Category: What We’re Reading

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

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

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

Here are the articles for the week ending 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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We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Which mineral is the hardest problem to solve of these?

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

Is there one specific rare earth in particular?

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

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

“Actually… something huge happened.”

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

Sometimes, though, they don’t give cash.

Sometimes they give hope.

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

His dad won.

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

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

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

His family became millionaires overnight.

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

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

But freedom from what, exactly?

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

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

5. National Bank of Detroit – Joe Raymond

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

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

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

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

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

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

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

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

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

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

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

The 10-year track record was satisfactory…

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

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

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

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

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

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

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

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

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

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

And right the ship he did.

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

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 12 October 2025:

1. GDP’s absurdity – Abdullah Al-Rezwan

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kedrosky: Nowhere near.

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

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

3. Why Warm Countries Are Poorer – Tomas Pueyo

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

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

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

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

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

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

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

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

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

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

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

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

…The pattern, and its logic, is unmistakable:

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

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

4. How Misleading Headlines Frame the Narrative – Michael Batnick

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

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

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

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

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

5. A Sleepy 5x – Joe Raymond

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

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

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

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

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

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

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

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

These are extremely pedestrian numbers.

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

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

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

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

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

These are “good, not great” numbers.

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

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

The price?

$152 per share…

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 05 October 2025:

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

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

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

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

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

They just walk away.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5. China’s AWS of Manufacturing – Thomas Chua

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

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

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

Five times the price. Similar product.

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

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

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

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

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

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

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

The pace of AI development is staggering.

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

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

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

DeepSeek and ChatGPT being the two most common.

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

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

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

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

Here are the articles for the week ending 28 September 2025

1. Is this 1996 or 1999? – Ben Carlson

From 1980 through Greenspan’s speech at the tail end of 1996, the S&P 500 was up more than 1,200% in total or a blistering 16.5% return on an annual basis. Valuations were up, up and away. The Netscape IPO occurred a year earlier. Things felt very toppy.

That didn’t matter…

…From the time of Greenspan’s speech through the rest of the decade the S&P would more than double, good enough for an annualized return of nearly 26% through the end of 1999. The market was up 33% in 1997, 28% in 1998 and another 21% in 1999.

The dot-com bubble finally burst in the spring of 2000, cutting the S&P 500 in half along with a drawdown of more than 80% in the Nasdaq…

…The AI capex spending binge is eerily similar to the telecomm buildout that occurred in the 1990s.

Speculative activity is all over the place too — SPACs, meme stocks, IPOs, leverage, story stocks, high valuations, deregulation, etc…

…Many people are trying to figure out whether this is the early stages of a bubble or the end of the road.

Investing would be a lot easier if there were a simple way to predict these types of markets. Unfortunately, there’s not. No one can predict when human nature will take things too far or when it will stop on a dime. The pendulum always swings; we just don’t know how far in either direction…

…If you had invested in the S&P 500 following Greenspan’s speech in December of 1996 and held on until today, you would be up just shy of 10% per year. You would have had to live through two 50% crashes in the next dozen years or so, 9/11, multiple wars, oil going to $150/barrel then negative, the pandemic, 40-year high inflation, the 2022 bear market and about a dozen other run-of-the-mill corrections…

…If you had invested at the peak of the market just before the dot-com bubble burst at the end of 1999, you would be up a little more than 8% per year. That’s not a terrible outcome considering all of the bad stuff you would have had to live through plus that was the most expensive valuations the U.S. stock market has ever seen.

2. Ethical investing, avoiding blow ups, and salacious indictments $RICK – Andrew Walker

But honestly, saying that “I’ll invest in anything, ethics be damned!” is kind of a trite point. Why do I bring it up?

Because I’m actually interested in the potential wisdom of having ethical limits. I wonder if having “ethical passes” on stocks is actually a way of identifying and passing on stocks with tail risks…

…For example, in the mid-2010s, Valeant was an unstoppable acquisition machine. The business model was truly incredible: Valeant acquired underpriced drugs and brought their pricing in line with what the market would bear. Often that pricing was 10x what the old company was charging. Valeant had kind of discovered the holy grail in pharma: they did no risky R&D, every acquisition was insanely and instantly accrettive, returns on investment were astronomical, the company gushed cash, etc.

Of course, what Valeant was really doing was price gouging. In 2015, Charlie Munger called Valeant “deeply immoral”. Valeant was a hedge fund darling at the time, and Munger’s comments raised a lot of eyebrows. I remember a ton of investors who said Munger had lost it, and some hedge funders3 came out swinging pretty hard against Munger.

Within months of Munger’s comments, Valeant was in deep distress (which continues to this day!). Much like raisins mixed with turds are still turds, when a business is a turd no amount of accrettive acquisitions or clever financial engineering can save it. It’s still a turd and, true to form, Munger called a turd a turd…

…Last night, RICK’s got hit with a pretty salacious indictment from the NY AG (the company denies all wrong doing). And it has me questioning my “no ethics in investing” rule…

…It’s the type of stock I very easily could see myself owning: an asset heavy business (RICK tends to own the real estate under their clubs) operating a sin business with a founder CEO who owned a ton of stock and was openly talking about running an “Outsiders” playbook / was planning to buyback tons of stock when it was cheap while also pursuing extremely accrettive (and low multiple) acquisitions?…

…There were/are a lot of issues at Rick’s that you had to get comfortable with to be long to the stock4; in general, the way you could get comfortable with the issues was something like “it’s a strip club business; the whole industry is shady so you kind of just need to accept that and realize ultimately the cash flow of the business + stock ownership of the CEO pushes this higher.” Given the upside here, I think there was a reasonable chance you could talk yourself into that if you were ignoring all ethics…. but, if you used an ethics based screen, then you wouldn’t have even been tempted by the cash flows / alignment issue. You would have seen the shadiness and instantly passed.

3. How to avoid value traps in Asia – Michael Fritzell

• Value traps are stocks that look cheap but end up delivering poor returns.
• The main reasons why stocks end up being value traps include hoarding cash, having obsolescent products, selling commodity products in a market with excess supply, related party transactions, aggressive accounting, industry cyclicality, high debt and government interference…

…How do you avoid the value traps that simply do not return cash to shareholders? Check the company’s cash flow statement.

In IMAX China’s case, you can see that they pulled the dividend in 2023 and spent almost nothing on share buybacks in 2024. So US$17 million of cash built up on the balance sheet, unfortunately out of reach for us minority shareholders…

…So how can you know whether the underlying demand for a product is rising or not? First, check the like-for-like volume numbers reported by the company. Second, observe consumer behavior through customer engagement metrics. Third, check alternative data sources such as Google Trends or Similarweb to see whether interest in the product is rising or falling…

…So, how do you know if a company is selling a commodity product or not?

• You can check the company’s market share: if it’s greater than 50%, then it probably has some type of competitive advantage.
• You can ask customers why they buy the product: is price the determining factor, or are they focusing more on other attributes when buying?
• Finally, is there a market price for the product that fluctuates with supply & demand? If so, then you’re most likely looking at a commodity…

…So, how do you check whether a company has a complex corporate structure? Search on TIKR using the company name and then click on the Ownership tab. If the parent is a holding company, ask ChatGPT what business the parent is involved in. Finally, open up the annual report and search for any related party transactions…

…If the accounting is aggressive, that means that profits are partly illusory. Once the market realizes what the sustainable earnings power of the business truly is, the shares will probably trade down.

How do companies play these games? They might adjust their depreciation schedules, push products to customers on looser payment terms, capitalize expenses, under-estimate credit costs, etc…

…In reality, I sometimes struggle to judge whether an industry has hit a bottom. But I like to look at a company’s operating margins over time, to see whether they’re mean-reverting or not. You can also look at the operating margins of companies in the same industry. Property developers, auto companies and chemical companies are famously cyclical. So to avoid value traps in these industries, consider whether margins may one day head lower…

…At one level, I think it’s helpful to invest in countries with a reliable rule of law, just to avoid negative surprises in the future. But if you have to invest in countries with a poor rule of law, it’s helpful to invest in entities that are aligned with the top leadership. Because if any government interference occurs, it will most likely be on the positive side.

4. Arc’teryx Is Cooked in China – Amber Zhang

On September 19, Chinese firework artist Cai Guoqiang and outdoor apparel brand Arc’teryx jointly staged a fireworks display called “Ascending Dragon” (“升龙”) in Relong Township, Gyantse County, in the Tibet Autonomous Region. The display — set at roughly 5,500 meters altitude — consisted of three sequences of fireworks along the Himalayan mountainous ridge, with imagery meant to evoke a dragon…

…Soon after videos of the event circulated online, the display triggered intense backlash over environmental and cultural concerns. Netizens began calling for a boycott of Arc’teryx, arguing that setting off fireworks in such a fragile alpine ecosystem risked disturbing wildlife, damaging slow-growing vegetation, and polluting the high-altitude environment. Many also criticized the spectacle as disrespectful to local traditions, which hold mountains as sacred and discourage loud disturbances. The sponsored firework show is the complete opposite of environmental protection and respect for nature—values that strongly resonate with China’s affluent urban middle class and outdoor enthusiasts, who form Arc’teryx’s core customer base.

Some netizens have even extended the boycott to Anta Sports (2020.HK), the Chinese sportswear conglomerate that acquired Arc’teryx’s parent company, Amer Sports (AS:NYSE), in 2019 and now effectively owns the brand…

…For a long time, corporate references to “environmental friendliness“ or “social responsibility“ were treated as nice-to-have branding or merely compliance with basic regulations, rather than as priorities with real financial impact. For one thing, investment decisions in China were rarely bound by ESG mandates, and it’s common for consumers to choose price and convenience over whether a brand truly embodied ESG values. (Realistically speaking, many consumers simply lacked the awareness, tools, or access to evaluate how a company performed on ESG benchmarks.)

But that is changing. In recent years, China’s urban middle class has begun voting with their wallets, willing to spend real money to support brands that align with their values…

…Arc’teryx, which first won over hardcore outdoor enthusiasts in the 1990s with its technical hardshell jackets, has in recent years faced criticism in China for drifting away from its image as a serious outdoor brand…

…Many outdoor enthusiasts argue that Arc’teryx’s management has lost touch with the outdoor spirit that once defined the brand. They point out that a true outdoor enthusiast would never have approved a fireworks show that risks damaging the very landscapes where Arc’teryx gear is meant to be worn. To them, the backlash over the event felt less like a one-off mistake and more like the inevitable result of a brand now led by people who no longer live and breathe the outdoors.

Apart from environmental issues, China’s urban middle class — especially those born in the 1980s and 1990s — is paying more attention to how socially responsible companies are. For example, this year more and more netizens are boycotting products from companies that follow the “996 schedule,” the notorious work culture requiring employees to work 9 a.m. to 9 p.m., six days a week, often without clear overtime pay. On Xiaohongshu (Red Note), people are sharing lists of companies that mistreat employees and avoiding their products…

…Generational divides are particularly stark. Those in power today—both in government institutions and corporations—were born in the 1960s and 70s, coming of age during China’s fastest industrialization. Meanwhile, the biggest consumers, born in the 1980s, 90s and 00s, have entirely different mindsets and values. The clash between these perspectives shapes much of the friction we see today.

For instance, apart from Arc’teryx’s terrible marketing decision, another major topic discussed among netizens is how this firework show was even approved in the first place. A firework show of this scale in such a fragile alpine ecosystem would not have been possible without prior authorization from local officials. (In fact, Cai had previously applied to hold the firework displays in Japan and France, only to be rejected by both countries.) While China does have environmental protection laws, the lack of awareness among those in power demonstrates how actual social responsibility, such as law enforcement, has lagged behind the rapidly growing economy…

…For some, over time, it became more about the “I”—the ego—and less about the community that upholds those values. For this reason, I believe the Arc’teryx and Cai firework incident is not merely a case of bad PR or environmental infringement, but an important and valuable reminder—especially for those who take consumers for granted and fail to adapt to these social and cultural changes in China today.

The change in aesthetics also reflects the shifting social climate in China. It offers a glimpse into the many differences in values and the conflicts between generations: how the older generation prizes hard work, while the younger generation rejects the 996 culture; how fierce competition and extreme efficiency has morphed into involution; or how the younger generation increasingly regards “grandeur” as “grandiose” and unnatural rather than aesthetically satisfying.

5. The resilience of consumer spending in the US – Abdullah Al-Rezwan

This graph basically helped me understand how the broader economy is chugging along just fine even though “vibecession” has increasingly become part of the conversation. The vibes are not great because a lot of people are indeed feeling the pinch whereas the high income group remains remarkably resilient. It is because of this high income group macro data may continue to be strong for a while:

the fact that credit card debt levels for the highest-income consumers are currently well below the pre-pandemic trend implies that these consumers have room to spend out of unused credit even if their cash on hand has been depleted.

US economy has increasingly been driven by the high income group for a while as half of the consumer spending (vs ~36% three decades ago) basically comes from just top decile of earners.

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

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

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

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

Here are the articles for the week ending 21 September 2025:

1. AI Will Not Make You Rich – Jerry Neumann

Fortunes are made by entrepreneurs and investors when revolutionary technologies enable waves of innovative, investable companies. Think of the railroad, the Bessemer process, electric power, the internal combustion engine, or the microprocessor—each of which, like a stray spark in a fireworks factory, set off decades of follow-on innovations, permeated every part of society, and catapulted a new set of inventors and investors into power, influence, and wealth.

Yet some technological innovations, though societally transformative, generate little in the way of new wealth; instead, they reinforce the status quo. Fifteen years before the microprocessor, another revolutionary idea, shipping containerization, arrived at a less propitious time, when technological advancement was a Red Queen’s race, and inventors and investors were left no better off for non-stop running…

…1. Containerization History: The benefits of the tech are obvious, leading many companies to enter. AI Rhyme: The idea that AI is the next big thing is widespread, and entrepreneurs and tech companies quickly enter.

2. Containerization History: There is immediate government and social attention, leading to pushback. AI Rhyme: The debate over AI that immediately surfaces in society, the media, and government limits experimentation.

3. Containerization History:  Shipbuilders and other infrastructure companies get a quick boost, but not a long-lasting one. AI Rhyme: Chip makers, data center builders, and data providers get a quick boost, but not a long-lasting one.

4. Containerization History:  Competitive intensity makes it difficult to keep prices high or lower costs, and forces high spending on capex, R&D, and talent. AI Rhyme: Prices start to drop even as companies spend heavily on capex, R&D, and talent. Companies will not be especially profitable.

5. Containerization History:  The industry searches for ways to limit competition through cartels and regulatory bodies. AI Rhyme: Investors become alarmed and push for rationalization, resulting in consolidation and convergence on a few business models.

6. Containerization History:  The value created by the innovation is zero-sum: who captures it (provider vs customer) determines the structure of the resulting industry. AI Rhyme: Companies vertically integrate into their customers’ businesses. Companies built on another company’s model have their margins or business model subsumed. Model companies become generalized AI providers.

7. Containerization History:  The longer-term beneficiaries of increased productivity are existing companies that dramatically reduce prices or open new markets to their products. Most incumbents don’t do this. AI Rhyme: The beneficiaries of increased productivity in “thinking” are existing knowledge-industry service providers. Those that won’t adapt will die.

In the “AI rhymes” column, the first four items are already underway. How you should invest depends on whether you believe Nos. 5–7 are next…

…The high capex of AI companies will primarily be spent with the infrastructure companies. These companies are already valued with this expectation, so there won’t be an upside surprise. But consider that shipbuilding benefited from containerization from 1965 until demand collapsed after about 1973.[19 If AI companies consolidate or otherwise act in concert, even a slight downturn that forces them to conserve cash could turn into a serious, sudden, and long-lasting decline in infrastructure spending. This would leave companies like Nvidia and its emerging competitors—who must all make long-term commitments to suppliers and for capacity expansion—unable to lower costs to match the new, smaller market size. Companies priced for an s-curve are overpriced if there’s a peak and decline.

All of which means that investors shouldn’t swim upstream, but fish downstream: companies whose products rely on achieving high-quality results from somewhat ambiguous information will see increased productivity and higher profits. These sectors include professional services, healthcare, education, financial services, and creative services, which together account for between a third and a half of global GDP and have not seen much increased productivity from automation. AI can help lower costs, but as with containerization, how individual businesses incorporate lower costs into their strategies—and what they decide to do with the savings—will determine success. To put it bluntly, using cost savings to increase profits rather than grow revenue is a loser’s game.

The companies that will benefit most rapidly are those whose strategies are already conditional on lowering costs. IKEA’s longtime strategy was to sell quality furniture for low prices and make it up on volume. After containerization made it possible for them to go worldwide, IKEA became the world’s largest retailer and Ingvar Kamprad (the IK of IKEA) became a billionaire. Similarly, Walmart, whose strategy was high volume and low prices in underserved markets, benefited from both cost savings and just-in-time supply chains, allowing increased product variety and lower inventory costs.

2. Getting Rich on Rocks – Joe Raymond

35% per year for 19 years results in a 300x return.

This is a Hall of Fame result. It’s an incredible feat in only two decades for a single stock…

…But what if I told you there was an obscure OTC stock that returned more than 35% annually from 1993 to its acquisition in 2012?

You almost certainly haven’t heard of this company. Its executives aren’t on the covers of any magazines and haven’t written any bestselling books. And its shareholders quietly made their fortune without anybody noticing.

To make matters even more interesting, this was an aggregates business. That’s right, the company sold rocks…

…Let me tell you about Western Lime…

…Western Lime was an unremarkable business in the ’90s.

Growth was around 5-6% per year, and ROE hovered around 10%. Decent, but not particularly noteworthy.

What was noteworthy was the price.

For much of the ’90s, WLC traded between $150 and $160 per share. Trades were very infrequent. The stock only changed hands a few times a year.

At $155 per share in 1993, Western Lime had a market capitalization of only $2 million. The company earned $1.3 million after-tax that year, good for a P/E ratio of 1.7x.

Shareholders’ equity was $12.7 million, so the P/B was 0.16x.

The company had no debt and paid a small quarterly dividend…

…In addition to being incredibly cheap, the company itself was repurchasing shares in private transactions at $550 (more than triple the OTC price). I don’t know if anyone was arbing this, but I bet somebody was…

…By 2010, Tweedy owned 27% of Western Lime’s outstanding shares…

…By this point, word had started to get out on WLC. It was no longer a completely undiscovered stock selling for less than 2x earnings. It was then trading for $5,600 per share…

…Performance had been solid from 1993 to 2009.

Net income grew at 13% per year, the share count was cut in half, and the P/E multiple more than doubled from 1.7x to 3.7x.

The result was a 25% CAGR before dividends from 1993 to 2009…

…In late 2010, we received a string of correspondence between the company and Tweedy, Browne. It was sent to all shareholders. And it made for compelling reading.

The company had offered Tweedy $7,600 per share to acquire their 27% interest (36% above the prevailing $5,600 share price).

This equated to about 5x trailing earnings and 86% of tangible book value…

…Tweedy pegged the intrinsic value of WLC at somewhere between $24,000 and $33,600 per share. This equated to 8.5x EBITDA (15.9x earnings) on the low end and 11.9x EBITDA (22.0x earnings) on the high end…

…Tweedy ultimately rejected the bid, saying that they would much rather buy shares at $7,600 than sell them…

…What’s interesting is that the company upped their bid to $10,300 per share based on “an independent valuation of WLC’s stock” which includes “a discount for lack of marketability of minority blocks of stock.”…

…WLC traded for less than $200 per share 15 years prior. The current market was around $5,600. The company was offering $10,300. And they showed no signs of getting serious about selling the entire company or uplisting the stock.

In other words, there was no other clear “catalyst” on the horizon, other than this seemingly juicy offer from the company.

But Tweedy stuck to their core principles and refused to sell below intrinsic value.

They declined the bid and continued to hold their shares…

…Western Lime ended up selling to Graymont a little over a year later in March 2012…

…Shareholders received $52,000 per share.

That’s more than 5x the price offered to Tweedy less than two years prior, and a 36% CAGR from the 1993 price of $155 (before dividends).

3. From flops to fortune: How tech’s biggest failures create tomorrow’s winners – Chin Hui Leong

Ever since OpenAI launched ChatGPT in November 2022, Alphabet has found itself in an unfamiliar situation – playing second fiddle to OpenAI’s popular artificial intelligence (AI) assistant.

But with the recent launch of Gemini 2.5 Flash Image, Google is starting to look innovative again. The new image feature (code-named Nano Banana) attracted more than 10 million new users in a week, with over 200 million images edited.

Here’s what most people don’t realise: Nano Banana’s success was about 15 years in the making. The story begins with Google+, the company’s catastrophic attempt to challenge Facebook. Launched in 2011, Google+ burned through hundreds of millions before being shuttered in 2019.

But buried within that failed social network was a gem – Google Photos. When Google Photos became a standalone product in 2015, it brought along the image editing and organisation capabilities developed for Google+. Those capabilities – during its failed social network experiment – would give Google’s image AI the headstart it needed.

Fast forward to today, the technology that couldn’t save a social network now powers Google’s comeback in the AI race. Nano Banana’s overnight success took about 15 years of patient failure…

…For investors, the lessons are:

1. High-profile failures may signal opportunity, not disaster.
2. Watch how executives handle failure. Do they admit mistakes openly like Nadella?
3. Look for companies with “failure labs” – autonomous labs, experiment budgets that embrace Bezos’ brutal math of taking a bet that has a 10 per cent chance of a pay-off of 100 times.

4. The bloom is off: the start of the DAT crash? – Andrew Walker

When I was writing my series ~a month ago, MSTR was trading for ~2x mNAV, and every company that announced a DAT [Digital Asset Treasury] deal with any crypto was seeing their stock price skyrocket.

Today, things have changed dramatically. Yes, you’ll still get an occasional squeeze on a buzzy deal in a company with a tiny float (see: OCTO jumping ~2500% on a worldcoin DAT strategy), but for the most part things have cooled down. Just take a look at the king of DATs: MSTR1 has traded down to ~1.5x mNAV….

… and the market seems to be looking at their strategy with increasing skepticism; most of their preferreds are trading below par (in the case of STRD, well below par), and, despite the drop in MSTR’s mNAV, MSTR has been forced to shift most of their capital raise to their ATM program in order to continue to buy bitcoin…

…And we’re already seeing formerly hot DATs need to pivot their strategy as their stocks trade below mNAV. For example, SBET has announced a share repurchase program as their stock slipped below mNAV, and they’re not alone. My favorite is Empery Digital, which announced a share repurchase program and had their CEO make an impassioned plea to shareholders about buying their stock to get discounted access to BTC…

…Despite the shareholder friendliness of the buybacks, I suspect they are a band aid on a bullet wound for most DATs.

Why?

Most of these DATs have fully deployed all of the proceeds they raised into their underlying assets. SBET, for example, has purchased over $3.5B of ETH and had just ~$72m in cash on their balance sheet at their last update; that’s a pittance versus their >$3B market cap…

…I think what’s really interesting about the bloom coming off DATs (the premiums fading away) is that it’s happened while crypto is still generally in favor. ETH is up ~70% over the past three months, while Bitcoin is up ~5%.

If DATs are starting to go out of favor will the underlying crypto is still doing reasonably well, what would happen if we hit another crypto winter and crypto prices traded down meaningfully?

And, if I might speculate a bit, if a lot of the recent rise in crypto has been caused by the huge rush of capital into DATs (which then gets deployed into the crypto, thus supporting the price), what would happen if that unwound for some reason? What if a bunch of DATs said “we’re trading at a discount to NAV; let’s practice good corporate governance, sell crypto, and buy our stock back (option 3 above)”? Or what if a bunch of DATs practice option 2 (leveraging crypto to buy back stock) and get margin called?

I suspect the underlying crypto could go a lot lower real fast as the same flywheel effect that’s sent crypto up recently unwinds.

5. What the Pentagon’s Rare Earths Deal Gets Right and Wrong –  Tracy Alloway, Joe Weisenthal, Arnab Datta, and Peter Harrell

Rare earth elements and magnets manufactured from them are used across defense and industrial applications: An F-35 fighter jet, for example, requires more than 900 pounds of rare earths, and in cars they are used for everything from batteries to power seats. Apple uses a rare earth magnet in the iPhone’s “haptic” engine that makes a user feel buzzes and other vibrations.

China’s dominance of rare earths (it processes nearly 90% of rare earths globally) is relatively recent. For much of the 20th century the U.S. produced both rare earths and rare earths magnets domestically. Indeed, MP’s mine in Mountain Pass, California, located near Las Vegas, started production in 1952.

In the early 2000s, however, low-cost Chinese producers came to dominate global markets, driving most non-Chinese companies out of business: the Mountain Pass mine, for example, stopped operations in 2002. By the time it reopened in 2012, China had built a market infrastructure to dominate all aspects of the trade. The mine closed again in 2015. GM sold America’s leading rare earth magnet manufacturer to Chinese companies in the 1990s. By 2004 it, too, had shuttered U.S. manufacturing. Even after MP acquired the Mountain Pass mine and restarted operations in 2017 it exported most of its product to China to be processed and turned into magnets.

The Defense Department’s deal with MP Materials is designed to end America’s dependency on China with respect to two specific rare earths, neodymium (Nd) and praseodymium (Pr). In addition to expanding mining and processing of the raw metals, the deal is intended to build up America’s capacity to manufacture the metals into magnets, specifically neodymium iron boron (NdFeB) permanent magnets, one of the most important types of rare earths defense and industrial magnets…

…First, MP committed to expand U.S. mining, processing, and magnet manufacturing facilities. The company will increase mining and processing operations, including possibly in heavy rare earths; expand its existing magnet manufacturing facility in California to be able produce 3,000 tons of NdFeB permanent magnets annually (up from 1,000 tons annually currently), and construct a new “10X” facility in Texas that will enable MP to produce a total of 10,000 tons of magnets annually after 2028. Combined, the facilities should be able to meet a substantial portion of U.S. demand for NdFeB magnets, including all of our defense needs.

Second, DoD set a guaranteed price floor of $110 per kilo of MP’s NdPr products, running for 10 years. If the market price, currently below $60/kilo, remains below $110, DoD will pay MP the difference between the market and $110/kilo. If market prices exceed $110/kilo, DoD is entitled to 30% of MP’s extra profits. This ensures that MP can make money on its mining and processing operations even if it has to sell minerals below cost to compete with Chinese producers.

Third, DoD has guaranteed that either it or commercial buyers will purchase all of the 10X facility’s NdFeB magnets, estimated at 7,000 tons a year for the next decade. DoD will pay MP its realized cost of production of the magnets, plus $140 million per year to guarantee MP a profit, with a 2% annual inflation increase in the guaranteed profit figure. With DoD’s consent, MP can sell some of its magnets to commercial buyers, in which case, DoD will take the first $30 million in MP magnet profits exceeding $140 million. Additional profits beyond that will be split 50/50 between MP and DoD. Similar to the price floor, the magnet offtake agreement ensures that MP can profitably make magnets even if low global prices would undercut MP’s manufacturing…

…Beneath the deal’s ambition, its structure raises significant policy design questions. The first is a fundamental question about the extent to which the government (versus the private sector) should bear the costs associated with addressing critical U.S. supply chain risks. The MP deal essentially puts the U.S. taxpayer on the hook for developing a reliable U.S. supplier of rare earths and NdPrB magnets. And while the U.S. government can share in the upside if global prices for rare earths and the magnets exceed expected levels, if the price trajectory looks similar to the last decade, the U.S. government could be on the hook for billions. Potential costs include $1.4 billion in guaranteed profits for MP ($140 million per year, adjusted up at 2% per year). The price floor alone could cost billions over ten years if MP hits their announced capacity of 6,075 metric tons and prevailing market prices stay constant…

…The deal elevates MP Materials as America’s de facto magnet champion, despite having no track record of commercial success in magnet production. By contrast, China’s national champions typically emerge through fierce domestic competition. Firms like CATL did not rise to global leadership through political selection alone; they fought their way to the top by outperforming rivals on innovation and scale: CATL remains one of the top patent recipients globally while leveraging partnerships with major automakers like Tesla and BMW. Government support was structured to spur this competition. Subsidies and pilot programs were spread across multiple firms before consolidating behind the winners.

The U.S. decision to back MP sidesteps this competitive process, effectively granting a monopoly franchise in magnet production. This risks locking the U.S. into a suboptimal path if MP fails to deliver on cost or performance, while crowding out rivals that could prove more innovative…

…The deal also hardwires U.S. fiscal exposure to the same market infrastructure that China uses to determine prices and stifle investment in competitors. Under the price‑protection term, DoD pays the difference between $110/kg and a reference price — specifically the Asian Metal Market price. A substitute ex‑China Index is only allowed at DoD’s election and with the company’s consent. That means core cash flows for a decade depend on a benchmark whose prints reflect Chinese production costs, market structure, trade flows, policy choices, and tax treatment.

This creates three, compounding problems: (1) basis risk; (2) manipulability; and (3) path dependence. NdPr is sold as concentrate, oxide, and metal with varying specs, impurities, tenors, and delivery terms; Asian Metal quotations often embed VAT regimes, logistics premia, and buyer restrictions that diverge from U.S. realizations. Even with upside sharing, those mismatches can cap clawbacks in booms and invite arbitrage in busts. Relatedly, when public payments hinge on a single, quarterly external print, an actor with market power can manipulate spreads, restrict eligible buyers, or flood spot supply to push the index below U.S. breakevens and eat DoD appropriations. And locking federal contracts to Asian Metal deepens liquidity and legitimacy in that price‑discovery ecosystem. The U.S. ends up validating the very benchmark that concentrates market power abroad, raising the fiscal cost of preserving domestic capacity and making future decoupling harder.

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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), Apple, Meta Platforms (parent of Facebook), Microsoft (its CEO is Satya Nadella), and Amazon (its founder is Jeff Bezos). 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 September 2025:

1. Secular Bull Market Peaks – Are We There Yet? – Tyler Grason

The concentration and valuation of the market today often draws parallels to the tech bubble.  We analyzed the Tech Bubble and the nifty fifty period (late 1960s to early 1970s), both of which marked the end of secular bull markets, to assess the similarities. As to the end of this secular bull market, as Mark Twain said, “The reports of my death are greatly exaggerated.”…

…Both the nifty fifty and tech bubble periods both coincided with a Fed hiking cycle. In January 1973, the market peaked 3 days prior to the first hike, and the Fed did not cut until December 1974. During the tech bubble, the Fed began hiking rates in June 1999, or about 9 months prior to the market peak. While the Fed continues to be on pause, fed funds futures prices suggest a 0% probability that the Fed hikes by year-end 2026.  Odds now show the Fed is 88% likely to cut rates in September, with 2 expected rate cuts this fall…

…Market today is cheaper than the tech bubble despite better fundamentals. The S&P 500 at 22x forward twelve-month earnings is ~15% cheaper than the peak of the tech bubble at 25.5x despite having 60% higher profit margins and 10% better ROE. When compared to the 10yr which traded at 15.9x at the height of the tech bubble, equities were 10x turns more expensive vs 1x turn less expensive today. On a justified P/E basis, fundamentals and bond yields would suggest the market today should trade at 24x, or slightly above the current multiple of 22x…

…Market concentration today looks much more aligned with fundamentals. During the tech bubble, the concentration of the top 10 largest stocks at 27% was nearly 2x above its earnings contribution. The expected earnings growth that was priced in failed to materialize. Today, the weight of the top 10 stocks relative to their earnings contribution is much more aligned at 35% and 32%, respectively. While the top 10 stocks in the S&P at 38.3x is above the tech bubble at 34.4x, Tesla at 145x is meaningfully skewing the data. Excluding Tesla, the top 10 today trade at 26.5x, or ~25% below the tech bubble peak despite returns on capital that are >2x higher.

2. This Is Why America Is Losing to China –  Ross Douthat, Sophia Alvarez Boyd, and Dan Wang

Wang: I decided to take two friends and go on a lengthy bike ride in China’s southwestern province of Guizhou. This is a land where a local said, “Not three feet of land is flat, not three days go by without rain and not a family has three silver coins.”

China’s fourth-poorest province, I was surprised to see, had much better levels of infrastructure than one could find in much wealthier places in the United States, like New York State or California.

We saw very tall bridges all around us. We saw a guitar-making hub. We saw a lot of fancy new roads that were a cyclist’s dream. And it was only afterward when I realized how bizarre it was that China’s fourth-poorest province — about the level of G.D.P. per capita of Botswana, much less than Shanghai or Guangdong — was able to build all of these things.

It is a province with 11 airports, 50 of the highest bridges in the world and brand-new, spiffy highways — and that’s because China was just building a lot in its equivalent of a South Dakota or West Virginia…

…Wang: I think that the first and most important part of China’s technological success has to do with something I call process knowledge.

Process knowledge is also known as tacit knowledge, also known as industrial expertise. In a kitchen analogy, it is something like the recipe, and the hardware is something like the stoves and the pots and the pans.

But let’s say, Ross, we give someone who’s never cooked a day in his life the most well-equipped kitchen, as well as the most exquisitely detailed recipe. Are we sure that this person will be able to do something as simple as frying an egg for breakfast?

I’m not sure if that person will burn the kitchen down in some big way.

Douthat: My children have often given evidence for that hypothesis.

Wang: Yes. And I think the crucial part of technology is actually all of this tacit knowledge, process knowledge that we can’t really write down.

That is the core part of what has been driving China’s technological advantage. It started when China started making pretty simple things — socks, T-shirts, all these things that we think and know are not terribly important — before they get to slightly more complex things, like shoes.

Then they get to everything that now includes iPhones and electric vehicle batteries, and they are really good at climbing this ladder.

China’s hardware capital, Shenzhen, was mostly a backwater — making textiles all the way up until 2008, when Shenzhen started producing Steve Jobs’s iPhones.

iPhones started rolling off the line and you had this enormous work force, hundreds of thousands of people making the most sophisticated consumer electronics in the world, making the next consumer drones, more sophisticated electronics. And I think that is really the basis of China’s technology advantage: It’s just these gigantic investments and work force.

The state sometimes gets in the way; the state sometimes harnesses this work force. You also have a lot of entrepreneurial energy. I’m not sure if I wanted to define it as state capitalism with Chinese characteristics, but I just view it as technological catch-up.

Douthat: Right, but what is the difference, then, between that model and ours? Part of your argument is that America has lost a lot of that knowledge through the process of outsourcing and allowing factories to move overseas and allowing deindustrialization to happen, and becoming an information and financial services and service economy — a very rich one, but not an industrial economy in the way that China is.

I want to understand how much of this is saying there are engineering minds in the Politburo who made these choices that maybe you can only make in an authoritarian society, or maybe we could have made different choices ourselves in the U.S.?

How much of it is that versus some other element of competition or culture in China right now?

Wang: I think the crucial mistake in the U.S. was that it wasn’t even a choice that the U.S. made to outsource a lot of manufacturing. Now, there is this line that politicians like to trot out that China stole all the jobs — and sure, that’s one framing of it.

But I think a more accurate framing is that since the 1990s, big American manufacturers had been actively moving their production to China, and the U.S. government did almost nothing to restrain them.

I’m not sure whether that was actually a really deliberate choice plotted out by the Council of Economic Advisers advising Bill Clinton. Maybe it was, but I think this was just a process of business lobbying saying: Well, we need to tap into this market and produce at these cheaper places.

And something that the Communist Party actively decided was that they were going to import big American manufacturers in the 1990s and 2000s, Apple, Tesla.

If they want to build their products here, we are going to completely welcome Steve Jobs and Elon Musk to train our workers and make them as good as they can be.

That was a more conscious decision, I think, made by engineers who realized they had to catch up to the global frontier. They couldn’t do it with China’s existing level of technology, and they were going to have Americans help them…

…Wang: I think you’re absolutely right that America is highly dynamic, and I don’t want to count out America in this stage of competition. I think at various points the U.S. will look weak. At various points it will look strong.

But what are the stakes here? Because I think there is still a broad view in the U.S. that deindustrialization has been pretty bad — not just for regions like Pennsylvania or Michigan, where the deindustrialization has been felt pretty badly.

There’s also a pretty clear loss of manufacturing expertise that is represented in the declining fortunes of American apex manufacturers. Companies like Intel, Boeing, Detroit automakers and now, increasingly, Tesla.

They’ve had mostly bad news over the last few quarters, last few years. In the case of Detroit, the last few decades. Apex manufacturers are not working very well.

If we take a look at the early days of the Covid pandemic, the U.S. manufacturers were not very good at making simple products either — necessary products, like cotton swabs and cotton masks. And they weren’t able to really rejig their supply lines in order to build out critical materials.

If we take a look at the U.S. defense industrial base, after the U.S. shipped a lot of munitions to Ukraine for its self-defense against Russia, the U.S. hasn’t really been able to rebuild its munition stockpiles.

If we take a look at naval ships with the U.S. Navy, every class of ships is now behind schedule…

…Douthat: As a potential scenario for Chinese success. How could China, how could this model fail? What do engineers get wrong?

Wang: Engineers are meddling extensively in the economy. And maybe we will wake up and find one day that central planning is a ginormous failure and the Chinese will not be able to fundamentally overcome these contradictions in the model of state capitalism with Chinese characteristics.

That is a potential scenario in which the extensive meddling that has scared the living daylights out of a lot of venture capital investors in China, as well as a lot of entrepreneurs who would really prefer not to suffer through a lot of the edicts of the Politburo — they decide to not contribute so much to the great rejuvenation of the Chinese people.

I think that a lot of people have been pretty extensively burned out by the mistakes and some of the foibles of the Communist Party. A lot of what I have seen is that many young Chinese are willing to take leave of the great rejuvenation that is conducted in their name.

We have a lot of data on Chinese entrepreneurs, a lot of wealthy Chinese people who would much rather live their lives in Chinese communities like Irvine, Calif., by buying some property and just having their businesses be established in Singapore, and still not really quite trusting the Communist Party to respect everything that they want to do.

Young Chinese creative types are interested in smoking dope, just as young California types may be. They are smoking dope in Chiang Mai. I’ve spent a little bit of time seeing these people who are just as into marijuana, as well as cryptocurrencies, as folks are in Silicon Valley.

We also see a lot of Chinese migrants who are not necessarily rich, who are not necessarily the creative types, dare to fly to Ecuador, which has been visa-free for a period of time to the Chinese, and try to walk across the Darién Gap — a perilous journey to cross to the southwestern border of the United States.

At its peak in 2024, the U.S. was apprehending something like 30,000 to 40,000 Chinese who were trying to cross over into Texas. It still blows my mind that many people would try to do that to escape the regime…

…Douthat: Let’s end with advice for the United States. What are the actual implications of your analysis — and especially the bull’s case that we started with, the Chinese century case for what the U.S. should do right now? What should we be doing differently if China is poised to be as powerful as you think it might be?

Wang: I think that the U.S. should first and foremost rebuild its manufacturing base. That follows quite naturally from a lot of my analysis of China’s greatest strength, which is that China is a manufacturing superpower and China is poised to further deindustrialize Europe and it is poised to further deindustrialize the United States as well.

I am skeptical that President Trump’s efforts to reindustrialize America through the tariffs have been very effective. I am more positive about the Biden administration’s policies on efforts to reshore through industrial policy. But we can still see a lot of flaws with that approach as well.

Douthat: Do you think tariffs — essentially trade war — can’t work, in your view, because China has become too strong and resilient?

Wang: I think that the trade war, as prosecuted right now through the tariffs, is not going to be very effective. If we just take a look at the manufacturing employment data since Liberation Day in April — with the next jobs release, I’m not sure if we’ll get that data probity back — the U.S. has lost about 40,000 manufacturing workers.

It is not a natural fit if the U.S. is to become a technological, scientific superpower to advance its science by denying a lot of funding to scientific agencies like the National Science Foundation and the National Institutes of Health.

I think that universities, flawed as they are, are still driving a lot of American innovation and scientific advancements, and it also doesn’t make a lot of sense to attack universities in order to save the scientific base.

And it really doesn’t make sense to try to deport a lot of workers who may be working in the construction industry or the manufacturing industry, or to frighten away a lot of high-skilled researchers who may want to be in the U.S. from Europe or Asia to do a lot of their work here. So I think that as prosecuted, the trade war is not making a lot of sense.

The industrial push in the U.S. is not making a lot of sense. Maybe there’s something positive to be said about Trump’s energy agenda in terms of building more nuclear power, in terms of building more facilities online. Maybe there’s something positive about the deregulatory agenda. I can certainly see that case, but I certainly see more headwinds than tailwinds.

3. Are We at Bubble-Level Valuations? – Ben Carlson

Here’s the monkey wrench — Bernstein also wrote about why regression to the mean can be so tricky outside of science:

There are three reasons why regression to the mean can be such a frustrating guide to decision-making. First, it sometimes proceeds at so slow a pace that a shock will disrupt the process. Second, the regression may be so strong that matters do not come to rest once they reach the mean. Rather, they fluctuate around the mean, with repeated, irregular deviations on either side. Finally, the mean itself may be unstable, so that yesterday’s normality may be supplanted today by a new normality that we know nothing about…

…This is the CAPE ratio going all the way back to a time when Francis Galton was still alive: [Average of 17.6x since 1881, and average of 28.3x over past 30 years]

What’s more relevant here — the 150+ year full history or the past 30 years? Which average is more relevant?…

…Last week I wrote A Short History of the S&P 500 which looked at the composition change to the index over time in terms of the types of stocks. The S&P 500 was full of capital-intensive industrials and railroad stocks for much of its history. These were relatively low-margin businesses that required a large number of employees and lots of physical assets that needed to be replaced over time.

Today’s companies have more intangible assets and are far more efficient.

Take a look at average margins by decade going back to the 1990s and you can see this shift happening:

Every decade the average moves a little higher.

This was supposed to be the most mean-reverting series in all of finance. Market historians have been shouting it from the rooftops for the past 15 years. And they were wrong…

…It’s interesting to note that the biggest crash on this list–the Great Financial Crisis–started at relatively muted valuation levels. Stocks were not insanely overvalued heading into the fall of 2007. It’s just that no one saw earnings were about to fall off a cliff.

Picking tops is not easy.

4. Finding Fraud – Farrer 36 Asset Management

One of the first things I do when reading an annual report is search the PDF for the term “Material Weakness” – you’d be surprised how often you get a positive hit. A material weakness is a flaw or combination of flaws in a company’s internal controls over financial reporting that creates a “reasonable possibility” of a significant error occurring in the financial statements. For example, take Evolv Technologies that declared a material weakness in its 2024 annual report.

The discovery of the accounting mishap (it turns out an employee was overstating sales) sent the stock tumbling 50%…

…Many ‘material weakness’ declarations get remedied, or don’t turn out to be much, but their existence is cause for more work…

…Swedish small cap Intellego has been on a tear recently – with the stock up more than 300% this calendar year. The stock is being driven by impressive revenue (+152% yoy in Q12025) and profit growth (+162%). Given this, you would expect that operating cash flow would have also exploded. But would it surprise you that it has instead decreased over the same time?

This is because much of Intellego’s revenue, while recorded, has not actually been received by the company. Receivables have increased 6x over the same period.

The above begs the obvious question – are the revenues real? Let me be clear, I am not stating that this is fraud – the company has explained that some of their older contracts gave too loose of terms to their clients, and newer contracts have stricter terms. However, such a large mismatch between profits and cash should give any investor pause…

…Many of Enron’s troubles lay with CFO Andy Fastow’s creation of SPVs which he and his family owned. These vehicles had the dual purpose of raising billions for Enron (and thus allowing the consolidated balance sheet to appear debt-free) and paying himself millions of dollars…

…Going back to the Enron example, even though they showed positive operating cash flow in three annual reports prior to declaring bankruptcy, their working capital assumptions raised alarms. You can see from the above table that from 1998 to 2000 (read right to left) that both receivables jumped (see the previous example for what that implies), but to compensate, there was also a significant jump in payables…

…For years Yes Bank had posted numbers too good to be true. Their loan book grew much faster than peers, margins and profits were higher than its comparable set, and all this despite exposure to troubled sectors like real estate, airlines, and telecoms. It turns out that Yes Bank was underreporting stressed loans (they reported NPAs under 1%, whereas the RBI showed a 400-500bp difference). When the truth was revealed we saw a 96%+ drop in stock price and jail for the founder.

5. Why retention is so hard for new tech products – Andrew Chen

Just as there’s the laws of physics, weirdly there are some constant patterns that keep cropping up over time. Here are a few that I’ll share:

• You can’t fix bad retention. No, adding more notifications will not fix your retention curve. You can’t A/B test your way to good retention
• Retention goes down, it doesn’t go up. And weirdly, it decays (oh, does it decay) at a predictable half life. Early retention predicts later retention.
• Revenue retention expands, while usage retention shrinks. Good news: You lose people over over time, but the ones that remain sometimes spend more more money!
• Retention is relative to your product category. There’s nature, and there’s nurture. Sorry, you’ll never make a hotel booking app a daily use product
• Retention gets worse as users expand and grow. The best users are early and organic. The worst users come after that
• Churn is asymmetric. It’s far easier to lose a user forever than to re-win them back
• Retention is weirdly hard to measure. Seasonality is a real thing. New tests throw things off. Bugs happen. D365 is a real metric but you can’t wait
• Crazy viral growth with shitty retention fails. We’ve run this experiment many many times already, across multiple platforms and categories
• Great retention is magic. When you see it out in the wild, it’s amazing…

…You might read all of this and still have a big question: So wait, how do you get to great retention? (If I knew the answer in a deterministic way, my job as a startup investor would be so much easier, wouldn’t it?)

But let’s try our best. In my points above, there’s a few clues:

• The idea really matters.
• If you want a high retention product, you need to pick a category that is high retention already.
• You need to pick a product category where you already use an existing product every day.
• You’re going to build something that directly competes against that.
• If you win, then you’ll stop using that other product and use your product instead.

That’s a high bar, but I think it’s a good start…

…The natural counterpoint is that new markets are often more exciting than existing ones. Isn’t tech about building brand new things rather than innovating 20% on old stuff? Of course this is true, but I think this is the tiny tiny minority of products.

My counterpoint to this counterpoint is that most products actually have some kind of prior lineage, even if those prior products are quickly forgotten.

Before Instagram there was Hipstamatic, which had become the #1 paid photo app in the early App Store. It demonstrated the success of photo filters. Of course Google was not the first search engine, it was actually #10 or whatever, after Lycos, Excite, Infoseek, etc., which demonstrated consumers wanted search but that it was impossible to monetize. Tesla was not the first electric car, nor iPhone the first smartphone. Sometimes it’s the 10th iteration that matters. Some call this “last mover advantage” rather than first mover. I think an important point.

Yet sometimes new things do happen. Uber was created to turn an existing offline action — calling a cab — into an app, not because there was already a hugely successful ridehailing app. (And no, not Lyft — it was a weird bus booking thing at the time). Of course a lot of ChatGPT, with OpenAI’s 5 year journey between inception and v3 which really took off, and without any real blueprints for what it might replace. These types of journeys are remarkable, and the tech industry is better off for it, because they involve real risk as part of new category creation.

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