in their image? Does that mean that the Democrats and the progressive movement are vanquished and will be sidelined for a generation or more?

The truth is arguably the opposite. When you look at what’s going on through the lens of long-ball politics, then you can see that Trump might be solving one other huge challenge that America needs solved right now.

America needs to finally end the roughly 50/50 political stalemate that has paralyzed the country for the last 25 years. We try the increasingly divergent political formulas of Blue America, then Red America, then back again, and back yet again.

We need a long-term 60/40 political coalition that can more fundamentally reinvent America over the course of the next 25 years so that it can thrive for the rest of the century.

Trump is in the process of creating that political opportunity — for the Democrats and Blue America…

…Throughout American history, populist movements have been great at dismantling and destroying things. They’ve also been horrible at building anything of lasting consequence — let alone new systems that will define the next era…

…In Trump’s case, he is an absolute master at channeling anger at existing systems and the elites who run and benefit from them. But now that he’s in power, he’s dredging up really outmoded ideas from a truly bygone era, like tariffs, as solutions to today’s problems.

Trump, his MAGA administration, and the current crop of Republicans now in Congress are not going to come up with the new systems that will reinvent America in a way that allows it to thrive in the 21st century. The odds of that happening are minuscule.

However, they almost certainly are going to create the space for some other political force, some other movement, some other set of leaders to pull that off. I expect that will come out of Blue America with new movements and a new generation of leaders looking forward with truly transformative ideas.

The political consequences for whoever dismantles America’s old systems are going to be profound, and I mean profoundly bad. The president and the party who dismantles those bureaucracies, as healthy as that process might be in the long run, will make enemies of all those who lose their jobs or benefits…

…By 2050, the general consensus was that Trump had made America great again — just not the way he had intended. Trump did dismantle the old Pax Americana and the old 20th-century bureaucratic welfare state, but he also dismantled the political efficacy of the Republican Party and conservative movement for a couple generations, too.

Trump unintentionally laid the foundation for the next era of American greatness to begin — not by looking backward to resuscitate the past, but by allowing others to look forward and reinvent a much better future.

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