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 14 December 2025:

1. When Mountains Become Cages: Lessons from the Sichuan Basin – Eugene Ng

The Sichuan Basin (四川盆地) is surrounded by mountains on all sides and is drained by the upper Yangtze River and its tributaries. The basin is anchored by Chengdu, the capital of Sichuan province, in the west, with the Chengdu Plain and Chongqing in the east…

…The Tibetan Plateau contains the headwaters of most of the streams and rivers in its surrounding regions. This includes the three longest rivers in Asia (the Yellow River, the Yangtze River, and the Mekong River).

The upper tributaries of the Yangtze River (长江 or 扬子江) flow through the Sichuan Basin, providing water for irrigation to grow crops, and for civilisation…

…Because of its relative flatness and fertile soils, the Sichuan Basin can support a high population density, providing staples such as rice, wheat, and barley…

…The Sichuan Basin was the strategic fortress that shaped the Three Kingdoms era (220-280 AD), following the collapse of the Han Dynasty. Wei (in the north) was led by Cao Cao, his son Cao Pi, and strategist Sima Yi. Shu Han (in the southwest) was led by Liu Bei, with strategist Zhuge Liang, and warriors Guan Yu, Zhang Fei, and Zhao Yun. Wu (in the southeast) was led by Sun Quan, strategist, Zhou Yu, and Sun Ce.

Surrounded by mountains and accessed through treacherous gorges, Sichuan was nature’s citadel. Easy to defend, nearly impossible to invade. Emperor Liu Bei built his entire kingdom in Sichuan. When he lost the battle for central China, Sichuan became his refuge and his power base.

However, the Sichuan Basin was both a blessing and a curse. It kept Shu Han alive for decades against stronger rivals, but the same isolation made it nearly impossible to project power outward after decades of failed northern campaigns.

The same mountains that kept enemies out also kept Shu Han’s armies in. Zhuge Liang launched five major northern expeditions against Wei, and all sputtered out for the same core reasons:

1. Geography was brutal. To attack Wei, Shu had to march through mountain passes and supply armies across hostile terrain. Wei just had to defend chokepoints. Offense is always harder; offense uphill through mountains is nearly impossible.
2. Economics didn’t add up. Shu was the smallest, poorest kingdom—one province against Wei’s nine. Every campaign drained resources Shu couldn’t replenish. Wei could lose battles and recover; Shu couldn’t afford to lose anything.
3. Talent ran thin. Zhuge Liang was brilliant, but he couldn’t be everywhere. When he died in 234 AD, Shu’s brain died with him. Wei had depth; Shu had dependence.
4. Strategic logic was flawed. The campaigns weren’t really about conquering Wei—they were about survival through offense, keeping Wei preoccupied so they wouldn’t invade Shu. Defense disguised as attack. It bought time but burned treasure…

…That is why Shu Han, despite having brilliant strategists like Zhuge Liang, could never quite break through to challenge Wei’s dominance in the heartland of the North China plains (华北平原). They were trying to play offense from the strongest defensive position in China…

…Shu Han’s mountains kept enemies out but armies in. Companies build defensive moats: loyal customers, proprietary technology, high switching costs, and then discover that those same moats prevent them from expanding into new markets. The thing that protects them eventually confines them. Ask BlackBerry how their keyboard moat worked out. Ask Intel if their x86 architecture saved them from irrelevance. Defense becomes offense becomes history…

…The North China Plain birthed Chinese civilization because the flat land, water, and soil aligned. In investing, today’s geography is market size, secular tailwinds, and competitive position. Invest in businesses riding massive currents, the Yangtze Rivers of commerce, not isolated mountain kingdoms. Find the disruptors and top dogs commanding vast plains of opportunity (i.e., large total addressable markets), where continued expansion is possible, and resources flow abundantly. The best investments are not defensive fortresses. They are empires with still abundant room to build and grow…

…The Yangtze River still flows through Sichuan. The mountains still stand. But Shu Han is gone. Geography endures. Dynasties do not. Companies do not last forever. Similarly, management does not, as they have to pass the torch on.

Niche businesses prosper, then calcify, then fade. Without access to vast markets, even genius becomes a footnote. The question is not whether you are smart. It is whether your terrain allows for growth or just survival.

2. Horses – Andy Jones

Engines, steam engines, were invented in 1700.

And what followed was 200 years of steady improvement, with engines getting 20% better a decade.

For the first 120 years of that steady improvement, horses didn’t notice at all.

Then, between 1930 and 1950, 90% of the horses in the US disappeared…

…I was one of the first researchers hired at Anthropic.

This pink line, back in 2024, was a large part of my job. Answer technical questions for new hires.

Back then, me and other old-timers were answering about 4,000 new-hire questions a month.

Then in December, Claude finally got good enough to answer some of those questions for us.

In December, it was some of those questions. Six months later, 80% of the questions I’d been being asked had disappeared.

Claude, meanwhile, was now answering 30,000 questions a month; eight times as many questions as me & mine ever did…

…But while it took horses decades to be overcome, and chess masters years, it took me all of six months to be surpassed.

Surpassed by a system that costs one thousand times less than I do.

A system that costs less, per word thought or written, than it’d cost to hire the cheapest human labor on the face of the planet.

And so I find myself thinking a lot about horses, nowadays.

3. Energy Predictions 2025 – Casey Handmer

In 2025, headlines scream that datacenters are pushing prices up and consuming all the power. I think datacenters are exposing the rot in a moribund power generation and delivery industry which has proven unable to meet demand in recent years. But it is a moot point.

Datacenters are already building their own captive power plants. As AI demand outstrips production of gas turbines, hyperscalers will turn to offgrid solar+battery power systems, which are already competitive with pure gas or gas+solar in the sunnier parts of Earth.

Depending on location, 10x overbuild of solar and batteries are sufficient to hit >99.5% uptime for the GPUs…

…On the flip side, these captive solar power plants will be curtailing approximately 75% of their generated power and will be able to provide net power on all but a few days per year. That is, 99% of the time, which is substantially higher utilization than any conventional thermal power plant.

Within the next five years, market power between utilities and datacenters will flip, with DCs becoming the preferred load growth power generation partner.

To spell out the implications, this means that consumers will get access to extremely competitive (cheap) power most of the time, and some combination of utility-owned and privately owned batteries will be needed to smooth out the gaps, as they would be anyway…

…If SpaceX or a competitor can ship inference compute to a 560 km unshaded sun-synchronous orbit which is 80% 1 kg/m^2 solar arrays by mass and 80% compute by cost, then it should be possible to make money. Otherwise, we can expect to see compute being developed on the ground…

…At Terraform Industries, we’re pioneering the technology to convert cheap solar power, air, and water into synthetic natural gas and other hydrocarbons. Within the next five years, solar cost reductions will drive our process to be cost-preferred in all hydrocarbon import markets, and geological sources of oil and gas will never again be able to compete. Our grandchildren will be swimming in copious cheap energy and wondering what all that drilling was for.

We believe that the path forward is lime-calcite captured CO2 + electrolyzed H2 to make CH4 and CH3OH (methanol). Methanol can be upgraded via a wide variety of existing petrochemical processes to make DME, ethylene, propane, gasoline, kerosene, and almost anything else you can imagine…

…In 2025, most gas is used for electricity generation, while most oil is used for cars, trucks, ships, and aircraft.

Solar is going to continue to displace all other primary electricity generators. And electric cars and trucks will continue to dominate growth in ground transportation.

By 2045, natural gas will be used as LNG primarily for high performance supersonic aviation, shipping, and industrial heat.

Methanol will be used as the universal industrial chemical precursor for plastics, paints, fertilizers, adhesives, as well as specialty fuels. Kerosene will service the legacy aviation fleet. Internal combustion piston engines will ultimately go the way of the piston steam engine…

…They don’t want you to know this, but rocks are made of metal oxides, and infinitely abundant commonly occurring rocks such as basalt contain basically every metal you could ever want.

With sufficiently cheap power, we no longer need to travel to the ends of the Earth to build mines. Instead, build a solar powered rock refinery at your local gravel pit…

…But much of the coast of Australia, Chile, Peru, Namibia, South Africa, Mexico, Saudi Arabia and other gulf states have essentially infinite quantities of cheap land, free solar power, and sea water. Democratized solar desalination technology can turn any and all these areas into arbitrarily lush paradises with <1% of the available land under solar arrays.

4. Why AGI Will Not Happen – Tim Dettmers

One of the most common misconceptions I see is that people assume hardware keeps improving and improving. This is an important misconception that explains a lot of the poor thinking around AI progress. The efficiency of GPUs has driven almost all innovation in AI. AlexNet was only possible by developing one of the first CUDA implementations that could compute convolutions over networked GPUs. Further innovation was mostly possible through improved GPUs and using more GPUs. Almost everybody sees this pattern — GPUs improve, AI performance improves — and it is easy to think that GPUs will improve further and will continue to improve AI outcomes. Every generation of GPUs has been better, and it would seem foolish to think that it will stop. But actually, it is foolish to think that GPUs will continue to improve. In fact, GPUs will no longer improve meaningfully. We have essentially seen the last generation of significant GPU improvements. GPUs maxed out in performance per cost around 2018 — after that, we added one-off features that exhaust quickly.

The first of these one-off features was 16-bit precision, then Tensor Cores, or the equivalent, then high-bandwidth memory (HBM),then the TMA or equivalent,  then 8-bit precision, then 4-bit precision. And now we are at the end, both in the physical and the idea space. I have shown in my paper about k-bit inference scaling laws what data types with particular block sizes and computational arrangements are optimal. This has already been adopted by hardware manufacturers. Any further improvement will lead not to straightforward improvements but to trade-offs: either better memory footprint at lower computational efficiency or higher computational throughput at higher memory footprint. Even if you can innovate – linear improvements, need exponential resources – further improvements will be trivial and will not add any meaningful advancement.

While GPUs can no longer improve meaningfully, rack-level optimizations are still critically important. Efficient shuttling of key-value caches is one of the most important problems in AI infrastructure. The current solution to this problem, however, is also relatively straightforward. Companies like OpenAI boast about their AI infrastructure, but it is relatively simple to design because there is essentially only one optimal way to design it. And while it is complex to implement, it just needs clear thinking and mostly hard, time-intensive engineering. But the overall system design is not particularly novel. OpenAI – or other frontier labs – have no fundamental advantage in their inference and infrastructure stacks. The only way to gain an advantage is by having slightly better rack-level hardware optimizations or data-center-level hardware optimizations. But these will also run out quickly – maybe 2026, maybe 2027…

…I believe in scaling laws and I believe scaling will improve performance, and models like Gemini are clearly good models. The problem with scaling is this: for linear improvements, we previously had exponential growth as GPUs which canceled out the exponential resource requirements of scaling. This is no longer true. In other words, previously we invested roughly linear costs to get linear payoff, but now it has turned to exponential costs. That would not be a problem on its own, but it sets a clear physical limit on scaling that is rapidly approaching. We have maybe one, maybe two more years of scaling left because further improvements become physically infeasible. The scaling improvements in 2025 were not impressive. Scaling in 2026 and 2027 better work out better.

Despite these exponential costs, the current infrastructure build-out is reasonable, particularly with the growth of inference use, but it still creates a very precarious balance. The biggest problem is this: if scaling does not provide much larger improvements than research/software innovations, then hardware becomes a liability and not an asset…

…The key value of AI is that it is useful and increases productivity. That makes it beneficial. It is clear that, similarly to computers or the internet, AI will be used everywhere. The problem is that if AI were just used for coding and engineering, it would have a very limited impact. While a lot of economic activity is supported by digital programs, these also have diminishing returns, and producing more software will not improve outcomes significantly if existing software is already good enough (just look at the SAAS failure in China). This makes wide-spread economic integration absolutely vital for AI effectiveness.

So in order to provide real value, AI needs to be used in ways that provide new benefits, not just improvements to what already exists. This is a difficult problem, but the right answer is to integrate AI into everything to squeeze out non-linear improvements, see what works and what does not, then keep what is working. China is taking this approach by subsidizing applications that use AI to encourage adoption. The Chinese population is very receptive to innovation, which facilitates this process. It is nothing unusual in China to see an 80-year-old grandma use AI to help her with their daily life. The US, on the other hand, bets on ideas like AGI and superintelligence, which I believe are fundamentally flawed concepts that have little relevance to future AI progress. This becomes clear when you think carefully about what these terms actually mean in physical reality…

…The concept of superintelligence is built on a flawed premise. The idea is that once you have an intelligence that is as good or better than humans — in other words, AGI — then that intelligence can improve itself, leading to a runaway effect. This idea comes from Oxford-based philosophers who brought these concepts to the Bay Area. It is a deeply flawed idea that is harmful for the field. The main flaw is that this idea treats intelligence as purely abstract and not grounded in physical reality. To improve any system, you need resources. And even if a superintelligence uses these resources more effectively than humans to improve itself, it is still bound by the scaling of improvements I mentioned before — linear improvements need exponential resources. Diminishing returns can be avoided by switching to more independent problems – like adding one-off features to GPUs – but these quickly hit their own diminishing returns. So, superintelligence can be thought of as filling gaps in capability, not extending the frontier. Filling gaps can be useful, but it does not lead to runaway effects — it leads to incremental improvements.

5.The cure for FOMO is…time – Josh Brown

Strategy, formerly known as Microstrategy. This is a publicly traded company that once sold software but now serves as the largest publicly traded “digital asset trust” or DAT. It created and defines the category. For those who haven’t been paying close attention, the idea behind these stocks is that the company sets out to accumulate as much of a crypto asset as it can (in the case of Strategy they’re buying Bitcoin) and the shareholders benefit as the underlying asset (BTC) appreciates. Why not just buy the asset itself or a spot price ETF? Because the digital asset treasury is accumulating the asset at a faster pace using the money it raises via taking on debt or secondary stock sales or preferred stock sales or all three at once.

MicroStrategy currently holds roughly 649,870 bitcoin, acquired at a total purchase cost of about $48.37 billion, which works out to an average price of approximately $74,433 per BTC. Based on the fixed 21 million-coin bitcoin supply, the company controls about 3.0%–3.1% of all bitcoin that will ever exist. Saylor’s going to continue to dilute his shareholders in his quest to accumulate even more of it so, the thinking goes, if you are bullish on the Bitcoin asset itself, you buy his stock and take the ride to even faster gains than you would otherwise get with the ETFs. In this way, he has convinced the faithful that dilution is actually good, not bad. It’s helping the cause.

I never could wrap my head around it. I get the theory, I think, but it hasn’t clicked in terms of why it would work. Maybe this is because I don’t have a mental price target of $1 million per Bitcoin or something like that. I don’t know. I sold all my Bitcoin and bought the BlackRock ETF IBIT a while back to replace it and that’s pretty much the extent of my involvement in the asset class. The appeal of Microstrategy as an investment is mystifying to me still.

But, I must confess, for a long while I was wondering what was wrong with me. Was I missing something? Was there some aspect to this I wasn’t getting? My uncertainty stemmed from the performance of the stock, which was stratospheric…

…Between August 10th, 2020 and last Thanksgiving, MSTR returned 3,050%. An investment of $10,000 would have become worth over $300,000. No other publicly traded company I can find did anything even close to that in the same timeframe. Nvidia, for example, merely 10x’d in the period.

On Wall Street, price is validation, even if price is only temporary. Saylor was validated for the time being. He knew what he was talking about. After all, millions of investors had agreed with him and those who did not had been rendered wrong by what Jeffrey Gundlach often refers to as “the bloodless verdict of the market.” I was dumbfounded…

…And then a funny thing happened. Time went by. Things changed. We got a dozen ETFs listed that could serve the same purpose MSTR had served for the stock market investor – a way to own Bitcoin exposure in a traditional brokerage account. Additionally, Fidelity and Schwab, Robinhood and Public, all became legitimate venues in which to buy, sell and hold the underlying asset. This was a tremendous unlock. Where once MSTR was the only game in town, now there were many options, none of which required people to pay a premium or remember a seed phrase or transact with Coinbase or get involved with cold storage wallets and the like. Bitcoin became as accessible as running water, everywhere and to everyone. Even in an IRA. That was the beginning of the reckoning for investors in MSTR. One year later and we see the result…

…Warren Buffett once famously said the stock market is not a game where the guy with the 160 IQ beats the guy with the 130 IQ every time. He says temperament is much more important than intelligence. Temperament keeps you from acting on impulse. It’s an innate sense that things might look different in the future than they do today. The cure for FOMO doesn’t come in a can or a bottle or a box. Sometimes it pays to just stick around awhile and watch.

The cure is time.

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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 07 December 2025:

1. Understanding ROIC on Low Growth Businesses – John Huber

A 20% FCF yield that is durable is just as good as a reinvestment moat that grows at 20% (in fact, I’d take the former over the latter in many cases because growth rates of 20% tend not to last past a few years). Of course, many 20% FCF yields are also fleeting, but there are enough examples of durable companies (some examples below)…

…People are placing too much emphasis on the stated ROIC of low growth mature companies that earn high FCF and don’t need to retain much of their earnings. It’s important to remember that the capital on a business’s balance sheet is the money that someone else invested (i.e. shareholders in past years).

If there is no place to reinvest capital going forward, then what matters going forward isn’t the ROIC (which is based on a historical balance sheet figure that is no longer relevant). What matters in this case is the FCF that we can collect going forward and the price we have to pay to acquire that FCF (i.e. the FCF yield)…

…Imagine a real estate developer invests $5 million to build a new apartment building that produces $200,000 of annual cash flow. This is a 4% FCF yield, or in the parlance of real estate, a 4% cap rate (technically the cap rate uses a pretax number based on what RE investors call net operating income, but we’ll ignore taxes for simplicity)…

…Viewing this building as a “business” suggests this is a mediocre one at best: a 4% return on capital is not creating value because the investor could have likely earned better returns investing in some other real estate investment, other stocks, or some other asset class altogether…

…So we have a 4% ROIC business that isn’t creating value. Let’s assume the market goes south, the developer’s business is overleveraged and on the rocks, and he decides to bring on a partner to help inject much needed cash. He offers you a 50% share of this building at a valuation of just $1.5 million…

…Let’s look at your result: you invest $750k and now have a $100k of cash flow (50% share of the building’s overall annual cash flow).

This means that your return on the capital you invest is not 4% but rather 13.3% ($100k / $750k).

The same building had an original cost basis of $5 million. That was the initial capital that went into funding its development. This same asset that traded at a 4% yield now trades at a 13.3% yield. However, if you viewed the financials and crunched the ROIC for this building using GAAP financials, it would still show an ROIC of 4% because that is the capital that the original developer invested into the building.

Would this stop you from investing at a 13.3% yield (assuming you like the long-term prospects for the building)? Of course not. You would view this as a great deal.

2. Blue Owl’s teachable moment for investors and asset managers chasing yield and ‘hot money’ – Isla Binnie

Blue Owl’s (OBDC.N), opens new tab turnabout decisions in the last two weeks – to merge, and then not merge, and then maybe merge two of its private credit funds at a later time – offer a cautionary lesson for retail investors in search of higher yields and the asset managers chasing the billions in “hot money” wealthy individuals bring.

The New York-based asset manager withdrew a proposal last month to merge a $1.7 billion non-public fund for retail investors with a $17 billion publicly traded fund for institutional and retail clients after news of the deal helped send Blue Owl’s shares down more than 10% in less than two weeks. The retail investors, who had to vote on the plan, were spooked by two things: it could have forced them to take a 20% loss at current prices and Blue Owl paused redemptions until early next year…

…”The reason that private credit can advertise more yield is because they’re providing you more credit risk … it’s more concentrated investments in riskier companies. Now that doesn’t sound like a trade that should be in a liquid fund,” said Robert Cohen, director of global developed credit at DoubleLine, a bond-focused investment firm managing $90 billion in assets, referring to private credit in general.

Blue Owl’s proposal touched a nerve in credit markets already rattled in recent months due to a few high-profile bankruptcies that have undermined confidence in private credit. Also, some in the market fret that expectations of interest rate cuts by the Federal Reserve could reduce the appeal of private credit investments, one of whose main selling points is their juicy yields.

3.Want This Hearing Aid? Well, Who Do You Know? – Steven Levy

Fortell is a hearing aid, one that claims to use AI to provide a dramatically superior aural experience. The chosen few included in its beta test claim that it seems to top the performance of high-end devices they’d been unhappily using.

These testers have made pilgrimages to Fortell’s headquarters on the fifth floor of a WeWork facility in New York City’s trendy SoHo neighborhood, where they were fitted for the hearing aids—which from the outside look pretty much like standard, over-the-ear, teardrop-shaped devices. But the big moment comes when a Fortell staffer takes them down to street level. There, among street clatter, honking cabs, and delivery trucks backing up to luxury stores, they are asked to conduct a conversation with a Fortell worker. Two other employees stand behind them, adding their own loud discourse to the urban cacophony.

Despite the din, the testers clearly make out what the person in front of them is saying…

… “A lot of people regard AI as something you’ll use to make businesses more efficient,” he says. “But people haven’t really internalized that you could use AI to make products exponentially better.”

De Jonge and Morris eventually dubbed the new company Chromatic, a name they later ditched, settling instead on Fortell. They realized that there would be two critical components in an improved approach to a hearing aid. The first would exploit the recent advances in AI for a better algorithm to selectively augment conversation. And the second would be a custom chip to process that algorithm in real time.

The first requirement became the province of Igor Lovchinsky, who had been Butterfly’s AI wizard. He’d come to the field late in life; up until his mid-twenties he’d been a Juilliard-trained concert pianist but left the field when he became enamored with science. Lovchinsky felt that the AI claims made by some other hearing aid companies were overblown; they were simply tweaking the amplification, he says, or aiming the microphones in a different direction.

“What became clear is that what was needed is source separation,” he says. “Take an audio wave that contains both things you want to hear and things you don’t want to hear, and separate them into just speech and just noise.” Even in 2021, it wasn’t clear that this was possible. “We all have this incredible neural network in our heads honed by billions of years of evolution to recognize speech,” he says. “If you do the source separation with the slightest deviation from full naturalness, your brain will immediately hear it.”…

…Having the right algorithms wouldn’t be worth much if you didn’t have a properly engineered chip to run them. To lead its silicon team, Fortell tapped as CTO Andrew Casper, another Butterfly alum who was a lead engineer on a Google team making AI chips. Casper also wasn’t sure that his task could be accomplished. “Your ear is very sensitive to latency,” he says, noting that if the altered sounds weren’t processed in 10 milliseconds—a hundredth of a second—it would throw users into a hellish uncanny valley. “We didn’t know if it could be done in that amount of time with a high enough fidelity so you aren’t going to notice distortions.” Only then, he says, could the company move to the final challenge: “Can we even put this thing into your ear?”

It was going to take years before the startup got those things right and could even begin to test on humans. Fortunately, the $9 million initial stake, the majority of which came from Kushner, provided a long runway. “For the first few years of the company there was no hearing aid in sight,” says de Jonge. “We needed to build for ourselves to see if the science problems could be solved.”

By 2023, Lovchinsky and Casper had made significant progress on their respective missions. Lovchinsky’s team realized that separating out the voices required creating a proprietary version of what is known in the industry as Spatial AI, involving a 3D understanding of the real world. (Confusingly, they also use the nonproprietary technology, spatial AI, in their product.) “It gleans perspectives from multiple microphones and can infer the same way that healthy people can, from both ears,” he says. His team also found a way to train their AI models with huge amounts of synthetic data that emulated all sorts of conditions. “It’s specifically useful in the most challenging environments,” he says…

…Now that the product is launched, Fortell will sell hearing aids in a single clinic on Manhattan’s Park Avenue. It’s decked out like a posh lounge, with the devices on display in a tasteful presentation that’s straight out of the Apple retail playbook. Hanging on the wall is a silicon wafer with the circuitry of the custom chips. In the early stages, his staff of four audiologists will serve only a couple of dozen customers a week, to make sure everything goes smoothly. In any case, while ramping up production, the supply will be limited.

This is great for Fortell, but it seems de Jonge’s initial impulse to usher everyone’s grandparents into the land of the hearing is in danger of being limited to the one percent, which doesn’t exactly qualify him for a Salk medal. When I ask de Jonge how his invention can scale to change life for the masses, his replies, whether due to secrecy on future plans or just not having a good answer, seem hand-wavy. In his defense, Fortell has resisted the temptation to jack up the traditional price of premium hearing aids—the $6,800 is actually a bit less than some other medically prescribed hearing aids. (As with other high-end hearing aids, the price is part of a package that includes fitting and support from professional audiologists.)…

…It’s hard to measure hearing quality, but Fortell has set out to prove scientifically that it has a better solution to hearing loss. It contracted researchers in NYU Langone’s audiology and neuroscience departments to consult on a blind experiment comparing Fortell with the leading AI-powered hearing aid competitor, a Swiss company called Phonak, whose devices retail for $4,000 and is considered the gold standard in AI hearing products. (In the study, Phonak isn’t mentioned by name and is identified only as the control hearing aid group.)

The test matched performance in environments where noise was coming at random intervals from three directions—kind of an emulation of the Cocktail Party Problem. “This is a configuration that’s particularly good to show the advantages of this aid, because what it does is actually extracting the various signals and getting rid of some of them,” says Mario Svirsky, the Noel L. Cohen Professor of Hearing Science at NYU School of Medicine, who consulted in the study (and was paid for his time).

Svirsky says the test and its goals were set out in advance. If it showed that Fortell notched a 4-decibel increase over its rival in boosting the desired signal, it would be a home run. But when they ran the study, the difference reported between the two devices was 9.2 dB in Fortell’s favor. “The results were overwhelming,” he says. “I’ve never seen such a categorical result in my career.” In one chart, the line representing the hearing improvement from Fortell virtually towered over the Phonak line. The study concluded, “In the most challenging multi-talker environment participants had 18.9X higher odds of understanding speech versus the top AI hearing aids on the market today.”

Naturally, I sought comment from Phonak about those results. Michael Preuss, the lead audiologist for Phonak’s AI platform, has been wearing hearing aids since he was 3 years old. Phonak, he says, has been in the business for 75 years and has been working with AI in its products for the last quarter century, and for the last seven years has pursued the idea of producing an AI chip—just like Fortell. Phonak, too, has spent years developing and testing its AI system, which rolled out last year to what the company describes as acclaim and adoption. When I tell Preuss about how some startup he never heard of trounced his product in a head-to-head test, he seems unruffled. “We have seen in the past that there is no industry standard in how you set up these studies and how you do these kinds of measurements,” he says. “You can design studies to enhance your own performance.” To be sure, Fortell did set up conditions that played to its strengths. But Svirsky says that those conditions were the ones that matter to hearing aid wearers. Also, unlike almost all studies performed by hearing aid companies, Fortell has submitted its work for publication in a peer-reviewed journal.

4. “Suspicion of Gross Fraud”: some notes from passing on Intellego Technologies – Andrew Walker

The company at the center of this story is a tiny little Swedish company named Intellego Technologies; when I was researching them over the summer, they had a ~$200m market cap (note: I used USD there, but Intellego reports in SEK; for ease going forward, I will use SEK through the rest of this article. 10 SEK roughly equals $1, so just divide by ten to get to a rough USD number)…

…Bears claimed the company was…. let’s say incredibly sketchy. The financials didn’t really make sense. Despite seemingly massive profits, operating cash flow was basically non-existent. Bulls said the bears were missing the forest for the trees and misconstruing normal small company growth pains with something more nefarious…

…Obviously, that bull / bear debate seems to have been settled now; the stock getting halted because the company’s cash was frozen / the CEO getting arrested for “suspicion of gross fraud” has a way of settling debates…

…As I’ll detail, to say Intellego had a ton of red flags around it is an understatement.

But, even if you put those red flags to the side, there was a pretty easy reason not to invest: it was literally too good to be true…

…Here’s where the too-good-to-be-true part comes in: UVC dosimeters aren’t exactly an unknown technology; a quick amazon search reveals a heck of a lot of options for dosimeters. Sure, maybe a hospital grade disinfecting system needs something better than a color changing chameleon sticker, but this technology isn’t some wild revolutionary breakthrough. Intellego was guiding to more than 700m SEK in revenue and 400m SEK in EBIT for 2025. In USD, that’s ~$70m in revenue and $40m in profits, making Intellego a very large and profitable business…. and an extraordinarily fast growing one; revenue was ~260m SEK in 2024, and the company was suggesting >10B SEK (~$1B in USD) in sales in five years.

I could never find a single person who could explain to me why Intellego had a right to make such enormous margins and insane growth on a technology that seemed so simple / commoditized. I’d hear bulls wave their hands and say “probably some type of patent?”, but I’d never really hear a good answer why this was a defensible market that should yield such high profits / growth…

…As mentioned, in August Intellego guided to over 700m in revenue and 400m in EBIT for all of 2025. Intellego’s initial full year guidance came in February 2025 had been for over 500m in revenue and 160m in EBIT for all of 2025 (which in itself represented insane growth from 2024’s ~260m in revenue). If you believed those numbers, the business was going parabolic. But look at those numbers: from February to August Intellego increased their sales guidance by ~200m and their EBIT guidance by over 240m, which implies that the business was experiencing negative incremental costs. How?…

… I did want to share one last tidbit from my Intellego research: my call with their (again, I assume soon to be former) CEO. I had a call with him in early August to talk about the company. It was a really weird call (my first notes from the call were “weird call”) for a bunch of reasons, including that he showed up ~ten minutes late. I won’t get into all of the details of the call, but there is one specific thing that I’ve been thinking about a lot with the benefit of hindsight that might be interesting.

I spent most of the call pressing on my key question: how could a product that seemed so simple / commoditized generate such high margins / insane growth? The CEO was pretty dismissive of those concerns (at least in my opinion), and on the heels of the call I would have a lot of mental debate with myself: was he dismissive because he was crazy, or was he dismissive because there was something so good about the product that he knew he had the right to be dismissive (was Steve Jobs crazy to be dismissive of the Zune?). The interesting thing is that he was quite cavalier on all of my questions about competition…. but he was completely honed in when I asked him questions about the company’s accounts receivable. Multiple times he told me “our one weakness is accounts receivable” or “we know that the receivables are our big weakness.”

I’ve heard CEOs mention receivable as an opportunity to improve (i.e. bring receivables from 60 days to 50 days and ROIC improves markedly!), but I’ve never heard a CEO say they were a weakness, let alone the company’s sole weakness! It just seemed like a really weird focus / Achilles heel for a company whose products were so in demand that revenue was set to ~triple, and it seemed like a strange thing for a CEO to be so singularly focused on.

5. The Untold Story of Charlie Munger’s Final Years – By Gregory Zuckerman

In the year before his death, Munger made over $50 million from a bet on an out-of-favor industry he had shunned for 60 years. He revved up his real-estate activities, working with a young neighbor to place big, long-term wagers, unusual for a nonagenarian. He faced down health challenges and wrestled with the future.

“Even a week or two before passing away, he was asking questions such as, ‘Does Moore’s Law apply in the age of AI?’” recalls his friend Jamie Montgomery, referring to whether artificial intelligence would see exponential gains like those experienced in computational power…

…Munger made his own investments, too. Sitting in a recliner in his library, he’d grab green Value Line binders from a nearby desk and pore through data on publicly traded companies.

For decades, he barely looked at coal stocks, friends say, but in 2023, these companies grabbed his attention. Coal usage was in a long-term decline, and investors saw a bleak future for the industry. Yet many producers remained profitable, trading at inexpensive levels. Coal will remain necessary as global energy demand grows, Munger argued to friends and others.

“He read an article that said coal was down the chute,” Borthwick recalls. “He said, ‘Horse feathers.’ ”

In May 2023, Munger purchased shares of coal miner Consol Energy. Later in the year, he bought shares of Alpha Metallurgical Resources, which produces coal for steel production. By the time of Munger’s death, Consol had doubled in value. Alpha had also surged. Together he scored paper gains of more than $50 million, friends say…

…Back in 1978, a surgeon had bungled cataract surgery, leaving him blind in his left eye. He learned to compensate, installing bright lights around the house. Around 2014, though, Munger experienced a problem in the optic nerve of his right eye. He faced the possibility of going blind—yet he took the setback in stride, says Li Lu, a regular visitor. Munger decided to adjust his life, asking others to read to him and contemplating other steps.

“I’ll have to learn Braille,” he told one friend. He had studied it after his botched cataract surgery but never mastered it. He was ready to try again.

That turned out not to be necessary. His right eye slowly improved, but Munger’s movement became constricted…

…Munger was counting down to a 100th birthday party on Jan. 1, 2024. Friends and longtime business associates including Jim Sinegal, Costco’s co-founder, planned to fly to Los Angeles for the festivities.

Munger’s health was faltering, though. He sensed the end was near. When a friend asked how he was feeling, he replied: “There’s a lot wrong with me.”

When he discussed his legacy, he said he was comfortable with his accomplishments and optimistic about Berkshire’s future. 

“Once it’s built, you don’t need to be Warren and Charlie,” he told a friend. “What we have is a framework for looking at investments.”

Near the end of life, Munger leaned on humor for strength. He told family members that Diet Coke was responsible for his longevity, lightening the mood.

​And he shared a wish with a visitor.

“Oh, to be 86 again,” he said.

Late on Thanksgiving evening two years ago, days before his death, Munger was admitted to a hospital near Montecito. He asked family members to leave the room so he could call Buffett one last time.

They shared a last farewell.

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

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

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

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

Here are the articles for the week ending 23 November 2025:

1. Blue Owl private credit fund merger leaves some investors facing 20% hit – Antoine Gara

Earlier this month, Blue Owl told its shareholders that it planned to merge its Blue Owl Capital Corporation II fund, which has $1bn in assets and was one of the first private debt funds targeting wealthy individual investors, with its OBDC fund, which has $17bn in assets.

Blue Owl Capital Corporation II investors are being asked to exchange their shares in the private fund for shares in OBDC at the stated net asset value of both funds. However, OBDC trades on public markets at a discount of about 20 per cent to the stated value of its assets. Blue Owl Capital Corporation II, meanwhile, is not publicly traded and instead offers investors the ability to redeem cash every quarter at the fund’s stated value.

If the mooted deal were to be approved by shareholders and completed at current prices, Blue Owl Capital Corporation II shareholders would see the value of their investments fall by about 20 per cent.

Blue Owl Capital Corporation II investors will be restricted from pulling money from the fund until the merger with OBDC closes in early 2026, at which time they will permanently lose the ability to redeem cash at the fund’s NAV…

…Jonathan Lamm, chief financial officer of OBDC, conceded in an interview with the Financial Times that at current prices, the investors in Blue Owl Capital Corporation II could take a potential haircut on their investments. But he said the merger came with significant benefits, such as the ability to own more liquid shares in OBDC, which trade on the New York Stock Exchange.

2. Blue Owl’s clever private-to-public deal makes investors see red – Sujeet Indap

Blue Owl, a US-based private capital firm, just took a bruising in such a skirmish. On Wednesday it cancelled a planned merger between two affiliates that lend to middle-market companies. One of these “business development companies” is publicly traded; the other is private, so its investors have more limited opportunities to sell their holdings.

While now dead, the merger deserves study. Here is how it worked: investors in the unlisted company would have received shares in the listed one. Measured in terms of fund assets, the swap was a wash: an owner of $1 of what sits in the unlisted bucket would still hold a claim on $1 of stuff in the enlarged, listed counterpart.

The catch was that the listed company’s shares were trading in the market at a 20 per cent discount to their net asset value. So in return for getting access to an investment they could sell whenever they liked, Blue Owl’s clients were taking a pretty sharp haircut if they wanted to sell immediately. Predictably, they cried foul.

While that’s the simplified version of events, the deal actually came with some pretty complex engineering. Had the acquiring publicly traded BDC been trading at a premium to its net assets, the exchange would be calibrated based on its share price, not the — lower — net asset value. In return for their $1 of assets they would get paper they could sell into the market also for $1, but representing a claim on stuff worth less than that.

Confusing? Welcome to private markets.

3. Going All-In on MSTR – Ben Carlson

A reader asks:

Let’s say I have a brother. Let’s say he was on a lucky hot streak this year YOLO’ing into the most speculative plays in the market (quantum, crypto, meme stocks, etc) and was up 100% YTD. Pressing his luck, he thought it was a good idea to put nearly all of his portfolio into MSTR (using margin for more leverage) when it was trading in the 300’s and he is now down 50%. I told him to never touch MSTR with a 10-foot pole and if he was bullish Bitcoin, just buy Bitcoin. I also told him many times to never use margin, especially on high risk stocks. He is at risk of a significant % of his net worth (>50%) going away forever with a home purchase on the horizon as well that’s in jeopardy. Now he suddenly wants my advice on how to get out of this mess. I told him I don’t know and I honestly don’t. It’s a darned if you do, darned if you don’t lesser of two evils situation. How do you deal with clients that consistently ignore your advice and now want your help getting out of a mess?…

..This is the problem with the bull market brain you get from making big gains in the markets. It’s difficult to know if you’ve morphed into a degenerate gambler when you’re making money. Investors who have taken on excessive levels of risk the past few years have been compensated for it.

Once you get a couple of big wins under your belt it’s easy to let things get out of control.

Strategy (formerly Microstrategy) was in the $300s when the brother got into the stock. Now it’s well below $200 and falling fast…

…Here’s the thing — you could try to offer sensible advice. Sell now before it gets worse and you get a huge margin call. Invest in something far more reasonable and diversified.

I’m not sure it will matter.

When I first started my blog I had this dream that I could somehow save people from making illogical financial decisions. After creating financial content for more than a decade now I’ve come to realize this but some people cannot be saved.

They are doomed to make money mistake after money mistake and there’s nothing you can do about it.

Then there are others who need to make a huge mistake before having an ah-ha moment of realization that they need to change their behavior. Some people do change their stripes but it’s not easy.

4. A Century-Old Classic Buffett Would Love – John Garrett

Every so often you stumble across a book so old, so unassuming, that it shouldn’t have any relevance to modern investing… and yet it reads as if it were written yesterday.

That was my experience with R.W. McNeel’s 1927 gem, Beating the Market. Nearly a century old, it feels startlingly contemporary…

…Although it was published three years before Warren Buffett was born, the lessons in this little volume closely mirror his own philosophy: buy below intrinsic value, bet on America, stay unemotional, seek value, avoid new issues, ignore brokers, be patient, resist the crowd, and focus on businesses with quality management — to name just a few.

You’ll find the similarities striking…

…“Before one starts in to speculate, therefore, he should paste this old creed in his hat: ‘I believe in my country – The United States of America. I believe in the American people, their genius, their brains, and their brawn. I believe in their honesty, and their integrity and dependability. I believe that nothing can stand in the way of their commercial advancement and prosperity.’” R.W. McNeel…

…“Charlie and I have always considered a ‘bet’ on ever-rising U.S prosperity to be very close to a sure thing. Indeed, who has ever benefitted during the past 237 years by betting against America? If you compare our country’s present condition to that existing in 1776, you have to rub your eyes in wonder. And the dynamism embedded in our market economy will continue to work it’s magic. America’s best days lie ahead.” Warren Buffett…

…“Hold firm the principles underlying all successful speculation, that earning power makes values, and values make prices in the long run, and, having in mind the value based on earning power of any particular stock.“ R.W. McNeel

“Put together a portfolio of companies whose aggregate earnings march upward over the years, and so also will the portfolio’s market value.“ Warren Buffett…

…“One chief reason many fail to buy stocks when they are low is because of fear. Periodically prices of stocks representing ownership in the great productive industries of the United States and her great railroad systems fall so far that ownership in them is selling for 25 to 50 cents on the dollar of the value of the bricks and mortar and working capital which the stocks represent. But the majority of people will not buy them then because they are afraid. If they would analyze the cause of their fear they would discover it to be due to doubt as to the very stability of American institutions, for nothing less fearsome would justify certificates of ownership in the great industries of the nation selling at such ridiculous prices.” R.W. McNeel…

…While Buffett ultimately built a far broader and more sophisticated investing framework than McNeel could ever have imagined, the foundations McNeel laid in 1927 remain remarkably solid. Strip away the technology, the speed, the data, and the noise, and you find the same timeless principles: discipline, patience, rationality, independent thought, and a focus on value anchored in real businesses run by real people.

That is why this nearly century-old book still feels so alive. Markets evolve, but human nature does not. The behaviours that drove booms and busts in McNeel’s era are the same forces we wrestle with today — fear, greed, impatience, imitation, overconfidence, and the lure of the crowd.

Or, as Buffett put it most succinctly:

“Humans behave the way humans behave, and they’re going to continue to behave that way in the next 50 years.”

McNeel understood that in 1927.

5. Robotaxis and Suburbia – Ben Thompson

Another classic of the Uber bear genre was this 2014 post by NYU finance professor Aswath Damodaran attempting to determine Uber’s true value; the startup had just raised $1.2 billion at a $17 billion valuation, and according to Damodaran’s calculations, “it is difficult to justify a price greater than $10 billion” (his actual valuation was $5.9 billion). Investor Bill Gurley — before his dramatic powerplay that led to the ouster of founder Travis Kalanick — explained what Damodaran got wrong in How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size:

The funny thing about “hard numbers” is that they can give a false sense of security. Young math students are warned about the critical difference between precision and accuracy. Financial models, especially valuation models, are interesting in that they can be particularly precise. A discounted cash flow model can lead to a result with two numbers right of the decimal for price-per-share. But what is the true accuracy of most of these financial models? While it may seem like a tough question to answer, I would argue that most practitioners of valuation analysis would state “not very high.” It is simply not an accurate science (the way physics is), and seemingly innocuous assumptions can have a major impact on the output. As a result, most models are used as a rough guide to see if you are “in the ball park,” or to see if a particular stock is either wildly under-valued or over-valued…

Damodaran uses two primary assumptions that drive the core of his analysis. The first is TAM, and the second is Uber’s market share within that market. For the market size, he states, “For my base case valuation, I’m going to assume that the primary market Uber is targeting is the global taxi and car-service market.” He then goes on to calculate a global estimate for the historical taxi and limousine market. The number he uses for this TAM estimate is $100 billion. He then guesses at a market share limit for Uber – basically a maximum in terms of market share the company could potentially achieve. For this he settles on 10%. The rest of his model is rather straightforward and typical. In my view, there is a critical error in both of these two core assumptions.

Gurley argued — correctly in retrospect, given that Uber’s gross bookings over the last 12 months were $93 billion in rides and $86 billion in deliveries — that Damodaran failed to consider how a radically better experience could dramatically expand the addressable market, and completely missed the potential for network effects leading to an outsized share of that expanded market…

…That last sentence was about Uber’s diminished bargaining vis-à-vis a centralized robotaxi operator versus individual drivers, and it’s an important one in terms of Uber’s long-term valuation. However, as robotaxis continue to expand — Waymo is now in five cities (three via their own service, two via Uber), Tesla (with human supervisors in the car) in two, and Amazon’s Zoox in one — I do wonder if I am making a similar mistake to Horan and Damodaran.

First, like Horan, am I too caught up in the current economics of robotaxis? As an apostle of zero marginal costs I am intrinsically allergic to the depreciation inherent in the cars themselves, along with the significant marginal costs in terms of energy and insurance; Uber side-stepped this by offloading those costs to the drivers. Can scale solve this? At some point — Cybercab already points to this future — vehicles will be purpose-built at scale to be robotaxis, and my experience with Full Self-Driving (Supervised) has me convinced that insurance costs will be manageable, not just because of scale, but because there will be fewer accidents.

Second, like Damodaran, am I limiting my thinking by focusing on the current market — even if that market is already massively larger than the taxi & limo market ever was? The experience of a Waymo is certainly magical; it’s also peaceful, and by removing the human from the equation, provides a sense of safety and security that Uber has always struggled with. This last point could address a major suburban point point, which is kids: the lockdown in kids’ freedom corresponded with a dramatic rise in organized activities, the sheer volume of which leaves lots of parents feeling like unpaid Uber drivers themselves. Some may rely on Uber to solve this problem; it seems likely to me far more would be willing to entrust their children to a Waymo.

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

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

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

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

Here are the articles for the week ending 16 November 2025:

1. Berkshire Hathaway Inc. News Release – Warren Buffett

One perhaps self-serving observation. I’m happy to say I feel better about the second half of my life than the first. My advice: Don’t beat yourself up over past mistakes – learn at least a little from them and move on. It is never too late to improve. Get the right heroes and copy them. You can start with Tom Murphy; he was the best.

Remember Alfred Nobel, later of Nobel Prize fame, who – reportedly – read his own obituary that was mistakenly printed when his brother died and a newspaper got mixed up. He was horrified at what he read and realized he should change his behavior.

Don’t count on a newsroom mix-up: Decide what you would like your obituary to say and live the life to deserve it.

Greatness does not come about through accumulating great amounts of money, great amounts of publicity or great power in government. When you help someone in any of thousands of ways, you help the world. Kindness is costless but also priceless. Whether you are religious or not, it’s hard to beat The Golden Rule as a guide to behavior.

I write this as one who has been thoughtless countless times and made many mistakes but also became very lucky in learning from some wonderful friends how to behave better (still a long way from perfect, however). Keep in mind that the cleaning lady is as much a human being as the Chairman.

2. BlackRock Faces 100% Loss on Private Loan, Adding to Credit Market Pain – Davide Scigliuzzo and Silla Brush

About a month ago, BlackRock Inc. deemed the private debt it had extended to Renovo Home Partners, a struggling home improvement company, to be worth 100 cents on the dollar. As of last week, the firm had a new assessment: zero.

The drastic revision comes as Dallas-based Renovo — a roll-up of regional kitchen and bathroom remodeling businesses created by private equity firm Audax Group in 2022 — abruptly filed for bankruptcy last week, indicating it plans to shut down…

…It was no mystery Renovo was in a tough spot. In April, lenders had agreed to take losses and convert some of their loans into equity as part of a recapitalization that was supposed to give the company a chance to turn its business around, the people said. In the third quarter, they also allowed for deferred cash interest payments on its restructured debt, an arrangement known as payment-in-kind, regulatory filings show.

Yet at the end of September, funds managed by BlackRock and MidCap Financial were still marking the new Renovo debt at par, which typically indicates investors expect to be paid back in full.

3. Not Joined at the Hip: The Relationship between the Fed Funds Rate and Mortgage Rates – David Pendered

A time-honored, but flawed, assumption about the relationship between mortgage rates and interest rates has been turned on its head as the two have moved in opposite directions following the Federal Reserve’s interest rate cuts over the past year…

…But the Federal Reserve doesn’t set mortgage rates. Instead, the Fed sets short-term interest rates—often called the fed funds rate—in an effort to fulfill its dual mandate from Congress: promoting maximum employment and stable prices. The Fed’s short-term rates factor into how banks and financial institutions set many other rates, such as those for business loans, credit cards, and auto loans. And, of course, mortgages…

…Kris Gerardi and Domonic Purviance, both of the Atlanta Fed, explained that the presumed connection between mortgage rates and the fed funds rate is a misconception. For the past 20 years, mortgage rates have been more closely associated with the interest paid on 10-year Treasury notes than with the fed funds rate set by the FOMC, according to Gerardi, a financial economist who studies real estate finance and housing economics, and Purviance, a subject matter expert who analyzes risk in the housing market and threats it could pose to the financial system.

“While mortgage rates do, typically, move fairly closely with short-term interest rates like the fed funds rate, they are more strongly linked to longer-term rates such as the 10- or 20-year Treasury yield,” Gerardi said. “This is because the average life of a mortgage is around seven to 10 years.”

Gerardi observed that many factors determine longer-term yields on Treasuries and that the Fed’s short-term interest rates are just one factor. Others include the market’s expectation for economic growth, the federal government’s fiscal policies on spending and taxation, inflation expectations, lender capacity as homeowners refinance their mortgages, borrowers’ credit risk, and so forth. Gerardi said, “This means that, at times, mortgage rates and short-term rates can move in opposite directions.”

4. The Benefits of Bubbles – Ben Thompson

Late last year Byrne Hobart and Tobias Huber made a new contribution to our understanding of bubbles with their book Boom: Bubbles and the End of Stagnation. While Perez focused on the benefits that came from financial speculation leading to long-term infrastructure, Hobart and Huber identified another important feature of what they called “Inflection Bubbles” — the good kind of bubbles, as opposed to the much more damaging “Mean-reversion Bubbles” like the 2000’s subprime mortgage bubble. First, here is Hobart and Huber’s definition of an inflection bubble:

Inflection-driven bubbles have fewer harmful side effects and more beneficial long-term effects. In an inflection-driven bubble, investors decide that the future will be meaningfully different from the past and trade accordingly. Amazon was not a better Barnes & Noble; it was a store with unlimited shelf space and the data necessary to make personalized recommendations to every reader. Yahoo wasn’t a bigger library; it was a directory and search engine that made online information accessible to anyone. Priceline didn’t want to be a travel agent; it aspired to change the way people bought everything, starting with plane tickets.

If a mean-reversion bubble is about the numbers after the decimal point, an inflection bubble is about orders of magnitude. A website, a PC, a car, a smartphone — these aren’t five percent better than the nearest alternative. On some dimensions, they’re incomparably better. A smartphone is a slightly more convenient tool than a PC for taking a photo and quickly uploading it to the internet, but it’s infinitely better at navigation. A car is not just slightly faster and more reliable than a horse (although in the early days of the automobile industry, it was apparently common for pedestrians to yell “Get a horse!” at passing motorists); cars transformed American cities. Modern-day Los Angeles is inconceivable on horseback. The manure problem alone beggars the imagination.

This is what makes inflection bubbles valuable:

The fundamental utility of inflection bubbles comes from their role as coordinating mechanisms. When one group makes investments predicated on a particular vision of the future, it reduces the risk for others seeking to build parts of that vision. For instance, the existence of internet service providers and search engines made e-commerce sites a better idea; e-commerce sites then encouraged more ad-dependent business models that could profit from directing consumers. Ad-dependent businesses then created more free content, which gave the ISPs a better product to sell. Each sector grew as part of a virtuous circle…

… In this case, the optimistic take would be that AI is already delivering tangible benefits, that those benefits are leading to real demand from companies and consumers, and that all of the money being spent on AI will not be wasted but put to productive use. That may still be the case today — all of the hyperscalers claim that demand for their offerings exceeds supply — but if history is any indication we will eventually overshoot.

There is, however, a pessimistic way to ask that question: will the AI bubble be beneficial like the positive bubbles chronicled by Perez and Hobart and Huber, or is it different? There have been reasons to be worried about both the physical buildout and the cognitive one.

Start with the physical: a huge amount of the money being spent on AI has gone to GPUs, particularly Nvidia, rocketing the fabless design company to a nearly $5 trillion valuation and the title of most valuable company in the world. The problem from a Perez perspective is that all of this spending on chips is, relative to the sort of infrastructure she wrote about — railroads, factories, fiber, etc. — short-lived. Chips break down and get superseded by better ones; most hyperscalers depreciate them over five years, and that may be generous. Whatever the correct number is, chips don’t live on as fully-depreciated assets that can be used cheaply for years, which means that to the extent speculative spending goes towards GPUs is the extent to which this bubble might turn out to be a disappointing one.

Fortunately, however, there are two big areas of investment that promise to have much more long-term utility, even if the bubble pops.

The first is fabs — the places where the chips are made. I’ve been fretting about declining U.S. capacity in this area, and the attendant dependence on Taiwan, the most fraught geopolitical location in the world, for years, and for much of that time it wasn’t clear that anything would be done about it. Fast forward to today, and not only are foundries like TSMC and Samsung building fabs in the U.S., but the U.S. government is now a shareholder in Intel. There is still a long path to foundry independence for the U.S., particularly once you consider the trailing edge as well, but there is no question that the rise of AI has had a tremendous effect in focusing minds and directing investment towards solving a problem that might never have been solved otherwise.

The second is power. Microsoft CFO Amy Hood said on the company’s earnings call:

As you know, we’ve spent the past few years not actually being short GPUs and CPUs per se, we were short the space or the power, is the language we use, to put them in. We spent a lot of time building out that infrastructure. Now, we’re continuing to do that, also using leases. Those are very long-lived assets, as we’ve talked about, 15 to 20 years. And over that period of time, do I have confidence that we’ll need to use all of that? It is very high…

…It’s hard to think of a more useful and productive example of a Perez-style infrastructure buildout than power. It’s sobering to think about how many things have never been invented because power has never been considered a negligible input from a cost perspective; if AI does nothing more than spur the creation of massive amounts of new power generation it will have done tremendous good for humanity. Indeed, if you really want to push on the bubble benefit point, wiping away the cost of building new power via bankruptcy of speculative investors — particularly if a lot of that power has low marginal fuel costs, like solar or nuclear — could be transformative in terms of what might be invented in the future…

…I’ve been less worried about the cognitive capacity payoff of the AI bubble for a while: while there might have been concern about OpenAI having an insurmountable lead, or before that Google being impregnable, nearly everyone in Silicon Valley is now working on AI, and so is China. Innovations don’t stay secret for long, and the time leading edge models stay in the lead is often measured in weeks, not years. Meanwhile, consumer uptake of AI is faster than any other tech product by far.

What is exciting about the last few weeks, however, is that there is attention being paid to other parts of the stack, beyond LLMs. For example, last week I interviewed Substrate founder James Proud about his attempt to build a new kind of lithography machine as the center of a new American foundry. I don’t know if Proud will succeed, but the likelihood of anyone even trying — and of getting funding — is dramatically higher in the middle of this bubble than it would have been a decade ago.

It was also last week that Extropic announced a completely new kind of chip, one based not on binary 1s and 0s, but on probabilistic entropy measurements, that could completely transform diffusion models. Again, I don’t know if it will succeed, but I love that the effort exists, and is getting funding. And meanwhile, there are massive investments by every hyperscaler and a host of startups to make new chips for AI that promise to be cheaper, faster, more efficient, etc. All of these efforts are getting funding in a way they wouldn’t if we weren’t in a bubble.

5. An Interview with Michael Morton About AI E-Commerce – Ben Thompson and Michael Morton

What we started to do is we took a couple different products and we ran them through the traditional funnel and we’ll go back to the first example I used, shoes for flat-footed runners. What I did to start the exercise was I did hours and hours of research reading literally podiatry magazine posts, and every single post about the best running shoes for flat feet, I organized them, I ranked them, so what shoes got first and second, and we came out with some clear winners. “Here are the one, two, and three best running shoes for people with flat feet”, so we know what the best answer is.

Now let’s put it in Google search, and what you found was the PLAs at the top, the carousel you’ll see a set of icons that are horrible for getting the right answer.

So are those pure payment to get there, or is Google actually making determination of what’s the best answer?

MM: Yeah, for the work we did, one of the six was of the top ranked running shoes and when you looked at the models, their slugging percentage was, I would say 60 to 80% of the time, what they showed you out of the five icons were the best running shoe. So if they had five, they’d get one bad one.

Now, that’s a good question people have pushed back, “Well, how can these people be at the top of the feed if they’re paying for it” and this inevitably boils down to a conversion game. Shouldn’t it really only be the best products? And in an ideal state, yes, but this is also an output of which websites have better conversion rates? Who has bigger marketing budgets? Who’s looking to build a brand at this specific time? No one knows a perfect answer for the weightings and outputs of Google Search. Well, there are people, but their emails have @google.com, not our email addresses.

So why did Google’s results get like this, to the extent that you feel one out of six was a good answer? And you contrast the ChatGPT where four out of six are good answers. Is this a matter of, to your point, they’re measuring things like conversion factors, what actually goes through? Is it some people just paid more? Was this something that they can fix or is it that the money flowing in is too much that they can’t actually recommend four out of six because two, three and four might not pay them very much? What happened?

MM: This is probably an hour podcast in itself, but to try to simplify it as best as possible, I think there’s a lot of influencing factors. We are all very familiar with the gamification that has occurred with search, the entire giant industry of SEO, an army of marketing consultants to tell you how to win the keyword bidding game…

…MM: Yes. And look, before I came on here today, I re-ran the exercise, and search was again one for six for the shoe. But then I did AI mode in Google for the flat-footed running shoes — basically batted perfect, just incredible.

So that’s the question. Can Google fix this?

MM: Yeah. Michael Nathanson and I, I was like the devil on his shoulder while Google was going down every day, ChatGPT is just adding users and the bear case is just building and building, building and I’m over there, I’m like, “Oh, they got a problem, Michael, they got a problem”, and Michael’s been doing this for long enough where it’s really hard in these moments to see through this overwhelming wave of negative sentiment. And the day after Google I/O, I go into Michael’s office, I’m like, “Okay, I think they’re going to run towards this problem”, and now you’re sitting on the biggest distribution network in the world, the best AI infrastructure stack, and you’ve increased the friction from moving from being a Google user to a ChatGPT user. So people like you and I were ChatGPT probably day one, my mom and wife are now just going to end up being AI overviews and AI mode and maybe never ChatGPT people. So I think Google has the tool set to win this…

…So, who is the number one winner? Let’s grant this is going to happen, it’s so much better, people are going to be searching on ChatGPT for products. Who wins?

MM: Amazon. (laughing) This is like where movie starts with the ending scene, and then you work towards it — Amazon should win. And the way to work through this is you can go a couple angles. Again, why I like searching this subject so much, and thinking about it is, ask the models. So, we ask ChatGPT, Gemini, Grok, and all the different models, “For a e-commerce query, what do you weight in your decision-making process?”, and from most important to least important. And the top three, number one is price, number two is trustworthiness, and number three is speed. Price, speed, trustworthiness, you start to see where this is going and then I asked them, “Okay, of these weightings, who does the best job at delivering?”, every singly model, Amazon is number one, Walmart is number two and you go down the list, Target, Best Buy, eBay-…

…MM: Yeah, let’s take a step back. I’m Brand A, I sell most of my stuff on Amazon, I order it, it gets sent to the warehouses in Amazon, but I have 40% of this business that’s not on Amazon, but I don’t want to have a 3PL that I use outside of Amazon, it’s just a pain in the butt, why don’t I use Amazon? Now what Amazon will let you do is for the stuff that you sell on your own store, not on Amazon, they will deliver in unmarked boxes. So, it’s not like the Amazon Prime labeled all over it, and it’s just multichannel fulfillment, and for a long time, Walmart said, “You can’t use that, if you’re a third party merchant selling on our marketplace, you have to use our fulfillment network, or UPS or FedEx, but you can’t use the…” — basically, you can’t use Amazon multichannel fulfillment, you got to play within these rules.

I think it was in April of 2025, Walmart removed the multichannel fulfillment limitation. So now if you’re a Walmart and you’re plugging in your first party and third party inventory into ChatGPT, the whole thing about Amazon’s mode is that FBA business.

I just want to make sure I understand this. By multichannel fulfillment, you mean that you can buy on Walmart and it’s delivered by Amazon or Walmart? Or Walmart will deliver for any product?

MM: No. So, you can sell it on a Walmart marketplace. Now one of the Walmart rules is is that it can’t be delivered by a truck with Amazon labeling on it. You’ll see the Amazon Flex workers that drive around in cars with stuff, so who knows exactly? And if everybody is going to follow the rules here. But it’s just interesting because Walmart runs towards this new channel, and, in theory, the third party sellers on Walmart’s marketplace that would be presented in a ChatGPT answer have the ability to use a multichannel fulfillment service that is not Walmart’s and is not their own, and it brings that incredible distribution network to ChatGPT.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Amazon, and 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 09 November 2025:

1. Return on Invested Capital (ROIC): Why High Returns Require More Than High ROIC – Eugene Ng

Investors have been fascinated with return on invested capital (ROIC) and, in particular, seek to invest in businesses that can generate high ROICs. And for good reason, the higher the ROIC, the better the business. Yet, businesses with high ROICs alone are insufficient to generate strong long-term investment returns…

…We seek to explain why a company with a high ROIC would not necessarily deliver a similar high long-term total shareholder return.

In addition, businesses must be able to continue reinvesting capital at an attractive ROIC that allows them to grow revenue, earnings, and free cash flows strongly and compound for a long time.

It is not one or the other; it has to be both (unless the business’s valuation/price is really low). Unfortunately, there are very few companies that can do both, especially over a long period…

…Currently, all tangible and intangible assets, whether purchased or acquired via M&A, are capitalized on the balance sheet and expensed in the income statement over their useful lives.

However, internally generated intangibles are not capitalized and are immediately expensed on the income statement rather than recorded on the balance sheet and amortized over time. This is because accountants are uncertain about the sales that these investments may generate. So, to be conservative, they do not apply the matching principle of sales and expenses, and expense the outlays immediately. This causes near-term expenses to rise, profits to fall.

This significantly depresses near-term profitability, making companies that are spending a lot on intangibles seem less profitable than they really are and more expensive by conventional valuation metrics (e.g., price-to-book (PB) or price-earnings (PE) ratios), particularly when they are heavily reinvesting early on…

… The best companies in specific sectors (i.e., 80th percentile) tend to generate much higher ROICs. For example, looking at adjusted ROIC, the sectors are software, computer & peripherals, semiconductor equipment & products, IT consulting & services, communications & equipment, internet software & services, internet & catalog retail, biotechnology, and tobacco…

…Companies only create value when they (1) keep growing durably for an extended period of time, and (2) earn a return on capital that exceeds their cost of capital consistently.

For a company to keep growing fast, there must be a significant opportunity and a large total addressable market (TAM) to reinvest new capital at high returns relative to costs and to penetrate and gain market share. The faster they can grow, the greater the cash flows and value creation.

Conversely, competitive advantage is what sustains growth and high ROIC. Companies with attractive profitability will tend to attract new entrants seeking to compete profits away from the incumbents, causing ROICs to mean-revert.

A business with a wide moat and numerous competitive advantages in a highly monopolistic/duopolistic/oligopolistic market structure with strong unit economics tends to sustain higher ROIC durably over extended periods…

…Only a small percentage of the entire universe (55,321 companies) has very high ROICs: ~5.5% have >20% ROIC, ~3.6% have >25% ROIC, ~2.4% have >30% ROIC, and ~1.5% have >40% ROIC…

…ROIC is a static snapshot in time. How ROIC changes over time matters as well. One should focus not just on the absolute ROIC, but also on return on incremental invested capital (ROIIC). Think of ROIC as stock, and ROIIC as flow. If incremental capital is reinvested at ROIICs that are even higher than high ROICs, it will drive ROICs higher over time, and vice versa…

…Revenue growth translating into earnings growth is the single most significant contributor to rising stock prices. If companies can keep growing earnings for years and decades, and if the stock market is not too exorbitantly expensive, one will likely still end up with a fine-looking result. Earnings are the weighing machine for stock prices over the long term…

…The reinvestment rate measures the percentage of earnings that a company plows back into the business every year (i.e., reinvestment / net income).

ROIC measures the return the company makes on these reinvested earnings…

…Suppose a 19.5% ROIC company is unable to reinvest any capital and does not grow earnings. Assume it trades at a 15x PE (assuming no change in valuation multiples), if the company chooses to return 100% of that capital via share buybacks or dividends, it would be an implied 6.7% (1/15) earnings yield that the shareholder will effectively indirectly receive via buybacks/dividends/higher enterprise value, and adding the 0% earnings growth, would render a significantly lower combined total shareholder return of 6.7% as compared to the company’s much higher ROIC of 19.5%.

Whereas if this 19.5% ROIC company reinvests more of its earnings to achieve higher earnings growth, its total shareholder returns tend to converge to the higher combination of its earnings growth and its earnings yield (assuming no change in PE valuation multiples). The math is counterintuitive. The implications are profound.

Notably, the price (i.e., PE ratio) matters much more when earnings are growing much more slowly, and matters less when earnings are growing much faster.

2. The Great Decoupling of Labor and Capital – Abdullah Al-Rezwan

Almost two decades ago, Hewlett-Packard (HP) was the first tech company to exceed $100 Billion annual revenue threshold in 2007. At that time, HP had 172k employees. The very next year, IBM joined the club, but IBM had almost 400k employees…

…Alphabet required 76k employees to get to their first $100 Billion. Their most recent incremental $100 Billion? Just 11,000! (assuming they add another 3k employees in 4Q’25)…

…Historically, Microsoft used to be much more human capital intensive company as they required 124k and 97k incremental employees to get to $100 Billion and $200 Billion revenue milestones respectively.

But their most recent $100 Billion? Only SEVEN thousand!…

…Meta is the youngest of these companies. They will likely reach $200 Billion revenue milestone next quarter. Their first $100 Billion took 63k employees while the recent one will likely take one-third of that number!…

…Amazon really didn’t exhibit much of a pattern for their journey to $500 Billion revenue milestone. In fact, their hiring pattern is perhaps the poster child of post-pandemic over hiring as the company really was in the thick of pandemic induced massive upward demand shock and misread the post-pandemic hangover. While historically they took 200k to 400k employees for their incremental $100 Billion revenues, they added their last $200 Billion revenue with only 36k incremental employees!…

…I wouldn’t be surprised if Amazon reaches $1 Trillion revenue in 3-4 years by adding only ~100-200k incremental headcount. If that happens, it would mean while Amazon required 1.5 million employees to get to $500 Billion revenue, the next $500 Billion revenue would come with only ~10-15% of incremental headcount!

Of course, I haven’t even mentioned the largest company in the world: Nvidia! When they reached $100 Billion LTM revenue in 2024, they only had 30k employees and they will likely reach their next $100 Billion with only ~6-8k incremental headcount!

The trend isn’t necessarily just confined to tech companies either. Walmart’s full-time employees number remained relatively constant for the last 10 years while their revenue grew by $200 Billion during this period. In fact, Walmart recently mentioned that the headcount will remain static for the next three years as well. So, it is likely that Walmart will add $300 Billion incremental revenue since 2015 with basically no incremental headcount!

3. Missing a bidding war: a mea culpa on Metsera ($MTSR) – Andrew Walker

Pfizer announced a deal to acquire Metsera4 (MTSR) for $47.50/share plus a CVR in late September (per the proxy, the all in value of that offer is ~$54.66/share; see p. 45). If you read the MTSR proxy, you could see that there was actually a higher bid for MTSR; page 44 of the proxy notes that “party 1” had made an offer that was valued at $59.46, but the board determined to go with the Pfizer offer for a variety of reasons, but most notably “potential regulatory risks.”

That proxy background got really interesting earlier this week, when party 1 (Novo Nordisk) lobbed in an unsolicited proposal to buy Metsera that was deemed a superior bid (over Pfizer’s strong objections, including a lawsuit filed Friday night!). The superior bid and prospect of a bidding war sent Metsera stock up ~20%. Not bad for a merger arb!…

…Why do I think you could have predicted a possible topping bidder?

Because the presence of a higher bid was right there in the MTSR proxy.

MTSR’s proxy came out October 17th. It discloses that party 1 (who we now know to be Novo) offered a package valued at $59.46/share (see p. 44) for MTSR. As mentioned above, MTSR ultimately turned down Novo in favor of the certainty of the Pfizer deal.

You’ll recall I mentioned earlier that boards often turn down higher bidders with some type of regulatory or financing uncertainty in favor of a lower offer with more deal certainty.

But bidders and boards often differ quite a bit in their assessment of risk. The funny thing about public companies is that they are required to file a proxy with the background of a deal, and bidders who were passed over can then read the proxy and say, “huh, the board was concerned about that? We think they were completely wrong” or “o, we didn’t realize this one item was a gating factor for the board; let’s fix that issue and go back with a better bid.” And, even if the board still thinks the offer is inferior, the higher bidder can always take the question directly to the company’s shareholders, and shareholders will very often let the board know they’d prefer the higher price and antitrust risk to the certainty of the lower price.

So MTSR fits into a unique and perhaps my favorite of all of the no lose set ups: a merger arb that is scheduled to go through where there is a publicly confirmed strategic that has offered a higher price and was turned down for some reason. The reason this set up is so interesting is the spurned bidder can wait, read the proxy, see all of the companies projections, see what the company was worried about when it came to antitrust, see what other bidders were bidding….. and then chose to swoop in at the last second with nearly unprecedented amounts of information!

Again, this set up is rare…. but time and time again I see that the market underprices the odds of a topping bid from a bidder who was offering more and got passed over for some reason (generally anti-trust9). Let me give a few examples:

• My favorite example is Disney / Fox. They announced a merger in late 2017 that valued Fox at about $28/share (plus a spinoff)…. but then a few months later Comcast swooped in with a $35/share offer, and Disney eventually bumped their bid to $38/share. So, If you had bought Fox stock the day the initial deal was announced, you’d have made ~35% in ~6 months through the course of the bidding war…. and, if Comcast had never shown up, you’d have still made a normal arbitrage spread!
• How could you have known that Comcast might come in over the top. Well, there were plenty of press reports that Comcast had been trying to buy Fox with a higher offer before Fox sealed the deal with Disney…. but you also could have read Fox’s initial proxy in late May 2018 and seen / confirmed that Comcast had made a much higher offer for Fox! Again, that proxy came out late May 2018…. Comcast made their (public) topping offer a few weeks later.
• Chevron announced a deal to buy Anadarko for $65/share in mid-April 2019; right when the bid was announced David Faber reported “Occidental was prepared to pay $70 a share for Anadarko and is currently exploring its options.” Anadarko traded slightly below the Chevron price when the deal was announced…. Sure enough, Occidental came with a topping bid less than two weeks after the Chevron bid was announced and eventually won that deal (I believe Andarko’s stock closed at $73.39/share when the definitive OXY deal was signed ~a month later, so that’s a very nice bump insider of a month…. btw, OXY’s CEO does not come off well in the Anadarko proxy).
• Marriott and Starwood announced a deal that valued Starwood at ~$71/share in November 201510. Starwood was a very hot commodity and there were plenty of rumors that other strategics were looking at buying it; those rumors were confirmed when the proxy came out in February 201611. It disclosed nearly unlimited strategic interest in Starwood’s portfolio, but in particular I’d note that Company G and Company F both sent offers to buy Starwood for $86/share that were dismissed for one reason or another. Sure enough, in March Anbang offered $76 and then $78/share, their bid was deemed superior, and Marriott eventually had to bump their bid to $79.53/share (or $85.36 if you included the value of the spin).
• One thing that was/is so unique about the marriott / starwood set up? Marriott’s CEO was acknowledging the potential for a bidding war when the deal was announced; this FT article from right after the initial deal was announced has an incredible quote from him, “Will other bidders crash the deal? We hope they won’t.” The article goes on to speculate that Hilton, Hyatt, IHG, or several Chinese companies could serve as interlopers.

4. The Risky Movement to Make America Nuclear Again – Michael Riley

When Oklo Inc., a nuclear power startup, applied in 2020 to operate its first reactor, the company rested largely on outsize ambition. Its MIT-educated co-founders, a married couple named Jacob and Caroline DeWitte, lived in a mobile home park in Mountain View, California, in space 38. Oklo, which had only 20 full-time employees, wanted to build small reactors across the country, transforming the way towns and industries are powered. To realize that dream, it needed the US Nuclear Regulatory Commission to say the company’s design was safe.

Two years later, Oklo had failed to pass even the first step of the approval process. In 2022, after months of frustrating back and forth, the NRC concluded that the company didn’t provide verifiable answers to the most basic safety questions. The regulator denied the application. A former senior agency official, who spoke on the condition of anonymity, says Oklo “is probably the worst applicant the NRC has ever had.”…

…In 2025, Oklo’s reactor design is still unlicensed. But, in a sign of how radically the safety landscape has changed for nuclear power, the company’s business promise seems bright. Oklo went public last year and now has a market value hovering around $20 billion. In May, Jake was in the White House when President Donald Trump signed four executive orders designed to herald a nuclear renaissance. “It’s a brilliant industry,” Trump said, DeWitte at his side.

The startup’s backers long had a Plan B: If Oklo couldn’t win approval from the agency charged with protecting the public from nuclear accidents, they would, essentially, go after the regulator, in much the way Uber Technologies Inc. and other Silicon Valley startups have obliterated regulatory roadblocks. One of the architects of Oklo’s attack-the-regulator strategy is a law professor-turned-venture capitalist with ties to the Koch empire. He says the public shouldn’t be worried…

…Not far from the massive silver dome is a patch of government land where the DeWittes have staked their future. Little more than a sign and a couple of porta potties stashed amid the juniper bushes, this is where the two are planning to build Oklo’s reactor, Aurora, which they’ve described as a more modern version of the EBR-II. They have vowed that their reactor will share the same inherent safety characteristics.

Edwin Lyman, a physicist and director of nuclear power safety with the Union of Concerned Scientists, says the assumption that reactors like EBR-II are “passively safe” is misguided. “It’s gaslighting,” he says. Sodium fast reactors are notoriously difficult to operate, which accounts for the technology’s long history of accidents and meltdowns. Sodium leaks can create fires that spray a toxic sodium-oxide aerosol into the air. If the coolant comes into contact with water, hydrogen explosions can result in both the reactor itself and the power generation plant. And compared with light-water reactors, fast reactors leak neutrons that need extensive shielding to make them safe. “If something goes wrong, the potential for a Chernobyl-like escalating event is actually much higher than it is with light-water reactors,” Lyman says.

When Oklo submitted its first application to the NRC in 2020, the agency was under pressure from Congress and the industry to show it could license new reactors more efficiently. The agency’s licensing team was eager to begin what it called a Phase 1 review—essentially checking that the application is complete enough to move to a more rigorous scientific and safety evaluation. With an experienced company, Phase 1 usually takes about two months. “We thought we could get Oklo to that point in about six months,” says a former agency official familiar with the company’s application, who asked for anonymity to talk openly about the company’s application.

Major sticking points soon emerged. The company declared that, based on its extensive calculations, Aurora was one of the safest nuclear reactors in the world and there was no plausible accident that would result in a release of radiation into the environment. Yet the NRC staff identified important scenarios that Oklo didn’t appear to consider: What if undulating pipes from a sudden leak wrecked key systems? What if the seals of the reactor capsule failed, creating a pathway for radiation to reach the outside? The regulators also asked about the risk of flooding inside the reactor capsule, which the NRC said “may represent a potential criticality issue.” Nuclear experts say that’s a technical way of saying that the agency was worried about the possibility of an uncontrolled fission event, which could result in a dangerous steam explosion inside the reactor vessel.

As the licensing team dug in, Oklo couldn’t provide the supporting analysis for many of its basic safety assumptions, according to four officials who spoke to Businessweek about the application, as well as public NRC documents. In some cases, supporting files the company claimed to have were not available when the NRC tried to examine them, one official says.

“We needed the evidence that this reactor could be built and operated safely, and it just wasn’t forthcoming,” says one of the four officials.

Finally, in January 2022, the NRC denied Oklo’s application. By that point, the company had raised more than $25 million, and its dream of mass producing small nuclear reactors had seemed in reach. But at the NRC, the company never made it beyond Phase 1.

In a flashy video posted on YouTube last year, the DeWittes, clad in jeans, stroll across the high prairie near the Idaho National Laboratory. They’re introduced by a narrator whose tone mixes soothing and serious. “Meet the husband-and-wife engineering duo that discovered a game-changing technology buried in a government lab in Idaho,” the narrator says.

The six-and-a-half-minute video was published on the YouTube channel of a Utah-based organization called the Abundance Institute, identified on its website as “a mission-driven nonprofit focused on creating a space for emerging technologies.” In contrast to other pro-nuclear outfits including Third Way and the Breakthrough Institute, the Abundance Institute has been ferocious in its criticism of the NRC. In January its CEO penned an op-ed in the Wall Street Journal that labeled the regulator “lawless,” then followed up with social media posts declaring that it was time to abolish the agency.

5. AI Could Be the Railroad of the 21st Century. Brace Yourself – Derek Thompson and Richard White

Even in these early answers, you can see both a difference and similarity between the transcontinentals and AI.

A difference: The transcontinental project was government-financed from the jump. It was launched as a wartime strategy to keep California in the Union and backed with government loans and land grants. The AI buildout, by contrast, is overwhelmingly financed by the richest companies in the private sector.

A similarity: The transcontinentals were “central” to the U.S. economy in the second half of the 19th century—so central, in fact, that whenever the railroads caught a cold, the entire economy sneezed. In 2025, AI is similarly eating the entire economy—from the stock market (AI-related stocks have accounted for 75% of S&P 500 returns since ChatGPT launched in November 2022) to the construction industry. According to JPMorgan, data centers “are eclipsing office construction spending” and pushing up electricity prices across the country…

…The railroads were built with debt. Debt, debt, debt. The whole thing was a tottering Jenga tower of leverage, and it came crashing down every 15 years or so. By contrast, the AI buildout has not relied significantly on borrowing. Most data center construction to date has been financed by free cash flow from the major US tech companies with capital from private-capital firms like Apollo and Blackstone.

But this might be changing—and fast. Last week, Bank of America Global Research reported that “borrowing to fund AI datacenter spending exploded in September and so far in October…

…In the 1800s, the railroad supply chain was partly owned by, or directly financed by, the government, which led to years of corruption that exacerbated the severity of the economic panics that followed. I am reminded of the news that the Trump administration has been taking minority stakes in US chip companies (Intel) and demanding a share of their export revenue (Nvidia, AMD). Maybe not the single most auspicious sign.

Second, go back to that Fahnestock paraphrase: “We have borrowed immense amounts of money, built relatively little, and the lines we’ve built go nowhere. We have nothing to carry. This is simply going to collapse.” I think it is fair to say that, to date, the AI hyperscalers have not borrowed immense amounts of money (yet); they’ve built a lot, and what they’re building is being broadly used by tens of millions of people. The folks at Exponential View estimate that total generative AI revenues this year will exceed $60 billion. Say what you want about AI, but it is not an empty railroad cart leading to the desolate Nevada desert! This is a train that people are riding…

…Thompson: What are some timeless lessons that the railroads offer for other transformative technologies, such as AI?

White: Transformative technologies are built by people who never under-promise. They always overestimate the beneficial consequences of what they’re doing in the short-term and underestimate the costs of what they’re doing.

Second, the people who hype these technologies, the people who control the companies that are seeking to master these technologies, very often do not understand the technologies themselves. They can over-promise because literally they know what they want to promise to get financing and to get money and to get profits. But they often have very little idea of what these technologies will do. And so these technologies turn out to be something of a black box. You open them up and all kinds of things pop out. Some of them are things you’re anticipated. Many things are going to be things that you don’t anticipate.

Third, these technologies virtually always become bubbles. Because they take on this belief that if you’re going to change the world, if this is the secret to the changing world, everybody should get in on this. The railroads were the American stock market and American financial market in the late 19th century. I mean, that’s where the money went. It dwarfed everything else. In that way, they invent American financial markets and they invent the way that the bond market and the stock market will later work. But it means a relatively few corporations can make the whole thing boom and make the whole thing bust.

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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, Amazon, Mastercard, Meta Platforms, Microsoft, and Visa. 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 02 November 2025:

1. Do We Want an Age of AI Robopets? – Jessica Roy

One August morning, Kaarage woke up and took the train from the Japanese countryside into the city. She went to a restaurant and enjoyed a lunch of vegetables and soup, as well as an iced coffee. Afterward, she studied a musical score on her iPad, then went home to relax.

Kaarage is not a person: She’s an internet-beloved Moflin, an AI-powered robopet made by Casio—yes, Casio—who shares a charming, bucolic life with her owner in rural Japan…

…Since Casio initially released Moflins last year in Japan, they’ve proven to be a surprise hit, with the company selling several million dollars’ worth of Moflins in a matter of months. Last month, Casio made them available in the U.S. too, offering the furry-haired critters on its website for $429 a pop in two colors: gold and silver…

…Casio markets them as an “AI companion and robot pet” that can offer “quiet reassurance,” “ease stress” and “bring comfort.” (Watch out, loneliness epidemic.) The wellness language here is purposeful—a very real attempt to imagine the softer, cuddlier side of AI…

…The fandom and internet subcultures devoted to the robopets include a Reddit board where Moflin owners share fur-care tips and celebrate their Moflins’ “50-day” birthdays, the point when Casio says the AI pet has fully studied its owners’ vocal tones and can best respond to them in a series of purrs and coos audible through a tiny, built-in speaker beneath their fur. In Japan, hardcore Moflin owners can even spend $49 annually to join Casio’s Moflin Membership Club, which gives them access to health checkups (for maintenance issues, like charging problems) and appointments at a salon to take care of their Moflins’ fur…

…The Moflin, which weighs as much as a small rabbit, comes equipped with an app, several auditory and touch-based sensors, and a battery that lasts about five hours. (It charges in a soup bowl–shaped bed.)…

… Its only facial feature is two beady eyes. This is by design.

“We intentionally avoided features like ears, tails or distinct facial characteristics because making them look like a real creature would only emphasize how they differ from one,” said Casio developer Daisuke Takeuchi. “The abstract design allows each person to interpret who Moflin is to them, which helps build a more personal bond.”

Moflins rely on what Casio calls “emotional AI” to learn and respond to their environments, developing different personalities based on their owners’ interactions. The companion app, MofLife, allows users to track a Moflin’s mood to see how affectionate and energetic it feels…

…Amy Wang, 27, of New York. Wang has bad allergies and a small apartment, so a real pet was out of the question, but she said her Moflin, which she named Roku, provides much of the same emotional support a pet would…

…Whether the Moflin appealing to a younger demographic is a good thing remains to be seen. Nataliya Kosmyna, a research scientist at Massachusetts Institute of Technology’s Media Lab who focuses on AI, said there’s not a huge amount of research into the effects of soft AI, like that used by the Moflin, on children’s brains, but that’s exactly the issue: Kosmyna argues there should be more research into the impact of emotional AI toys on kids before they hit the market. 

2. Argentina Could Be a Superpower – Tomas Pueyo

Argentina used to be rich.

Its capital, Buenos Aires, was “the Paris of South America”.

For decades, Argentina (which means “the country of silver”) was among the richest countries on Earth—richer than France, Germany, Japan, or Italy…

…Not only did the Western world leave Argentina behind. Traditionally poorer countries like Chile and China are now richer! And Brazil is catching up!

How is this possible?

Because, unlike most countries I write about, Argentina is poor despite its amazing geography. With better management, it could become the United States of Latin America…

…Argentina is basically the US of the Southern Hemisphere:

• Very similar defensibility, with oceans, mountains, and ice on three sides, and weak neighbors on the other
• The huge exception is Argentina’s neighbor, Brazil.
• Very similar land and climate, allowing for a world-class agriculture industry and cheap infrastructure.
• A very similar navigable river basin in the heartland, helping reduce transportation costs, and creating wealth and political harmony, all controlled from Buenos Aires.
• Huge, untapped mineral deposits.

Despite these striking advantages, Argentina has not been able to translate them into immigration and wealth. Geography is not destiny.

One way to put it: Geography is the hardware, our institutions are the software. When both work well, a country is unstoppable. With bad hardware but intelligent software, a country can go far. But it’s easy to waste good hardware with very bad software. This is what Argentina has done. Another way to put it: Geography is the chessboard: How you play on it determines your success, and Argentina hasn’t played very well.

3. The AI Boom’s Real Economy Problem – Bob Elliott

Meta’s release showed revenue grew 26% from the same quarter last year, or roughly 10bln, claiming that AI is now helping improve the way ads are being placed on the platform. The ads of course being the only source of revenue for the business…

…On the surface those numbers sound great for any company, but in context it’s a pretty mediocre outcome. For instance the rise of 26% y/y is only at a marginally faster rate than previous years 3Q reads which grew 19% and 23% respectively. All that AI investment for a few extra percentage points.

To achieve these goals Meta is spending upwards of $70bln on AI capex to say nothing of rising operational expenses all chasing the hope that it’ll drive increased income…

…Of course all the AI investment is driving more income, but at best it’s maybe 3-5bln more than they would have had relative to the underlying trends pre capex spend. I’m no individual company analyst, but investing 70bln/yr to get 3-5bln/yr of revenue seems like a pretty shitty ROI…

…The whole sector faces the same basic problem. Already they are spending upwards of 60% of their operating cash flow on CAPEX at this point…

…The math is pretty simple, unless there is a surge in revenues from these activities, big tech is going to pump nearly all their free cash flow into CAPEX in just a few years…

…Blowing all this cash on investment means that they need to start to generate significant incremental cash flow from their investment on real economy activities (not just self referential activities to each other on things like cloud, etc)…

…Cumulative investment has surged and yet actual revenues either direct or indirect from these activities has been, has been … lets call it subdued…

…But the reality is that there are already signs that the AI adoption curve for companies is starting to bend downward even as forward expectations are high, a real threat to the idea that revenues will surge ahead…

…Increasing revenue may not be the primary benefit of AI for the economy, because most of the benefit will come in the form of increased efficiencies…

…But higher margins do not come free of impact. Workers earnings by definition finance the vast majority of spending in the real economy. So the trouble is that if you fire a bunch of workers, they have less income to spend, and with less revenue earned. The real economy realities make what looks like a free lunch actually a drag.

4. Is AI Eating CSI? – Dragon Field

As the ChatGPT turns three in November 2025, the most popular recent riff is “AI is eating SaaS”, which has claimed countless victims of the once popular software companies such as Duolingo, Shutterstock, Coursera, Gartner, Adobe, and Constellation Software. Everyday we have hundreds of TikTok influencers and YouTubers hyping the notion that even people without any coding experience and no technical education can simply type a few prompts into ChatGPT, and the AI will automatically create a software application in a few minutes. We also have high-profile tech CEOs like Ali Ghodsi of Databricks and Satya Nadella of Microsoft all announcing that “AI is eating SaaS”.

In fact, the expression that “AI is eating software” was first mentioned by the Nvidia CEO Jensen Huang in a 2017 LinkedIn post…

…About 25 years ago, I became an IT operations manager for a metal stamping plant for one of the Detroit Big Three auto companies. The plant is 2.4 million square feet, sitting on 118 acres of land. It produces automotive parts like hoods, door panels, bumpers, floor pans, and hundreds of other smaller parts. The plant had about 1,600 employees working three 8-hour shifts for six days a week at the time. At its peak in the 1950s, the plant had several hundred presses and employed over 6,000 people…

…In stamping plants we don’t usually let the manufacturing execution system (MES) have full control of the production because if the IT system is down, we would not shutdown the press lines. This is a situation we call “running blind” and usually you want to restore the system as quickly as you can. In addition, our VMS system was integrated with our warehouse inventory and corporate ERP systems, so a sustained downtime can cause a lot of issues thus we consider it mission-critical. Accuracy and reliability are the most important for us.

At the core of our MES is a VMS for production monitoring. It was first developed in the 1980s by a small vendor in Michigan when the US Big Three auto companies started to automate and install IT systems in their manufacturing plants…

…This VMS is deeply imbedded in every aspect of our production and workflow as depicted in the chart below. It has integration with our ERP system that is running on IBM mainframe with blue screens. It’s used by most departments in the plant, even the Finance and HR people use the system regularly for production and labor hour reports…

…For many years, this small VMS vendor only had three employees: One hardware engineer who liked to hide in the workshop fiddling with all kinds of gadget, a software engineer who focused on the the software development and upgrades, and the third engineer who worked as the leader and the face of their company…

…For many years, we also tried to find a replacement for this VMS, either from another vendor or develop one by our own internal IT. Sometimes the pressure from my own IT headquarters was intense. Like most legacy VMS systems, this VMS was first procured by the business people and they did not confirm to our new IT standards. They called VMS like this “Shadow IT”. Our internal IT spent a few million dollar developed a replacement and it was pushed to many plants. It caused a lot of trouble to the business and headache to the manufacturing IT operations. Because the new system did not well, we had to keep the old vendor system running in a “passive mode” in case the new system broke. It was also needed to run data collection layer, the barcode system, and to provide data via SMS to the phones and emails. When our new system acted up (which happened a lot especially in the early years), we would quickly switch back to the old “passive” vendor system. We ended spending a lot of more money and manpower plus it tarnished IT’s reputation.

The last time (~7-8 years ago) I heard about the VMS and its vendor was when a friend mentioned to me my former company had decided they would retire the new corporate system and reverse back to the old vendor system (which was never truly replaced anyway). They announced the older vendor VMS the new corporate standard and called it “strategic”. The vendor had to hire a couple more engineers to support the added scope.

5. Stumbling Onto a Goldmine – Joe Raymond

One sunny afternoon in the late-80s (more than a decade after the Interstate Stores transaction), Larry and Nate were having lunch together on Long Island.

After eating, Nate asked Larry if they could swing by the bank so he could make a deposit. Larry was enjoying the good weather and friendly company. “Sure,” he said, “Let’s do it.”

They walked into the bank and up to the counter to grab a deposit slip.

Larry noticed on the counter a copy of the bank’s most recent quarterly balance sheet. It was one of the cleanest, most secure bank balance sheets he’d ever seen…

…He looked around the lobby and saw on the other side of the room a thick wood door with a big brass knob and the word “PRESIDENT” emblazoned across the front…

…A man in a suit opened the door and asked how he could help.

“You have a beautiful balance sheet,” Larry said. “I’d love to know how and why this came to be, and if there are any other banks out there like yours!”

The president invited Larry into his office and explained to him how the bank had recently converted from a mutual to a stock bank….

…Imagine a make-believe mutual bank with $1 million of tangible equity. Let’s say this bank wants to convert to a stock bank and offers 100,000 shares at $10 per share in an IPO. Only depositors are invited to participate in the offering.

On a pro forma basis, the converted bank will have $2 million of tangible equity (the original $1 million plus the $1 million of IPO proceeds), which equates to $20 per share of tangible book value ($2 million of equity divided by 100,000 shares).

As an IPO investor, you were able to purchase the shares at $10. You paid only 50% of tangible book value…

…The president explained all of this to Larry, including how he himself had made a killing on the bank’s conversion…

…”You should check out this little bank in Queens,” he said. “They are preparing for a conversion themselves, and I think it will be a good one.”

That little bank in Queens was called Jamaica Savings Bank. And JSB ended up being a killer investment…

…Less than two years later, on June 24, 1990, JSB Financial went public. Santa Monica bought 59,000 shares at $10 per share for an initial investment of $590,000. The pro-forma book value was $21 per share (0.48x P/TBV)…

…The shares shot up 30% to $13 right after the IPO. Many investors sold for a quick profit. Larry decided to hold on as he saw a bigger pot of gold down the line…

…BVPS could be north of $25 within three years and the company would be worth $35 per share to a strategic buyer at 1.4x TBV. This works out to a 51% annualized return over three years.

Given the nature of the balance sheet (liquid, overcapitalized, and invested primarily in short-term government securities), the downside was minimal.

Thus, Larry found himself in investment nirvana: low downside paired with big upside.

JSB became an avid repurchaser of its own stock, buying back 7% of its outstanding shares in 1991 and another 8% in 1992…

…The share count was further reduced by 10% in 1993, 9% in 1994, 2% in 1995, and 7% in 1996. Shares outstanding fell by a cumulative 38% from 1990 to 1998. And most of these buybacks were done at or below tangible book value…

…JSB entered a stock-for-stock merger with North Fork Bank (NFB) in 1999. Every one share of JSB received three shares of NFB…

…As for Larry, he held onto his stock until NFB sold to COF, at which point he elected to receive cash. The $590,000 investment in 1990 turned into more than $5.5 million in 2006.

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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 Adobe, Mastercard, Meta Platforms, Microsoft, and Visa. 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 26 October 2025:

1. Sanity Check – The Brooklyn Investor

When I say that if 10-year Treasury yields stay at around 4%, then the market should average a P/E of around 25x over time, it sounds crazy, but given that the market has traded at 22-23x P/E in the last 35 years, this is not so crazy to me.

Of course, one can come back to me and say, well, as much as 22-23x P/E was shocking in 1990, who’s to say that the market can’t shock us again, going back to 6-7% interest rates and 14x P/E ratio over the next 20? This is also true. I can’t say that can’t happen. But I’ve always said that I think 4% or so 10-year rate seems reasonable given 4% nominal GDP growth over time.

So, given that, how does the market look today? The market today looks like it is priced correctly. The 10-year Treasury rate is 4% today, and the S&P 500 index P/E is 25.5x, almost exactly where it should be according to the model. Next year’s estimate P/E is 22x.

In past bubbles, the rubber band was stretched. The table below is from an earlier post. Just before Black Monday, the rubber band was stretched as 10-year rates spiked to close to 10% while the earnings yield declined to 4.7%, creating a near 5% gap. On a price basis, the market was overvalued by 100%! During the internet bubble, the gap increased to 1.5% and the market was overpriced 40%. Today, there is no stretch in the rubber band…

…So, the other thing is all this talk about an AI bubble. It is really interesting and I have no idea what is going to happen. But there seems to be two extreme views that both can’t be right. On the one hand, some people fear all these trillions being invested into AI infrastructure (energy, data centers etc.) will not offer decent returns on investments as there is still very little revenue associated with many of these big AI models. On the other hand, there is a big fear that AI will wipe out entire industries. There are already reports that huge increases in productivity is being actualized in the coding world, so much that the word is that entry level computer science positions are completely gone, wiped out. Big tech have also been firing a lot of engineers as they are replaced by AI.

They both can’t be right.

Here’s some bogus math, just as a sanity check too. Let’s say AI replaces 10% of jobs in the U.S. There are 160 million workers in the U.S. Ridding 10% of them is 16 million jobs gone. Many of these replaced jobs will be office jobs (well, AI will eventually replace Uber and truck drivers, farmers, factory workers too). Let’s say office workers cost companies $100K / year, including benefits. I’ve heard this somewhere before. That’s $1.6 trillion in expenses that you can cut. How much would you be willing to invest to cut $1.6 trillion?

You are now talking about trillions of dollars in investments. Now, all those numbers people throw around don’t sound so silly anymore. Of course, you can’t just say spending $10 trillion to eliminate $1.6 trillion is a 16% return on investment, as AI costs money to keep running / maintaining and you need to replace servers every 3-5 years etc. But still, you start to see the magnitude of what can happen if AI really starts to replace workers.

2. How Silver Flooded the World – Tomas Pueyo

A century earlier, around 1350, the Black Death spread across Europe, killing 25-50% of its population.

Men were dying, but coins were not.—David Herlihy

For a century, there was more coin than people, so they didn’t notice when silver and gold production slowed down. But it did; first, because of fewer miners.

Second, because mines ran out of gold and silver.

Third, the supply of gold from Africa collapsed after the Mali Empire civil war in the 1360s and the Songhai Empire instability.

Fourth, mines in southeastern Europe, in Serbia and Bosnia, fell to the Ottoman Empire.

So new sources of silver and gold shrunk. Meanwhile, silver sinks continued. Europeans kept buying Chinese silks, Indian cotton cloth, dyes, and spices, Middle Eastern sugar and drugs… But Europeans had little to export: wine, slaves, wood, salt, and little more. Italian traders paid one third in merchandise and two thirds in precious metals.

As silver and gold became scarcer, people started debasing the currency: Diluting it with other metals, clipping its edges…

Between the Black Death, the scarcity of metals, the debasement of currency, the incessant warfare, and taxes, people did everything they could to hoard and hide their precious metals, whether through hidden coins, filled chests, plates, and any other conceivable way…

…When we say some resource is exhausted, what we generally mean is… with current technology. People abandoned mines when they couldn’t figure out how to reach more ore, or when they couldn’t get more metal out of them.

One of the most typical issues was that ore is in mountains, but mountains also have something else: rain. Mining shafts would get flooded, so mining was restricted to the surface…

…Romans knew about waterwheels and pumps, but they never used them for extracting water out of mines. Central Europeans put them together into ever more complex systems to dry up mines and extract more ore…

…But there were two significant innovations that allowed Europe to increase its silver production by 5x between the 1460s and the 1540s.

Both innovations were new processes to extract more silver from ore. The first one is called liquation, and was first discovered in southern Germany in the mid-1400s, just as the Great Bullion Famine was hitting hardest. Of course, that’s not a coincidence: It was the bullion famine that was spurring mining innovation. Within 15 years, it had spread throughout Germany, Poland and the Italian Alps…

…We’ve already explored how Portugal’s discovery of an alternative path to Asia was made to bypass the Ottomans, who had taken control of Istanbul and blocked Christian trade through the Silk Road. But that trade still required gold and silver, and Europe didn’t have any. So the Portuguese were also looking for gold and silver deposits to mine. They found some in Western Africa—remember the Mali Empire—but that was not enough.

Now you know why Spanish Conquistadors were so obsessed about finding gold and silver in the Americas. It was not just a matter of greed. It was an existential matter for Europeans after the Great Bullion Famine. This is why Columbus mentioned gold 65 times in his diaries!

Spaniards didn’t find much gold in the Americas, but they did find silver. Unfortunately, the high-quality silver ore quickly ran out, and Spaniards were left with ore that didn’t contain enough silver to be extracted.

That’s when they invented a new technique to get more silver from the lower quality ore: amalgamation, via the patio process.

3. Microsoft’s Cloud & AI Head on the AI Buildout’s Risks and ROI (Transcript Here) – Alex Kantrowitz and Scott Guthrie

Alex Kantrowitz: I totally understand that, but I have to go back to the diminishing returns of training question. Where do you stand on that?

Scott Guthrie: If you look at training broadly, I think you’re going to continue to see more value from the models by doing more training. But going back to my answer earlier, I don’t know if that’s always going to be pre-training. I think increasingly lots of post-training activities are going to significantly change the value of the model. By post-training I mean take the base model and how do you add financial data or healthcare data or something that’s very specific to an application or a use case.

What’s nice about post-training is that you don’t have to do it in one large data center in one location. Part of the technique that we’ve been focused on is how do we take this inferencing capacity around the world and a lot of it is idle at night as people go to sleep. How are we doing increasingly post-training in a distributed fashion across many different sites? Then when employees come to work in the morning, we serve the applications. Having that kind of flexibility and being able to dynamically schedule your AI infrastructure so that you’re maximizing revenue generation and training ideally in a very swappable dynamic way—I think is one of the things we’re investing in heavily and I think is one of the differentiators for Microsoft.

Alex Kantrowitz: Okay, but you’ll forgive me for going back to this scaling pre-training question. I’m just trying to see what you believe here. You haven’t said it outright, but from your answers, it does seem to me like you believe that spending wildly on scaling pre-training is a bad bet.

Scott Guthrie: I wouldn’t necessarily say that. I think we’ve definitely seen as the scale infrastructure for pre-training has gotten bigger, we are seeing the models continually improve and we’re investing in those types of pre-training sites and infrastructure. We recently, for example, announced our Fairwater data center regions around the US. We have multiple Fairwaters. We did a blog post recently of one of our new sites in Wisconsin. These are hundreds of megawatts, hundreds of thousands of the latest GB200s and GB300 GPUs. We think the largest contiguous block of GPUs anywhere in the world in one giant training infrastructure that can be used for pre-training. We’re investing heavily in that, as you could see from the photos from the sky in terms of massive infrastructure. We do continue to see the scaling laws improve.

Now will the scaling laws improve linearly? Will they improve at the rate that they have? I think that is a question that everyone right now in the AI space is still trying to calculate. But do I think they’ll improve? Yes. The question really around what’s the rate of improvement on pre-training? I do think with post-training, we’re going to continue to see dramatic improvements. That’s again why we’re trying to make sure we have a balanced investment both on pre-training and post-training infrastructure.

Alex Kantrowitz: Just to parse your words here, you can see improvement by doubling the data center, but that’s why I use the word bet—because are you going to get the same return if it doesn’t improve exponentially and just improves on the margins? That I think is the big question right now, right?

Scott Guthrie: It’s a big question. The thing that also makes it the big question is it’s not like a law of nature that’s immovable. There could be one breakthrough that actually changes the scaling laws for better, and there could be a lack of breakthroughs that means things will still improve but do they improve at the same rate that they historically did from a raw size and scale perspective? That is the trillion dollar question…

…Scott Guthrie: Yeah, going back to the comments we had earlier on balance, I think as you think about your GPU buildout, one of the things that we think about is the lifetime of the GPU and how we use it. What you use it for in year one or two might be very different than how you use it in year three, four, five, or six. So far we’ve always been able to use our GPUs, even ones that we deployed multiple years ago, for different use cases and get positive ROI from it. That’s why our depreciation cycle for GPUs is what it is…

…Scott Guthrie: If you are for example building one large data center that only does training and it’s not connected to a wide area network around the world that’s close to the users, it’s hard to use that same infrastructure for inferencing because you can’t go faster than the speed of light. Someone elsewhere around the world that wants to call that GPU—if you don’t have the network to support it, you can’t use it for those inferencing needs…

…Alex Kantrowitz: Okay. All right. It’s good to get something definitive on that. You mentioned your 39% Azure growth. I’m looking at your quarterly numbers every quarter and often talking about them on CNBC and the numbers are massive. The other side of it though is that’s spend coming from clients, right? There have been multiple studies that have come out recently that have talked about how enterprises aren’t getting the ROI that they’ve anticipated on their AI projects yet. When you see those studies, do they ring true to you? How do you react to them?

Scott Guthrie: I think when you say AI in general, it’s a very broad statement.

Alex Kantrowitz: This is in large part generative AI where companies everywhere have tried to adopt LLMs and try to put some version of that into play. It’s not recommender engines basically.

Scott Guthrie: But I think what you need to do is double-click even further from GenAI to GitHub Copilot or healthcare or Microsoft 365 Copilot or security products built with GenAI. I do think ultimately, the closer you can double-click on is this really delivering ROI, then you have much more precise data.

I do think a lot of companies have dabbled or done internal proof of concepts and some of them have paid off and some of them haven’t. But I think ultimately a lot of the solutions that are paying off that we continually hear from our clients and our customers are a bunch of the applications that we’ve built. Similarly, a bunch of the applications that our partners have built on top of us. Ultimately the Azure business is consumption-based, meaning if people aren’t actually running something, we don’t get paid. It’s not like they’re pre-buying a ton of stuff. We recognize our revenue based on when it’s used.

The good news is when you look at our revenue growth, it’s not a bookings number. It’s actually a consumption number. You can tell that people are consuming more. The last two quarters, our revenue growth has accelerated on a big number. That is a statement of the fact that I think people are getting a lot of ROI, at least with the projects that they’re running on top of our cloud…

…Scott Guthrie: I think increasing the number of tokens you can get per watt per dollar is going to be the game over the next couple years. Maximizing the ability of our cloud to deliver the best volume of tokens for every watt of power, for every dollar that’s spent—where the dollar is spent on energy, it’s spent on the GPUs, it’s spent on the data center infrastructure, it’s spent on the network, and it’s spent on everything else—is the thing that we’re laser-focused on. There’s a bunch of steps as part of that, GPUs being a critical component of it.

One of the things that our scale gives us the ability to do is to invest for nonlinear improvements in that type of productivity and that type of yield. If you’ve got a million dollars of revenue on a couple hundred GPUs, you’re not going to be investing in custom silicon. When you’re at our scale, you will be. You’re not just investing in custom silicon for GPUs for pre-training or for inferencing. You’re looking at what could we be doing for synthetic data generation with silicon. What can we be doing from a network compression perspective with custom silicon? What can we be doing from a security perspective?

We have bets across all of those, many of which are now in production and are actually powering a lot of these AI experiences. In fact, I think every GPU server that we’re running in the fleet right now is using custom silicon at the networking, compression, storage layer that we’ve built. The GPUs themselves are also going to be a prize that people are going to try to optimize—the actual instructions for doing the GPUs.

Nvidia is a fantastic partner of ours. We’re probably one of, if not the biggest customer in the world of theirs. We partner super deeply with Jensen and his team. At the same time, and partly why they’re so successful is they’re executing incredibly well. If you look at the history of silicon, it’s rare to have a silicon company that every single year is doing the absolute perfect work that’s differentiated. Kudos to Jensen for what he’s done, and I know he’s going to keep trying to do it going forward. But there will be other opportunities from other companies where people are going to look for a niche that’s going to be big enough in this AI space to be truly differentiated versus what Nvidia is delivering. Then we’re doing our own silicon investment in-house because we’re going to be going after those same opportunities.

Ultimately, the way we’ve tried to build our infrastructure, none of our customers know when they’re using Microsoft 365 or GitHub or any open models what silicon they’re running on. We’re going to be constantly tuning the use cases based on the applications. If we find ways that are breakthroughs, we’re absolutely going to be taking advantage of them for those use cases. At our balance of scale and our balance of use cases, I’m very confident that we’re going to find use cases where custom silicon will make a difference. I’m also very confident we’re going to continue to be a great partner to Nvidia and others in the world that are going to be selling us great solutions.

4. The coming debt deluge? – Abdullah Al-Rezwan

For example, last week Meta entered in a Joint Venture (JV) with Blue Owl Capital for their $27-Billion Hyperion Data Center campus, of which Meta will own 20% and the rest will be owned by funds managed by Blue Owl Capital. Meta is signing an “operating lease” with an initial term of only four years. They have the option to extend the lease every four years, but they are not obligated to.

To persuade the JV to accept the short four-year leases, Meta provided a “Residual Value Guarantee” (RVG) covering the first 16 years of operations. If Meta decides to leave (by not renewing or terminating the lease) within the first 16 years, they guarantee the campus will still be worth a certain amount of money (undisclosed). This payment is “capped” i.e. there is a pre-agreed maximum limit to how much Meta would have to pay. Again, we don’t know the exact capped limit in this deal.

The structure of this deal, featuring short 4-year leases combined with a long-term RVG on a highly specialized asset, closely resembles a financial tool known as a Synthetic Lease.

In a synthetic lease, the tenant (Meta) gains the flexibility of short commitments and favorable accounting treatment (keeping the debt off their balance sheet). However, to convince investors (Blue Owl Capital) to fund the construction, the tenant must assume the majority of the financial risks of ownership. The RVG achieves this risk transfer. To secure financing for such a massive, specialized asset, this cap must be set very high. While we don’t know the exact number, my guess is it’s likely somewhere between 80% to 90%. If we assume it to be 85%, for the $27 Billion Hyperion campus, Meta’s maximum possible exposure is $22.95 Billion.

If Meta decides to terminate the lease within the 16-year RVG period, the payout is determined by the following calculation:

Guaranteed Value at time of exit – Actual Market Value = Shortfall

Meta pays the shortfall, but only up to the agreed-upon cap (estimated at $22.95B)…

…Given Meta’s backing, the bonds issued to fund this investment received investment grade credit rating. However, the bonds were issued at 6.58% yield which is closer to junk bond yield.

Why is the yield so high? If the value of the data center catastrophically collapses due to obsolescence or for some other reasons, Meta’s RVG covers most of the loss, but the investors bear the portion exceeding the cap. Moreover, the debt belongs to the project entity, it is “structurally subordinated” to Meta’s own corporate debt. Investors demand a higher yield to compensate for this “tail risk”.

More importantly, the underlying collateral is a hyper-specialized AI data center. If Meta leaves, it’s likely that the facility cannot be easily repurposed. While the RVG mitigates the financial loss, the specialized nature of the underlying asset still influences the perceived risk and pushes the yield higher.

My guess is Meta (and other big tech) will do more of these deals going forward. In fact, just yesterday, Oracle appears to be raising debt even larger than Hyperion deal: $38 Billion for building data centers in Texas and Wisconsin. If the deal goes through, it would be the largest debt deal so far in AI infrastructure.

5. Thoughts on the AI buildout – Dwarkesh Patel and Romeo Dean

With a single year of earnings in 2025, Nvidia could cover the last 3 years of TSMC’s ENTIRE CapEx.

TSMC has done a total of $150B of CapEx over the last 5 years. This has gone towards many things, including building the entire 5nm and 3nm nodes (launched in 2020 and 2022 respectively) and the advanced packaging that Nvidia now uses to make datacenter chips. With only 20% of TSMC capacity1, Nvidia has generated $100B in earnings…

…Further up the supply chain, a single year of NVIDIA’s revenue almost matched the past 25 years of total R&D and capex from the five largest semiconductor equipment companies combined, including ASML, Applied Materials, Tokyo Electron…

…For the last two decades, datacenter construction basically co-opted the power infrastructure left over from US deindustrialization. One person we talked to in the industry said that until recently, every single data center had a story. Google’s first operated data center was across a former aluminum plant. The hyperscalers are used to repurposing the power equipment from old steel mills and automotive factories.

This is honestly a compelling ode to capitalism. As soon as one sector became more relevant, America was quickly and efficiently able to co-opt the previous one’s carcass. But now we are in a different regime. Not only are hyperscalers building new data centers at a much bigger scale than before, they are building them from scratch, and competing for the same inputs with each other – not least of which is skilled labor…

…Labor might actually end up being the most acute shortage – we can’t simply stamp out more workers (at least, not yet).

The 1.2 GW Stargate facility in Abilene has a workforce of over 5,000 people. Of course, there will be greater efficiencies as we scale this up, but naively that looks like 417,000 people to build 100 GW. And that’s on the low end of 2030 AI power consumption estimates. We’re gonna need stadiums full of electricians, heavy equipment operators, ironworkers, HVAC technicians,… you name it.

For reference, there’s 800K electricians and 8 million construction workers in the US…

…Anthropic and OpenAI’s combined AI CapEx per year (being done indirectly, mostly by Amazon and Microsoft in 2025) seems to be around $100B.

Revenues for OpenAI and Anthropic have been 3xing a year for the past 2 years. Together, they are on track to earn $20B in 2025.

This means they’re spending 5 times as much on CapEx as they’re earning in revenue. This will probably change over time – more mature industries usually have CapEx less than sales. But AI is really fast growing, so it makes sense to keep investing more than you’re making right now.

Currently, America’s AI CapEx is $400B/year. For AI to not be a bubble in the short term, the datacenters currently being built right now need to generate $400B in revenue over their lifetime. Will they?…

…Do you think that AI models will be able to do much of what a software engineer does by the end of a decade? If the 27M Software engineers worldwide are all on super charged $1000/month AI agent plans that double their productivity (for 10-20% of their salary), that would be $324B revenue already…

…A key question is whether datacenters will go “off-grid”—generating power on-site rather than connecting to the utility grid. Some of the largest datacenters are already doing this, e.g., Meta’s Orion or XAI’s Colossus.

Why would datacenters want to make power themselves rather than relying on the grid? They’re trying to get around interconnection delays. Connecting large new electricity sources to the grid now takes over 5 years…

…What will the distribution of individual datacenter sizes be? Here’s the argument for why we might end up seeing what looks like a thick sprinkle of 100 MW datacenters everywhere:

• If you can plop down a medium sized datacenter here and there, you can soak up any excess capacity in the grid. You can do this kind of arb with a 100 MW datacenter, but there’s no local excess capacity in the grid at the scale of 1 or 10 GW – that much power is on the scale of a whole grid itself.
• For pretraining like learning, you want to have large contiguous blobs of compute. But already we’re moving to a regime of RL and midtraining, where learning involves a lot of inference. And the ultimate vision here is some kind of continual learning, where models are widely deployed through the economy and learning on the job/from experience. This seems compatible with medium sized datacenters housing 10s of thousands of instances of AIs working, generating revenue, and learning from deployment.

Here’s the other vision. 1-10 GW datacenters, and then inference on device. Basically nothing in between.

• If we move to a world with vertically integrated industrial scale production of off-grid datacenters, maybe what you want to do is just buy a really big plot of land, build a big factory on site to stamp out as many individual compute halls and power/cooling/network blocks as possible. You can’t be bothered to build bespoke infrastructure for 100 MW here and there, when your company needs 50 GW total. A good analogy might be how a VC with billions to deploy won’t look at any deal smaller than deca millions…

…Why doesn’t China just win by default? For every component other than chips which is required for this industrial scale ramp up (solar panels, HV transformers, switchgear, new grid capacity), China is the dominant global manufacturer. China produces 1 TW of solar PV a year, whereas the US produces 20 GW (and even for those, the cells and wafers themselves are manufactured in China, and only the final module is assembled in the US).

Not only does China generate more than twice the electricity than the US, but that generation has been growing more than 10 times faster than in the US. The reason this is significant is that the power build out can be directed to new datacenter sites. China State Grid could collaborate with Alibaba, Tencent, and Baidu to build capacity where it is most helpful to the AI buildout, and avoid the zero-sum race in the US between different hyperscalers to take over capacity that already exists.

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

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

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

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

Here are the articles for the week ending 19 October 2025:

1. Why AI Is Not a Bubble* – Derek Thompson

The dot-com bubble was genuinely insane. The internet companies didn’t have real revenue, and the telecom firms didn’t have real users. In its first fiscal year, Pets.com earned less than $700,000 in revenue and spent nearly $12 million on advertising. Telecom firms laid so much fiber-optic cable that as late as 2005, 85 percent of broadband capacity in the U.S. was going unused.

Today’s AI boom is nothing like that. The modern hyperscalers are among the most profitable enterprises ever. The eight biggest tech firms—the Magnificent Seven plus Broadcom—now account for 37 percent of the S&P 500 and are expected to grow profits by 21 percent this year. Return on equity for the S&P 500, around 18 percent, is the highest since at least 1991—and achieved with less leverage than the late 1990s. These are not fragile companies playing with borrowed money…

…So, you could boil down the whole “is AI a bubble?” thing to one simple question: Where’s the cash?

The answer is that three years ago, it was nowhere, and now it’s surging. According to Azhar, generative AI revenue has grown by ninefold in the last two years…

…What we’re really interested in is revenue that comes in from new businesses and customers. This comes from three sources.

• The first source is internal: Hyperscalers using their own AI to make money from their existing businesses, such as Meta using its AI to sell $1 billion more in ads (or, just as good for cash flow, using AI to save $1 billion).
• The second source is external: Companies like OpenAI and Microsoft getting money from companies that use their AI, such as a legal AI firm building a bespoke model off of GPT-5.
• The third source is novel: Using AI to create a business that doesn’t yet exist, like Tesla is trying (and mostly failing) to do with its fleet of Optimus robots.

The first two revenue sources seem to be firing on all cylinders. Microsoft and Amazon’s cloud divisions are surging as enterprise customers integrate generative-AI tools. Meta is using AI to sell more ads and to cut costs. Internal AI use (Meta’s ad tools, Microsoft’s Copilot, Amazon’s logistics optimization) plus external adoption (customers building on GPT-5 or Claude) means two of Azhar’s three revenue streams are already working.

The most important test for whether AI is a bubble is what you could call the “Triple-Digit Test.” The TDT says: If AI revenue grows more than 100 percent annually (or, even better, 200 percent) for the next few years, there probably won’t be a huge bubble pop. So far, that’s happening—or, at least, the biggest AI investors claim that it is happening.

• Microsoft says its AI business has surpassed a $13 billion annual run rate, up 175 percent year-over-year.
• Amazon claims its AI revenue is growing at “triple-digit percentages” year over year.
• The VC firm Menlo Ventures estimates OpenAI, Anthropic, Scale AI, and Perplexity are all doubling or tripling annual revenue, which means they’re growing at 100 percent or more…

…AI’s revenue problem is really a white-collar workforce creativity problem. It’s notable that AI’s profitability doesn’t just depend on how fast frontier labs innovate. It depends on how creatively their customers use the tools, as Smith points out. But most of the world’s companies are not prompt-engineering wizards. Researchers at Harvard and Stanford recently found that many firms are misusing AI so badly that workers spend more time fixing AI-generated “workslop” than doing their jobs. If that pattern persists, AI will increase frustration rather than productivity, and a slow adoption curve will blow up the Triple-Digit Test and make these companies vulnerable to a major correction in valuation or investment.

If everybody builds the same thing, where’s the moat? Something I still can’t figure out is how all of these companies are going to make money when they’re all building similar products. Anthropic’s Claude technology isn’t that different from OpenAI’s GPT technology, and when several companies build an interchangeable product, competition tends to drive down prices, which is great for consumers and bad for firms outlaying a trillion dollars to build that thing. Meanwhile, cheap Chinese or open-source models that are “99.8 percent as good for a tenth of the price,” as Smith writes, could commodify the industry overnight. In that world, the real winners wouldn’t be OpenAI or Anthropic but the downstream companies that use cheap AI to build their own moat to get rich.

2. Is AI eating Vertical Market Software? – Best Anchor Stocks

Much like some people already believed, AI has not “eaten” VMS software (at least not yet). Mark Leonard started the call with a powerful story that demonstrate his integrity and also the fact that some people might be running ahead of themselves with their forecasts:

In 2016, Geoff Hinton made a long-term forecast. For those of you who don’t know him, Geoff is known as the godfather of AI and is a Nobel Prize winner for his work in the field. And long-term forecasting is very difficult. I talked about this before, and I’m happy to send you some sources/information if you’d like to delve into that further.

Geoffs’s forecast in 2016 was that radiologists were going to be rapidly replaced by AI, and specifically, he said people should stop training radiologists. In the intervening nine years since he made that forecast, the number of radiologists in the US has increased from 26,000 (these are US board-certified radiologists) to 30,500 or a 17% increase. Now that’s outpaced the population growth in that period. So the number of radiologists per capita is up from 7.9 to 8.5. Now, Geoff wasn’t wrong about the applicability of AI to radiology. Where he was wrong was that the technology would replace people. Instead, it has augmented people. The quality of care delivered by radiologists has improved. And the number of practicing radiologists has increased.

So I told you this story to make two points. Firstly, you and I will never know a tiny fraction as much about AI as Geoff did. And secondly, despite his deep knowledge of AI, he was unable to predict how it would change the structure of the radiology profession…

…Management also discussed how the company leverages LLMs and how their strategy protects them from worsening unit economics. Both things are related, so let’s start first with how Constellation has structured its access to LLMs to avoid being “price-gouged:”

So we’ve essentially created our own centralized sort of platform that essentially removes the various factions that are currently going on where to a certain extent, you have to largely be within this cloud provider to have access natively to this LLM and so on and so forth. So there’s these turf wars being kind of created across the various cloud providers and whatnot.

And so with our strategy has been to really play a very neutral sort of Switzerland type role, where by centralizing things through strategic relationships, either directly with the model providers or with the platform providers and so on and so forth. We’ve managed to negotiate, I think some, some, some really aggressive deals and remove the element of these sort of factions. They’re all willing to kind of play nice with us in the sandbox. So that puts us in a very unique position where sort of technically we have access to 15,000 sort of unique models. And that’s because we’re essentially sort of coalescing sort of anything that otherwise couldn’t be or reside within other platforms. The other piece that sort of I had touched on very briefly, and Paul sort of alluded to as well, is sort of using a on prem based assets where and when possible.

So to the extent that the LLM needs to be or the AI model needs to be hyper specific or, you know, a specific trained one that it resides with a pre-existing best of breed provider, then sure, that may make sense to kind of tap into that one, but for basic, let’s say sort of translation service, summarization, service, and a myriad of other hosts of functionality and whatnot, you know, the on prem one is plenty sufficient and capable of doing it’s, you know, its own sort of thing.

This flexibility basically means that CSU will benefit from price wars across the different LLMs (which they expect will happen) and will also be able to take advantage of their on-prem infrastructure to lower costs for consumers (when able to).

3. An Interview with Gracelin Baskaran About Rare Earths – Ben Thompson and Gracelin Baskaran

GB: We have so much supply on the market right now, and that’s really coming from China, they keep overproducing and it’s actually forcing western companies out of operation. So to put this into context for you, in the last three years, lithium prices have fallen by 85%, nickel prices by 80%, and cobalt by 60%. So companies are struggling to operate at a time when we know we need a lot of these materials because the economics of it aren’t checking out.

And is this overproduction on purpose? Is it to knock out all these western companies to result in China dependence?

GB: I can tell you one thing is Chinese companies aren’t operating profitably by-and-large either, but they are willing to absorb long-term losses in order to gain a strategic monopoly on a lot of these sectors.

I’ll give you an example: Chinese companies in Indonesia five years ago were producing about 500,000 tons of nickel a year, now they’re producing over 2.5 million tons a year, and what’s happened is nickel prices have fallen so much that BHP, an Australian company, has closed their operations in Australia and Glencore, a Swiss company, has closed theirs in New Caledonia.

So that dominance, that willingness to absorb loss, has given them a dominance and the ability to weaponize minerals and cut us off…

…But I want to go back to your question about rare earths, there’s two things that are important. First of all, rare earths are not actually rare, they’re everywhere, but finding them in these large scale quantities that are again economically viable is actually much harder, number one.

Number two is you can mine rare earths in a lot of places, but we don’t actually process rare earths or we historically have not, which it means that no matter where the rare earths are mined — I mean, even this year until February, the rare earths that we mined in California still went to China for that processing phase, so that allowed them to build that dominance. But it’s a very small market. I need a ton of lithium, I need a ton of cobalt, rare earths are actually a small market…

…What is it about them that makes them so useful?

GB: They are the most powerful permanent magnet, which is actually really important. If you put a really good permanent magnet next to a fridge, you would basically pull the fridge off, so for defense technologies in particular, there’s nothing really that you can substitute at this point.

Got it. So is it just the magnetic properties or are there — for example, what’s their use in chips? I know particularly as chips have become more advanced, there’s questions about rare earths in there. Is that a magnetic thing or are there other properties as well?

GB: Rare earths are actually used in advanced semiconductors including memory chips and logic chips and actually when you look at the most recent export restrictions that China has applied, they’re actually reviewing the semiconductor end use on a case by case basis…

...Right. So what’s the trade-off there? Because you see numbers and you reference this before, I think China actually mines 60 to 70% of rare earths, but the actual processing is well over 90%. So what’s the bigger hole for us here? Is it the actual acquiring the rare earths or is it the processing/refining?

GB: Our big chokehold is processing because I can get rare earths from other places. So for example, now we are putting US government financial support, not just at mines, for example, Mountain Pass here in the United States, but we’re also providing financing to a project in Brazil that has rare earths. You can source feedstock from a variety of places, but it doesn’t actually matter when it goes back to China because then China can cut us off and we don’t have any of it. So we’ve got to build those processing capabilities here or else it’s like we never have access to them anyway.

So how does that happen? Is this an issue where basically there needs to be some combination of tariffs? Does China imports need to be blocked? There need to be a guaranteed price floor? How do you make the economics work? And you mentioned that these massive permitting issues when it comes to mines, is it better or worse when it comes to building these processing facilities?

GB: So really what we need is an all-of-the-above approach, and here’s what I mean by that. Again, it varies by commodity, the reason you need a price floor is this is when that US, the Department of Defense and MP Materials deal was signed earlier this year, which had a lot of support mechanisms. NdPr, neodymium-praseodymium oxide, which is one of our key rare earth compounds, was about $54 a kilogram. So at that price point, by 2030, there would only be eight projects outside of China that could even break even with their production costs because it was so commercially unviable. What you need in that case is you do need a price floor because what I don’t want is I don’t want my Western companies to go bankrupt or have to stop operating because prices are so low, and we’ve already seen it happen. In 2023, the United States opened its only cobalt mine and it closed it in the same year because prices had fallen so much. So we complained, we’re like, “Oh, I don’t want to lean into the Congo”, but we couldn’t keep our own mine open. We don’t want that to replicate for rare earths, so part of the story is a price floor story and the reality is you shouldn’t need a price floor forever.

What we saw after that deal, General Motors signed offtake, you saw Apple sign offtake, and already those prices have gone from $54 to about $84 or $85, the price floor is $110. So I’ve already closed what my fiscal responsibility by over 50%. As there’s more demand for a reliable supply chain and companies are now willing to pay that premium. I’m a Midwesterner, and what we saw after the rare earth export restrictions in April was that Ford actually had to stop manufacturing its Explorer model in Chicago because it couldn’t access these materials, so of course now we’re willing to pay a bit more to know that I won’t have to stop producing. Price floor is one part, but I need more than that.

So other mechanisms that become really important is I need concessional financing. Capital markets, because a lot of the risks that we’ve talked about, often tend to view this sector as too risky to lend to, but when the US government provides cheaper financing, we also see that banks are more willing to invest in that because they see it as a key de-risking mechanism.

The third thing I would add is government offtake helps because you’re not going to be able to sell everything to an American firm and so what we’ve seen this government do is say, “Okay, well we want a stockpile”. The recent budget in the US included $2 billion for a stockpile because if there is a supply chain disruption, I want to have enough to cover our national and economic security insurance, so they can backstop that by buying some of it…

…Given the massive risk that is here, let’s sketch out that risk. What happens if China actually just cut off rare earths tomorrow? What happens?

GB: Our manufacturing stops. Even in April 4th when those restrictions hit, US government officials said, “Maybe we get to June before we run out”…

…GB: I can manufacture for days, but the US at the end of the day has less than 1% of the world’s nickel, cobalt, we have about 2% of the world’s rare earths, we have less than 1% of graphite, we are not going to win this race alone no matter how we cut the cake. The question is how do we form — I mean, think about it — we used to form strategic alliances over oil, our relationship with Saudi was the defense for oil agreement that kept our economy open for a long time. The question is, “How do we work with our partners in a way that our supply chains are as close to us as possible?”, but we can’t do it alone, God didn’t give us the rocks.

Which mineral is the hardest problem to solve of these?

GB: I would say that the most complicated mineral is probably actually rare earths, and there’s a few reasons for that.

Is there one specific rare earth in particular?

GB: The United States Geological Survey just undertook its review of what is a critical mineral, and of the 55 or so minerals, samarium is ranked number one. The reason samarium is number one is when I take out a ton of rock from the ground, there’s a different percentage of every mineral in that ton, and samarium is such a small percentage of that rock that and I need more of it than that percentage is in there. So samarium is our most critical, which means that it is a high likelihood that there’s a failure of that supply chain. Niobium is right up there and rhodium is up there, and rhodium is a platinum group metal. So you pull it out with platinum, tiny percentage. So that’s what I mean by geology, I can’t will myself to have more Samarium in a ton of rock.

4. My friend became a millionaire at 17, and I got two book recommendations – Thomas Chua

“Actually… something huge happened.”

He told me slowly, almost reluctantly. His dad’s boss had given his father a red packet for Lunar New Year—a pretty standard gesture in Singapore. Bosses give employees red packets during the festive season as a bonus, usually cash.

Sometimes, though, they don’t give cash.

Sometimes they give hope.

In his dad’s case, his boss had given him a lottery ticket—the Singapore Sweep, with a top prize of over $2 million.

His dad won.

My jaw dropped. My teenage brain couldn’t comprehend that level of luck.

Over $2 million. The boss had fought to get the ticket back—or at least demanded a portion of it. I never learned exactly how it ended, but his dad quit his job not long after, so I assumed he kept everything. The family became estranged from relatives who’d expected generosity with the windfall, who’d wanted their own slice.

My friend and his siblings each received a tidy six-figure sum from their dad.

His family became millionaires overnight.

Looking back now, I realize that the Chinese New Year was probably their last normal one as a family. The last time money was just money, not a test of relationships. The last time people showed up because they wanted to, not because they wanted something…

…At seventeen, that kind of windfall looks like freedom. Looks like every door opening at once. No more worrying about tuition, about scholarships, about starting life in debt. Just pure possibility.

But freedom from what, exactly?

My friend hadn’t built anything yet. Hadn’t struggled for anything. Hadn’t earned the quiet confidence that comes from overcoming something you weren’t sure you could overcome. He hadn’t had the chance to discover what he was capable of when things got hard.

And here’s the thing about struggles: they don’t just test you. They build you.

5. National Bank of Detroit – Joe Raymond

Long story short, Buffett, Munger, and Guerin acquired control of Blue Chip Stamps in the late ’60s. The main appeal of the stock was the cheap price in relation to the large amount of deferred revenue from stamp sales. By taking control of the company, Buffett & friends could invest this “float” in securities.

Blue Chip had $89 million of stamp-related float in March 1972, $134 million of securities, and $74 million of common equities…

…Buffett needed to keep Blue Chip’s balance sheet liquid enough to handle stamp redemptions, but he knew he could do better than short-term debt instruments. Instead, he bought a group of solid companies at reasonable valuations.

Nearly two-thirds of the stock portfolio was made up of 10 banks…

…Blue Chip got out of most of these stocks within a decade. Nevertheless, I thought it would be fun to go through each of these banks and see how things played out over the long run.

It turns out this is a great way to learn about bank investing…

…This post will be dedicated to Blue Chip’s biggest position in 1972 – National Bank of Detroit – which is an interesting (and moderately successful) story…

…In March 1972, Blue Chip owned 218,380 shares of NBD (3.64% of the total outstanding) worth nearly $11 million. This equated to about 8% of the securities portfolio, 15% of the stock portfolio, and 24% of Blue Chip’s common equity.

All of this is to say this was a sizable bet.

The average price in 1971 (when Buffett was buying) was $50 per share…

…So, NBD was a dominant regional bank with a 12%+ ROE trading at a discount to book value. Loans to deposits was less than 60%, with the rest invested in conservative securities.

The 10-year track record was satisfactory…

…By the mid-80s, many states had passed reciprocity laws allowing bank holding companies from approved neighboring states to buy or merge across state lines.

Merger mania ensued; NBD did its fair share, making dozens of acquisitions from the mid-70s to mid-90s.

Despite the feverish M&A activity, results weren’t bad.

Book value per share grew from $57.24 in 1972 to $237.66 by 1995 (6.4% CAGR). The company also paid substantial and growing dividends over this period.

Annual BVPS growth adjusting for dividends came in around 11-12%…

…In 1995, NBD completed an all-stock merger of equals with First Chicago Corporation. The two banks had complementary business lines in adjacent geographies. The surviving entity operated under the combined name First Chicago NBD.

Then in 1998 First Chicago NBD merged with Banc One – a Columbus, Ohio based bank. Every one share of FCNBD received 1.62 shares of Banc One and the combined company was renamed Bank One (with a “k” instead of a “c”)…

…But Bank One’s fortunes started to turn south in the late ’90s shortly after the merger.

Earnings fell sharply in 1999 as growth slowed and anticipated cost savings failed to materialize. The credit card division from Banc One imploded due to bad loans and regulatory scrutiny. The stock fell by 50%. Analysts described Bank One as “the sick man of big banking.”

In 2000, a young executive by the name of Jamie Dimon was brought in to right the ship.

And right the ship he did.

Dimon wrote off billions in bad loans and goodwill. He centralized operations and established new risk controls. Tech systems were updated and unified. The credit card business was rebuilt.

By 2003, Bank One stock had tripled from its 2000 low.

In 2004, JPMorgan Chase and Bank One decided to merge…

…Each share of Bank One received 1.32 shares of JPM. Dimon was made President and COO for a year before taking the CEO title in 2005 and Chairman in 2006…

…Every one share of National Bank of Detroit Buffett purchased in 1971, if he had held for the next 54 years, would have turned into 14.58 shares of JPMorgan Chase today (as a result of multiple stock splits and stock-for-stock mergers).

NBD traded for an average price of $50 per share in 1971 whereas JPM trades for $310 per share today. As such, every $1,000 invested in NBD 54 years ago would be worth a little over $100,000 today.

Buffett’s $11 million stake would have grown to more than $1.1 billion.

Astute readers will note that this “only” equates to a 9% annual return. The buy-and-hold investor would have also received growing dividends over the decades, pushing the total annual return into the low-teens.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Amazon, 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 12 October 2025:

1. GDP’s absurdity – Abdullah Al-Rezwan

To understand the flimsy nature of such belief, we first need to understand the nuances around calculating GDP:

Discourse over GDP is frequently confused because there are actually three different calculation approaches: the income approach, the expenditures approach, and the value-added approach.

Each approach has its uses, but you have to be careful with which you use. What percent of GDP is healthcare? You get two different numbers depending on the approach. With the expenditures approach, healthcare is 17% of GDP, but for the value-added approach only 8%. Why? Because the value-added approach only counts expenditures on hospital and clinic workers toward the healthcare category. Money spent on manufacturing medical devices counts as manufacturing; money spent building hospitals counts as construction. For measuring healthcare’s share of the economy, it is probably better to use the expenditures approach because it is reasonable to include pharmaceutical production and hospital electricity bills as part of healthcare.

GDP is a very complicated statistical construct that is made by government bureaucrats behind closed doors without any ability of the public to replicate, audit, or verify assumptions. Sometimes, these kinds of constructs can be useful for accurately representing real-world phenomena, like manufacturing capacity. But a dive into how the sausage is made makes clear that GDP is not one of them.

So, how is the sausage made here? It is particularly striking to take a look at manufacturing:

If you want to see what percent of the economy is manufacturing, and how that has changed over time, you can only use the value-added approach. Only the value-added approach separates out each step in the economic chain: from mining the iron ore to transporting it to the factory to manufacturing the product to selling it at the store. The value-added approach categorizes each step, so you can sum together just the increase in price from the manufacturing step across all categories of spending.

2. This Is How the AI Bubble Will Pop – Derek Thompson and Paul Kedrosky

Thompson: How do you see AI spending already warping the 2025 economy?

Kedrosky: Looking back, the analogy I draw is this: massive capital spending in one narrow slice of the economy during the 1990s caused a diversion of capital away from manufacturing in the United States. This starved small manufacturers of capital and made it difficult for them to raise money cheaply. Their cost of capital increased, meaning their margins had to be higher. During that time, China had entered the World Trade Organization and tariffs were dropping. We’ve made it very difficult for domestic manufacturers to compete against China, in large part because of the rising cost of capital. It all got sucked into this “death star” of telecom.

So in a weird way, we can trace some of the loss of manufacturing jobs in the 1990s to what happened in telecom because it was the great sucking sound that sucked all the capital out of everywhere else in the economy.

The exact same thing is happening now. If I’m a large private equity firm, there is no reward for spending money anywhere else but in data centers. So it’s the same phenomenon. If I’m a small manufacturer and I’m hoping to benefit from the on-shoring of manufacturing as a result of tariffs, I go out trying to raise money with that as my thesis. The hurdle rate just got a lot higher, meaning that I have to generate much higher returns because they’re comparing me to this other part of the economy that will accept giant amounts of money. And it looks like the returns are going to be tremendous because look at what’s happening in AI and the massive uptake of OpenAI. So I end up inadvertently starving a huge slice of the economy yet again, much like what we did in the 1990s…

…Kedrosky: The market is rewarding [the big tech companies] for investing in AI even though it makes no economic sense to spend at this level because there’s no way they can recoup the value of the capital spending over the next three years. So they’ll be forced to do these kind of wacky shell games where they say, “Well, the building itself will actually be valuable in five years, because it’ll still have energy, it’ll still have water, it’ll still be able to cool things, the walls will still be standing, and I’ll just swap out the GPUs.” But the problem is the GPUs are the majority of the cost. The shell is the thing I’d like to write off, since I don’t want to have to write off GPUs every three years. But they’re the majority of the cost of what we call a data center.

Unlike telecom, unlike the fiber boom, unlike in railroads, there are actually two assets here. One that’s long-lived, a building, which is essentially a small fraction of the cost of the center; and one that’s very short-lived, which is the GPUs, which are the thing we’d like to have last and don’t, yet represent as much as 60 percent of the cost of the data center. So there’s the perversity.

Thompson: I want to talk about how some of this might go badly in the next few years, and I want to preface that discussion by saying that when I talk about AI as a bubble, I think some people see me as being pessimistic about the technology. The railroads were a bubble. There was a panic of 1857, of 1873, and of 1893. There were constant railroad depressions, and also the railroads changed the world. Broadband was a bubble, it also changed the world. Big infrastructure buildouts that changed the world often passed through a bubble phase. So it’s not pessimistic to say that AI is currently in a bubble. You could say it’s actually historically in tune to say that we are very likely in the middle of a bubble, because every industrial revolution passes through bubble phases.

Let’s start here. How close are the hyperscalers—Meta, Google, Microsoft, the big boys—to getting AI revenue to match AI spending?

Kedrosky: Nowhere near.

The hyperscalers are spending as much as 50 percent of income on capital expenditures, which is unprecedented. This doesn’t happen. Normally, if I did that as Microsoft or Amazon, I would be taken to the woodshed and beaten by investors because that’s such an incredible investment on one narrow slice of CapEx. They’re not being punished for that.

What I’m watching is how they’re moving the financing off their balance sheet. That for me is a reflection of not wanting the credit rating agencies to look at what they’re spending. What we’re seeing is these SPVs— special purpose vehicles—being created. Meta has a stake, some giant private debt provider has a stake, and the data center at the end is under Meta’s control, but they don’t “own” it. And so it doesn’t go on their balance sheet in terms of assessing creditworthiness. We’re seeing for the first time over the last six, seven months, the beginnings of a wave of these special purpose vehicles and other more exotic financing structures. We’re seeing the equivalence of some of the old collateralized debt obligations emerge. These are all, for me, the beginning of the sign that the bubble is becoming tired because the market is beginning to punish—at least there’s a perception that the market will punish—if I continue to keep this on my income statement. So I move it somewhere else. And that makes the entire process much more opaque. That’s the thing to watch. How hard are they trying to hide the expenditure?

3. Why Warm Countries Are Poorer – Tomas Pueyo

Societies that live closer to the equator are warmer. Why are they also poorer?…

…Here’s the kicker—I’m so excited about writing this, I have a huge grin on my face right now: We did not evolve in such warm places, and humans in warm countries don’t live where you think they live!…

…Lisbon, the capital of the first global empire of the West, actually gets warmer than Nairobi! Nairobi’s temperature is not that high, and is quite stable throughout the year…

…The answer is obvious when you think about it: The higher you are, the cooler the temperature. Normally, temperatures decrease by ~4–9ºC every 1000 meters higher (2 to 5 °F/1000 ft). Since Bogotá is at 2,600 m of altitude (8600 ft), its annual temperature is 14ºC (25ºF) cooler than Barranquilla, which is farther north from the equator but at sea level, on the coast.

Bogotá was created far inland in the mountains in 1538, only a few decades after the Spanish discovery of America. The colonizers had a much harder time with disease and conflict in coastal flatlands. It was worth traveling hundreds of miles inland and up thousands of meters to survive. That region is agriculturally much better than the sea-level flatlands too, because of the same lack of disease and the soil that doesn’t get leached as much. This logic is true of all three main Colombian cities: Bogotá (12.7M people), Medellín (4.4M) and Cali (4.2M) are all in the mountains…

…Arguably, civilization would have had a much harder time developing in the Americas if the land had been much flatter and low-lying. It’s not a coincidence that the Incan Empire was a mountain empire and was the only independent one in the world to form on the equator!

Even today, the Latin American population concentrates in the Andes!…

…So the trend is clear that, closer to the equator, people tend to live in higher altitudes. What are the consequences of that?…

…Mountains mean people need to travel up and down mountain passes and huge slopes to get anywhere. They mean no navigable rivers. They mean much higher costs of infrastructure, so there’s much less of it. This means transportation costs are much higher…

…This, in turn, means there’s dramatically less trade, and so less money is made, and less wealth accumulated. We’ve seen how these facts have dramatically impoverished countries like Mexico and Brazil, and the generic process in A Science of Cities…

…The other thing that happens with mountains is conflict. As transportation costs are so much higher, people don’t move as much from their valley. There’s substantially less regional integration, and people trust and like each other less. They develop their own independent customs and mistrust those of their neighbors. This leads to more conflict between valleys, regions, and countries.

This process is called Balkanization, for the mountainous Balkans in Europe. But we also see it in Mexico’s and Colombia’s cartels—in fact, nearly all cartels in Latin America are in the mountains. We saw it in Iran, a highly mountainous country that requires a very strong state suppressing dissent to keep the country together…

…The pattern, and its logic, is unmistakable:

• Humans evolved in the African highlands, where temperatures are stable throughout the year, and close to that of spring & fall in temperate regions. This is why we feel most comfortable there.
• Close to the equator, if we’re not in the mountains, the temperatures are too high for us. We can’t think or work properly because we overheat, and our sweat can’t cool us off because humidity is too high.
• We also suffer from many more diseases, more common in hot moist climates, but also because we didn’t evolve there.
• This also affects food, as agriculture is much harder in these hot moist climates, given the pests, the speed of rot, and the work required by crops.
• This prevented maladapted Westerners from efficiently transferring culture and institutions to these hot, humid, low-lying areas, yet another way these regions suffered.
• In order to avoid all that, people close to the equator tend to live higher up, in mountains, where temperatures are cooler and the dew point is lower, allowing people to cool down with sweat when necessary.
• The big tradeoff for this comfort though has been much higher transportation costs, so less trade, so less wealth.
• This also leads to much more ethnic diversity.
• This diversity breeds conflict, which makes everybody poorer.
• Ethnic diversity and conflict also mean institutions are much harder to make and keep.

This is how mountains are the most significant underdiscussed topic in economic development, and how they must be considered to better explain why warmer countries are poorer.

4. How Misleading Headlines Frame the Narrative – Michael Batnick

The Financial Times recently ran a story on pension funds and private credit with the headline, “US public pension funds pare allocations to private credit. Pullback highlights concerns about looser underwriting standards and rising credit risks.”

On the surface, it was about institutional investors growing cautious on the booming asset class. But look closer, and you’ll see something more telling about the way news gets written — and consumed.

The article opens with a small pension fund in Cincinnati that has tapped the brakes on private credit. The narrative builds around skepticism, risk, and pullback. Only at the very end do readers learn that the New York City pension fund — with over $300 billion under management — is fully committed to private credit. In other words, the story’s most significant character wasn’t just positive on the space, but “all in.”…

…For investors, policymakers, and the public, this matters. Media framing shapes how we understand markets, risk, and opportunity. When negativity consistently drowns out proportion, we risk making decisions based on skewed perceptions.

And for society at large, the same forces are at play. Politics, economics, health, culture — the most pessimistic interpretations tend to dominate. Not because they’re always right, but because they’re the most clickable.

5. A Sleepy 5x – Joe Raymond

In my experience, stocks with the following characteristics tend to do well on average over time:

1. Boring businesses with long histories of profitability
2. Clean balance sheets (more cash than debt)
3. Honest insiders (even if they aren’t terribly talented)
4. Trading cheaply (say, 5x EBIT or less)

Once in a while one of these sorts of stocks might do poorly. But in aggregate, this group does tremendously well – at least in my experience and based on conversations with many other investors…

…Bryan Steam Corporation (BSC) was founded in Peru, Indiana way back in 1916. The company started out making steam-powered cars and tractors…

…By the mid-1920s, it was clear that gasoline powered engines were winning out over steam in automobiles. BSC switched course and focused on boilers and related steam equipment, rather than vehicles.

And that’s basically what the company did for the next 80 years…

…1993 is the earliest year I have data, so that’s where we’ll start. This was around the time my friend was buying shares…

…Growth was modest and choppy, and the operating margin fluctuated between 5% and 10% depending on activity levels. ROE in most years came in somewhere between 7% and 12%.

These are extremely pedestrian numbers.

Most investors wouldn’t have been excited to sit on the bid and patiently build a stake in Bryan Steam. Sure, it was cheap, but it had single-digit margins and single-digit ROE most years. Growth was lackluster. The dividend yield was a mundane 3%…

…But, if you think about it, what was the risk buying BSC at $30 in 1993?

You were paying half of tangible book value. The balance sheet was net cash. The company had a multi-decade history of profitability. Earnings could be cut in half, and you’d still only be paying 10x profits…

…Bryan Steam grew revenue from $16.4 million in 1993 to $26.2 million in 1998 (9.8% CAGR). Cumulative earnings over the period were $6.5 million, which was more than the entire $5.7 million market cap in 1993.

Book value per share grew from $58.84 to $78.50 (5.9% CAGR). The company also paid $8.45 per share of total dividends over those five years.

These are “good, not great” numbers.

Yet the stock finished 1997 trading for $58.25 (18% CAGR before dividends from the 1993 price of $30). And it still traded for only 77% of TBV and less than 7x earnings…

…In September 1998, Bryan Steam entered into a merger agreement with Burnham Corporation (OTC: BURCA/B).

The price?

$152 per share…

…My friend who bought BSC in 1993 at $30 earned a 44% IRR, including dividends. More importantly, he did it without taking a whole lot of risk…

…What if Burnham hadn’t come in and offered $152 per share?

Remember, BSC had compounded at 18% over the prior four years before Burnham entered the picture. And the valuation was still sub-1x book value for a decent (7-12% ROE) business.

Let’s say there was no acquisition and Bryan Steam kept plugging along at its prevailing pace, compounding book value at 6% for the next 20 years.

By 2018, BVPS would have been north of $250 per share and annual earnings would have been around $25 per share. At 12x earnings, BSC would be worth $300 per share.

This results in a hypothetical 10% annualized return over the 25-year period from 1993 to 2018. Including dividends, the IRR would have been in the neighborhood of 12-13%.

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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 05 October 2025:

1. Nature’s cruel lesson for bag holders – Thomas Chua

On the screen, a cheetah was stalking a gazelle through tall grass, and I found myself holding the stretch longer, mesmerized. My body frozen in tension—part from the stretch, part from watching this life-or-death chess match.

Then the gazelle’s head shot up. The cheetah immediately stopped. No hesitation. Just turned and walked away.

As I eased out of the stretch and settled onto the mat, I realized something: successful investors act like that cheetah, but most people? They do the exact opposite.

Here’s what fascinates me about predators: they never chase a losing cause. The moment their cover is blown—the second the distance starts widening—they quit. No ego. No “sunk cost” thinking. No “but I’ve already come this far.”

They just walk away.

But when you look at the markets, you see the opposite everywhere…

…The thing about us humans is that our prefrontal cortex—the part that makes us “smarter” than animals—actually makes us worse at knowing when to quit. We tell ourselves stories. We rationalize. We create elaborate justifications for why this time is different.

But the cheetah? It just knows: energy spent on a failed hunt is energy that can’t be used on the next opportunity.

This isn’t about giving up easily. It’s about recognizing when persistence becomes stupidity. When determination becomes delusion.

The sunk cost fallacy tricks you into thinking backward—valuing what you’ve already invested over what you could gain elsewhere. But successful investors, like predators, always look forward.

They ask: “From this moment, right now, is this my best opportunity?”

If the answer is no, they walk away. No drama. No hesitation.

Like nature’s hunters, the smartest investors know exactly when to abandon the hunt. They don’t chase dead ends. They stalk fresh opportunities.

2. America’s top companies keep talking about AI – but can’t explain the upsides – Melissa Heikkilä, Chris Cook and Clara Murray

The FT has used AI tools to identify these mentions of the technology in US Securities and Exchange Commission 10-K filings and earnings transcripts, then to categorise each mention. The results were then checked and analysed to help draw a nuanced picture about what companies were saying to different audiences about the technology.

SEC filings require companies to disclose risks to the businesses, and are necessarily more cautious than the sales pitches made by executives on earnings calls. But the increasing array of risks described in filings appears not to be weighing on executives in the public pronouncements.

374 of the S&P 500 mentioned AI on earnings calls in the past 12 months — with 87 per cent of the calls logged as wholly positive about the technology with no concerns expressed…

…The FT sought to categorise the expected positive benefits of the technology. Most of the anticipated benefits, such as increased productivity, were vaguely stated and harder to categorise than the risks.Companies anticipated being able to optimise workflows through automation, and hope to achieve market differentiation through their use of AI. Some hoped to be able to use the technology to improve the personalisation of their products.

Filings do reveal that the companies able to give clear AI upsides include those that serve the rising AI-driven data centre boom. Energy companies First Solar and Entergy cited AI as a demand driver.

Freeport-McMoran, which has a stockpile of copper, stated that “data centres and artificial intelligence developments” would support the metal’s price. The company also said the technology can help with material characterisation and mineral extraction.

Equipment manufacturer Caterpillar reported that its energy business was benefiting from supporting “data centre growth related to cloud computing and generative artificial intelligence”…

…As the number of companies discussing AI has grown, fewer businesses are expressing positive views about the technology than they did in 2022.

The most commonly cited concern was cyber security, which was mentioned as a risk by more than half of the S&P 500 in 2024.

3. The 100 faces of China’s retiree wallet – Nina Chen

The heterogeneity within generations is not unique to China, nor is it exclusive to the elderly. But what sets China’s current seniors apart is the unprecedented structural complexity born of rapid social upheaval and institutional ruptures. This has made them the most heterogeneous and fragmented generation in the country’s modern history.

A closer look shows that different age cohorts in China were shaped by profoundly different circumstances. Take the post-1950s cohort: as children, they endured the Great Famine, carrying lasting memories of hunger through their formative years. In adolescence, their schooling was interrupted by the Cultural Revolution. At an age when they should have been applying their knowledge and skills, they were sent to the countryside for “re-education” through manual labor. Many missed out on the economic dividends of China’s later boom simply because they lacked access to formal education.By contrast, the post-1960s cohort came of age in a very different world. They benefited from the reinstatement of the college entrance exam and the rapid expansion of education. Their youth coincided with the early years of reform and opening, full of energy and opportunities. By middle age, they had experienced soaring property prices, volatile stock markets, the rise of the internet, and widening wealth gaps. The difference between these two generations is not one of degree, but of kind—a qualitative rupture rather than a quantitative stretch…

…In many reports and media narratives, seniors are depicted as embracing new trends and eager to spend: the spotlight often falls on smiling tourists, silver-haired influencers in stylish outfits, and the curated lifestyles of upscale retirement communities. Such portrayals carry strong visual appeal but obscure the underlying consumption attitudes of the majority.

For most seniors, the guiding principle is frugality: “money must be spent where it matters.” Family resources are first directed toward projects deemed vital and long-term—children’s housing, weddings, or cars—seen both as investments in future security and as obligations rooted in traditional family responsibility. Frugality is regarded as a virtue, so spending always requires a clear justification. Tangible goods with lasting value are far more acceptable than abstract or experiential services, with subscription models in particular often dismissed as “non-essential.” Seniors are highly price-sensitive, prioritizing cost-effectiveness over brands or aesthetics…

…This generation lived through China’s abrupt transition from a production-oriented society to a consumer-driven one. Having grown up under scarcity and a planned economy, they later faced a sudden explosion of marketization and commercialization in adulthood. Without systematic guidance—whether from family or school—on new retail channels, advertising formats, rules, and risk awareness, most lack strong consumer judgment. Social isolation and emotional vulnerability further make them particularly susceptible to highly personalized, “caring” marketing.

This dynamic often leaves them swallowing losses in contradictory ways. Some engage in compensatory spending when finances allow, yet remain drawn to bargain hunting. They might skip a RMB 79.9 buffet but stockpile dozens of RMB 9.9 trinkets online. When warned about scams, they retort, “You just don’t understand.” They defend dubious products with, “It has a factory address.” And to children who question their “adopted sons and daughters” from livestreams, they reply, “At least they call me more than you do.” The cautionary phrase they once told their children—“Everything online is a scam”—has boomeranged back to them.

Seniors thus represent both an underserved market and a lucrative yet highly fragmented, information-poor, and emotionally fragile consumer base. Until high-quality elder-focused supply matures, low-cost, easily replicated, high-margin “elder exploitation” businesses will fill the gap. Often, all it takes is a livestreamer repeatedly calling viewers “grandpa” or “grandma” to trigger enthusiastic purchases…

…Within the senior consumer base, those with limited resources and capabilities make up a large share. Behind the silver economy narrative lies a stark truth: the majority of seniors remain low spenders, with consumption disproportionately shaped by a small, visible minority…

…Supplements: Over 80% of seniors do not take them. Among those who do, more than 80% spend less than RMB 3,000 annually. Although supplements are often viewed as the quintessential product for older consumers, a survey on the Living Conditions of Urban and Rural Seniors data shows otherwise: only 16.6% of seniors report using supplements. Of these, 60.6% spend less than RMB 1,000 per year, 24.0% spend RMB 1,000–2,999, 6.5% spend RMB 3,000–4,999, and just 8.9% spend over RMB 5,000…

…Elder care: More than 80% of seniors cannot afford standard retirement home costs. According to CEIC data, in 36 major cities, the average monthly fee for self-sufficient seniors exceeds RMB 2,600—including accommodation, meals, and basic care—and is even higher for semi-dependent or disabled residents. Survey data show that only 15.8% of seniors can afford RMB 3,000 or more per month, meaning over 80% remain priced out of institutional elder care…

…Over 80% of seniors did not travel in the past year. Among those who did, more than 80% spent less than RMB 5,000 annually. According to the 2021 Survey on the Living Conditions of Urban and Rural Seniors, only 9.1% traveled in 2020, and even with some growth in recent years, the share is still estimated at under 20%. Among the small group who do travel, the majority spend under RMB 5,000 a year—pointing to a market still dominated by short, budget-friendly trips…

…First, draw the finest slice, not the biggest circle. Before a single yuan is spent, nail down exactly who you’re serving: how many grandmothers and grandfathers within a ten-year birth band, how much they can actually pay, and how often they’ll open their wallets. Over-count the grey tide and you’ll build a palace for a village—then watch inventory rot and margins drown.

Second, once you’re inside the right yard, seniors likely stay. Familiarity beats flashy ads; trust is a lifelong contract. Win them once and they’ll keep the same travel agency, the same pill brand, the same breakfast stall—year after year—turning your customer-acquisition cost into a one-time entry fee.

Third, profits may also come from the quietest voices—but only if you’re willing to do the hard, on-the-ground work. Village grandpas without apps and grandmas without data plans don’t appear on dashboards, but their needs are vast and competition is thin. Reaching them is hard: you must squat on the lane curb, piggy-back existing clinics, and price for a pocket that holds only folded bills. Yet thin-margin, high-frequency sales—subsidised just enough by local government—add up quickly when idle assets are put to work. I searched online and found some uplifting examples:

• In Ningxia’s Tongyi village, a derelict fish pond and drying yard were simply re-leased; anglers’ tickets and night-market stall rents now fully finance an 18-bed nursing home that charges residents zero fees.
• In Gutian, Fujian, a ¥3 lunch canteen covers its costs with a tiny on-site grocery counter plus monthly on-site pharmacy sales, and the same micro-format has already been copied in 48 neighbouring villages.
• In Caoxian, Shandong, 200 shared e-tricycles (¥1 per 3 km) pay themselves off in 18 months through side ads and parcel deliveries, proving that even a village road can become a revenue-producing asset.

4. AI isn’t replacing radiologists – Works in Progress and Deena Mousa

Radiology is a field optimized for human replacement, where digital inputs, pattern recognition tasks, and clear benchmarks predominate. In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’. If the most extreme predictions about the effect of AI on employment and wages were true, then radiology should be the canary in the coal mine.

But demand for human labor is higher than ever. In 2025, American diagnostic radiology residency programs offered a record 1,208 positions across all radiology specialties, a four percent increase from 2024, and the field’s vacancy rates are at all-time highs. In 2025, radiology was the second-highest-paid medical specialty in the country, with an average income of $520,000, over 48 percent higher than the average salary in 2015.

Three things explain this. First, while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions. Second, attempts to give models more tasks have run into legal hurdles: regulators and medical insurers so far are reluctant to approve or cover fully autonomous radiology models. Third, even when they do diagnose accurately, models replace only a small share of a radiologist’s job. Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians…

…Over the past decade, improvements in image interpretation have run far ahead of their diffusion. Hundreds of models can spot bleeds, nodules, and clots, yet AI is often limited to assistive use on a small subset of scans in any given practice. And despite predictions to the contrary, head counts and salaries have continued to rise. The promise of AI in radiology is overstated by benchmarks alone.

Multi‑task foundation models may widen coverage, and different training sets could blunt data gaps. But many hurdles cannot be removed with better models alone: the need to counsel the patient, shoulder malpractice risk, and receive accreditation from regulators. Each hurdle makes full substitution the expensive, risky option and human plus machine the default. Sharp increases in AI capabilities could certainly alter this dynamic, but it is a useful model for the first years of AI models that benchmark well at tasks associated with a particular career.

There are industries where conditions are different. Large platforms rely heavily on AI systems to triage or remove harmful or policy-violating content. At Facebook and Instagram, 94 percent and 98 percent of moderation decisions respectively are made by machines. But many of the more sophisticated knowledge jobs look more like radiology.

In many jobs, tasks are diverse, stakes are high, and demand is elastic. When this is the case, we should expect software to initially lead to more human work, not less. The lesson from a decade of radiology models is neither optimism about increased output nor dread about replacement. Models can lift productivity, but their implementation depends on behavior, institutions and incentives. For now, the paradox has held: the better the machines, the busier radiologists have become.

5. China’s AWS of Manufacturing – Thomas Chua

Guangzhou and its neighbors—Shenzhen, Dongguan, Foshan—form the Pearl River Delta manufacturing cluster. Decades of development have created an ecosystem so dense that suppliers, manufacturers, and assemblers for almost any product sit within hours of each other.

I took this trip to visit some of these wholesalers and it’s amazing how much they can do.

Walking through the wholesale markets, I saw many products that retail in Singapore and on online platforms selling at a fraction of the price. Take compression boots, for example—under $200 here. A similar device with a different brand slapped on in a Singapore mall near my house? Around $1,000.

Five times the price. Similar product.

I’ve known friends who’ve come here with specifications for products, whether clothing retail or electronics, and they’re able to establish their products and start selling abroad very quickly. All without ever having to sink heavy investments into building a factory or dealing with hiring anyone to produce these items…

…The Laifen hair dryers in hotels across China cost around $50. Dyson? $600.

The performance difference? About as noticeable as the taste difference between Pepsi and Coke.

We’ve also seen DJI and Insta360 run laps around GoPro in their offerings. If businesses can’t innovate fast enough, they’re going to be left behind.

This changing landscape created lots of new value, with some accruing to these newer, more nimble businesses. Consumers capture a nice chunk of the value as competition intensifies…

…On the first day, I had to use DeepSeek for my daily tasks—research, responding to my tour guide in mandarin, planning.

I’d tested DeepSeek during its Sputnik moment in January 2025 and found it comparable to ChatGPT. But now, having to use it due to the firewall, I realized just how rapidly Claude and ChatGPT have advanced. These models improve incrementally day by day—you don’t notice until you’re forced to switch between them.

The pace of AI development is staggering.

I ended up getting LetsVPN to access Claude and ChatGPT again—reliable for short China trips if you need Western services…

…During my daily hour at cafes, coffee in hand, doing my reading, I noticed something.

Many people around me were perpetually on LLM tools. Not just occasionally checking—constantly working with them.

DeepSeek and ChatGPT being the two most common.

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