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 04 January 2026:

1. Authenticity after abundance – Adam Mosseri 

Everything that made creators matter—the ability to be real, to connect, to have a voice that couldn’t be faked—is now suddenly accessible to anyone with the right tools. Deepfakes are getting better and better. AI is generating photographs and videos indistinguishable from captured media. The feeds are starting to fill up with synthetic everything…

…We are now seeing an abundance of AI generated content, and there will be much more content created by AI than captured by traditional means in a few years time. We like to talk about “AI slop,” but there is a lot of amazing AI content that thankfully lacks the disturbing properties of twisted limbs and absent physics. Even the quality AI content has a look though: it tends to feel fabricated somehow. The imagery today is too slick, people’s skin is too smooth. That will change; we are going to start to see more and more realistic AI content.

Authenticity is fast becoming a scarce resource, which will in turn drive more demand for creator content, not less. The creators who succeed will be those who figure out how to maintain their authenticity whether or not they adopt new technologies. That’s harder now—not easier—because everyone can simulate authenticity. The bar is going to shift from “can you create?” to “can you make something that only you could create?” That’s the new gate…

…But flattering imagery is cheap to produce and boring to consume. People want content that feels real. We are going to see a significant acceleration of a more raw aesthetic over the next few years. Savvy creators are going to lean into explicitly unproduced and unflattering images of themselves…

…Social media platforms are going to come under increasing pressure to identify and label AI-generated content as such. All the major platforms will do good work identifying AI content, but they will get worse at it over time as AI gets better at imitating reality. There is already a growing number of people who believe, as I do, that it will be more practical to fingerprint real media than fake media. Camera manufacturers could cryptographically sign images at capture, creating a chain of custody…

…In a world of infinite abundance and infinite doubt, the creators who can maintain trust and signal authenticity—by being real, transparent, and consistent—will stand out.

As for Instagram, we’re going to have to evolve in a number of ways, and fast. We need to build the best creative tools, AI-driven and traditional, for creators so that they can compete with content fully created by AI. We need to label AI-generated content clearly, and work with manufacturers to verify authenticity at capture—fingerprinting real media, not just chasing fake. We need to surface credibility signals about who’s posting so people can decide who to trust. And we’re going to need to continue to improve ranking for originality.

2. 2025’s biggest investing lesson – slow down – Chin Hui Leong

HERE Is the uncomfortable truth about 2025: The year’s biggest wealth destroyer was not tariffs, AI disruption, or interest rate uncertainty.

It was speed…

…At the start of 2025, traders reacted swiftly to every hint about interest rate movements.

A strong jobs report? Sell immediately – fewer rate cuts ahead.

A weak inflation print? Buy before everyone else does.

This behaviour assumed that being first to interpret the data would translate into superior returns.

Let us test that theory with 2024’s track record. Goldman Sachs predicted five rate cuts. We got three.

Traders priced in a 73 per cent chance of a March 2025 cut. The first cut came in September, six months later. The market expected 1.5 percentage points of cuts. We got one.

In other words, the number of cuts was wrong, the timing was off, and the size of the cuts were lower than expected.

Yet despite these spectacular misses, the S&P 500 rose more than 23 per cent in 2024.

The lesson: You can be completely wrong about interest rates and still do well in the market – if you stay invested.

The investors who traded every data point, trying to front-run the US Federal Reserve, generated fees and anxiety.

The investors who ignored the noise generated returns…

… In his book Your Money and Your Brain, author Jason Zweig explains that our minds recognise patterns even when none exist.

But this is the kicker: We cannot switch this mechanism off at will…

…Consider this: Every major decline in 2025 was accompanied by an avalanche of negative headlines – detailed articles on what went wrong, podcasts dissecting the damage, and social media hot takes piling on.

Amid that onslaught, any good news was buried.

The investors who reacted to the noise sold at lows. The investors who waited for the noise to clear bought those shares from them.

Speed did not protect portfolios. Patience did.

3. Running Out of Runway – Poe Zhao

Last week’s dual IPO filings from Zhipu AI and MiniMax reveal a paradox at the heart of China’s AI model market. Both companies have proven they can build competitive technology. Both have validated their business models at the unit economics level. Both are running out of time…

…Zhipu grew from ¥57 million in 2022 to ¥312 million in 2024, a 130% compound annual growth rate. MiniMax achieved even more dramatic expansion, with revenue surging 782% to $30.5 million in 2024. In the first nine months of 2025, MiniMax generated $53.4 million, already exceeding its full-year 2024 results.

But losses grew faster. Zhipu’s adjusted net loss exploded from ¥97 million in 2022 to ¥2.47 billion in 2024. That’s 20x growth. MiniMax went from $7.37 million in losses in 2022 to $465 million in 2024.

The cash burn is brutal. Zhipu: ¥300 million monthly. MiniMax: ¥2 billion monthly. Zhipu’s mid-2025 reserves stood at ¥2.55 billion. Do the math. Six months later, both companies rushed to file IPOs. The December timing was necessity, not choice…

…Research and development consumed ¥2.2 billion of Zhipu’s budget in 2024. That’s a 26x increase from the ¥84 million spent in 2022. Within that R&D figure, ¥1.55 billion went directly to compute services. Computing infrastructure alone ate 70% of the entire R&D budget.

MiniMax shows better cost discipline but faces the same fundamental pressure. Training-related cloud computing costs reached $142 million in the first nine months of 2025. The company has managed to improve efficiency. The ratio of training costs to revenue dropped from 1,365% in 2023 to 266% in the first three quarters of 2025. But even at 266%, you’re spending nearly $3 on training for every $1 of revenue.

This creates the first paradox. At the transaction level, these businesses are profitable. Sell an API call or a subscription, you make money. Scale that up, you should make more money. But scaling requires maintaining competitive model quality. Competitive model quality requires constant compute investment. The compute investment grows faster than revenue. The more you sell, the more you lose…

…China’s entire large language model market totaled ¥5.3 billion in 2024, according to Zhipu’s prospectus. Enterprise customers contributed ¥4.7 billion of that. Individual consumers accounted for just ¥600 million.

Do the math. Zhipu burns ¥300 million monthly. MiniMax burns ¥2 billion monthly. Combined, that’s ¥2.3 billion per month. Annualize it and you get ¥27.6 billion. The two companies alone are burning through more than five times the entire current market size annually. And they’re not alone. Multiple other companies compete in the same space…

…Zhipu bet on scale. The company invested heavily in frontier model development. R&D spending jumped from ¥529 million in 2023 to ¥2.2 billion in 2024. Compute infrastructure dominated that budget. The strategy assumes that leading-edge capabilities justify the burn rate. Stay at the frontier, win the highest-value customers, eventually reach economies of scale.

MiniMax took the efficiency route. The company’s prospectus explicitly positions itself as capital-efficient. Cumulative spending from founding through September 2025 totaled approximately $500 million. The prospectus contrasts this with OpenAI’s estimated $40–55 billion in cumulative investment. That’s a 100x cost difference for comparable multimodal capabilities…

…This reveals what makes the situation structural rather than cyclical. Your strategy becomes irrelevant when competitive dynamics dictate behavior. Zhipu chose scale. MiniMax chose efficiency. DeepSeek’s emergence forced both to spend more regardless of their chosen path. In a true market, companies can differentiate on cost, quality, or features. In this market, everyone must match the pace of iteration or become obsolete. The pace keeps accelerating. The costs keep compounding.

4.Why We Worry – Part I – Fawkes Capital

This year alone, Google will spend roughly $60 billion more in annualised capex than it did before ChatGPT launched. Since late 2022, the company has deployed an additional $85 billion in cumulative capex on AI-related development. With similar spending levels expected next year, Google’s capex now exceeds its net profit – a sharp departure from pre-AI years, when capex represented only about 25% of profit…

…What is Google receiving in return for this extraordinary level of investment? At present, Google processes roughly 1.4 quadrillion AI tokens per month. If we make a simplifying assumption and apply Google’s API input pricing across all of those tokens, the result is an additional $21 billion of annualised revenue.

For context, this is not an especially compelling trade-off: $85 billion of incremental capex for $21 billion of low-margin revenue. In effect, Google is deploying vast sums of capital for what amounts to a modest 5% uplift in annual revenue, and materially lower returns than its core search and advertising franchise generates. A major outlay for just a 5% uplift in annual revenues doesn’t sound like a great use of capital to us.

And if this is the underlying economic reality for the industry leader, it is difficult to see how outcomes will be more favourable for its competitors. Over time, we doubt that the return on capital employed (ROCE) from datacentres will meaningfully improve from today’s levels…

…If Big Tech and data centre operators collectively spend around $400 billion on AI infrastructure in 2026, then, by our estimate, at least $80 billion in annual net income would need to be generated to justify that investment. The hurdle is high because processors, which make up the bulk of capex, have a useful life of only about five years. Back-solving this requirement implies that something like 333 million paying users of ChatGPT – roughly the entire US population – would be needed to support such economics.

Today, the numbers fall drastically short. Only around 5% of users (about 20 million people) pay for ChatGPT, and both paid and non-paid user growth has begun to stall in recent months. OpenAI’s attempt to introduce advertising as a revenue stream has met fierce consumer resistance. And unlike Google, directing users to websites does not generate economic value for OpenAI. This raises the critical question: how will OpenAI, or any non-advertising-based AI provider, monetise its service at the scale needed?…

…. A similar pattern emerged during the late-1990s dot-com bubble. Telecom operators, despite enormous capital outlays, found their services rapidly commoditised. Usage growth slowed, pricing power collapsed, and the industry could not extract the household revenues required to justify the capex binge. High returns initially attracted more competition, which eventually eroded margins for even the leading “pick-and-shovel” equipment suppliers. Investor belief in unassailable competitive advantages proved illusory. Once reality set in, the bubble burst and triggered a shallow recession…

…SemiAnalysis notes that Google’s TPU infrastructure now rivals NVIDIA’s latest commercially available GPUs – at significantly lower cost. Sensing the threat, NVIDIA has resorted to taking equity stakes in companies that depend on its support (OpenAI among them), effectively subsidising its customer base to stave off competition and preserve margins. This is not a sustainable strategy. Amazon’s upcoming Trainium 3 chip has also narrowed the performance gap and is likely to be cost-competitive upon release.

With credible alternatives emerging, NVIDIA’s 75% gross margins – the foundation of its current valuation – will not hold indefinitely. When investors fully appreciate this, and when that realisation intersects with the economic unsustainability of OpenAI’s model, the conditions for a sharp correction may be in place.

5. AI will kill all the lawyers – Sean Thomas

‘Last week we did an experiment, a kind of simulation. We took a real, recent and important case – a complex civil court appeal which I wrote, and it took me a day and a half. We redacted all identifying details, for anonymity and confidentiality, and we fed the same case to Grok Heavy AI. And then we asked it to do what I did. After some prompting, the end result was…’ He shakes his head. ‘Spectacular. Actually staggering. It did it in 30 seconds, and it was much better than mine. And remember, I am very good at this.’

He sits back, wry yet resigned. ‘It was at the level of a truly great KC. The best possible legal document. And all done in seconds for pennies. How can any of us compete? We can’t.’…

…James believes AI will work its way up the legal hierarchy. First the gruntwork, then the drafting, the citation, the argumentation. Eventually the majority of legal jobs will be replaced. ‘Process lawyers are obviously doomed. AI will handle the most complex probate and conveyancing cases in seconds. The most complicated human skill will be,’ he chuckles, sadly, ‘to scan and digitise paper documents. Barristers will make arguments in courtrooms that are drafted by AI, and then people will wonder why they are paying human barristers £200,000, and they too will disappear.’…

…I ask what he thinks this will do to his colleagues – psychologically, economically, emotionally. ‘At first, they will fight, like radicals. A losing battle. There will be attempts to outlaw the use of AI in various legal areas. But it won’t work, the economics will see to that. So lots of people who make a lot of money will, suddenly, not make that money. God knows what that might do to property prices, to politics, to all of us. Because it won’t just be the law.’

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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), Mastercard, Meta Platforms (parent of Instagram), 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 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.