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 19 July 2026:

1. The Great Wave Has Arrived – Tang Jie

If there is one thing we have learned over the past twenty years, it is this:

The greatest commercial opportunities never lie in minor adjustments to products or business models. They arise when the ceiling of intelligence itself makes a leap…

…Whoever can push that ceiling even one inch higher will be able to redefine the boundaries of what thousands of industries are capable of achieving. That single inch is precisely what the new generation of AI companies grounded in first principles are competing to secure…

…We have a simple but demanding definition of AGI:

AGI is not the intelligence of a single genius. It is the aggregate of all human intelligence.

It should be capable of creating original knowledge on the level of the theory of relativity. That is the only standard by which we measure whether the true summit has been reached.

On the road toward that destination, several mountains must be crossed. They are also where today’s technological wave is surging most powerfully.

The First Mountain: Long-Horizon Task Capability

The most exciting breakthrough today is teaching models to complete extremely long tasks—not merely answering questions immediately, but planning and executing over weeks, months, or even years…

…The Second Mountain: Fully Autonomous Agent Systems

Building on long-horizon capabilities, groups of agents that can operate independently, collaborate with one another, and work around the clock will become a new form of productivity…

…The Third Mountain: Self-Evolution

This is the most difficult—and also the most compelling—mountain of all.

AI training AI is already taking shape. Models are beginning to write their own code, clean and synthesize their own data, and train themselves…

…What will happen after these three mountains have been crossed?

AI will begin to learn what the “self” is and what self-awareness means. Beyond that, it may begin to touch human emotion. Farther still lies consciousness itself.

From perception to cognition, from cognition to general intelligence, and from general intelligence toward artificial superintelligence, or ASI—the road has already been laid…

…When AGI arrives, today’s applications may all need to be rebuilt as AI-native systems—or may no longer be needed at all.

Operating systems themselves may be rewritten. In the future, when you turn on a computer, what you see may be an “LLM OS,” with every function generated on demand.

Going deeper still, this represents a challenge to the von Neumann architecture that has underpinned computing for the past eighty years…

…As the supply of high-quality human-generated data approaches exhaustion, we will turn computing power into fuel for evolution.

This means building factories for high-quality synthetic data, using AI-versus-AI competition through self-play to generate knowledge from scratch, and giving systems the ability to reconstruct their own code within secure sandboxes.

The goal is to free the pace of evolution from the physical limitations of human engineers…

…The more powerful AI becomes, the more robust its safety constraints must be.

From the very beginning, Zhipu established a guiding principle:

AI must serve human well-being and national strategic priorities.

The company rejects bolt-on safety patches. Instead, it seeks to encode human ethics, social norms, and national laws and regulations into the model’s value function as foundational axioms.

We plan to commit resources on the scale of tens of billions to advancing mechanistic interpretability—clarifying the neural logic behind model decisions and transforming black-box systems into transparent, explainable ones.

2. The Reverse Information Paradox – Satya Nadella

In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.

You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!

Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return.

That is what I think of as the Reverse Information Paradox…

… That is why enterprises need a real trust boundary for their human capital and token capital to compound. It is where an organization’s data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models.  I think of this as every firm’s right to align models to their enterprise accountability obligations.

3. Away From the Casino Tables: The Wildest Value Gap Since 1999 – Sam Ziff

The chart below splits global equities into four valuation buckets, running from deep value to extreme growth, with each cohort’s current valuation shown as a premium or discount to its own 40-year median…

…The cheapest bucket globally (“deep value”) trades at a 45% discount to its historical average, while extreme growth sits at a 40% premium. Shallow value outside the US remains meaningfully cheap. The further you move from US large-cap growth, the more likely you are to find something trading below its long-run valuation. This is the backdrop against which we invest…

…The last time markets were this concentrated and this expensive, the next decade belonged to the parts of the market the crowd had forgotten. From the end of 1999 to the end of 2009, the MSCI World and S&P 500 delivered close to a zero total return, while emerging markets doubled and value outperformed growth.

4. The 221-year-old company that reinvented itself — 4 times – Eric Markowitz

Meet Jean-Joseph D’Ieteren.

In 1805, the 14-year-old orphan took over a small workshop in Brussels and started making wheels. Over the next 220 years, the company D’Ieteren founded became a carriage maker, then a builder of custom car bodies, then an importer of American automobiles, then the exclusive Belgian distributor of Volkswagen, then the world leader in automotive glass repair, and then — in a move that raised eyebrows across Europe — the owner of Moleskine, the Italian notebook brand.

Today, the D’Ieteren Group operates in more than 40 countries, employs over 32,000 people, and generates annual revenues exceeding $8.2 billion…

…The D’Ieteren family maintains a private museum tucked inside an ordinary working building on the Rue du Mail — roughly 5,000 square feet that most people in Brussels don’t know exists. There’s no sign outside. You call ahead, state your reason for visiting, and wait to hear if you’ve been accepted. If you are, you walk up a ramp, down a long corridor, and through a large sliding door. Then the city disappears…

…The company had walked away from its core business four times, yet it had preserved, with extraordinary care, a museum’s worth of carriages, photographs, and tools. It wasn’t the behavior of a company that had escaped its past. It was the behavior of a company that understood its past so precisely that it knew exactly what to keep and what to release…

…For most of the 19th century, D’Ieteren was a carriage maker of growing renown. It won medals at international exhibitions, earned the title of Supplier to the Royal Court, and built some of the most celebrated horse-drawn vehicles in Europe…

…In 1898, while still producing horse-drawn carriages, they built bodywork for 12 electric vehicles commissioned by Camille Jenatzy, the Belgian race car driver who would soon become the first person to break the 62 mph (100 km/h) land speed record…

…After a fire destroyed the old workshops around 1903, the family rebuilt with modernized facilities capable of handling the new work on a greater scale. For the next two decades, horse-drawn and automotive production coexisted — in many cases with the same craftsmen serving the same clients, who often owned both kinds of vehicles…

…On the eve of the 1929 stock market crash, D’Ieteren employed nearly 500 craftsmen and was exporting 65% of its production to Argentina, Egypt, Spain, and the United States. A single car would cost approximately $468,700 in today’s dollars.

But Lucien had also spent 15 years sitting with an uncomfortable truth.

During World War I, while assigned to a military vehicle depot in Le Havre, he had watched American cars roll through by the thousands: Studebakers, Packards, Fords. Standardized, efficiently built, priced for ordinary people. He came home understanding that the future was volume, not bespoke cars…

…What the crash did for Lucien — what crisis so often does — was to remove the social and psychological costs of changing. The reputation, the pride, the loyalty to craftsmen: all of it was real, and all of it had kept him from acting on what he already knew. The crash stripped that away…

…Crisis has a way of burning off the unnecessary, leaving behind only what has always been true.

One of the most commonly asked questions is, “What do you do?”…

…The better question — the harder question — is not what we do, but who we are.

Consider D’Ieteren.

In a narrow technical sense, they were a carriage maker, then a carmaker, then a glass repair company. If you’d asked them in 1850 what they did, they’d have said carriages. But that wasn’t really who they were. Who they were was a tight-knit family obsessed with quality, committed to the long view, and devoted to finding better ways for people to move through the world. The carriage was just the expression of that deeper question at the time.

5. A Framework for Frontier AI and the Dawning of a New Age – Demis Hassabis

AGI cannot be compared to standard technological breakthroughs, not even ones as consequential as the internet or mobile – it is much more akin to the discovery of electricity or fire…

…The magnitude of this technology’s impact will be unprecedented, perhaps 10x of the Industrial Revolution at 10x the speed. It will help us solve some of the biggest problems society faces from accelerating drug discovery to developing new clean energy sources to creating novel advanced materials. We could even reach a point where resources are no longer the limiting factor for human progress, leading to an amazing new era of abundance…

…Urgent action is needed to address risks that might arise as we get closer to AGI. We’ve already seen the challenges frontier models pose for cybersecurity, and other threats including nuclear and bio risks may soon emerge as capabilities continue to advance. On the horizon, we will need robust safeguards to maintain control of increasingly agentic, recursively self-improving systems – and tackle unknown issues that will only become clearer over time…

…The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous. The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organisation, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives…

…A model would qualify as ‘Frontier-class’ if it meets certain thresholds on a set of benchmarks determined by the Standards Body and regularly updated to keep pace with evolving AI capabilities. Organisations with ‘Frontier Models’ as defined by those benchmarks would be deemed ‘Frontier Labs’, and be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research, and more.

Initially, Frontier Labs would voluntarily share models with the Standards Body for review up to 30 days before release. Once the assessment protocol is shown to be effective and robust, formalisation could quickly follow, meaning that Frontier Models would be required to pass it to be deployed in the US market…

…Model assessments should include rigorous scientific evaluations of capabilities in cybersecurity, biological threats and other high-risk domains…

…Even if we solve these hard technical challenges, there will be further complex economic and philosophical questions to tackle: what sorts of new economic models will be needed to help everyone thrive in a post-scarcity world? What values do we want to live by, what will meaning and purpose be, and how might even the human condition itself change? Resolving these questions obviously cannot and should not be left to technologists alone. It requires every part of society to come together to help define this new chapter.

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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 (Demis Hassabis is the CEO of Google Deepmind, an Alphabet company) and Microsoft (Satya Nadella is the CEO of the company). Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 12 July 2026:

1. AI’s Value Capture problem – Jaya Gupta

Imagine State Farm, Progressive, Allstate, Travelers, Chubb, AIG, Liberty Mutual, and 100-plus smaller carriers all running claims through the same model. Every carrier feeds it the same stream of context: the accident description, photos, repair estimate, adjuster’s note, borderline approval, fraud flag, override, payout, appeal, recovery outcome.

At first this is obviously useful. The model moves claims faster, flags suspicious cases, learns which repair estimates run inflated, which medical patterns look strange, and which overrides later become losses.

But if the same model learns from every carrier, is your claims judgment still your advantage? The underwriting exception that protected your loss ratio becomes a benchmark. The fraud pattern your team caught early becomes a feature sold back to the market…

…If everyone gets the same edge, customers keep it. Imagine an auto manufacturer using a model to negotiate semiconductors, resin, freight, contract manufacturing capacity, and substitute parts. The edge is buying better than the next manufacturer: knowing which supplier shortage is real, which quote embeds excess margin, and when preserving supply matters more than squeezing price. If every manufacturer runs procurement through the same model, the model does not just lower costs. It makes buying more “similar”. The best buyer loses the spread between its process and everyone else’s…

…The model also captures what compounds. Imagine 1,000 resource-constrained biotechs using Claude for Life Sciences because they do not have the internal platform of massive pharma company. Each company owns its compound, lab cost, failed program, and regulatory trail. But the workbench can see the pattern across all of them: which tox signal killed the program, which assay gave false confidence, which endpoint was weak, and which patient subgroup wasn’t the right one. If it sits across enough biotechs and pharmas, it can see failure patterns no single company can see. While data advantage is in exclusivity, a shared workbench breaks exclusivity by aggregation. And because Anthropic intends to develop drugs of its own, the tool you adopt for efficiency is built by the entity whose endgame may be to do what you do, using what it learned by watching the field do it…

…Data rights are not learning rights. Companies know how to negotiate retention, confidentiality, security, access controls, and training opt-outs. But the more important question is who owns the derived judgment: tasks, feedback loops, evals, workflow traces, corrections, failure modes, decision patterns, agent skills, and product insights. Once the model company knows the hard problem, it can acquire the job logic another way…

…The gain is front-loaded; the dependency compounds. The first adoption creates a real productivity jump. But once competitors run the same model, that jump becomes the baseline, and what remains is not your edge, it is your dependency on the next upgrade. Everyone will capture the first uplift but the vendor captures the recurring learning curve. Year one, the factory model reduces downtime, but then every rival has the same predictive maintenance workflow and the vendor owns the process intuition you now depend on.

2. High Bandwidth Flash: The Full Report – Austin Lyons

What’s interesting is that it has the same read bandwidth as an HBM4 stack, but with roughly 10x the capacity. And it’s made of NAND, not DRAM like HBM. (NAND is the cheap stuff.)

The first samples of the memory itself are expected very soon from Sandisk, sometime in the second half of 2026. Samples of the first AI inference devices built with HBF follow in early 2027…

…NAND flash has traditionally been used for information storage, i.e. where data lives when it’s not being used. Cheap, dense, and non-volatile. But slooooooowwwww….

But memory is where the accelerator keeps information handy during computation, and it has to be fast enough that compute never waits…

…So how could NAND flash possibly be used as memory?

Well, could there be a way to get the right data to the accelerator in time for computation, even with NAND’s slow reads? If you start the read it WAY before the accelerator needs the data, it could work?

In addition to intelligently scheduling when data is requested, one can also try to achieve high bandwidth from NAND. Bandwidth is the amount of data that arrives per unit of time. So if you know the read is going to take a long time, well, might as well have a bunch of reads running in parallel, if possible, right?

HBF unlocks high bandwidth by stacking NAND dies to increase the “width” or the parallelism. Each die is divided into many sub-arrays, which are small blocks of NAND that can each be read at the same time, independently of one another. An HBF stack places 16 of these dies behind a single interface, thousands of bits wide, so a huge number of sub-arrays are available to read at once. Any single read still takes thousands of nanoseconds, but thousands of reads can run in parallel, so a lot of data can be moved simultaneously…

…Conventional flash ships about 14 GB/s behind a PCIe 5.0 NVMe controller. Packaged as HBF, the same material delivers 1.6 TB/s. Roughly 100x the bandwidth, from packaging alone…

…HBF’s latency is still 10-100x slower than HBM. But if the accelerator knows what data it needs in advance, it can prefetch it and avoid waiting on any single slow read. The argument for HBF is that inference decode is the perfect workload…

…Inference cost scales with GPU count, and for today’s massive frontier models, GPU count is often driven by memory capacity per GPU.

Why?

Well, you need enough HBM to hold all the weights, but HBM is co-packaged with the accelerator; a fixed amount of HBM is bonded into each GPU package. So you can’t add memory without adding GPUs. Hence, bigger models mean more GPUs.

Of course, weights aren’t the only thing the accelerator needs to store in memory and acccess quickly and often. The KV cache and activations sit in memory too, and both stay in HBM or DRAM.

But the KV cache takes new writes every token; NAND’s endurance can’t handle that. NAND has much lower write endurace.

And activations need low-latency random access that NAND is too slow to give.

So HBF is for storing model weights.

And frontier weights are huge and always wanting to be even bigger. Wouldn’t it be nice to hold the model in significantly cheaper memory than HBM?

Recall that a 70B parameter model at fp16 (2 bytes per parameter) requires 70 × 10⁹ × 2 = 140 GB just for weights. A 1T parameter model thus needs 1,000 × 10⁹ × 2 = 2 TB. But that’s a lot of HBM; today’s shipping HBM4 stacks hold 36 GB (12-Hi); 16-Hi parts push 48 GB, and the JEDEC spec tops out at 64 GB. So a large model needs many stacks, which means many GPUs. Expensive!

That also means more interconnect (to move data around all those GPUs), which requires more power and, ultimately, a higher cost per output token (watts and $).

But HBF can provide 512 GB of capacity per stack!

So 512 GB per stack versus 48 GB for HBM4 is roughly 10x the capacity at the same bandwidth…

…For the same bandwidth, HBF has up to 8-16x the capacity at roughly 2x the power.Yes, HBF has worse bandwidth per watt, but for model weight storage one can argue the metrics that matter most are capacity and capacity per watt, where HBF wins.

SanDisk also claims a similar cost to an HBM stack despite 8-16x the capacity, which works out to roughly 10x lower cost per GB.

I thought NAND was way cheaper?!The raw NAND is cheap per bit, but an HBF stack isn’t cheap.

3. A Penny on the Dollar: Li Lu’s Bet on Russian Privatization, Part 1 – Tim Isgro

Starting in October 1992, each Russian citizen would be given a paper voucher that could be used, in government-organized auctions, to purchase a portion of a Russian business. 147 million vouchers would be printed, one for each Russian citizen, and distributed until January 1993 via local branches of the State Savings Bank. To obtain the vouchers, a citizen needed only to pay a nominal fee of 25 rubles (equivalent to 10 cents US)…

…By January, 144 million of the vouchers had been picked up and were circulating in public.

Importantly, the vouchers were freely tradable, which could help poorer Russian citizens monetize their vouchers if they chose to (and many did), and free tradability helped attract larger pools of capital that were needed in privatizing the economy.

Once Russian citizens had their vouchers, they could either hold them and use them at the auctions or they could trade them immediately for cash, typically on the streets of Russia. As soon as the vouchers began trading, it was apparent that they were trading for very low prices…

…As the chart shows, from 1992 to 1994, these vouchers were trading on the streets of Russia for anywhere from 5 to 25 USD each…

…144 million privatization vouchers were distributed to Russian citizens. These vouchers were mandated by the government to be converted into the ownership of 29% of all businesses in the country…

…Using a generous value of $20 per voucher, that implies the entirety of Russian businesses was worth $14.4bn at the time (equal to $20 x 144,000,000 vouchers / 20%). As if such an extreme undervaluation needs a comparison, the market cap of Exxon alone, a single US company, at its stock price low in 1993 was $71.7bn…

…Why were the vouchers trading at such incredibly low values?…

…First, inflation was rampant in Russia at that time. Prior to the fall of communism, the prices of many goods and services were set by the state. Starting in 1989 and in a few stages in the years following, Russian leaders slowly freed prices from government control. The effect was a massive inflation that gripped the country in the 1990’s…

…Second, and perhaps most obviously, the primary owners of the vouchers were ordinary Russian citizens. Now, the voucher auctions were designed to be as simple as possible, to the point where a citizen did not even need to know anything about a company to participate…

…Finally, and for interesting reasons, the vouchers were given a “face value” of 10,000 rubles (approximately 25 USD as of the end of 1992, albeit highly volatile)…

…The apparent effect of that 10,000 ruble face value (again, about 25 USD as of late 1992) on the trading price cannot be overstated.

It inadvertently created an anchor around which the vouchers traded…

…From December 1992 to June 1994, 15,052 Russian businesses were taken private, in whole or in part, almost all of them at prices that were incredibly low compared to their counterparts in other countries.

Boycko, Shleifer, and Vishny describe two of the bargains:

1. VAZ, the auto maker of the popular Lada cars, came out of its auction with a total market value of $45 million. As a point of comparison, in 1991, Fiat reportedly offered the Russian government $2 billion for the company.
2. Gazprom, the gigantic Russian natural gas monopoly, emerged from its auction with a market value of $228 million. This was roughly 1/1000th the value of put on the company by foreign investment banks, presumably by comparing it to other natural gas companies around the world…

…1994, for example, was an incredibly volatile year. The ROS index of 30 Russian stocks (created by CSFB) surged from 116 at the start of the year to a peak of 1,669 in September of that year. Yes, the index was up 1,338% in nine months. That is not a typo. It later dropped to a low of 443 in April 1995…

…So, 1994 was an important year for Russian privatization as an investment. It marks the end of the “voucher period” when Russian vouchers were trading at absurdly low prices and marks the deeper involvement of foreign investors (and the re-pricing and volatility that came with them). It is the time when the price of Russian businesses went from absurdly low to merely very low.

4. A Penny on the Dollar: Li Lu’s Bet on Russian Privatization, Part 2 – Tim Isgro

In Part One of our case study of Li Lu’s investment in Russia, we discussed the the fall of communism, the government plan to privatize Russian businesses, and the voucher system that was used to convey interests in those businesses to private citizens.

Now, I would like to focus on one of the two companies mentioned by Li to try to get a more specific sense of what he saw at the time. That company is Lukoil…

…Lukoil was formed in 1991 with the merger of three companies, the Langepas Oil Company, Urai, and Kogalym (the “Luk” in Lukoil), and in November 1992, Boris Yeltsin officially designated Lukoil one of three integrated holding companies. In those early days of the company’s formation and Russian market privatization, the trading of stocks was in its infancy and systems were rudimentary…

…Lukoil was cheap in those days, but how cheap was it? Let’s take a look at the valuation of Lukoil versus that of another leading oil company at the time, Exxon.

While Lukoil’s trading prices implied a market cap of 20 cents to 50 cents per barrel of proven oil and gas reserves around the time of Li’s purchase, Exxon’s valuation implied a market cap around $6 to $8 per barrel. In other words, Lukoil was trading about 5% the value of Exxon on a reserve basis ($0.35 divided by $7). At the time, the price per barrel of crude oil on world markets was around $20.

5. A Penny on the Dollar: Li Lu’s Bet on Russian Privatization, Part 3 – Tim Isgro

Li speaks briefly and vaguely about the market prices of Lukoil and Gazprom around this time and is not very specific about the exact time when he bought and sold the stock, but he does provide some clues:

Forget about the earnings. Just… the assets on the balance sheet. At the time oil prices [in the world market], I think the four or five year average was around twenty dollars [per barrel], and the [value per barrel of proven oil reserves for Lukoil was] at really low prices… about 10 cents to 20 cents per proven barrel of oil on the balance sheet and that’s not even counting the earnings… This is how low it went. It was ridiculous.

From this quote and others throughout the talk, it seems that Li’s main focus was just how cheap Lukoil was trading relative to how much proven oil reserves were on its balance sheet. He seems to have virtually ignored any loss (or any income) that the company was making at the time, reasoning that such an extreme undervaluation relative to the company’s assets dwarfed the numbers on the income statement…

…All in all, with the limited information we have, I think the most reasonable conclusion is that Li made his initial investment in Lukoil somewhere in early 1995 to mid-1996 when the stock was trading around $3 billion to $5 billion in market cap and $0.30 to $0.50 per proven barrel of oil and gas reserves, and he sold it somewhere around mid-1997 to mid-1998, in the region of $12 billion to $20 billion of market cap and $1.25 to $2.00 per proven barrel of reserves. (Recall his comments from earlier, that he sold “two years after” he first bought it and that the $2.00 price per proven barrel of oil no longer looked protected.)

With those crude, round numbers, his investment would have made him anywhere from 2.5 to 6.6 times his money in just over two years of time…

…Li talks about the dramatic cheapness of Lukoil and other Russian stocks at the time, and he was right to some extent. But that cheapness had its limits. Below, I show the price of Lukoil’s stock from 1993 to 2021. I also show a variation of the chart with Lukoil’s market cap versus the value of its proven oil reserves (a measure of Lukoil’s “cheapness”).

The charts paint a picture that is difficult to rectify: For virtually the entire period from 1993 to 2021, Lukoil appeared to be cheap, trading at a market cap that almost never valued the entire company greater than 8% of the value of its proven oil and gas in the ground. Exxon (and later Exxon-Mobil), by comparison, averaged a market cap of 34% of the value of its reserves from 1993 to 2021.

So, at all points, an investor might have thought that Lukoil was cheap. And yes, buying the company’s stock in 1995 and holding it for two years, like Li, would have produced a great return. Even holding it for 10 years, from, say, June 1995 to June 2005 would have produced an annualized return (excluding dividends) of 21%, as the stock went from $5.21 to $34.75.

However, the next 10 years, through June 2015, would have only produced an annualized return of only 3%, despite the company appearing cheap in June 2005, when its market value was only 4.7% of the value of its reserves…

…This case study was particularly enjoyable for me because the lessons are so difficult to tease out. Simply buying Lukoil stock at any point in its history because it was cheap relative to other companies around the world would have been a mixed bag. Buying in the 1990’s or early 2000’s would likely have worked out great. Buying in the late 2000’s or the 2010’s would likely have been poor. At all times, Lukoil looked cheap versus Exxon and other western oil companies.

It is very difficult to know how to think about this issue, but one thing to keep in mind is the timing of Li’s investment. In the early to mid 90’s, Russia was emerging from communism and still getting accustomed to the cultural shift toward capitalism and democracy. One could argue that, although corruption was still rampant, the prevailing winds were blowing in the direction of a country getting more used to democracy and slowly reaping the benefits of capitalist markets. These trends could serve as a gradual but important kind of catalyst to close the gap between price and value. In Russia, for example, these changes would slowly lead to more Western investors participating in Russian markets through the 1990s and 2000s.

But it’s important to realize too that the lack of such change (or timing) could make for a difficult investing situation, whereby an investor thinks a stock is cheap by some measure but that situation sticks around for many years.

So one takeaway from Li’s investment is that extreme cheapness is a great thing to hunt for, but seek to have it come along with a changing situation or an outright catalyst.

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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 no vested interest in any companies mentioned. Holdings are subject to change at any time. 

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

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

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

Here are the articles for the week ending 05 July 2026:

1. As AI Companies Race for Power, Amazon and Google Have the Lead – Lee Jinjoo

Amazon has an incumbent advantage. It is the world’s largest cloud provider and has been building a lot of data centers over the past two decades. The company’s operating, self-built data centers in the U.S. consume up to roughly 9 gigawatts of power, according to Aterio, a data provider. That is comparable with the generation capacity of North Dakota.

By comparison, Microsoft and Alphabet’s Google each have self-built data centers that use up to about 5 gigawatts of power, while Meta Platforms’ data centers have a roughly 4 gigawatt capacity. So far, most of the hyperscalers’ data center capacity is self-built, rather than rented from data-center operators…

…Based on estimates from Aterio, which tracks company announcements, utility filings, building permits and satellite data, Amazon is expected to add the most data center and power capacity in the U.S. through 2030. But Google is expected to add capacity at the fastest rate. In fact, including leased capacity from third-party data center owners, Google will have significantly closed its gap with Amazon by 2030, according to Aterio…

…Amazon plans to build out most of its own capacity, while Google is expected to rely more heavily on leases. Based on Aterio’s data, about a quarter of Google’s expected data center capacity in 2030 is expected to come from leases. Self-built can take longer but is the cheaper option over the long term…

…Google has proved that it can get speed with clean energy. At least three of its planned Texas data centers will be able to skip the long queue to connect to the grid because they are being built next to solar and wind projects, according to a report from Cleanview. In two of these data centers, Google will build the solar and wind capacity through its Intersect Power subsidiary. Texas’ power market rules allow faster grid connection if the data center is co-located with a new source of power. In all three cases, the solar and wind capacity well exceeds that of the data center…

…Microsoft, for example, struck a 20-year agreement with Chevron to power its AI data center in Texas with an off-grid, natural-gas-fired power plant. Both Meta and Amazon have plans for such projects, according to data compiled by Cleanview. Amazon hasn’t publicly confirmed its involvement, but its data center in Fayette County, Ohio, is the only planned source of large power demand near a permitted off-grid, natural-gas-power project, according to Cleanview’s Thomas. In most cases, hyperscalers eventually want these data centers to connect to the grid.

2. What’s the Real Depreciation Curve of a GPU? It Depends on What It Actually Did – Pietro Sette 

Identical GPU hardware can age very differently depending on how it’s used:

• A GPU running steady inference at, 60–70% utilization, under moderate thermals, day in and day out
• vs. a GPU running irregular training workloads that repeatedly spike to 95–100% utilization and push thermal limits every afternoon

On paper these two might be the exact same model of GPU. In practice, their aging is radically different. One might still be going strong and profitable after 5+ years (indeed, some 2016-era GPUs are still in active cloud service), while the other might effectively be worn out – or at least no longer economically viable in 3 years or less…

…Consider a mid-market lender financing several GPU deployments in the 0–50MW range (hundreds of high-end GPUs across multiple customers).

Their original underwriting assumed:

• ~80% steady utilization on each GPU (a consistent workload level)
• ~5.5 year useful economic life for the GPUs (before resale or obsolescence)
• No meaningful variance across different customers or workload types (every GPU in the fleet treated uniformly)

But when real telemetry data was collected at the GPU level, here’s what was actually observed:…

…Result: The fleet’s effective depreciation curve varied by 30–45% across different end-customers, even though the GPUs were identical models. In other words, certain customer workloads drove their hardware to lose value almost half again faster than others…

…Different operational events and stressors affect how fast a GPU “ages” or loses reliable performance. Thermal stress, power stress, and workload intensity are chief among them.

3. Rory Johnston on Why His $200 Oil Prediction Didn’t Turn Out Right (Transcript here) – Joe Weisenthal, Tracy Alloway, and Rory Johnston

[Joe Weisenthal]: Let’s start back in early March. Remind listeners what your take and the general wisdom was in the first couple of weeks of March about how long this could persist. And remind people why the Strait of Hormuz was seen as the choke point among choke points when it comes to oil.

[Rory Johnston]: Yeah, let’s transport ourselves back to our last conversation.

[Joe Weisenthal]: We need to get the time-travel-machine music, right?

[Rory Johnston]: They can add that. So, the reason it was such a massive deal — and still remains, I’d say. While we’ve avoided the doomsday prophecies, it is still by far the largest supply disruption in the market’s history. And for the numbers, for the barrel counting, for Tracy: the total flow through Hormuz prior to the war was roughly 20 million barrels a day. We knew we weren’t going to lose all of that, because we had some offsets — the Saudi East-West pipeline, the Emirati pipeline to Fujairah on the Gulf of Oman. But netting out all of those known rerouting options — which, again, at the time we didn’t know if they would fully work, because they’d never been fully tested, though they did work, thankfully — even after netting those off, we were still down roughly 13 million barrels a day of Gulf oil production, excluding Iran, that had been forcibly shut in for the duration of this crisis and is only now beginning to pick back up. That’s a lot of oil. That’s more than 13% of global supply. The reason we thought prices were going to hit $150 or even $200 a barrel is that when you have a supply shock that large without any more offsets, you end up at demand-destructive pricing really, really fast. And to destroy that level, the depth of that demand — we had never seen that before, but $200 a barrel seemed like the reasonable price at which it would happen. Now, thankfully, we did not have to destroy that demand. And what we’ll talk about shortly, I’m sure, is all the ways the system adapted and flexed. I think we saw this most notably, above all, in China.

[Tracy Alloway]: Okay, why don’t we just dive into it? Give us your overview on what happened and why we didn’t actually hit $200 a barrel.

[Rory Johnston]: The two biggest things — one on the fundamentals, the barrel-counting side — was China. We always knew China had huge stockpiles of oil, but we didn’t know how it was going to react to this crisis. What we’ve seen is that Chinese crude oil imports — into the world’s largest crude oil importer — fell by upwards of 5 million barrels a day between the three-month average prior to the war and June. We’re not quite done this month, but that’s roughly where we’re trending for June so far. That 5 million barrels a day was upwards of half of the total spot-market supply hit to Asia, and it allowed a lot of those other Asian importers to not have the competition they would otherwise have had for the barrels they were importing. So the countries that were hit hardest, and the governments that were most panicked — South Korea, Australia, Japan, Taiwan, and so on — there was a period where the Prime Minister of Australia was coming out daily and announcing the government’s successful acquisition of a cargo of diesel. It felt very COVID-y. Those importers saw imports collapse through March and April, but through May and into June they actually recovered basically to pre-war levels. And the largest facilitator of that was the fact that China was not competing for any of the other barrels — it absorbed so much of the shock itself.

[Tracy Alloway]: Just on China specifically — I have so many questions already — do you have any sense of how much of this was genuine demand destruction or substitution in China versus just releasing from stockpiles?

[Rory Johnston]: It’s a good question, and the firm answer is we don’t have 100% certainty as to the exact composition of that swing. We know the oil going in fell by 5 to 6 million barrels a day, products included. But in terms of actual demand destruction — and you guys were actually in China very recently — all the mobility indicators showed no notable decline. The level of implied demand destruction we see is striking, and importantly, China does not publish official demand data, and, very importantly, it doesn’t publish official inventory data either. So we’re left feeling around in the shadows. The implied demand destruction through this crisis was on par with the steepest in history, and on par in volume with the COVID-zero demand shock in 2022. But you guys were in China; I have not seen any reporting that indicates that level of lockdown. So we start asking, what’s going on in the middle? Typically, you’d assume that level of demand destruction without COVID-zero lockdowns would have to be driven by massive price increases. But part of what happened here is that China basically throttled the ability of domestic retail prices to rise through their normal regulatory procedures. Petrol prices in Beijing only rose maybe 30%, versus the doubling we saw globally. So again, it just doesn’t track for me that all of that, or even most of it, was demand destruction.

So then we go to substitution or outright releases of strategic petroleum reserves. The one thing we can say is that the inventories we can see — the floating-roof crude oil storage tanks — are still very, very high, roughly where they stood at the beginning of this crisis. As far as we can tell, they’re not drawing down aggressively on those stocks, at least not yet. The caveat is that with satellite analysis we can’t see underground storage caverns and their proper SPR. They have at least six storage caverns that we know of, about 131 million barrels. The likelihood is that they’ve been drawing those down, because crude oil imports fell far faster than refining run rates. So again, it had to be made up somewhere. The Occam’s Razor here is that they’ve been silently releasing additional crude inventories. But above and beyond that, crude refining run rates also fell dramatically, by 3 to 3.5 million barrels a day. So where’s the implied demand destruction? This is where we get one of two things. Either a very large release of refined product stocks — we know China has large stocks of refined products like gasoline, diesel, and jet fuel. We have virtually no firm information on those levels, and we can’t track them closely day-to-day or week-to-week, because unlike crude they don’t have floating roofs, so we have to infer. Those stockpiles are upwards of a billion barrels, but the implication is that they’re drawing them down very rapidly. The other thing we could have seen — and your colleague Javier Blas was on this very early — is the potential to switch some petrochemical feedstocks from oil-derived products like naphtha and LPG toward more gas-based products, natural gas, or even, in the extreme, coal-based chemical products.

4. How to Buy Cheap Claude Tokens in China – Qian Zilan

Underneath the handful of labs sits a much larger market, one that has been operating in public on GitHub, Taobao, Twitter, and Telegram. It is a grey economy of API proxies (commonly called “transfer stations,” 中转站) that lets Chinese developers access Anthropic’s models at as low as 10% of the official price. The participants extend far beyond selective experienced AI researchers, and the motivations are much broader than building a frontier model to catch up. Everyone who wants to use more advanced AI models or tools, be they university professors and students, tech workers, individual developers, or hobbyists, uses API proxies.1 The logs they generate may have become a commodity, traded for purposes ranging from model training to targeted fraud.

Meanwhile, every layer of control frontier US AI companies have added (geoblocking, phone verification, credit card requirements, and now live biometric KYC checks) has produced a corresponding layer of evasion infrastructure. These new SMS farms and biometric harvesting operations have implications that extend beyond geopolitics into how frontier AI safety frameworks are designed…

…A transfer station (中转站) is what the Chinese developer ecosystem calls an API proxy–an overseas server that sits between a developer and Anthropic’s infrastructure. It accepts API requests, forwards them as if they originated from the transfer station’s location, and passes the response back.2 The user redirects their software to the proxy’s server instead of Anthropic’s, and pays the API proxy RMB via WeChat or Alipay.3 This sidesteps both the VPN and the overseas credit card needed for direct access. Prominent transfer stations are catalogued in community repositories and ranked by real-time price and uptime. Below them, a longer tail of small and individual projects comes and goes.

While this setup sounds functionally identical to legitimate Western API aggregators like OpenRouter, transfer stations operate in an entirely different universe of legality and trust. Legitimate aggregators exist to simplify developer workflows, charging standard rates based on transparent enterprise agreements. Transfer stations, conversely, are built explicitly for evasion, routing data through unaccountable middlemen…

…A transfer station is not a sole entity. It sits in the middle of a layered supply chain, with most participants never interacting with each other directly.

Upstream are the resource providers: account merchants who bulk-register or acquire Anthropic accounts at scale; SMS verification platforms that supply the foreign phone numbers needed to pass sign-up checks; and, at the more technical end, reverse engineers who analyze Anthropic’s client code to find authentication shortcuts or detect when detection logic has changed. The payment infrastructure with card merchants and proxy networks also enables overseas billing from inside China…

…Almost no one operates the full chain. Most participants own one or two links and monetise those well, resulting in a resilient, modular system. AI model providers can suspend individual operators, but the upstream account pools and downstream customer base remain intact. So long as there are developers who want access to Claude and identity black markets willing to supply the credentials, which are both durable features, a replacement can be stood up quickly…

…The most curious thing, however, is not how to get access to Claude or Claude Code in China, but how to get it at a ridiculously low price–usually priced at 1 RMB per $1 of tokens — 70–90% below official prices. According to public discussions, there are at least three ways a transfer station makes this possible–often described as “one fish, three meals (一鱼三吃):

Meal 1: The markup on access. This is possible because of the upstream resource providers who can stack proxies using at least five relatively “innocent” tactics:

• bulk-registering API accounts to farm Anthropic’s $5 free credit
• reselling unused quota from others’ accounts
• corporate/educational discount arbitrage
• “APImaxxing” — one $200 Max plan carved up among multiple users via tokens-per-hour quotas, exploiting the gap between Anthropic’s flat subscription price and the far higher cost of equivalent pay-per-token API access…

…Meal 2: Swapping models and inflating tokens. Because users’ inputs and model outputs are mediated through a proxy, users cannot verify which model their request was actually routed to. A user selects Opus 4.7, but the proxy can silently route to Sonnet, Haiku, or, in the worst case, GLM or Qwen, and fraudulently relabel the output…

…Meal 3: The logs are the product. This is perhaps the most important part as it intersects with data privacy and distillation. Every request that passes through a proxy — full prompt, full response, tool calls, iterations — is sitting on the proxy operator’s server. For AI coding agents, those logs contain long reasoning chains, real engineering decisions, repository context, and human-verified correct outputs. This makes them an ideal dataset for post-training: for supervised fine-tuning on real engineering tasks, and, where full reasoning traces are captured, for distilling Claude’s reasoning patterns into smaller models.

5. How funerals keep Africa poor – David Oks

A modest, mid-level funeral in Ghana costs about $5,000 U.S. dollars; a “befitting” one can easily cost $15,000 or $20,000. And all this in a country with a median income of about $1,500 per year. Ghana is known for its particularly ornate funeral culture; but it’s not the only place in sub-Saharan Africa with a culture of exorbitantly expensive funerals. The average household in KwaZulu-Natal in eastern South Africa, for example, spends the equivalent of an adult’s annual income on a single funeral. We see the same tendency for ultra-expensive funerals in a striking number of places: the Democratic Republic of the Congo, Kenya, Nigeria, Benin, Cameroon, Mozambique, the Ivory Coast. It’s often observed, in fact, that families will spend more money on burying the dead than on keeping the sick alive: indeed, in the Kagera region of northern Tanzania, families spend 50 percent more money on funerals than on medical care…

…The answer, I think, is that the funeral isn’t really about the deceased. Funerals function as a costly signal of kinship group loyalty: and in that context, the expense of the funeral is the point. And, in turn, funerals tell us quite a lot about why so many societies across Africa have had so much trouble achieving economic “takeoff.” Kinship societies are actively hostile to economic growth, because economic growth undermines the basis of kinship: that is why kinship societies demand constant, visible sacrifices of wealth—funerals being the most spectacular—that make it extraordinarily difficult for any individual to accumulate capital, reinvest their assets, and pull ahead. The funeral is a window into a system of wealth destruction that serves, above all else, to keep people poor…

…African societies, by and large, are kinship societies.

So what are kinship societies?

You can think of modern societies as large collections of individuals, their lives structured by impersonal institutions like states and corporations. Kinship societies are much older: they are, in fact, the oldest and most durable type of human society. In a kinship society, life is centered on the extended family: the “clan,” the lineage, the tribe—a group that often includes many people who aren’t actually related. These kinship networks don’t act anything like nuclear families in modern societies. They are highly functional organisms: most of the functions provided by states in the modern world—protection from harm, credit, dispute resolution, eldercare, social insurance—are instead provided by the kinship network. If you fall sick, the kinship group will care for you; if you need cash, the kinship group will lend you money; if a stranger wrongs you, the kinship group will avenge you.

Of course, a kinship network isn’t a charity. It’s more like a mutual aid society that you’re born into and can’t leave: what the kinship group gives, the kinship group must also take. A huge amount of life in kinship societies is structured by the obligations that people owe to their kin…

…In a kinship society, nothing that you earn is truly yours. If you make money beyond the point of subsistence, you’ll be expected to share it with your less-fortunate relatives; if you start a business, you’ll be expected to hire your cousins or nephews or in-laws, even if they’re not the best possible employees; if you buy a car, you’ll be expected to lend it out to relatives who need it.

The result is a constant process of redistribution from the most productive members of a kinship group to the least productive. This informal redistribution is a constant feature of life in African societies: 93 percent of Kenyan entrepreneurs agree that success in business leads to financial demands from family and friends. South Africans even have a name for the sharing obligations that define African kinship groups: “the black tax.”…

…If the productive members of the group can defect—removing their resources from the common pool—then the whole system of mutual obligation begins to unravel. If a productive individual can simply withdraw from sharing obligations, then the network must demand more from those who remain, increasing the incentive to defect: so the entire delicate machinery of mutual obligation collapses in a slow cascade. This is the death spiral for kinship networks.

So from the perspective of the kinship network, wealth is a threat…

…You can think of funerals as another wealth destruction ritual. The genius of it is that it can’t be evaded: it is a public ceremony virtually dedicated to the immolation of wealth. In private, you might be able to evade your sharing obligations by hiding your earnings or your savings; but in public, at the funeral, the claims that your kin make on your wealth are at their most visible and least avoidable. You can’t simply not show up to your uncle’s funeral; and, if you show up, you will obviously be expected to contribute a handsome sum.

And this logic is even more powerful for those who are suspected of shirking their kinship obligations. It’s at the funeral where you must signal your willingness to honor sharing obligations most loudly. The lavishness of the funeral is a costly signal of continued commitment to the system of mutual obligation that holds the kinship group together. The point is that it’s expensive and incommensurate with your means.

This is why Ghanaian funerals, for example, have tended to grow only more lavish with time…

…And so the lavish funeral, in the end, is not a strange cultural quirk of African life, but the most visible manifestation of a social order oriented toward the destruction of accumulated surplus. And until the grip of that social order loosens, much of the wealth that Africa produces will continue to go, quite literally, into the ground.

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

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

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

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

Here are the articles for the week ending 28 June 2026:

1. The State of the AI Economy – Azeem Azhar, William Gildea, Hannah Petrovic, Nathan Warren and Marija Gavrilov

$110bn trailing 12-month revenues – now at a $175bn pace…

…AI is scaling three times faster than any IT wave…

…AI demand is reigniting a moribund US power sector

1950-2008: +6 TWh/month annual growth

2008-2024: ±0 growth

2024-today: +9 TWh/month annual growth…

…Against GDP, AI revenue is still a rounding error

Still tiny: AI revenue is equivalent to 0.42% of US GDP (vs IT sector’s 9.4%)…

…Seven in ten GenAI claims focus on cost savings or efficiency

Claimed AI outcomes 

S&P 500, Q4 2022 – Q1 2026

Revenue gain: 6%

Conversion improvement: 7%

Quality improvement: 18%

Throughput increase: 22%

Time savings: 23%

Cost reduction: 25%…

…Revenues cover the ongoing expense, not yet the cumulative bill

Q4 2025: Quarterly revenues first exceed CapEx depreciation…

…Still ~half-covered: cumulative revenue has nearly covered cumulative depreciation, but still has to cover the expected headroom…

…AI infra revenue now just clears today’s depreciation hurdle

GenAI revenues now cover the quarterly depreciation of AI infrastructure. Q1 26 headroom reached 19% for hyperscaler/neocloud revenues and 32% across all GenAI revenues.

Coverage remains thin. Depreciation absorbs roughly 81% of hyperscaler/neocloud GenAI revenue and 68% of total GenAI revenue before additional costs.

The next test is incremental coverage. As committed AI capex enters service, the depreciation base will rise. Revenue growth, utilization and pricing must continue to compound or headroom will compress again…

…Gross rental yields suggest useful lives extend past six years

Older GPUs earn yields long beyond their six-year depreciation life…

…This efficiency is increasing monetization per GW of capacity while revenues per token fall

Revenue per trillion tokens has fallen since its 2023 peak, mirroring price declines.

Efficiency gains drive lower token prices, which are more than offset by higher demand…

…AI demand is more revenue-validated than any prior platform shift. The investment case comes down to whether falling prices can move enough token volume to earn a return on CapEx.

2. Morgan Stanley Pitches Clients on a New Market for Data Center Loans – Dakin Campbell

Over the last few months, Morgan Stanley has suggested to clients that the next time they need to raise money for data center projects, they consider the leveraged loan market rather than the bond market, according to a person familiar with the matter who asked for anonymity to discuss private conversations.

Leveraged loans are those made to companies that don’t have investment-grade credit ratings, typically because they don’t have businesses that throw off lots of cash or they already have lots of debt. Such borrowers could include AI firms like OpenAI or new cloud providers such as CoreWeave…

…Leveraged loans are typically underwritten by an investment bank like Morgan Stanley. Most are then sold to financiers that bundle the loans into a single pool. That pool is then sliced up and resold to other investors based on their risk tolerance. These pools are known as collateralized loan obligations…

…Last month, Morgan Stanley brought the first AI-linked offering to the leveraged loan market when it sold $3.1 billion of notes on behalf of CoreWeave, which said it would use the proceeds to buy chips for OpenAI and Cohere. Investors placed more than $19 billion of orders, Bloomberg reported…

…Until now, most data center financing has been done via the bond market, either as junk bonds or—in the case of cash-rich tech firms like Google—less expensive investment-grade bonds…

…Other questions include the identity of the company actually leasing the space in the data center, and whether loans to finance chips get paid down on a schedule parallel to the chips’ expected useful life.

CLOs are a type of structured credit product, similar to the collateralized debt obligations that bundled mortgage loans and derivatives in the run-up to the financial crisis—debts that then suffered tens of billions of dollars in losses. CLOs haven’t experienced a similar blow-up, but many industry watchers worry that they contribute to financial instability by spreading the risk into corners of the financial system that can be hard to track.

3. China’s tribute system and the new world order – Ray Dalio

China is earning huge amounts of money from its exports, so Chinese companies and banks are building up large capital surpluses and accumulating buying power. This is exerting upward pressure on the Chinese renminbi relative to the US dollar and leading to its increased use for trade and capital transactions. Chinese investors and capital markets are emerging as competitors to their American counterparts…

…The tribute system was informed by Confucian values — in particular the idea that order comes from having clearly defined hierarchical roles. Relations within it are not between equals, but between superiors and subordinates that recognise their relative positions. The more powerful ones in the hierarchy should treat the less powerful well, and the less powerful should treat the more powerful well, so that there is harmony. If a lesser power treats the greater power inappropriately, the more powerful one punishes it, typically not violently but through pressure and deception. As Sun Tzu wrote in The Art of War, “to subdue the enemy without fighting is the acme of skill”…

…A military blockade that stops chip exports is just one of many potential pressure-points that China can exploit, but it is notable because the Chinese have a plan to be self-sufficient in chip production by late 2028, while the rest of the world will remain dependent on Taiwan.

Given these circumstances, China could put the US into the awkward position of needing to choose between fighting or not fighting, with each choice not to engage leading to the perception of diminished American power, so that China can gain ground by simply making threats. 

4. Is Ray Dalio correct that China is reviving the tribute system? – Arnaud Bertrand

China’s ancient tribute system – called 朝贡 (cháogòng) in Chinese – is typically very misunderstood in the West: we typically think it involved tributary states paying some form of “tribute” to China in exchange for protection – the way medieval vassals would pay fealty to a lord in Europe…

…The system was basically a quid-pro-quo where China would get “得名” (dé míng, literally “getting name/prestige”) while tributary states would get “得实” (dé shí, literally “getting substance/material benefit”) in exchange. It was about China paying huge amounts of money and other material benefits for the recognition of its centrality…

…Very concretely the way it worked is that tributary states would pay largely symbolic tribute to China (like local specialties and curiosities, the system codified that tribute should be “easy to obtain and not costly”, 必易得而不贵) and they would in exchange receive 3 layers of economic benefits:

Immediate payback in the form of money and expensive goods (silk, brocade, porcelain, tea, silver, etc.), which value was typically dozens of times the value of the tribute received by the emperor The right to trade during their tribute visit: the envoys’ entourage could trade with specially licensed Chinese merchants at the Huìtóngguǎn (会同馆, the official guesthouse in the capital) Most importantly, and that’s where the real money was, they would be granted the right to trade at Chinese ports. Under the Ming maritime prohibition, tributary status was the only legal entry point into the Chinese economy…

…He is however wrong to describe the tribute system as one fundamentally based on pressure and intimidation. As we’ve just seen, it was pretty much the opposite: the basic idea was to be so generous that everyone wants in (to the extent that countries would literally fight to be tributaries), not so threatening that nobody dares leave…

…That being said, he is ironically correct – I think – that there is some form of revival of a tribute-like system but not in the way he understands it: China will (and does) use trade – its “generosity” – as a gravitational force to pull countries into its orbit. Not by threatening to cut them off, but by making the relationship too valuable to walk away from. THAT is much closer to how the actual Chaogong system worked…

…Which, incidentally, is why you can be extremely confident that China will go to enormous lengths to develop its internal market, and why the current situation where China runs huge trade surpluses is facing mounting pressure to change from within China itself. If countries don’t feel they’re benefiting enough from trade with China, the entire logic collapses. That’s why developing domestic demand isn’t some target China sets itself to assuage Western demands, as some claim: it’s genuinely a strategic imperative.

It’s also why it’s ironic that the West is so keen on pushing China to boost domestic consumption: in effect, it means we’re already in a de-facto Chaogong-like system and they’re asking that the carrot be bigger.

5. Oil Prices Make a Stunning Retreat to Prewar Levels. Where Do We Go From Here? – Collin Eaton and Benoît Morenne

The U.S. war with Iran—and the economic war the latter waged in return—was supposed to be an apocalyptic moment for the oil market. Instead, oil prices are on the cusp of falling back to their prewar levels.

Their stunning round trip, just 11 days after President Trump reached a 60-day deal to reopen the Strait of Hormuz, has disrupted widespread expectations that the global oil market’s recovery would take months, at minimum…

…Tankers loaded with crude are leaving the waterway in droves; gulf countries are racing to resume crude exports; and some of the largest buyers of crude on the planet are proceeding without using as much oil. Analysts at JPMorgan Chase said this week that global energy flows had shifted in ways they hadn’t expected.

“The market has rebalanced through a meaningfully different mix of demand losses and inventory withdrawals than we initially assumed,” they said.

The reprieve could be short-lived. Some oil analysts are warning that the sinking prices don’t fully reflect how tight the market remains after months of draws on global oil inventories, which are now flirting with operational limits…

…Tanker traffic through the strait has climbed swiftly since the U.S. and Iran struck an accord on June 14. A postwar record of 78 tankers sailed through the waterway on Wednesday, up from a previous high of 49, according to S&P Global. That represents 57% of prewar traffic levels…

…Oil demand in China, the world’s largest importer of crude, appears to have fallen faster than JPMorgan analysts anticipated, implying that its economy might be adapting to higher energy prices more efficiently than experience would indicate, they said…

…Whether China picks up new purchases in the coming weeks will have a huge influence on the markets. Analysts said the country might not want to reduce its strategic reserves further…

…Over the past three weeks, roughly 2 million barrels of oil a day has come back on to the market, with Iran pumping out barrels faster than Saudi Arabia and the U.A.E., according to the research firm Rystad Energy. But it will likely take until October for Iraq, Kuwait and other gulf countries that had to slash production to pump oil at full speed, analysts said.

These barrels of oil aren’t immediately available to stocks around the world, which are still being depleted.  

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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 no vested interest in any company mentioned. Holdings are subject to change at any time. 

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

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

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

Here are the articles for the week ending 21 June 2026:

1. Tenneco Automotive: Charlie Munger’s $80 Million Bargain, Part 1 – Tim Isgro

The story of Charlie Munger’s investment in Tenneco Automotive is a fascinating one. And as far as I can tell, it’s only been told in a cursory way before now.

Munger made the investment in 2001 and it likely returned to him somewhere between 4 1/2 to 7 times his money and an annualized return over three years of 65% to 93%…

…What remained at the end was Tenneco’s automotive business, which sold emissions products (exhaust systems) and ride control products (like shocks and struts). It was this business, Tenneco Automotive Inc., that Munger was considering in 2001.

To say the above series of transactions dramatically changed the nature of Tenneco’s business is an understatement. The company went from being a large, diversified conglomerate with $13.2 billion in revenue in 1993 to a smaller single-line automotive businesses with just $3.5 billion in revenue in 2000…

…After all these spinoff transactions were finished, Tenneco was left with approximately $1.5 billion of long-term debt. After reading the history of Tenneco above, I suspect the automotive business was a victim of circumstance with respect to its debt load, being the last business standing after management spun off or sold five others…

…Focus for a moment on the company’s Operating Income (EBIT) and Interest Expense. Prior to the spinoff of Pactiv, the company was doing well, earning $633mn in EBIT in 1998 and spending $240mn in interest payments, but after the spinoff, the company was earning only $115mn in EBIT and spending $186mn in interest payments…

…To add insult to injury, Tenneco’s revenues were also suffering from the 2001 recession.

Tenneco served two broad sets of customers, original equipment manufacturers (auto makers) and the aftermarket (auto repair shops). Both were suffering lower sales…

…Not only were the interest payments on Tenneco’s debt too much for the company to handle in the years after completing its spinoff, but, on top of that, principal payments were starting to come due in 2001. The annual report from 2000 lists those upcoming maturities as $54 million, $109 million, and $99 million for 2001, 2002, and 2003, respectively. From the perspective of a casual analyst or observer, it was not clear at all how Tenneco could make those payments or how likely they were to work with their lenders on renegotiating terms.

2. Tenneco Automotive: Charlie Munger’s $80 Million Bargain, Part 2 – Tim Isgro

Tenneco produced auto products in two business segments: Emission Control and Ride Control. In both of those business segments, it had brand names with an excellent reputation and market share…

…Moreover, Tenneco’s list of original equipment manufacturers was large, including just about every major auto maker in the world. And the largest automaker (GM) accounted for only 16.6% of the company’s sales, indicating the sales were nicely diversified…

…Munger understood the great reputation of Tenneco’s products, as indicated by his brief comments at the Daily Journal meeting, when he stated:

I kind of knew based on experience how sticky some of that auto secondary market was, and how many old cars needed Monroe shock absorbers.

I think this point is critical to understanding Munger’s willingness to purchase these securities. Since customers loved and needed Tenneco’s products, the company still had fundamental value as an ongoing concern…

… Importantly, and perhaps underappreciated by the market, Tenneco was still in the midst of a major transition in its business. It had gone through five major spinoffs or sales of business units since 1993, it was facing its first recession since that time, and it was coping with all the debt it was saddled with after those spinoffs.

But digging in a bit to the company’s annual and quarterly reports makes it clear that management was keenly focused on right-sizing company expenses and running a more efficient organization…

…So, as of the end of 2000, management expected to generate a total of $92 million of savings by right-sizing its workforce and by adopting more efficient processes and practices.

In fact, it was already becoming evident by Q3 of 2001 that those efforts were working better than expected. Figure 10 below (which is Figure 8 reproduced) shows annualized operating costs (plus DD&A) that were $104mn lower than those for the year 2000. And those lower operating costs boosted EBIT by 32% to $152mn…

…I think these positive points are ultimately what caused Munger to believe that the company’s bonds, around a price of 35, and the company’s stock, around a price of $1.55 per share and a market cap of only $59mn, were way too cheap. For reference, the entire enterprise value of the company was $1.1bn, a figure I arrive at by conservatively assuming that the company’s debt is valued at par (apart from the 11 5/8% bonds, which I value at 35 cents).

I think Munger saw a very difficult financial situation for the company, and he probably acknowledged that a further, prolonged downturn in the economy and/or a group of unfriendly bank lenders could have pushed the company into bankruptcy. And in bankruptcy, in the wrong economic environment, it was quite possible that his debt and equity got wiped out.

The fact that Munger did not invest fully in Tenneco’s equity, which was more likely to be wiped out in bankruptcy, and chose to split his investment between bonds and equity, shows that he realized this was a possibility…

…I think Munger likely reasoned about how a potential bankruptcy might play out, and I think this was the most important point of all, prompting him to make his investment.

If Tenneco was forced into bankruptcy, its lenders would then have to decide on the best course of action that might get them a full recovery on their lending amounts. The total amount of long-term debt outstanding was $989mn plus the $500mn of subordinate bonds which Munger would invest in.

Tenneco’s lenders would rightly ask themselves: How are we best off to recover our $989mn?

1. We could force a liquidation of the business and attempt to be paid in full. That would involve a few years of wind-down work, staggered employee layoffs and plant and equipment sales, along with the severance and interim operating costs that come along with it. Plus, we would also need to engage in a process to sell the valuable Walker and Monroe brands, two of the most valuable assets the company had.

Or…

2. We could effectively realize the value of those brands by recapitalizing the company and operating as usual. One way to do that might be to forgive Tenneco’s debt completely, take an equity stake in the new company without debt, and then sell the equity in the new company to make ourselves whole on the lending amounts…

…We also know that Munger made “$80mn” on the investment. But we don’t know specifically how much he invested in either of the securities…

…Assuming Munger invested somewhere between 25% and 75% in Tenneco’s bonds (and stock), he likely made anywhere from 4.5 times to 7.2 times on his investment in three years, from December 2001 to the time the bonds were called in December 2004 (and when he likely sold his stock as well). Those returns imply an annualized return of 65% to 93%…

…I think there is one clear takeaway from Munger’s Tenneco investment.

When you encounter a company with a quality, in-demand product and/or a great brand, and that company is suffering, look twice. 

3. Systems of Record Won the SaaS Era – Clearinghouses Will Win the Agents Era – Jamin Ball

In financial markets, the clearinghouse sits between different parties that aren’t able to fully trust each other. The clearinghouse verifies / authorizes / settles trades, and ultimately keeps the receipt. Nobody really loves the clearinghouse, but it’s clear it has to exist for the ecosystem to transact.

Now think about where enterprise software is heading. Agents from tons of different vendors, acting autonomously, touching your most critical data, and even in the future spending real money. Some company has to sit in the middle of all that and decide: which agent is cleared to act? On what data? With what limits? And can you prove what happened after the fact? Whoever holds that seat holds incredibly “strategic real estate.” (and every founder I’ve worked with has probably heard me discuss strategic real estate over and over). That’s the clearinghouse.

This may sound counterintuitive, but owning the clearinghouse for agents (given agent companies themselves will want to be the clearinghouse) may create a deeper moat than the one systems of record had. A system of record controlled your data. It kind of controlled your workflows (but not always, oftentimes someone else controlled the workflows, but the data in the system of record was a critical part of the path). The Clearinghouse controls four things: memory (what your agents know), context (what they see and how it’s served), execution (what they’re allowed to do), and governance (who’s allowed to do what, plus the audit trail behind all of it). If migrating off a system of record was painful, migrating off the thing that holds your policies, your permissions, and your entire audit history is probably harder (especially when the agents start to handle more and more of the work). AND – I think these agent companies that become The Clearinghouse will start to look more and more like systems of record in their own right. Data in systems of record were oftentimes transactional data. Data in agent systems of records (ie Clearinghouses) will be agent traces, agent evals, agent telemetry data, agent A/B data, etc

4. Automation’s Asymptote: Part 2 – Abdullah Al-Rezwan

Tom Reed wrote a very good piece last month arguing that we may be pursuing what he calls “Goodhart Singularity”. Reed’s counter to automation doom is disarmingly simple: you cannot get good at solving problems without access to a source of problems, and the only source of most problems is slow, expensive interaction with the real world. Without that contact, the recursive loop produces something far less impressive than advertised. From Reed’s piece:

“The output of the R&D produced by an isolated datacenter of geniuses would be a mere Goodhart Singularity.4 An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.”

Why would self-improvement stall outside the lab? Because models get good at what they practice, and for most economically valuable work, there is nothing to practice on. Reed’s most clarifying observation is about what kind of data exists at all:

“For most tasks in the economy, the pretraining corpus contains writing about the task, but not a record of the task itself. This is of course one of many reasons coding has progressed faster than other domains – code is one of the neat cases for which the task itself is almost entirely reducible to its token trace.”

The internet contains commentary and advice in abundance, but the actual steps of closing an M&A deal or deciding which drone prototype to ship were never serialized into tokens. The natural rebuttal is that a sufficiently smart system can simulate whatever data it lacks. Reed is skeptical that simulation is a viable path:

“Consider that almost half of SWE-Bench submissions accepted by AI auto-graders would be rejected by the actual human maintainers of the relevant repositories. The fact that you can pump SWE-bench scores without increasing actual merge rates is, to me, suggestive of the situation the datacenter-genius will find itself in.

The great Zhengdong makes this point about the progress of AI research itself. Not only are “evals” the only things that models are capable of getting good at, but “the researchers [themselves], they just wanna optimise… they just want an important problem to solve, a clear evaluation that measures progress towards it, and then they just wanna optimise it.” I suggest that AI companies need real-world deployment as a source of problems, or else they will have no good targets for optimisation.”

5. Mao’s economic record wasn’t bad, actually – Arnaud Bertrand

One number for you: under Mao, China’s GDP PPP per capita (meaning per person) was multiplied by about 2.5x from just above $400 in the early 1950s to nearly $1,000 in 1978. These figures aren’t from a “communist source”, they’re taken straight from a report by the Congressional Research Service, the research arm of the U.S. Congress…

…This is confirmed in another report by the extremely serious National Bureau of Economic Research (NBER), one of the most prestigious economic research institutions in the U.S., who found in a report entitled “The Economy of People’s Republic of China from 1953” that “the Chinese economy in 1952-1978 grew rather rapidly” with an average annual growth rate of real GDP of 6%. This equates to the overall Chinese economy being multiplied by 5 over the Mao era, which is consistent with China’s GDP per capita nearly tripling since the Chinese population simultaneously increased by 75% during the period (5 divided by 1.75 equals 2.85)…

…The data is overall clear: during the Mao era, China outperformed both its most comparable peers. It grew roughly 25% faster annually than India (5-6.7% vs ~4%) and modestly faster than Indonesia (5-6.7% vs 4.8-4.9%). Which means that whatever criticisms one might make of Mao’s policies, the prevalent Western narrative that he presided over an “economic catastrophe” is demonstrably false. The reality, confirmed by American research institutions, international databases, and comparative studies alike, is that Mao presided over significant economic expansion that exceeded comparable peer nations.

Sure, it wasn’t all plain-sailing, to say the least. For instance during the Great Leap Forward, according to the Penn World Table data, China’s GDP contracted by 20.8% from its 1959 peak to the 1962 trough – a severe three-year recession that took until 1965 to fully recover from. Similarly, at the beginning of the Cultural Revolution, GDP contracted by 5.9% from 1966 to 1968, with back-to-back annual declines of 3.3% and 2.7% before rebounding strongly with 9.9% growth in 1969…

…We shouldn’t dismiss the human toll that the Great Leap Forward inflicted. It remains the most severe policy failures in modern Chinese history, causing genuine excess mortality and widespread suffering. But we shouldn’t exaggerate the catastrophe either: probably the best way to assess mortality rates during the Great Leap Forward is to look at population numbers and reconcile them with birth rate data (which dropped from 37 per thousand in 1959 to just 21 per thousand in 1960…

…But let’s be clear though: the Great Leap Forward was a largely man-made economic catastrophe stemming from disastrous policies that backfired spectacularly. Mao didn’t intend to cause a famine, but his policies – including unrealistic production quotas and the diversion of agricultural labor to backyard steel furnaces – undoubtedly did. He himself acknowledged some responsibility for the disaster, as did the Party officially, with Liu Shaoqi (then Chairman of the PRC) stating at the Seven Thousand Cadres Conference in 1962 that the famine was attributed to “thirty percent natural disasters, seventy percent man-made problems.”…

…Overall, China’s GDP nearly doubled over the entire 10-year period of the Cultural Revolution and the 1969-1975 period at 6.86% annual growth was the fastest sustained growth period during the Mao years, even exceeding the celebrated First Five-Year Plan period (6.53% average annual growth). This really goes against the widespread perception that the Cultural Revolution was an economic disaster comparable to the Great Leap Forward: not only it wasn’t, but China’s economy was actually booming during the period!…

…This is what resolves an oft-discussed paradox (discussed, for instance, by Branko Milanovic here): How could a “thoroughly inefficient system” create the basis for explosive subsequent growth? The answer is that the Mao era, despite its inefficiencies and disasters, created specific tangible foundations – human capital, physical infrastructure, industrial capacity, organizational systems, and transformed property relations – that made the reform era’s success possible.

You couldn’t have had the TVE explosion without the organizational legacy of communes. You couldn’t have absorbed foreign technology without an educated workforce. You couldn’t have rapidly expanded manufacturing without existing industrial infrastructure and millions of workers with basic industrial skills. You couldn’t have sustained 10% growth rates for three decades without the healthcare improvements that gave China a healthy, productive workforce. 

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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 no vested interest in any company mentioned. Holdings are subject to change at any time. 

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

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

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

Here are the articles for the week ending 14 June 2026:

1. Gas Prices, Stock Bubbles, Grad Advice — And Teaching Personal Finance In School (Transcript here) – Morgan Housel 

I want to start with what is the biggest economic news story of this year: the war in Iran. For most of you listening or watching, the biggest impact that’s had on your life is the rise in gas prices and oil prices. I want to make a very nuanced point here about making predictions about the future, which is so common in economics, and so difficult and humbling.

When the war in Iran first started about three months ago, it was very common among the smartest, most astute, most educated economists, oil analysts, and talking heads to make predictions along these lines: if the Strait of Hormuz is closed for another week or two, you’re not going to see a rise in oil prices — you’re going to see an explosion of oil prices. Not $100 a barrel, but $150, $200, $250. Not $4 gas, but $7, $8, $9 gas, with flights being cancelled. Those predictions have been made for months, and it was always along the lines of “if it stays closed for another week or two, this is going to happen.”

I want to make this point without minimizing what’s happened to gas prices all over the world and what could happen in the future. I don’t want to say, “Look at all these people — they were wrong,” because the price of oil today is about where it was three months ago when the war first started. It surged and then plateaued at this level. That is something almost no one watching this three months ago would have predicted. Virtually everybody, if you had told them we would be three months into this war with the Strait of Hormuz closed, would have said this is going to be a Mad Max scenario in oil. And so far, as I record this, it has not been.

I want to make an important point here without making any predictions about what might happen next — almost the opposite point, about why these kinds of things happen. There is such a long history in economics, in politics, and in any kind of social world that makes predicting what’s going to happen next so hard, even when it seems like the most rational conclusion. It’s so appealing and so easy to make simple predictions: if X happens, Y will be the result. Very appealing, and I think very comforting, because when you make a prediction like that, it gives you — or the person listening to that forecast — a sense of control in a world that is uncertain, if not unpredictable.

I say this with the glory of hindsight and nothing else; I would not have known any of this three months ago. But from my understanding, a lot of why oil has not yet reached those Mad Max levels, despite being three months into the Strait of Hormuz closure, comes down to a few reasons. Number one, the United States is exporting oil and gas like never before, which has taken some of the supply-crunch pressure off. Number two, Saudi Arabia has a series of oil pipelines that have been massively extended and expanded over the last three months — one big pipeline going to the Red Sea has gone from 2 million barrels a day to 7 million barrels a day, taking a lot of pressure off oil that used to go through the Strait of Hormuz. Number three, China has massively decreased the level of its oil imports. And number four, all over the world, we’ve been draining down oil stocks and reserves. Can that last forever? Of course not. I’m not making any predictions about what’s going to happen next.

The point I want to make — in a much broader way that applies to so many more things in the world of money and economics than just the Strait of Hormuz and oil prices — is that it is extremely difficult to know how people are going to adapt and evolve to a change in the economy. It’s very easy to say “if X, then Y,” and it makes a lot of sense and it’s very comforting. It’s much more difficult to say, “If the Strait of Hormuz closes, then people are going to adapt in this way, and this way, and this other way, and therefore we don’t really know what the end result is going to be.” There are so many cases like this, whether it’s housing prices, stock prices, whatever it might be.

I’ll give you one example that was crazy at the time. 2009 was one of the worst years in economic history since the Great Depression — absolutely dreadful, during the financial crisis. Stocks finished up that year. They increased. It’s so easy to say that the second coming of the Great Depression would be bad for the stock market; that’s a very easy prediction to make. It was much more difficult to see how people, prices, and valuations would adapt and respond in that era.

I was thinking about this recently because gas prices in my town went up tremendously in the last three months, but they’ve been about the same for the last two and a half. They exploded at the beginning of the war and then plateaued. Looking into how the global oil market has adapted and evolved — and again, maybe that doesn’t last forever, I’m not making any prediction — it’s so important to have a sense of humility about how complex the global economy is, and about people’s ability to adapt in ways you never saw coming. That’s what makes predictions about what’s going to happen this year, next year, or over the next month so difficult.

The last thing I’ll say about the psychology of making predictions: it is very common that the higher the stakes, the more people are willing to believe forecasts. When the stakes are really high — gas prices could explode so much that you can’t afford your commute, or your flights are cancelled — people are willing to believe anybody who says, “I can tell you what’s going to happen next.” That becomes very appealing. The irony is that when the stakes are that high and things are moving that quickly, that’s when forecasts become the least reliable. The demand for forecasts increases exactly when the forecasts themselves become least reliable, because people are adapting and changing so quickly. That is why there is such a long history of economic forecasts for things that never happened.

2. Avoiding Death on the Yellow Brick Road – Joe Schmidt IV

The Yellow Brick Road is our shorthand for the path the labs are walking, where they’re committing extraordinary resources. The reason the labs are best-suited for problems like code generation, writing, or image-creation is because these problems improve with raw model capability: every dollar spent on pre-training and post-training improves product quality. Meanwhile, the rest of Oz is inhabited by more complex, often vertical problems, that aren’t as simple as giving a business user a horizontal tool with access to standard tools and computer use. The value comes less from the underlying model’s raw capability (though that’s still important!) than from the scaffolding around it that makes the output trustworthy, compliant, and operational inside a specific industry…

…The labs will certainly improve, but I’d argue there are a few ways the rest of Oz can defend themselves over time:

Data and learning flywheels: A lot of what you internalize isn’t in any training set — unwritten industry norms, undocumented standards, the tribal knowledge that lives in practitioners’ heads. None of it is on the public web. No amount of training compute substitutes for being inside the workflows where this knowledge actually lives. There are two flywheels stacked on top of each other here: an across-customer one — patterns that compound as you see more variants of the same problem — and a within-customer one — the why behind specific decisions, the unsaid exceptions, the firm’s own rules of thumb that only surface through real interaction with the system…

…A horizontal agent could in principle build the same learning infrastructure. The reason it doesn’t, beyond pure focus, is UX: capturing this kind of knowledge depends entirely on the workflow surfaces you give the user, and vertical players can shape those surfaces around exactly what their workflow needs to surface. Horizontal tools can’t. Eval sets, labeled outputs, and edge-case taxonomies can compound into a vertical-specific data flywheel which can fuel fine-tuning the next entrant can’t generate without comparable production exposure. Whether this is possible depends on data rights, the volume of production exposure accumulated, and the structure of customer contracts, but pattern recognition accrues regardless.

Managing model variability and complexity: The labs are already routing internally — different model classes for different requests, ensembles under the hood. What they can’t do is route across vendors, or evaluate a competitor’s model for a specific sub-task, or use an open-source fine-tune for the narrow piece where it’s actually best. The Rest of Oz company picks the right model for each sub-task across the entire model market, not just what its parent lab ships. It also does the work nobody wants to do — re-running evals on upgrades, recalibrating prompts for the customer’s edge cases, rolling out without breaking production — every time a new model lands. The labs aren’t doing this on the customer’s behalf; they sell you their next model and tell you to migrate…

…Cost optimization: Running every query through Opus 4.7 is the fastest path to negative gross margins. The best Rest of Oz companies route across tiers of models — frontier models for the hardest tasks, mid-tier for the bulk, smaller custom or fine-tuned models where they’ve earned the right to use them. Some are now post-training their own models on top of that, optimizing them for the narrow slice of work their customer cares about and serving them at a fraction of the cost of a frontier API call…

…Governance: There is considerable value in becoming the control plane for how their customers run AI in that vertical – the place where permissions, auditing, what-the-agent-is-allowed-to-do, and what-the-agent-actually-did all converge. That control plane is built out of use case specific guardrails that look completely different across industries and job types. Because they own the tools, the workflows, and the data the agent touches end-to-end, they can provide deterministic outcomes in ways horizontal tools will struggle to. They are also the entity that absorbs the regulatory complexity for the end buyer — FRCP and bar rules in legal, HIPAA in healthcare, SEC and FINRA in finance, state insurance regulations, and so on. A horizontal player can’t credibly do that without becoming a hundred different verticals at once. CIOs want to have a partner that contractually states they are handling compliance for the agents they are providing.

All of these come back to the same thing: focus. That could be a vertical (insurance, legal, accounting) or a function done deeply (sales, customer support, finance). Either way, the work needs a team that’s heads-down on one customer set — its workflows, its edge cases, its regulations. The labs aren’t built for that. They have to be everywhere, for everyone, which is how they built the Yellow Brick Road in the first place. 

3. Sergey Brin: Where Frontier AI Is Headed | Unscripted Q&A @ AGI House × Google DeepMind (Transcript here) – Rocky Yu and Sergey Brin

Sergey Brin: That’s a great question—what’s next after we hit AGI? Everybody is pretty focused on accelerating the growth in AI right now. You’re right: we started with the web and internet search, went through the mobile generation, which was another big explosion, and now AI is a huge new industry trend. What comes after that? I think if you can answer that, you’ll have a fantastic company on your hands…

…Audience Member: I have two questions. First, now that we talk about superintelligence, and AI can help us drive cars and do office work—what kind of thing do you think only humans can do after superintelligence? Second, 20 years ago Google was famous for connecting people, and now it’s a company focused on AI. So my question is about strategy: what do you think Google’s role will be over the next 20 years?

Sergey Brin: Small questions, I guess—what is humanity’s role in this world, and what is Google going to do for the next 20 years? The definition of intelligence has always shifted with what machines can do versus what people can do. For a long time, chess was the measure of intelligence, and then Deep Blue beat Kasparov in the 1990s. The interesting thing is that people kept playing chess. How many people here know who the top-ranked human chess player is? Anyone can yell the name—I’m assuming it’s Magnus Carlsen, and people bounce up and down. But how many know the top-ranked AI program?

Audience Member: Stockfish?

Sergey Brin: That’s the most popular—is it number one? You don’t think AlphaZero can beat Stockfish? Okay, well, you’re the only one who named the top chess program; let’s point that out. My point is that computers doing things well hasn’t stopped humans from getting better and better at them, getting more recognition, and enjoying them. We’ve adjusted our view over time—it used to be that chess was the intelligent thing, then Go was the intelligent thing, then poetry or painting. I think we’re going to find that AIs can do a whole lot of surprising things, but they also help advance people in doing those things. Since AlphaGo, the game of Go has advanced a lot—the players who played against Lee Sedol became vastly better afterward, and Ke Jie did too after he played AlphaGo. It pushed the state of the art. So people will be able to enjoy and do a lot of things even with AI assistance. As for the 20-year question—I don’t know. I think we should let somebody else ask. That’s a big one.

Audience Member: Do you believe transformers are sufficient for AGI?

Sergey Brin: Great question. I’ve asked myself that a bunch of times. Transformers have been weirdly flexible—we use them for image and video in addition to text, and they’ve exceeded their original capability. To be fair, they’ve also changed along the way: we have sparse transformers and a lot of little details that have shifted, so it’s not exactly the same thing as the transformer paper. If I had to guess whether something close to that could be AGI, I’d say yes—just because they’ve been able to evolve so much. But they are changing; it’s not the exact same thing as the original transformer paper…

…Audience Member (Boris): What’s your perspective on how world models can help reach AGI?

Sergey Brin: World models are basically video models. People talk about AGI pretty broadly. I think of AGI as the idea that the AI can actually improve itself. Other people—and they’re probably more correct—think AGI means the AI can do anything a person can do. Those are two different things. To do anything a person can do, you absolutely need to understand and interact with the physical world. So being able to dream or imagine what’s going to happen in the world if you do something, and to comprehend it, is obviously important. If you’re going to do everything—and that extends to robotics—world models are key. You all have probably had more time to play with our Gemini Omni model than I have, honestly, because I’m deep into the self-improvement game. But we’ve been working on that for a long time, and Omni is the latest version. Omni is also pretty cool because it’s the same Gemini—we train it with all the text and all the other things, exactly the same way. The fact that these converge is amazing. But yes, you need that capability for the ability to interact physically.

4. Blackstone Investors Ask to Pull $4.4 Billion From Private-Credit Fund – Matt Wirz

Investors in Blackstone’s flagship private-credit fund, known as Bcred, asked to redeem 10% of their shares in the second quarter, up from about 8% in the first quarter. That amounted to investors asking for $4.4 billion.

Blackstone will limit redemptions from the $79 billion fund to 5%, a reversal from its strategy in March when it opted to pay the full amount requested. The about-face highlights rising financial strain on managers of large private-credit funds marketed to individual investors who continue to ask for their money back…

…“BCRED remains well capitalized, and repayments [from loans] and inflows have outpaced shares repurchased,” the firm said Thursday. It said the fund’s structure, allowing it to limit redemptions, is a core feature that is meant to trade some liquidity for long-term performance…

…Wealthy individuals piled into private-credit funds—known as business-development companies, or BDCs—which invest in high-interest loans to midsize companies and distribute most of the income they collect to shareholders via dividends. The boom ended this year when investors turned bearish over increasing loan defaults and the potential for future losses from lending to software companies.

The Blackstone fund is the largest of the bunch, surging to a high of $82 billion at the end of 2025, but it is now shrinking, cutting into the fees the firm can collect. 

5. The AI Price War Is Here, Piling Pressure on OpenAI and Anthropic – Bradley Olson and Tina Li

Big companies and startups, chafing at rapidly escalating artificial intelligence costs, are increasingly turning to tools that tap in to cheaper AI models, including some from China. That’s raising pressure on industry leaders OpenAI and Anthropic to lower their prices, a prospect that could hurt their ability to grow into profitable enterprises…

…The ecosystem allows autonomous AI systems, or agents, to use cheap models—including those made by Chinese companies like Alibaba and DeepSeek—for many functions. The agents only tap the most capable versions of OpenAI’s ChatGPT and Anthropic’s Claude for more complex tasks. That can reduce costs for some AI-assisted work by as much as 95%, according to executives using the tools.

“Once we find something that is working well and engineers love, we find ways to make it cost effective,” said Dan Robinson, founder of Detail, a startup that identifies bugs. “There’s really an embarrassment of riches right now coming out of the open source labs.”

Robinson shifted 90% of Detail’s workload from Claude and Google’s Gemini to custom models and GLM, a family of models developed in China…

…OpenAI is considering drastic cuts to the prices it charges AI users, ahead of similar cuts the company expects at Anthropic, The Wall Street Journal reported. The company sees itself as having an advantage in such a scenario because it spent massive sums in the past year to secure access to computing resources at far lower prices than what’s available now…

…Open-source Chinese models have been rising in popularity across American businesses. DeepSeek’s share of AI usage rose from 1% in April to 17% in May on the startup Vercel’s platform, the company said.

On OpenRouter, another startup that processes AI queries, DeepSeek has been the most-used AI company since mid-May. Among their highest-spending customers, open-source token usage grew four times faster than closed-source between fall 2025 and spring 2026, OpenRouter said. The company has also seen more than 500 organizations swap from proprietary to open-source models…

…Anthropic’s recently-released Fable 5 model is more than 50 times more expensive per token than DeepSeek’s V4 Pro, for example.

But the top proprietary models from companies like OpenAI, Anthropic or Google remain four to six months ahead of open-source competitors, researchers say. In some cases that means they can complete a complex task using fewer tokens, equating to a lower total cost…

…Many companies have begun to design their own AI models using open-source alternatives and say they are managing to reduce AI costs. When companies build in-house models and train them with company data, their performance can improve or even exceed the capabilities of frontier AI models, executives say.

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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). 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 07 June 2026:

1. Most of the Economy Won’t Run on the Best Model – Rihard Jarc

When a company hires an accountant, it does not go out and hire a PhD in pure mathematics to reconcile the ledgers. Not because the PhD couldn’t do it — they obviously could, and probably faster — but because it makes no economic sense. The PhD is overqualified, which is just another way of saying they are too expensive for the value the task produces. The economic output of bookkeeping is capped. There is only so much upside in getting the books done. So you hire the cheapest person who clears the quality bar, and you pocket the difference…

…If you are running a drug-discovery program, you absolutely want the PhD — in fact you want five of them, plus a Nobel laureate consulting on the side. Why? Because the economic output of a single discovery is enormous, almost unbounded…

…This is, I think, exactly how the AI model market is going to bifurcate…

…Today, essentially everyone uses the state-of-the-art (SOTA) model for everything. You want to summarize an email? SOTA model. Classify a support ticket? SOTA model. Extract three fields from an invoice? SOTA model. We do this for one simple reason: the frontier models have only just crossed the threshold of being broadly truly impactful for knowledge work, and when something has only just started working, you reach for the best version of it you can find. You don’t optimize cost on a capability you weren’t sure you had last quarter.

But I believe this is a transitional behavior, not a stable equilibrium…

…We have a rapidly falling price for any given level of capability and frontier that is already shrinking in size in terms of what is actually being deployed, and we have companies burning through their annual token budgets in a matter of months.

As such, I believe that for the overwhelming majority of economically valuable knowledge work, the correct model is not the SOTA model. It’s the cheapest model that clears the task’s quality bar. And as pilots move into full production (which is the stage we are in today) — where you’re suddenly paying for millions or billions of tokens a day instead of running a demo — intelligence-per-dollar becomes the only metric that survives contact with a CFO…

…The sellers of new compute (semis) are only winners in a world of continued high-cadence spending on new compute. And my thesis specifically questions whether that cadence is necessary. So let me lay out the two states the world can be in, because the asymmetry between them is the whole argument.

Scenario 1: Capex falls or stabilizes. If you can squeeze an order of magnitude more useful tokens out of the hardware you already own — because models got smaller, cheaper, more efficient and verticalized — then you no longer need to spend $100bn+ every single year just to stay relevant. In this world, the owners of the installed base win and the sellers of new compute lose. Hyperscaler free cash flow inflects sharply upward, because capex was the one thing suppressing it. Multiples re-rate higher as the cloud business converts from a capex incinerator into a cash machine running largely paid-for, partly-depreciated hardware. And the semis de-rate, because the market finally realizes the upgrade treadmill has slowed.

Scenario 2: Capex stays high — and revenue explodes. This is the Jevons-paradox-on-steroids case. Demand is so strong that hyperscalers do both: they extract enormous output from cheap, long-lived existing hardware and keep buying new gear. Here everyone wins at once — but the hyperscalers win more, because their incremental revenue now lands on a cost base that is partly depreciated and dramatically more efficient per token. Operating leverage goes vertical.

2. Calling the Top – Dirtcheapstocks

Spacex is set to go public next month.

I read the S1 and felt like I was watching “Whose Line is it Anyway?”. You know, the show where everything’s made up and the points don’t matter…

…Spacex is eyeing a ~$1.8 trillion valuation, from the latest reports I’ve seen. SPCX did $18.7B of revenue and generated a net loss of $4.9B in 2025. Free cash flow was severely negative: -$15.8B (adjusting for stock-based comp).

So the business is valued at ~100x revenue, and revenue has been growing at a ~34% CAGR over the last two years. Q1 2026 revenue grew 15% yoy.

The business has never sustained profitability, as evidenced by a $41B accumulated deficit…

…If you pay $1.8T for a business, and want a 10% return, you need it to send you $180B in year one. If it sends you $0 in year 1, you need it to produce $198B in year 2, and every year after that until the end of days. If year 2 also produces $0, you need year 3, and every year beyond that, to produce $218B.

I don’t think it’s likely that SPCX will reach profitability in the next couple years…

…According to ChatGPT, General Motors (in the 1950’s) was the largest company in American history when measured on GAAP revenue as a percent of GDP.

This isn’t a perfect metric, but I think it helps us get a rough feel for how large a company can become as compared to the ecosystem in which it exists.

GM’s revenue was equal to ~2.3% of American GDP. This shouldn’t be surprising as GM had ~50% market share in the second most expensive asset Americans owned…

…Now let’s take this metric and apply it to SPCX.

U.S. GDP is ~$32T today. Historically speaking, it would be difficult for a single business to earn more than $750B in annual revenue.

But SPCX will conquer the world (and Mars), so let’s assume it shatters the record. Maybe SPCX revenue can be 3% of GDP, beating out every business in history by 30%!

That would imply SPCX revenue of $960B. So what kind of profit margin can we expect for this business…

…But let’s say SPCX is a killer business at scale and it can achieve 20% operating margins, and 15% net margins. And let’s say it takes us 10 years to work our way there.

So, at a $1.8T valuation, we need $180B of cash in our pocket this year to generate a 10% return.

If we are unable to earn an cumulative profit above $0 for the next 10 years, then year 11 (and every year after that) needs to pay us $466B!

Alright, so we need $466B of profit in year 11. At 15% net margins, that means we need $3.1T of revenue.

If nominal GDP compounds at 7% for a decade, then GDP will have grown to ~$64T. So, SPCX in year 11, will need to have grown its revenue to ~4.8% of GDP ($3.1T / $64T) – a percentage more than double any company in history.

To get to $3.1T of revenue in 10 years, SPCX will need to grow its top line at 67% annually. The past couple years have shown revenue growth in the 30’s…

Hmm, this is getting difficult.

3. X thread on the difference between HBF (High Bandwidth Flash) and HBM (High Bandwidth Memory) – Eugene Ng

HBF is essentially HBM but with NAND flash dies instead of DRAM. It uses similar 3D stacking and TSV technology, delivering 8-16x higher capacity than HBM in a comparable footprint, while offering similar bandwidth, much lower cost per GB, lower power, and acceptable latency for read-heavy AI inference workloads (e.g., massive model weights, long context windows, and large KV caches)…

…HBF Shines in Inference: AI inference (LLM serving) is dominated by read-heavy, capacity-bound tasks, loading huge models, managing long contexts, and high-throughput batching. HBF excels better than HBM…

…Limitations: HBF has significantly higher latency (~10 µs, roughly 100x slower than HBM), slower write performance, and limited endurance (~100k write/erase cycles), making it unsuitable for frequent updates during training…

…Training vs. Inference Shift: As inference grows faster than training in overall AI compute, hybrid HBM + HBF setups are superior to HBM alone. HBM dominates training, while HBF’s capacity and cost advantages break the “memory wall” for cheaper, higher-throughput inference at scale…

…Bottom line: HBF expands the total AI memory TAM without cannibalising HBM. It creates a new high-value inference tier, making the overall market more competitive, multi-layered, and resilient, which is great for innovation and supply diversity.

4. Project Glasswing: what Mythos showed us – Grant Bourzikas

Mythos Preview is a real step forward, and it’s worth saying that plainly before getting into anything else. We’ve been running models against our code for a while now, and the jump from what was possible with previous general-purpose frontier models to what Mythos Preview does today is not just a refinement of what came before.

It’s a different kind of tool doing a different kind of work, and that makes a clean apples-to-apples comparison to earlier models difficult. So rather than trying to benchmark Mythos Preview against general-purpose frontier models, it’s more useful to describe what it can actually do, and two features that stood out across the work we did with Mythos Preview:

• Exploit chain construction – A real attack rarely uses one bug. It chains several small attack primitives together into a working exploit. For instance, it might turn a use-after-free bug into an arbitrary read and write primitive, hijack the control flow, and use return-oriented programming (ROP) chains to take full control over a system. Mythos Preview can take several of these primitives and reason about how to combine them into a working proof. The reasoning it shows along the way looks like the work of a senior researcher rather than the output of an automated scanner.
• Proof generation – Finding a bug and proving it’s exploitable are two different things, and Mythos Preview can do both. It writes code that would trigger the suspected bug, compiles that code in a scratch environment, and runs it. If the program does what the model expected, that’s the proof. If it doesn’t, the model reads the failure, adjusts its hypothesis, and tries again. The loop matters as much as the bugs it finds, because a suspected flaw without a working proof is speculation, and Mythos Preview closes that gap on its own.

Some of what we describe above is not entirely unique to Mythos Preview. When we ran other frontier models through the same harness, they found a fair number of the same underlying bugs, and in some cases they got further than we expected on the reasoning side too. Where they fell short was at the point of stitching the pieces together. A model would identify an interesting bug, write a thoughtful description of why it mattered, and then stop, leaving the actual chain unfinished and the question of exploitability open.

The Mythos Preview model provided by Anthropic, as part of Project Glasswing, did not have the additional safeguards that are present in generally available models (like Opus 4.7 or GPT-5.5).

Despite this, the model organically pushes back on certain requests – much like the cyber capabilities that made it useful for vulnerability hunting, the model has its own emergent guardrails that sometimes cause it to push back on legitimate security research requests. But as we found, these organic refusals aren’t consistent – the same task, framed differently or presented in a different context, could produce completely different outcomes…

…When we first started AI-assisted vulnerability research last year, our instinct was the obvious one: point a generic coding agent at an arbitrary repository and ask it to discover vulnerabilities. This approach works, in the sense that the model will produce findings, but it doesn’t work in producing meaningful coverage of a real codebase and identifying findings of value…

…Four lessons came out of running the work at scale, and each one pointed to the need for a harness that manages the overall execution:

• Narrow scope produces better findings – Telling the model “Find vulnerabilities in this repository” makes it wander. Telling it “Look for command injection in this specific function, with this trust boundary above it, here’s the architecture document and here’s prior coverage of this area” makes it do something much closer to what a researcher would actually do.
• Adversarial review reduces noise – Adding a second agent between the initial finding and the queue – one with a different prompt, a different model, and no ability to generate its own findings – catches a lot of the noise that the first agent would miss if it just checked its own work. It turns out that putting two agents in deliberate disagreement is way more effective than just telling one agent to be careful.
• Splitting the chain across agents produces better reasoning – Asking “Is this code buggy?” and “Can an attacker actually reach this bug from outside the system?” are two different questions, and the model is better at each one when you ask them separately, because each question is narrower than the combined version.
• Parallel narrow tasks beat one exhaustive agent – Coverage improves when many agents work on tightly scoped questions and we deduplicate the results afterward, rather than asking one agent to be exhaustive.

Each of those observations is about model behavior, and put together they describe something that isn’t a chat interface anymore. It’s a harness that helps you achieve the final outcomes.

5. Open-source agents with frontier advisors: matching frontier performance through training and harness engineering – Fireworks AI

On LAB’s continuous mean-score metric, GLM 5.1 ranks highest among the open-source models we evaluated, at 0.8921 mean score putting it directly alongside frontier: Claude Opus 4.7 at 0.911, GPT-5.5 at 0.892. Kimi K2.6 (0.863) and DeepSeek V4 Pro (0.871) come in just below, both still clearly viable for production legal workloads.

On the LAB all-pass metric, the production-readiness measure, the closed frontier holds a small lead: Opus 4.7 at 14 / 100, GPT-5.5 at 11 / 100, GLM 5.1 at 12 / 100. That gap is where the rest of this post lives; the two interventions we describe below close most of it.

Cost is the headline. GLM 5.1 reaches its 0.8921 mean for $121 across the 100-task run. GPT-5.5’s nearly identical 0.892 costs $560. Claude Opus 4.7’s 0.911 mean and 14 / 100 all-pass runs $954, roughly 8× any open-source candidate.

“The customer ask is no longer ‘how do we get the smartest model on every query.’ It is ‘how do we get frontier-quality outputs on the queries that need them, and a model we control on the queries that don’t.’”…

…A single LLM call is the wrong unit of work for a legal task: reasoning chains run long, citation discipline is unforgiving, and under all-pass grading any missed criterion costs the entire task. To solve the problem, the team built a small, opinionated multi-agent harness with the open-source worker at its core. The configuration is straightforward: open weights at the core, orchestration the team can inspect and tune, and the frontier model invoked as a callable tool rather than a load-bearing dependency.

A frontier advisor as a callable tool. Treating Opus 4.7 as an advisor the worker can call on hard sub-tasks unlocked the cost savings on the harness. The GLM 5.1 worker does the bulk of the reasoning, drafting, and tool calls. There is no external router or orchestrator. The worker pulls the advisor in itself, wherever it needs a second opinion: retrieval, drafting, validation. Across the run, the advisor is invoked just 0.83 times per task on average — sparse-but-targeted use. That captures most of the quality lift of running the frontier end-to-end, at a small fraction of per-query cost, and it gives us a tunable cost/performance knob: dial advisor calls up on complex matters, down on routine ones.

The harness traces show a recognizable pattern. The worker’s turn count rises meaningfully versus a GLM 5.1-only run: the model reaches an uncertain step (typically during validation, occasionally mid-draft), calls the advisor for guidance or review, then resumes the trajectory with additional turns informed by the response. The advisor is doing less of the writing and more of the steering; the worker is doing the rest of the work it would not have known to do on its own. Sparse advisor calls, denser worker activity downstream of them.

The harness moves GLM 5.1 from 12 / 100 all-pass to 18 / 100 — higher than Claude Opus 4.7’s 14 / 100 — at $368 across the 100 tasks, roughly 39% of Opus’s $954 standalone cost (Figure 1). Against Opus the comparison is clean on both axes: −$586, +4 tasks all-pass. Against the GLM-only baseline, the advisor adds +6 tasks all-pass for +$246 — the cost increase is real, but it is the cost of beating Opus while still running the open-source worker at the core.

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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 no vested interest in any company mentioned. Holdings are subject to change at any time. 

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

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

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

Here are the articles for the week ending 24 May 2026:

1. The end of the resource exponential – Brandon Carl

Financial analysts are currently engaged in a collective exercise in ruler-drawing. By mapping the trajectory of compute spending and GPU sales, they have constructed a future that is essentially a larger version of the present. In this view, the path to artificial intelligence is a matter of pure capital expenditure. If $100 billion buys a certain level of reasoning, then $1 trillion must buy ten times as much. It is an investment thesis built on extrapolation.

History, however, is not linear. In any technological cycle, the most dangerous moment is when the market begins to treat the status quo as a permanent law. Today’s AI logic—that hardware scale is the primary lever—is less a rule of physics and more a temporary workaround for inefficient architecture. As investors calculate the return on ever-larger clusters, they are ignoring a more fundamental lesson: what is built today is rarely what defines tomorrow.

The shift from brute force to elegance is not just likely; it is a mathematical necessity. Much of modern AI is built on transformer architectures that exhibit quadratic complexity. Double the input, and the requirements for compute and memory grow fourfold. Quadratic consumption of any limited resource will eventually consume everything. Efficiency is not an optional optimization; it is a condition for survival…

…This does not mean the end of the massive GPU cluster, but it does mean that algorithmic efficiency, rather than just raw silicon, will increasingly solve resource shortages. As architectural pivots reduce the dependence on brute-force scaling, the “mix” of hardware required by a data centre five years from now will look nothing like the procurement lists of today.

The risk for investors is to over-index on “selling the shortage” based on current constraints. Subsidizing the construction of yesterday’s architecture is a recipe for stranded assets. In the history of technology, the greatest returns have rarely accrued to those who simply bought the most hardware, but to those who understood how the math was changing. Ideas travel much faster than silicon.

2.AI Chip Mania Sows Seeds of Its Own Destruction – James Mackintosh

Memory chips are a perfect example of a highly cyclical industry. Heavy investment is required to build a fabrication plant, or fab. When demand rises, it takes several years for supply to catch up, during which prices and profits jump. Those high profits encourage CEOs to expand supply. And the high fixed costs encourage producers to run fabs at full capacity—even when supply overshoots demand. The cycle turns when excess supply pushes down prices and profits plunge, as they did in 2022-23.

Already the high profitability has encouraged heavy capital spending. Micron is spending $150 billion to build or expand fabs in New York, Idaho and Virginia, and new Korean fabs are opening…

…The risk of a downturn is embedded in Micron’s valuation. Two weeks ago it was the S&P’s third-cheapest stock measured by price to forward earnings, and it’s still at under 10 times, tame for a highflying stock. That doesn’t make it cheap, though. It just means investors recognize that the boom times in memory chips never last.

History shows how this works. In the last cycle Micron stock peaked at the start of 2022, with the forward P/E at just nine times, ahead of a halving in the shares that year. The stock bottomed out and subsequently doubled after the loss was baked into predictions…

…The biggest risk is impossible to quantify: AI technology could become far more efficient in its use of memory, meaning data centers need less of it. Memory stocks took a hit in March when Alphabet researchers published a paper showing dramatic improvements in memory efficiency, but have recovered. Large language models are an immature technology, and engineering improvements for specialized data centers should be expected—but how big they are and when they come is unknowable in advance.

Other risks apply to the whole AI supply chain: Data-center plans may be scaled back, AI uptake prove slower than hoped, or a political backlash may hinder expansion. All are plausible; none are considered that serious by the AI bulls driving stock prices.

A final risk is that supercharged profits attract new rivals to enter the market. For now, that seems unlikely in the superfast memory Micron makes, but it’s already happening with other highly profitable chips used in AI.

3. The toll booths of lending – Michael Fritzell

To manage risks, banks and companies gather information on their counterparties. And one way to do so is to buy data from so-called “credit bureaus”, also known as “credit reporting agencies”.

These credit bureaus gather information on borrowers’ creditworthiness. These include consumers, corporate borrowers, and trade counterparties. The data is then used to support lending decisions, ensuring that each lender is comfortable with their exposures…

…On the corporate side, credit bureaus collect all sorts of data on private businesses: business registration numbers, legal addresses, ownership data, the executive leadership, name changes, etc. And more importantly, they collect data on revenues, profitability, and leverage from public filings, interviews, payment data, etc. They also cooperate with debt collectors to understand whether each business has had payment issues in the past.

All this data then ends up in credit reports, which you can purchase for US$150 each. Historically, these credit bureaus made money by selling credit reports a la carte. But today, the entire industry has moved towards subscriptions that generate much higher-quality, recurring, and sustainable revenue. If you’re an ongoing subscriber, you’ll get alerts if there are any changes to the creditworthiness of any particular counterparty…

…Buyers of corporate credit data tend to be small- and medium-sized enterprises that want to know whether they extend favourable credit terms to their counterparties. Or banks that want to know how to extend credit to. The local Asian credit bureaus have almost impenetrable market positions, as they’ve gathered detailed information on millions of businesses. And the reports can be purchased for very little money, while costing almost nothing to produce. No serious lender would skip a US$50 credit check before extending a half-million loan…

…And because collecting consumer data is sensitive, it is highly regulated and therefore protected. The buyers of credit data tend to be financial institutions that want to know whether to extend a mortgage or consumer loans.

There are clear network effects: in many cases, credit bureaus get data on consumer borrowers from their bank customers, who willingly provide the information in exchange for data on other banks’ borrowers. So the bureaus almost become central exchanges that become difficult to displace.

On the other hand, the heavy regulation also means that pricing power tends to be limited. So it’s a scale business, with significant operating leverage if credit growth for whatever reason starts to accelerate.

And this is the exact bull case for Asia’s credit bureaus: the credit penetration in this part of the world remains low, especially in emerging Asian nations like Indonesia and the Philippines.

4. 18% IRR for 57 Years – Joe Raymond

George Batten founded the Batten Company in New York in 1891. At the time, advertising was mostly about placing ads in newspapers.

In 1919, Barton, Durstine & Osborn emerged, focused more on messaging, copywriting, and persuasion.

The two merged in 1928 to form Batten, Barton, Durstine & Osborn.

Over the next several decades, BBDO became a core player on Madison Avenue, helping large corporations build brands as radio and television expanded their reach.

BBDO International started trading over the counter in 1968…

…As Larry recalls:

“I came to realize advertising was a royalty business. If you had a consumer product, you needed to advertise. And you needed to use an ad agency like BBD&O. I viewed it as a royalty on consumer spending.”…

…He paid less than 8x earnings for a business generating 20% return on equity, growing in the low-double-digits, and yielding 7.5%…

…BBDO grew revenues from $49 million to $155 million from 1969 to 1979 (12% CAGR).

Net income tripled from $4 million to $12 million. Shares outstanding declined from 123 million to 106 million. As a result, EPS quadrupled from 3 cents to 12 cents (15% CAGR).

The P/E multiple ended the period at about the same 7.6x it started.

The stock went from 25 cents in 1969 to 85 cents in 1979 while also paying out 46 cents per share of dividends.

Including dividends, the IRR for his first decade of ownership was 20%…

…EPS over the 11 years from 1979 to 1990 grew from $0.12 to $0.25 (7% CAGR) while paying out a cumulative $0.99 per share of dividends. Not spectacular performance, but not terrible either.

The stock started the decade at $0.85 and finished at $2.73. Thus, Larry had a 10-bagger in his first 20 years of ownership, plus dividends worth nearly 6x his purchase price.

1979 to 1990 was a mediocre stretch for earnings growth. But dividends were consistently paid and the multiple expanded 45% from 7.6x to 11.0x. The result was a 17% IRR for the 11-year period…

…Like many other stocks (and the market averages), 2000 to 2010 represented a “lost decade” for Omnicom shareholders.

The business itself grew at a decent rate–EPS compounded at 8% and $5.48 of cumulative dividends per share were paid.

Counteracting these factors was a 50% reduction in the multiple. 32x in 2000 fell to 15x in 2010. The net result was a 1% IRR for the decade.

Operationally, the 2000s didn’t look that different than the 1970s (8% EPS growth in the former vs 7% in the latter). Yet the 1970s produced a 17% annualized return while the 2000s yielded only 1%.

Such is the power of valuation. The same quality business can deliver wildly different results depending on the price paid. In this case, paying 8x earnings resulted in an annual return of 17% for a decade while paying 32x delivered almost nothing for 10 years….

…BBDO was an ideal buy and hold investment in the 1960s and 1970s.

The economics were attractive (20%+ ROE) and growth prospects solid (decades of global advertising growth ahead). Capital allocation was sensible (small bolt-on acquisitions, share repurchases, and dividends), and the valuation was cheap (sub 10x earnings).

$10,000 invested in 1969 and held through today would be worth $3.2 million, with an additional $1.7 million of dividends received as well.

5. The American Rebellion Against AI Is Gaining Steam – Amrith Ramkumar, Katherine Blunt, and Lindsay Ellis

Delivering a commencement address at the University of Arizona, Schmidt told students the “technological transformation” wrought by artificial intelligence will be “larger, faster and more consequential than what came before.” Like some other graduation speakers mentioning AI, Schmidt was met with a chorus of boos.

In one poll after another in recent weeks, respondents have overwhelmingly voiced concerns about AI, a challenge to claims by industry executives that their technology would gain popularity by improving people’s lives…

…Pollsters and historians say the souring of public opinion is all but unprecedented in its speed. “I don’t think I’ve ever seen something intensify this quickly,” Gregory Ferenstein, who conducted a recent poll with researchers at Stanford University and the University of California, Berkeley, said of the backlash…

…Voters in Festus, Mo., ousted four city council members a week after they approved a $6 billion data center. Dozens of communities in states from Maine to Arizona are trying to ban new data centers. Some 360,000 Americans are in Facebook groups opposed to the facilities, roughly quadruple the number from December, figures from organizations fighting the AI build-out show…

…AI has risen in importance most quickly among 39 political issues studied by polling firm Blue Rose Research in the past year, though it still trails priorities including the economy, immigration and foreign policy…

…But all over the country, community-level organizations have been succeeding in blocking data-center projects. Local opposition blocked or delayed at least 48 projects valued at some $156 billion last year, according to Data Center Watch, an organization tracking the trend. A record of 20 were canceled in the first quarter of the year because of local backlash, figures from climate-media outlet and data provider Heatmap show. Dozens more are currently facing similar obstacles on top of obstructions because of permitting snafus and equipment shortages.

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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 (parent of Facebook). Holdings are subject to change at any time. 

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

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

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

Here are the articles for the week ending 17 May 2026:

1. A Government Debt Crisis? – Ben Carlson

One of my favorites is the 1972 Time Magazine cover story:

This sounds like it could have been written today:

Debt service is now the third highest public expense, exceeded only by spending for defense and education; most of the money goes to banks, which are the major buyers of bonds that governments at all levels sell to cover their deficits. Moreover, debt functions as a wrong-way income redistribution device, channeling tax money that is paid in large part by the poor and the middle class into the pockets of wealthy holders of trust accounts or stock in banks.

When this cover was published, government debt was roughly $430 billion.

Today it’s fast approaching $40 trillion in total…

…The Wall Street Journal shows that publicly held debt to GDP is now 100% for the first time since WWII..

…Here’s the trillion dollar question — why have none of the government debt crisis predictions come to fruition?…

There are two big mistakes people make when they predict a catastrophe from U.S. government debt levles:

1. Conflating U.S. government debt with household debt. Government debt is not like a mortgage that needs to be paid back. As long as the economy keep growing, debt levels will likely keep rising.1 Plus, the U.S. government has the ability to print the global reserve currency. You can’t print more dollar bills in your basement.

2. The government’s liabilities are someone else’s assets. Treasuries are bonds owned by pensions, insurance companies, fund managers, and households. It’s the largest, most liquid bond in the world and there isn’t an alternative…

…So what would make me worry about government debt levels?

The biggest risk of large deficits and government spending is inflation…

…Continuously rising interest rates would also be cause for concern…

…Another concern is the fact that interest expenses are becoming a larger share of the government’s budget…

…Interest expenses now exceed the defense budget.

The good news is that interest expense as a percentage of GDP is at 1980s levels.

The bad news is that it has risen like a rocket and rates were a lot higher back then…

…Is there a line in the sand where a government debt crisis automatically kicks in?

No one knows.

2. China’s $3 Trillion of Hidden Bad Debt Prolongs Economic Pain – Bloomberg News

By any measure, Tom Hu should be in default on a $730,000 bank loan for his plastics business in China. He barely brings in enough revenue to pay expenses and can’t cover the debt costs.

Yet rather than calling in the loan, his bank lets him defer payments — keeping him afloat, while avoiding another past-due loan on its books…

…Stories like Hu’s are playing out across China as banks grapple with a growing pile of bad debt. It’s impossible to quantify the true extent of the problem, though most economists say the ratio of bad loans is significantly higher than the 1.5% official rate. One analyst at Absolute Strategy Research in London pegs it at about 10%, which would mean a staggering $3 trillion in loans that should be classified as past due are not. Others say it could be double that amount…

…The apparent stability of the official bad loan rate is all the more surprising given that the economy has experienced a major property collapse and posted the slowest nominal growth outside Covid since the 1970s. In March, China lowered its 2026 growth target to between 4.5% and 5% — its least ambitious goal since 1991.

Regulators have taken note. Despite seemingly strong capital buffers and stable NPL ratios, officials have moved to bolster the nation’s six biggest banks with more than $100 billion in fresh capital…

…The primary culprit for the surge in bad loans is a mountain of credit extended to companies whose earnings are insufficient to cover interest payments. About 10% of listed non-financial firms have failed to cover interest payments from their earnings before interest and tax for three consecutive years, according to Absolute Strategy Research. As a result, the non-performing loan ratio is probably closer to 10% than 1.5%, according to Adam Wolfe, an emerging markets economist at the firm…

…China’s official NPL ratio has always been a bit of a mystery. In good times and bad, it’s rarely wavered much from 1.5%, and most economists say it greatly understates the true stress in the system. The figure captures only loans officially classified as “substandard,” “doubtful,” or “loss.”

In reality, the classification is often a subjective assessment and banks have different internal criteria. A much larger pool of troubled credit remains in the “special mention” — those that may have already become overdue but yet to be categorized as nonperforming — or “normal” categories, thanks to an aggressive use of leniency known as forbearance.

Existing rules stipulate that when repayment on a loan is overdue by more than 90 days and the borrower can’t fully repay the amount, it should be marked as nonperforming.

Economists including Wolfe estimate that about 40% of loans are either eligible or already in some sort of forbearance program, where banks are strongly discouraged from seeking repayment or recognizing losses…

…In other words, rather than cracking down on deadbeat borrowers, China’s banks are encouraged to cut them some slack. Regulators have for years urged the big banks to keep their reported bad loan ratio under 2%, according to people familiar with the guidance.

With the forbearance policy — a legacy of Covid support programs that’s been extended to property developers and other firms — Beijing is signaling its desire to maintain financial stability. It wants to avoid a rash of bank failures that would follow a surge in reported bad credits and company defaults.

A leniency policy for small businesses that was introduced during the pandemic was extended in 2024 to encourage banks to roll over loans for companies enduring temporary difficulties. This policy is effective until late next year, and applies to 9.4 trillion yuan ($1.38 trillion) worth of loans, according to officials.

As a result, banks routinely roll over maturing loans, extend repayment periods, or allow interest to be capitalized to avoid triggering NPL recognition. Local governments also exert pressure on lenders to maintain stability by avoiding cuts to risk classifications on loans tied to sensitive sectors. Those include property developers, local government debt and small businesses in weaker regions, according to a dozen bankers interviewed by Bloomberg News…

…All this leniency comes at a cost. Financial resources are trapped in unprofitable and even inactive firms, hindering banks’ ability to promote growth in healthy businesses. Overall loan growth is slowing significantly after fixed-asset investment experienced an unprecedented contraction last year…

…Chinese banks are also accelerating write-offs and transfers of bad assets. Lenders have disposed of more than 3 trillion yuan of non-performing assets a year since 2020, with the total rising to roughly 3.8 trillion yuan in 2024, the highest on record.

Banks have stepped up transfers of NPL portfolios to asset management companies, which typically hoover up bad assets in China. Still, these firms entrust collection back to the originating banks in many cases, according to people familiar with the matter. The funds used to purchase bad loans largely come from the banks, meaning the risks aren’t fully removed from the financial system.

3. The Inference Shift – Ben Thompson

Specifically, coding with LLMs requires a human in the loop. It’s the human that defines what is to be coded, checks the work, commits the pull request, etc.; it’s not hard to envision a future, however, where all of this is completely handled by machines. This will apply to agentic work broadly: the true power of agents will not be that they do work for humans, but rather that they do work without human involvement at all.

This, by extension, will mean that the likely best approach to solving agentic inference will look a lot different than answer inference. The most important aspect for answer inference is token speed; the most important aspect for agentic inference, however, is memory. Agents need context, state, and history. Some of that will live as active KV cache; some will live in host memory or SSDs; much of it will live in databases, logs, embeddings, and object stores. The important point is that agentic inference will be less about GPUs answering a question and more about the memory hierarchy wrapped around a model.

Critically, this articulation of an agentic-specific memory hierarchy implies a necessary trade-off of speed for capacity. Here’s the thing, though: lower speed isn’t nearly as important a consideration if there isn’t a human in the loop. If an agent is waiting around for a job that is being run overnight, the agent doesn’t know or care about the user experience impact; what is most important is being able to accomplish a task, and if entirely new approaches to memory make that possible, then delays are fine.

Meanwhile, if delays are fine, then all of the focus on pure compute power and high-bandwidth memory seems out of place: if latency isn’t the top priority, then slower and cheaper memory — like traditional DRAM, for example — makes a lot more sense. And if the entire system is mostly waiting on memory, then chips don’t need to be as fast as the cutting edge either. This represents a profound shift in future architectures, but it also doesn’t mean that current architectures are going away:

• Training will continue to matter, and Nvidia’s current architecture, including high-speed compute, large amounts of high-bandwidth memory, and high-speed networking, will likely continue to dominate.
• Answer inference will be a meaningful market, albeit a relatively small one, and speed from chips like Cerebras or Groq (I explained how Nvidia is deploying Groq’s LPUs here) will be very useful.
• Agentic inference will gradually unbundle the GPU, which alternates between stranding high-bandwidth memory (during the prefill process) and stranding compute (during the decode process), in favor of increasingly sophisticated memory hierarchies dominated by high capacity and relatively lower cost memory types, with “good enough” compute; indeed, if anything it will be the speed of CPUs for things like tool use that will matter more than the speed of GPUs…

…To date the invocation of “scaling with compute” has implicitly meant Nvidia bullishness. However, much of Nvidia’s relative advantage to date has been a function of latency: Nvidia chips have fast compute, but keeping that compute busy has required big investments in ever-expanding HBM memory and networking. If latency isn’t the key constraint, however, then Nvidia’s approach seems less worth paying a premium for…

…China, meanwhile, for all of its lack of leading edge compute, has everything it needs for agentic inference: fast-enough (but not leading-edge) GPUs, fast-enough (but not leading-edge) CPUs, DRAM, hard drives, etc. The challenge, of course, is compute for training; it’s also possible that answer inference is more important for national security, at least when it comes to military applications.

4. 50 Learnings from the War in Iran – Tomas Pueyo

Missile and drone launching can be dramatically curtailed, because you can track where they’re launched from and destroy that.

But they’re very hard to fully eliminate. This is the beginning of aerial drone warfare. It suggests it will be super important in the future as an asymmetric weapon: Countries can produce drones in a decentralized way and launch them from many different, constantly changing places.

The other way in which drones and missiles can be intercepted is at the destination. Israel has proven that this can work quite well: Iran has been unable to cause critical damage in the country despite trying over and over again…

…Iran’s entire fleet was destroyed in a matter of days (Ukraine did something similar over the last few years, virtually wiping out Russia’s fleet in the Black Sea).

This marks the end of naval warfare as we know it. Few countries will invest in a full traditional naval force anymore…

…Israel and the US blew up a lot of the command chain, but they couldn’t have done that just with airplanes. They needed intelligence, satellites, cyber penetration, AI, amazing communications, and fast command decisions. Doing all of these steps well and integrating them seamlessly is beyond the capability of most countries today…

…For the first time in history, Israel deployed an Iron Dome system in a foreign country—the UAE—manned by Israeli soldiers. This is unprecedented: Israel defending Arabs against other Muslims!…

…Iran finally executed their biggest threat, which gave them lots of leverage in negotiations: They closed the Strait of Hormuz.

It wasn’t clear that this was a threat they could actually follow through with. But it is. They closed it.

They did so even without air supremacy or a naval force. This is very counterintuitive! It turns out you can use small boats and drones to close a big international highway…

…Although US opponents have more incentives to de-dollarize, one thing is to want it and the other to succeed. The dollar has actually risen during the war, and its position as a reserve currency hasn’t changed.

5. An Ode to Restraint: Lessons from the Tim Cook Legacy! – Aswath Damodaran

If you were to create a profile of Tim Cook, the manager, based upon the choices that he has made at Apple during his tenure as CEO, two very divergent views emerge. To his admirers, his actions on some fronts (initiating dividends, massive stock buybacks, borrowing money) and inaction on other fronts (no big acquisitions, diffidence on AI investments), represent an exercise in discipline and restraint, preserving the company’s crown jewel (the iPhone) and fending off the bankers and consultants, with their false promises. To his critics, and there are quite a few, Cook’s caution has cost Apple its disruptor status, when it could have used its ample cash reserves to buy its way or invest in into almost every new business that has bloomed in the last fifteen years. In fact, they point to chances that Apple has had to buy some of the biggest stars in the market, from Tesla and Netflix more than a decade ago to Anthropic, Mistral and Perplexity in more recent years.

It is impossible to argue that one side is right and the other side wrong, but it is undeniable that both pathways (the restrained pathway that Apple adopted and the more aggressive pathway that it could have taken) include trade offs. It is true that Apple’s restraint has led it to miss out on some of the biggest trends in technology over the last decade, but it has also avoided the overpayment that is so common with high profile acquisitions of big companies. The argument that Apple would be worth a lot more today if it had bought Netflix or Tesla a decade ago falls flat for two reasons. The first is the selection bias in picking two companies that, in hindsight, have emerged as winners, when in fact there were at least a dozen other worse-performing companies that were also on Apple’s radar. The second is the presumption that companies like Tesla or Netflix would have been just as successful, owned by Apple, as they were as stand alone enterprises. The clash of corporate cultures that would have ensued if Apple had bought either Tesla, a company that reinvents its business narrative every few hours, or Netflix, an entity that makes content in quantity with the hope that some it sticks, would have been epic, with the risk that both Apple and its acquired target would have gone down in flames.

More generally, though, the question of whether you want a visionary or a disciplined business builder at the top of a firm is not one that has an easy answer, since it depends on the firm in question. In my work on corporate life cycles, I focus on the management skills that are needed most in a company, based upon where it is the life cycle, and that may help address the choice between vision and restraint:…

…With young companies, vision dominates, as managers work to sway investors, employees and nascent customers that their product or service will find a market. As the vision takes hold, converting it into commercial products and services requires trading off some portions of vision for pragmatism, in the interest of getting the business going. As products and services find demand among customers, business building becomes a key difference-maker, with the grunt work of marketing, production facilities and supply chains coming into play. Assuming that you have made it through these three stages, the trade offs of scaling up come into focus, and as you hit market limits, success depends on being opportunistic in finding new products and markets, but only if they exist. In corporate middle age, pathways to easy growth, especially at scale, become difficult to find, and to the extent that value comes from moats and core products, playing defense against competitors takes priority. Finally, in decline, a phase that no company ever wants to enter, but is inevitable at some point, you need to be willing to shrink a firm, shutting down businesses that no longer deliver value and selling other assets to high bidders.

Given these very divergent management functions, it should come as no surprise that there is no prototype for the perfect CEO, McKinsey and Harvard Business School blueprints notwithstanding.

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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 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 10 May 2026:

1. Corporate dark arts: when incentives tell you what might be coming $GME $EKSO $VAC $RPD – Andrew Walker

EKSO is a tiny little company; its market cap for most of last year was <$10m. But it’s a perfect case study in the dark arts and why paying attention to them can be profitable. In late November they gave all of their executives’ PSUs that vested only if the company underwent a change of control and the stock was “at least $7.50” per share within the next five years. The stock was trading in the mid-$4s at the time.

I’m not sure I’ve ever seen a single PSU grant that flashes “we are for sale” harder than that grant.

Sure enough, at the end of December EKSO announced a deal to merge with APLD’s cloud business spinoff. A few weeks later, EKSO did a private placement; it will shock you to learn the placement was priced at $8.22/share, above the mark that vested EKSO’s PSUs.

It wasn’t guaranteed that the market would respond positively to EKSO’s merger…. but I’d suggest EKSO’s board and management knew something was in the pipes when they made those grants, and that whatever was coming was likely to excite the market.

As I write this, EKSO is trading at $12/share.

2. OpenAI’s AI Chip Deal With Broadcom Hits $18 Billion Financing Snag – Anissa Gardizy

When OpenAI and chip designer Broadcom announced last fall that they would make custom artificial intelligence chips together, they positioned it as a done deal.

The companies said the deal would bring enough chips online before 2030 to consume 10 gigawatts of power, equivalent to five Hoover Dams’ worth of electricity, in a bid to lessen OpenAI’s costly dependence on Nvidia hardware.

What they didn’t say was that they hadn’t figured out how OpenAI would pay for the project.

Months later, the companies are negotiating an agreement for Broadcom to finance the first phase of chip production, which would consume 1.3 GW of data center capacity and would cost around $18 billion, according to an internal memo and two people involved in the talks. At that rate, the full 10 GW program, code-named Nexus, could cost $180 billion in chip production alone before factoring in data center construction and other costs…

…But the negotiations have run into a potential problem. Broadcom has said it would finance the first phase only if Microsoft agrees to buy roughly 40% of the chips, an OpenAI executive told colleagues in a memo last month. Microsoft would install the chips in its data centers and then rent them back to OpenAI.

A purchase commitment from Microsoft, one of the world’s most creditworthy companies with decades of data center experience, would give Broadcom confidence it would get its money back, said a person involved in the talks.

But Microsoft could choose not to buy OpenAI’s chips, which would change the financing terms for the project, the memo said…

…OpenAI has made a habit of announcing landmark partnerships without ironing out the details. A month before the Broadcom announcement, for instance, OpenAI said Nvidia would provide up to $100 billion in funding, allowing OpenAI to build its own data centers and use Nvidia’s chips to power them. The headline-making deal eventually fizzled, though Nvidia later made a $30 billion equity investment in OpenAI…

…And in January 2025, OpenAI announced Stargate, a joint venture with SoftBank and Oracle to spend $500 billion developing data centers. But the effort floundered as the three sides disagreed over details and lenders balked at backing multibillion-dollar projects tied directly to a company with an unproven business model…

…Despite the risks from Microsoft’s sway, talks between Broadcom and OpenAI have been progressing. Broadcom had long insisted that OpenAI put up one dollar of its own for every dollar Broadcom provided in financing, a typical arrangement to limit the chip vendor’s risks. That requirement had become a sticking point in the talks, according to the memo and an executive involved in the talks.

But Broadcom recently decided to relax that demand and invest more capital up-front than OpenAI, breaking from Broadcom’’s “long-held hard-line requirement,” the OpenAI memo said.

3. The Fertilizer, the Bond Market, and the End of the Country Banker – Dirt Cheap Banks

Chapter 12 farm bankruptcy filings rose 46% in 2025. That followed a 55% rise in 2024. That is the third consecutive annual increase. The Midwest jumped 70%. The Southeast jumped 69%. Montana, of all places, jumped 200%. Pennsylvania jumped 160%. Arkansas led the country in absolute filings — the most this century for the nation’s top rice-producing state. Total farm debt is projected to hit a record $624.7 billion in 2026. The American Farm Bureau Federation surveyed 5,700 farmers and 70% of them said they could not afford all the fertilizer they needed for the spring. The U.S. Department of Agriculture itself — not some doom-pusher on the internet, the actual government department whose job is to make this look fine — projects that 2026 corn will cost roughly $5.00 a bushel to grow and sell for $4.20. Soybeans, $12.27 to grow and $10.30 to sell…

…Nearly 40% more new farm operating loans were opened in Q4 2025 than in Q4 2024. The average operating loan in 2025 was 30% larger than 2024, with maturities running three months longer. Farmers are not borrowing more because they are growing. They are borrowing more because they are bleeding. And the only reason aggregate farm income looks anything like solvent is that the federal government will spend roughly $55 billion this year — $44.3 billion in direct payments, plus crop insurance subsidies, plus the $11 billion Farmer Bridge Assistance Program — propping up an industry that is, in market terms, no longer functional. Strip the subsidies out and 2026 net farm income falls off a cliff that nobody in Washington wants to look over. Agricultural lenders surveyed by the American Bankers Association expect only about 58% of farm borrowers to remain profitable in 2025, down sharply from 78% in 2023. NDSU’s Agricultural Risk Policy Center projects $44 billion in net cash income losses on the 2025-26 crops alone…

…The North Dakota State University agricultural trade modeling team ran the fertilizer scenarios and they are worth your attention because they are the most rigorous public modeling that exists.

Under their “Quick Reopening” case, urea peaks at $782/short ton in June 2026 and eases gradually. Under their central “Contested Transit” case, peak urea hits $784/st in July with prices staying above $700/st through November; fall 2026 prepay urea averages $733/st (56% above pre-crisis); winter fill at $643/st; spring 2027 top-off at $590/st. Add another fifty to eighty dollars per ton for freight and dealer margin to get the actual interior Corn Belt retail price. Under their “Extended Conflict” case, fall prepay climbs to $989/st; winter fill to $945/st; spring 2027 spot prices remain near $791/st. The World Bank’s Commodity Markets Outlook, released April 28, expects global fertilizer prices to rise more than 30% in 2026, with urea closing the year at $675 per ton — nearly 60% above 2025 levels.

For the farmer, this means 2026 is the easy year. Most spring 2026 nitrogen had already been contracted before the Strait closed in February. The real budgeting concern is 2027. American Farm Bureau Federation survey data shows that for every farmer more concerned about fertilizer for 2026, nearly two are more concerned about 2027. Damage to liquefied natural gas production and sulfur output in the Persian Gulf could take years to repair, even if shipping normalizes tomorrow. The infrastructure does not just turn back on.

If the central NDSU scenario plays out, the 2027 crop year sees farmers face fertilizer costs roughly 50% above pre-war levels at exactly the moment their working capital — the cushion that lets them absorb a bad year — has been exhausted by 2025 and 2026. This would be the fourth consecutive year of negative crop margins. Operating loans would grow even larger, even longer. Chapter 12 filings would push past 600 a year. Agricultural bank delinquency rates, currently 1.09% as of July 2025, would climb to 2.5% to 3.5%. Still well below 1985’s peak of 6.7% at agricultural banks, but moving in the wrong direction at speed.

If the extended conflict case plays out — Strait remains contested through 2027, fertilizer at near-1980-level real prices, fifth consecutive year of negative margins — the trajectory accelerates. 2028 starts to look uncomfortably similar to 1984. The structural buffers begin to fail in sequence, not in parallel…

…The American Enterprise Institute has been making the case openly: most farm households receive over half their income from non-farm sources; the agricultural sector’s debt-to-asset ratio is 13.75%; the system can absorb shocks without the level of subsidization currently in place. That argument is not winning yet. But it is being made by serious people in Washington, and it is being made at a moment when every other federal spending priority is under similar pressure. If a debt-ceiling fight or a continuing-resolution fight produces a sequester or a freeze, agricultural subsidies are not exempt. They are politically vulnerable in a way they have not been in a generation.

If subsidies are cut even modestly — say, a 30% reduction from the projected $55 billion to roughly $38 billion — the market-based losses that currently get masked by federal payments become visible all at once. Farm income drops by an amount equivalent to roughly 11% of total receipts. The farms that are barely solvent stop being solvent. The farms that depend most heavily on subsidies — the commercial row-crop operations in the Midwest and Plains, the largest borrowers, the ones holding the biggest loans at the most concentrated agricultural banks — fail in clusters.

If subsidies are cut substantially — back toward the 2024 level of roughly $10 billion — the math becomes cataclysmic. Net farm income outside government payments would fall by roughly $40 billion. The structural protection that has kept the current stress from becoming a 1980s-style crisis disappears. Farmland values, which have so far held in part because farmers can still service their debts, begin to crack. The 220 community banks that the FDIC identifies as having agricultural loan concentrations above 300% of capital become acutely vulnerable.

This scenario is the dark mirror of 1985. In 1985, there were no subsidies of this scale to remove. The crisis happened anyway. In 2027 or 2028, removing the subsidies would be the trigger that closes a system that is currently holding together by their grace alone…

…The 1980s farm crisis killed 205 agricultural banks between 1984 and 1987 — 37.4% of the 548 total bank failures during that window. There were 14,483 FDIC-insured commercial banks in 1984; by 2023 that number had fallen to 4,027 — a 72.19% decline. At the end of 2024 there were approximately 4,050 community banks left in the United States. Roughly 220 of them carry agricultural loan concentrations above 300% of capital, clustered in eight states: Illinois, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota. Most have under $200 million in assets. Most are not publicly traded.

4. Warren Buffett Case Study – East Sullivan Mines 1962 – Dirt Cheap Stocks

At yearend 1962, the Buffett partnership was managing $9.8 million.

East Sullivan was a $106,000 position.

East Sullivan was a mining business that produced copper, gold, silver and zinc.

It was headquartered in Quebec and formed in 1944.

East Sullivan had profitable operations. In 1962, it produced millions of pounds of zinc and copper along with 4,600 ounces of gold and 168,000 ounces of silver.

In 1962 the business had 33% EBIT margins. 1961 had 20% EBIT margins.

It was a nice little business. Of course, margins would swing wildly in this kind of operation, but still, it was doing well when Buffett owned it.

The business had cash and investments in excess of its market cap. It was profitable and paying a sizable dividend.

East Sullivan’s investments were largely made up of ownership in affiliated companies.

Members of the Beauchamin family made up the majority of the management team and the board.

Then there were a bunch of related businesses that were also interconnected and controlled by the Beauchamin family…

…East Sullivan was doing $1.2mm of EBIT from its own operations.

Let’s assume that the $9.6mm of marketable securities and affiliated businesses could produce a 7% return. That’s probably conservative.

7% on $9.6mm is an additional $672k of look-through ebit.

The market cap was $8.9mm. EV would’ve been $7.8mm if only giving credit for East Sullivan’s cash account.

The look through EBIT is $1.9mm (1.2mm + 672k).

That’s ~4x EV/EBIT…

…We don’t know how long Buffett held. But the investment was likely a good one for him.

Shares touched $3.00 in 1963. By 1964 they were $5.70. And they peaked at $9.40 in 1965.

If Buffett had held to the top in 1965 he would’ve earned a 73% IRR.

If he held through the end of his partnership in 1969, he would’ve earned a 34% IRR.

5. Iran war is crushing Asia’s farmers, threatening global food supply – Rebecca Tan and Wilawan Watcharasakwej

Saithong Jamjai has just finished harvesting the rice on the 19 hectares of farmland she owns in central Thailand and now is the time to sow again. But she won’t, she said, because of the U.S.-Israeli war against Iran.

She has gone over the math for weeks. Because of surging prices, driven by the war, of fuel, fertilizer, plastics and other necessities, planting and harvesting will cost her at least $33,000, she said. The grain that she’ll produce, she estimates, will sell in August for only $22,000.

“A confirmed loss,” Saithong, 53, concluded. She’d rather let her land bake under the yellowing husks from last season…

…Addressing world leaders in Rome on Thursday, Dongyu Qu, the director general of the U.N. Food and Agriculture Organization, said the war had created not only a geopolitical crisis but “a disruption at the core of the global agrifood system.”

Iran’s destruction of gas infrastructure in the Gulf and the dueling U.S.-Iran efforts to choke the Strait of Hormuz have prevented crucial supplies of fuel and its derivatives like urea — a potent source of nitrogen that enhances harvests — from leaving the Middle East. Because fuel infrastructure takes years to build, there is no ready replacement for these supplies.

In effect, 30 percent of the world’s urea has been “wiped out,” said Pranshi Goyal, senior analyst at the market intelligence firm CRU Group. China, a major fertilizer producer, has restricted exports to ensure its farmers have enough. Russia, another big manufacturer, is seeing demand soar, potentially boosting its economy and aiding its war in Ukraine. On what is known as the spot market, urea prices are up 40 percent since February…

…The longer the production plants in the Middle East stay closed, the longer they will take to restart. “This problem builds in a nonlinear fashion,” Goyal said.

So do its repercussions.

In Thailand, the Philippines, Bangladesh and Australia, which are the first since the war to enter key sowing periods, farmers are choosing to skip or reduce planting, or cut fertilizer use, which will lower yield.

As the war stretches deeper into the crop calendar, farmers from more countries will be forced to make similar choices, said Maximo Torero, chief economist for the FAO. “Right now, the impacts are more severe in Asia,” Torero said. “But clearly, this is moving east to west and south to north.”

In June, India and Brazil, two of the world’s biggest agricultural producers, will ramp up orders for urea. If, by then, vessels carrying urea are not sailing, there will be “significant yield loss” across many countries, Torero said…

…Thailand’s Commerce Ministry, for example, said in April the country still has 343,000 tons of urea fertilizer, sufficient to support the upcoming planting season. Driving through the vast flatlands surrounding Thailand’s Chao Phraya River basin, however, reveals a different picture.

Across Ayutthaya and Suphan Buri provinces, fertilizer shops large and small were completely out of urea — and said they had been for weeks. Distributors are offering only Russian compounds that farmers are wary to use, shop owners said. Seansdee Teerasattayaporn, 62, who runs a fertilizer wholesale business, sent a truck to a marketplace frequented by large dealers to try to procure urea but after waiting four days, he said, the truck returned empty.

Heading into planting season, many farmers said they are facing the worst conditions in their lifetimes. Not during the outbreak of the Russia-Ukraine war were shortages or costs this dire, they said. Nor during the pandemic…

…In an interview, Foreign Minister Sihasak Phuangketkeow asserted that Thailand still has sufficient farming supplies and Thai leaders are jetting across the world to procure more. But he acknowledged the country is competing against bigger nations with deeper pockets, amid extraordinary logistical challenges. “We have not faced such a crisis before,” he said.

On Tuesday, two weeks after a trip to Moscow, Thailand’s agricultural minister said an attempt to secure urea from Russia is likely to fall through. Because of shipping disruptions, it would take at least two months for Russian urea to arrive in Thailand — far too late for the current planting season.

Agricultural experts say the Iran war has underlined the need for farmers to become more self-reliant, for example, weaning themselves of diesel by switching to solar power or swapping out chemical fertilizer for organic alternatives that can be produced locally. But to make these switches, farmers need government subsidies and time, both of which are in short supply, said Esther Penunia, secretary general of the Asian Farmers Association…

…Thai farmers have been doubly hurt because the Middle East is also one of their biggest export markets. The region accounted for 17 percent of Thailand’s rice exports in 2025, according to customs data. Iraq was the single largest destination for Thai rice.

The day U.S. and Israeli forces bombed Iran, ship operators at a Bangkok port told sellers to lift containers of rice bound for Gulf countries off ships and back into warehouses, said Chookiat Ophaswongse, president of the Thai Rice Exporters Association. Since then, there have been no shipments of rice to the Gulf. Malaysia and the Philippines have absorbed some of Thailand’s excess supply but not all of it, leaving a glut that has kept rice prices low, Chookiat said.

Even before the war, many Thai farmers were in financially precarious situations, relying on loans to survive from one season to the next. Now, the squeeze of higher planting costs and lower projected rice sales could drive millions of farmers into spiraling debt that will take years to clear, said Pramote Charoensilp, 64, president of the Thai Farmers and Agriculturists Association. 

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