What We're Reading Archives : The Good Investors

What We’re Reading (Week Ending 06 September 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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 box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

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

Here are the articles for the week ending 06 September 2026:

1. X post on AI demand – Philippe Lemoine

Dwarkesh’s basic intuition seems to be that AI will soon be able to fully automate many white-collar jobs, so to get a sense of how much revenue OpenAI/Anthropic can generate by replacing them, you can just look at the wage bills for those workers, but that’s not how things work.

First, if AI can fully automate most tasks performed by those workers but even a handful of them prove resistant to automation, AI will still generate a lot of value but it will presumably be captured mostly by the remaining workers, their firms or their customers and the current wage bill for those workers will massively overestimate the future revenue of frontier labs.

In that case, demand for AI may not explode in the way Dwarkesh and Dylan expect, because it will be bottlenecked by humans in production.

But let’s assume, as Dwarkesh and Dylan presumably do, that AI capabilities will improve so much that it can automate every task those workers perform and their jobs can be fully automated. It still doesn’t follow that demand will be as high as they think.

Even when it has become technically possible to replace most white-collar jobs with AI in principle, before it can happen in practice, there will be enormous transition costs and frictions as firms will have to undergo massive reorganization. This will be very slow even if politics doesn’t interfere, which it will.

Dwarkesh’s argument implicitly assumes that frontier AI companies will be able to charge about the same for tokens that provide services equivalent to that of the worker being replaced, but as long as there is enough competition, the prices they can charge will be pushed down and if he’s right about how fast capabilities will improve they may fall very quickly…

…Now, even if prices do fall rapidly, this could in theory be compensated by increased demand in volume. But there is only so much legal, managerial, engineering, etc. services that people want, even if prices fall.

The price elasticity of the demand for those services will eventually fall and, even if improved capabilities shift the demand curve outward, this will only generate so much demand because no matter how good your lawyer gets and how low his rates fall, at some point you don’t need more of his services…

…the thing about AI agents is that even if they reach the point where they can fully replace human workers on the production side, they can’t replace them on the consumption side because they don’t consume anything except compute and a few B2B services.

The income will still go to someone, but they will probably have a lower propensity to consume and different spending patterns, so there is a macroeconomic problem here about where the ultimate demand for the output of consumer goods and services produced by the customers of OpenAI and Anthropic will come from.

2. AI Semiconductor Endgame 2026 (III) – Fin

Hardware/infrastructure revenue growth in the internet revolution had exponential growth along only one dimension of penetration: user count

Because everyone was on a subscription — $50 a month for internet access, and there’s nowhere to upsell from there. It’s a flat-rate service. Looking at ten times as many web pages doesn’t make you pay ten times as much. A flat-rate service has no second dimension of exponential growth; once it saturates, it saturates…

…AI hardware demand, by contrast, comes from the exponential growth of tokens. The reason this round of AI is so hardware-hungry is fundamentally that the ceiling is determined by two dimensions: exponential growth in user count/penetration, and exponential growth in usage per person

These two dimensions of exponential growth multiply, and both convert into a single unit of account: tokens — which raises the infrastructure ceiling by far, far more

Where does each of the two dimensions stand in our era?

The first dimension, user penetration, crossed 50% a few months ago — meaning the derivative of the sigmoid is about to inflect…

…The second dimension, tokens per person, is still early in 2026. The median AI spend inside US enterprises is currently $12 per person per month. Long term, that number could plausibly reach at least 10% of a white-collar salary — roughly $1,000 a month. There are nearly two orders of magnitude in between…

…This should be historically rare: compute/infrastructure demand created by a relay of sigmoid curves across two dimensions, and with it a semiconductor supercycle

But all exponential growth is an illusion before the ceiling arrives, and this round of AI buildout is no exception — the illusion just lasts much longer, because there’s an extra dimension of exponential growth taking the baton…

…Right now, broadly defined coding tasks account for roughly 60–70% of all ARR revenue. If you count only the narrow developer definition it’s maybe 40%; the other 20–30% is likely tasks from other industries converted into a coding-agent execution structure…

…What takes the baton from developer coding is not some other isolated mega-scenario. The fastest-growing thing right now is precisely “non-programmers using coding infrastructure to complete non-coding product tasks”, coding is still the default execution kernel, but it isn’t necessarily the product category the user sees…

…As of June 2026, among OpenAI’s enterprise customers, Codex already accounts for 64% of combined Codex + ChatGPT output tokens. Since February, Codex weekly active users have grown 108x in legal, 41x in sales and recruiting, 26x in marketing — and only 5x in engineering

Looking at Anthropic’s revenue mix by vertical, narrow developer/software-company coding is only 40%, financial services/insurance is over 20%, and legal, pharma/life sciences, and consumer/retail e-commerce are all very substantial, each on the order of 10%.

The logic of software eating the world, once LLMs lowered the barrier to coding by a hundred times, has even intensified into coding eating the world, or put another way, coding is the modality agents are most fluent in, and agents are using coding to spread their utilization into all knowledge work…

…From the broad agent-coding angle, I don’t currently see a hard ceiling on large-model/agent demand. We’re still in the phase where the more you use it, the more efficiency you gain — the marginal positive return is still large, and the main bottleneck is how much budget revenue/profit can support. Willingness and use cases aren’t the problem…

…Inference is simply too profitable right now, so overbuild is an inevitable outcome, because everyone wants to grab a piece of the inference market, every player is in the arms race, nobody can stomach losing share and failing to ride the wave of high-speed growth, and no one is going to back down

Closed-source inference gross margins clear 70% easily (even after the CSP takes its cut), and token factories hosting open-source models and selling tokens run 60%+ gross margins as well (DeepSeek’s GM is beyond 80%), GPU prices keep rising, rent for 1GW is heading toward $20B, and even just selling short-term GPU cluster contracts carries 60%+ gross margin. This AI infrastructure market is so profitable and so tempting that anyone with distribution and technology is piling in as a neocloud, and because there’s generally a one- to two-year gap between build plans and delivery, overbuild is certain to happen — it’s only a question of when

“A question of when” means that at current demand and current compute plans, overbuild isn’t visible for at least two years. Any step-function improvement in models (reasoning -> agent, for instance), or any innovation in the application paradigm that sustains AI spending growth, keeps extending the overbuild timeline further out

When those extensions can no longer keep up with the pace of the buildout, overbuild will slowly start to surface

3. The Teaser Period: Why the AI Boom Is Hitting a Reset Wall – Groundbreaker

Nothing looked wrong in the summer of 2006. Home prices had risen for the better part of a decade. Delinquencies were near historic lows. Credit spreads were tight, the ratings held, and the securitization machine hummed. If you had asked a hundred people on a trading desk whether the American mortgage market was months from seizing, most would have laughed.

Millions of subprime borrowers were, at that moment, paying the low introductory rate on a two-year adjustable rate mortgage – the 2/28 ARM. A low fixed-rate for two years, then the rate reset to a payment 30% to 50% higher. During those first two years the loan performed beautifully: the borrower paid, the servicer collected, and the bond paid its coupon. Nothing looked wrong because the whole complex – housing, mortgages, securitization – was sitting inside the teaser period.

Every ARM reset was known, dated, and contractually inevitable from the moment of origination. Aggregate those reset schedules and you get the most damning exhibit of the era: the reset wall. Roughly a trillion dollars of adjustable-rate mortgages were contractually set to reset across 2007 and 2008 – thirty to forty billion dollars a month at the peak…

…The parallel to 2006 is exact and it explains the single most-cited absurdity of this cycle: How does OpenAI, a company with some $40 billion of run-rate revenue, sign $1.4 trillion of compute commitments? The same way a household with $60,000 of income signed a $600,000 mortgage: because the terms at signing do not require the payment yet, and because everyone at the table – borrower, lender, and the market – believes the growth will arrive before the payment does…

…The compute commencement wall can be made visible in exactly the way the reset wall was visible in 2007 – from disclosed contracts and delivery schedules…

…A frontier lab signs a $12 billion, ten-year capacity commitment with a compute provider. The contract is take-or-pay, meaning the lab commits to payments once the capacity is delivered, and delivery requires a campus that does not yet exist: two years of construction, procurement, and power work stand between signature and completion.

Now look at what each party’s financial statements show during the two-year teaser.

The seller – a hyperscaler or neocloud – books the arrangement into RPO or contracted backlog on day one – the full $12 billion, disclosed, quoted, and celebrated. The market values it as contractual future revenue. Meanwhile the seller’s cash flow statement hemorrhages: the campus is being built, so capex runs far ahead of receipts. Booked backlog rises; reported earnings feel none of the buildout; financing frequently sits off-balance sheet.

The buyer – a frontier lab like OpenAI or Anthropic – announces access to the compute it needs to pursue its scaling roadmap, and its private valuation reprices on the announcement. The commitment is a future obligation, disclosed – if at all – deep in a contractual-obligations footnote or, for the private labs, nowhere public. No expense hits the P&L because no service is being received. A lab that has committed tens of billions across multiple providers carries a cost structure that reflects only its commenced capacity.

The market sees a seller with explosive backlog and a buyer with secured compute capacity, and prices both as growth stories. Nobody is lying. Every number is GAAP-clean. The structure simply guarantees that during the teaser period, the system’s reported economics and its committed economics diverge by the full value of everything signed and not yet commenced…

…In residential credit, the interval between origination boom and reset wall was twenty-four months, because that was the teaser’s term. In compute, the interval is the construction timeline – twenty-four to thirty-six months. The 2025–26 signing boom therefore mathematically guarantees a 2027–28 commencement boom, exactly as 2005–06 originations guaranteed 2007–08 resets…

…This is what it means to say we are in the teaser period. The booked figure is enormous; the billed figure is a fraction of it and only beginning to turn up. Everything about the present looks like strength. The obligations that will govern 2027 and 2028 are already signed, already dated, and already sitting in RPO. What has not happened yet is the conversion – the moment booked becomes billed and the take-or-pay clock starts running regardless of the revenue and the counterparty’s ability to pay…

…The claim is not that commencement causes a lab to fail. It is that commencement is the date on which a pre-existing mismatch – fixed obligation against assumed revenue – becomes cash-due, and that, as in 2008, the mismatch is visible in the fundamentals well before the date makes it unavoidable. You do not need demand to fall. You need it only to decelerate below the rate the booked compute was underwritten to.

The compute contracts commencing in 2025 and early 2026 cleared, or very nearly cleared, the required growth rate. This is the crucial point, and it is the reason there is no alarm anywhere in the system: the early vintages worked.

They worked the way the 2005 and 2006 subprime resets worked. The collateral appreciated fast enough. The refinancing happened. Everyone who signed was vindicated, and vindication is the input to the next round of underwriting. Success in the early vintages is the mechanism that manufactures the late ones…

…For committed compute merely to equal revenue in 2027 – not to be comfortably covered, simply to reach parity, before a single dollar is spent on wages, research, sales, or tax – revenue would have to compound at 217% annually off the 2025 base. The dashed line at 100% represents revenue doubling every single year and sustaining it, which no company at this scale of revenue has ever done for a multi-year stretch. The obligation is accelerating at more than double the rate of the best case for the cash flow meant to cover it.

The obligation curve is contractually fixed and steep – it ramps according to a defined construction timeline. The revenue curve is a growth rate. If the growth rate rolls over – the two curves cross. That is the reckoning: not a demand collapse, but a demand deceleration meeting a cost schedule that was set in a more optimistic year.

4. 90% of executives say AI hasn’t boosted productivity. Some are still cutting jobs – Mark Ma and The Conversation

One Atlanta Federal Reserve study found that about 90% of executives believe AI has not yet boosted productivity at their companies. Other evidence suggests that the broader increase in productivity seen since 2021 is more likely due to remote work or factors other than AI, like downsizing in sectors such as technology…

…Managers at publicly traded firms typically make decisions based on whether a new investment improves short-term profitability and share price. So after investing heavily in AI, managers face pressure to show a strong financial return. The expectation is that if AI makes employees more efficient, the company will need fewer of them to complete the same work.

As a result, a quick way for managers to help businesses realize that anticipated return is by cutting headcount and lowering labor costs. Some of the companies we studied even started to lay off employees before pouring money into AI, as a way to free up capital for future AI investments…

…To see how employees perceive and react to AI adoption, we analyzed millions of employee-satisfaction reviews on the workplace review site Glassdoor.com. By identifying and analyzing AI-related comments, we found them to be much more negative than the overall tone of employee reviews.

This negativity reflects widespread concerns over corporate AI adoption and anti-AI sentiment among workers. At the same time, there’s a strong association between employee sentiment toward AI and firm productivity based on the employer’s financial information. This suggests that anti-AI sentiment among workers actually lowers productivity and offsets the potential efficiency gains caused by AI…

…What businesses need to understand, I believe, is that managing how employees feel is key to unlocking AI’s benefits. In turn, that means creating an environment where workers feel that AI is working with them, not against them.

5. Prediction: AI will collapse – wordgrammer

The modern AI discourse is making a similar mistake to saying “everyone will have a website” back in 1995.

I hear stuff like:

People want personal software
Everyone will build a mobile app
The market for software is infinite

No.

You are directionally correct, but you are missing something big.

Every human will use agents. But less than 1% will use coding agents. (In the same way every human has a “personal page” through social media, but less than 1% have a personal website.)

You need an “idiotproof” sandbox. Your consumer agent, as well as agents for enterprise use cases, must have a 0% chance of failure. Your consumer agent must be usable:

While drunk In bed
Having sex
On the toilet
During lunch break
In the background while working on homework
While your brain is 99% preoccupied with a difficult task at work

It must be impossible for AI to: make purchases you did not intend, send messages you did not intend, and access information you did not want to share. It cannot be “difficult” for these to happen. It must be impossible. (In the same way that YouTube videos have a 0% chance of phishing, cross-site scripting, malware, and viruses.)

No amount of models getting better will give them a 0% chance of failure. To get to a 0% chance of failure, you need to be creative. You need to innovate. You need to radically rethink your abstractions.

Every human has a “personal page” through social media, but less than 1% has a personal website. What is the same shift for AI?

Every human will use _____ agents, but less than 1% use coding agents. What is _____?

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

What We’re Reading (Week Ending 30 August 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 30 August 2026:

1. How Countries Go Broke: The Dynamic Behind What is Happening Now – Ray Dalio

To me, the credit/market system is like the human circulatory system, bringing nutrients to all parts of the body that make up the markets and economy. If credit is used effectively, it creates productivity and income that can pay back the debt and interest on the debt, which is healthy. However, if it isn’t used well so it doesn’t produce enough income to pay back the debt and the interest on the debt, debt service will build up like plaque that squeezes out other spending. When debt service payments become very large, that creates a debt service problem and eventually a debt rollover problem as holders of the debt don’t want to roll it over and want to sell it. Naturally, that creates a shortage of demand for debt instruments like bonds and the selling of them, and when there is a shortage of demand relative to supply that either leads to a) interest rates rising, which drives markets and the economy lower, or b) the central bank “printing money” and buying debt, which lowers the value of money, which raises inflation from what it would have been. Printing money also artificially lowers interest rates, which hurts the lenders’ returns. Neither approach is good. When interest rates rise because the selling of debt becomes too large to curtail and the central bank has bought a lot of bonds, the central bank loses money, which hurts its cash flow. If this continues, it leads to the central bank having a negative net worth.

When this becomes severe, both the central government and the central bank borrow to make debt service payments, the central bank prints money to provide the lending because the free-market demand is inadequate, and a self-reinforcing debt/money printing/inflation spiral ensues.

In summary, the classic things to watch are as follows:

1) The amount of government debt service there is relative to government revenue (which is like the amount of plaque in the circulatory system),

2) The amount of selling of government debt there is relative to the amount of demand for government debt (which is like the plaque breaking off and causing a heart attack), and

3) The amount of central bank printing of money to purchase government debt to make up the shortfall in demand for government debt relative to the supply of government debt that needs to be sold (which is like the central bank administering a heavy dose of liquidity/credit to ease the liquidity shortage, producing more debt, which the central bank has an exposure to)…

…Now, imagine that you are running a big business called the US government. That will give you a perspective that will help you understand the US government’s finances and its leadership’s choices.

The total revenue this year will be about $5.5 trillion while the total expenses will be about $7.5 trillion, so there will be a budget shortfall of about $2 trillion. So, this year, your organization’s spending will be about 40% more than it is taking in. And there is very little ability to cut expenses because almost all the expenses are previously committed to or are essential expenses. Because your organization borrowed a lot over a long time, it has accumulated a big debt—approximately six times the amount that it is bringing in each year (about $32 trillion[1]), which equals about $240,000 per household that you have to take care of. And the interest bill on the debt will be about $1 trillion, which is about 20% of your enterprise’s revenue and half this year’s budget shortfall (deficit) that you will have to borrow to fund. But that $1 trillion is not all that you have to give your creditors because, in addition to the interest you have to pay on your debt, you have to pay back the principal that is coming due, which is around $10 trillion. You hope that your creditors will either relend or lend it to you. So, the debt service payments—in other words, the paying back of principal and interest that you have to do to not default—is about $11 trillion, which is about 200% of the money coming in.

That is the current situation…

…I believe that this situation needs to be dealt with via what I call my 3% 3-part solution. That would be to get the budget deficit down to 3% of GDP in a way that balances the three ways of reducing the deficit, which are 1) cutting spending, 2) increasing tax revenue, and 3) lowering interest rates. All three need to happen concurrently so as to prevent any one from being too large because, if any one is too large, the adjustment will be traumatic. And these things need to come about through good fundamental adjustments rather than by force (e.g., it would be very bad if the Federal Reserve unnaturally forced interest rates down)…

…Throughout history these debt cycles have occurred in virtually every country, typically several times, so there are literally hundreds of historical cases to look at. They go back as far as there is recorded history. Said differently, all monetary orders have broken down and the debt cycle process I’m describing is behind these breakdowns. This is the process that led to the breakdowns of all reserve currencies, like the British pound and the Dutch guilder before the pound. In my book, I show the 35 most recent cases.

Q2: If this process happens repeatedly, why are the dynamics behind it not well-understood?

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

…Q5: Do you know of any analogous cases of the budget deficit being cut so much in the way you describe and good outcomes happening?

Yes. I know of several. My plan would lead to a cut in the budget deficit of about 4% of GDP. The most analogous case of that happening with a good outcome was in the United States from 1991 to 1998 when the budget deficit was cut by 5% of GDP. In my book, I list several similar cases that happened in other countries…

…Q7: Japan—whose 215% debt-to-GDP ratio is the highest of any advanced economy—has often served as the poster child for the argument that a country can live with consistently high debt levels without experiencing a debt crisis. Why don’t you take much comfort from Japan’s experience?

The Japanese case exemplifies and will continue to exemplify the problem I describe, and it demonstrates my theory in practice. More specifically, because of the high level of the Japanese government’s over indebtedness, Japanese bonds and debt have been terrible investments. To make up for a shortage of demand for Japanese debt assets at low enough interest rates to be good for the country, the BoJ printed a lot of money and bought a lot of Japanese government debt, which has led to holders of Japanese bonds having losses of 51% relative to holding US dollar debt since 2013 and losses of 76% relative to holding gold since 2013. The typical wages of a Japanese worker have fallen 55% since 2013 in common currency terms relative to the wages of an American worker.

2. AI Jitters Have Suppliers Preparing for Data Center Boom to Go Bust – Brooke Sutherland

Manufacturers say they still have more orders from data center customers than they can handle. With no near-term end in sight for the demand surge, they’re racing to add factory capacity for electrical, power generation and cooling equipment. Siemens AG, for example, is investing more than $200 million in new electrical and power distribution products facilities in Georgia and Texas, the latest in a string of expansion projects for the industrial giant and its peers meant to support the data center buildout. But with the market getting increasingly jittery about sky-high valuations and the ultimate payoff from this massive debt-fueled spending bonanza, industrial companies are also taking precautions to make sure they don’t get stuck holding the bag…

…Even as it adds factory space, Siemens is tapping third-party manufacturers to fulfill some of its equipment orders, both to help it meet high demand and to give it some flexibility if that demand falters, Powell said. “If we were to get to a situation where the market dropped 15%, we’d have the opportunity to lower some of those third-party orders and not hit our own factories as hard,” he said. “They understand that we’re not necessarily giving them stuff forever.”

The German industrial giant is also trying to stay diversified in its electrical products business, even as data centers make up an increasing percentage of the market. The data center construction boom is now so huge that private spending has surpassed that for both the general office and healthcare markets…

…Suffolk Construction, one of the largest contractors in the US, is similarly seeking to make sure it doesn’t become “intoxicated” with data centers, says Charles McCarthy, president of mission critical projects. “We’re bringing on a lot of people, bringing on a lot of resources,” he said. “We want to make sure that there’s a backup plan. And when the market does turn, when some of these things do happen, there’s an avenue for us to keep our growth moving forward.”…

…Siemens similarly sees more risk with so-called neocloud data center companies that rent out access to leading AI chips amid concerns about circular financing. “There might be one or two really big winners out of these neoclouds, and you don’t want to miss out on that,” Powell said. “But if something goes bad in the data center market, it’s probably not going to start with the big cloud providers. It’s probably going to start with one of these big neoclouds.”

3. There are ~180 net nets in Korea, and I’m writing up all of them – Oliver Sung

Chaebols themselves are old (Samsung dates to 1938 and both Hyundai and LG go back to 1947) but the system that made them what they are was built in the 1960s and 70s. Park Chung-hee took power in a coup in 1961, and after he gained power, he put the commercial banks under government control and decided to point cheap credit at a handful of families he’d picked to industrialize the country. If these families hit their export target, well then more loans would follow at rates that were negative in real terms. The money that built corporate Korea came from the state and then from the banks, never really from shareholders, so the shareholder was never the constituency that mattered. That conflict between controllers and minority holders has been running ever since, and it’s been incredibly difficult to resolve.

Each one of the companies under a chaebol, some listed and some not, is called an “affiliate.” Samsung, Hyundai, and LG each run dozens of affiliates, and the largest 81 chaebols in the country count >3k affiliates under their umbrella. Why this is an issue when it comes to governance is that the families keep a tight grip on their affiliates through a spaghetti-fashion of cross-holdings between them. It’s not abnormal for a family to own, say, just 3.7% of a company but control 62.4% of the votes through a block of affiliates, meaning you could buy as much of the company as you’d like and still be the minority next to a family that owns <4% of it. If you wonder why I picked such odd percentages for illustration, you’ve probably already guessed that those aren’t illustrative but are the real numbers. 3.7% ownership vs 62.4% control represents the average across the country’s chaebol affiliates…

…Underlying that issue is a bunch of things that have traditionally been wrong with the machinery and have caused the chaebols to not only treat minorities unfairly but also hoard cash and create a jumble of corporate pyramids. Even worse, this machinery incentivized the controllers to in fact keep their own share prices down, and there have been at least four moving parts to it:

Dividend taxation. In Korea, once an individual’s financial income passes KRW20mn/year, dividends get folded into progressive rates that approach 50%, so the rational move for controllers has been to hoard the cash and pile it into low-return assets and further cross-holdings.
Inheritance taxation. Korea taxes inheritance at up to 50%, with a surcharge on controlling stakes that takes the bill toward 60%. And because the taxable value of listed shares is the average market price over the four months around the transfer, a family planning succession has a large and entirely legal incentive to keep their share price down for years.
Merger rules. A Korean merger ratio is typically set by averaging recent market prices rather than by any fair-value opinion. If the merger ratio is decided by the marginal buyer in the market rather than negotiated in the boardrooms, then that’s been good enough for the regulators. In 2015, the Samsung chairman was dying and his son, Lee Jae-yong, needed to end up controlling Samsung Electronics, which he barely owned any of, without triggering the inheritance bill. What he did own was a large slice of Cheil Industries, a small company in the group. Samsung C&T, a separate one, held a block of Samsung Electronics shares. So Samsung decided to merge C&T into Cheil right when C&T was trading at historic lows and Cheil at historic highs. The national pension fund, C&T’s biggest shareholder, swung the vote. People went to prison over that vote (including Lee Jae-yong, but he was later acquitted of all charges related to the merger), and the deal stood anyway.
Misuse of treasury shares. Because treasury shares haven’t traditionally been cancelled in Korea, in many cases they’ve been used to abuse shareholder value. There have been numerous cases of controllers swapping treasury shares with friendly parties, which is precisely what happened last year when Muhak, a local brewer, executed two treasury stock cross-swaps with its main glass bottle supplier and Samsung Gongjo, an auto parts company in the same region. This has meant that a Korean buyback is less a return of capital than a block of dormant votes bought with shareholders’ money and parked until the controller needs them…

…But the government is taking much deeper stabs at it from multiple fronts:

In July 2025, amendments to the Commercial Act were approved to require directors to balance corporate and shareholder interests. This is similar to what Japan enacted in its Stewardship Code around 2014. Before this amendment, a director’s duty ran to the company, which in a family-controlled company meant the family. This amendment is likely to mean more fairness in mergers, spins, splits, delistings, and other corporate transactions going forward.
Then in December 2025, the National Assembly approved a massive reduction in the dividend tax rate to a range of 14-30% for “high-dividend payers.” A “high-dividend payer” is one that has a payout ratio of >40% or has a payout ratio of >25% and increases it by 10% from the prior year. Crucially, to qualify, the company must also have a Value-up plan disclosed on the KRX. This is the first reform that really rewires the incentives for return of capital.
Finally, in February this year, the National Assembly passed another amendment requiring mandatory cancellation of treasury shares. Companies must now cancel newly acquired treasury shares within one year, and existing treasury stock got an 18-month grace period. This is a big deal…

…It’s important to mention that regulators have also decided that Korea has too many listed zombiecos, and so the exchange has started clearing out from the bottom. The minimum market cap for staying on KOSDAQ went to KRW20bn in July and will reach KRW30bn in January 2027. 30 trading days under the line brings a warning, then 90 more days to climb back above it, or the company gets delisted. The first company went out in June, another 36 were flagged just a week ago on August 12, and something like 1/10 of KOSDAQ could be gone by the end of the year.

4. We Bought a $500 Counterfeit Rolex So Good, Even Rolex Didn’t Spot It – Alistair Charlton and Jeremy White

I’m sitting upstairs in Rolex’s flagship London store on Old Bond Street. I’ve just handed over a fake Rolex to the staff and requested that its bracelet be adjusted, a simple bit of maintenance that requires removing a few links. I haven’t volunteered that the watch is a phony, but I haven’t said it’s legit, either…

…After what feels like far too long, the salesperson returns. He, like his colleagues downstairs did when I arrived, congratulates me on a beautiful watch and hands me back the counterfeit timepiece, now perfectly sized. The removed links have been placed reverently in a delicate, tiny paper bag, marked with the Rolex logo in cadmium green. Heart still pounding, I walk out…

…Mass-produced in huge quantities by Chinese factories, these watches are sold through dealers who advertise on TikTok and Instagram, broker through WhatsApp, and arrange discreet delivery to your door. Since reputation by word-of-mouth is paramount (these businesses can’t exactly show their wares in a digital shop window), they even claim to offer customer service channels, warranties, repair centers, and guarantees to send a replacement if your purchase is seized by customs…

…Quality control, or QC, is at the heart of this community. The subreddit r/RepTime has 269,000 weekly visitors and over 18,000 weekly contributions, according to Reddit’s own statistics. Posts about new counterfeit watch purchases pile up by the hour, both in r/RepTime and r/RepTimeQC, the offshoot subreddit focused on quality control. The posts contain photographs sent from the dealer to the customer, who then seeks advice from the RepTime community on whether they should “GL” (green-light) or “RL” (red-light) their purchase…

…Even a Reddit community with more than a quarter of a million weekly visitors represents a mere fraction of the global fake-watch industry. The Federation of the Swiss Watch Agency has estimated that tens of millions of fake watches are produced every year, dwarfing the number legitimately manufactured by Swiss brands. And, while many super clones sell for between $500 and $700, there are cases of ultra counterfeits posing as watches worth millions.

“There’s a very well-known Patek,” says counterfeit watch expert Adrian Hailwood, referring to an automatic model with the reference number 3448. It was the first serially produced automatic perpetual calendar wristwatch, and there have long been rumors that two examples were made in rose gold for a South American retailer. Only one is publicly known, a 1968 example first sold in Uruguay. In 2011 it was sold at auction by Christie’s for CHF 2,099,000 (about $2.3 million at the time), then sold again in 2025 for CHF 2.7 million (about $3.3 million).

“Allegedly there’s another one,” Hailwood says. “But I’ve seen three—all pretending to be that one, and all of them with faults. So somebody is churning out rose gold 3448s. And if it’s potentially got a list price of a million pounds, there’s a lot of incentive to put them together.”

Hailwood says that because such a piece would have real Patek innards and a totally fake outside, requiring a lot of work and resources and access to parts, each would cost between £20,000 to £30,000 to create. “You can imagine someone going, ‘Look, we can’t put this through public auction, it would be a million pounds. But, say, £400,000 and ask me no more questions.’ People with more money than sense would potentially go for that,” he says.

Million-dollar Pateks tend not to crop up on r/RepTime. Instead, the vast majority of watches subjected to quality control checks are replica Rolexes. Posts scrutinizing the fine details of Submariners, Datejusts, and Daytonas are especially frequent, along with a smattering of Tudor, Omega, IWC, and Cartier replicas. Most tend to be stainless steel, since their low manufacturing cost means these clone watches cannot be made from real precious metal…

…For a thorough expert evaluation of our cloned watches, WIRED met Adrian Hailwood at the UK offices of Watch Collecting, an online watch auction platform he helped launch in 2021, and for which he now provides authentication services. Since much of the production cost of super clones goes on the dial, Hailwood suggested inspecting other parts of the watch.

Under the jeweler’s loupe, he spotted bracelet screws that “really aren’t very round,” and the underside of a clasp that “is very much raw metal” and “not particularly nice.” These imperfections were followed by a Rolex logo on the bracelet which had been “milled out quite amateurishly … Almost looks like someone has done it with a Dremel.”

Hailwood plowed on. “The luminous material looks like it’s not set particularly nicely … The pip is not centered in the triangle, and it’s slightly too yellow for a modern watch.”

One startling piece of accuracy, however, is the coronet (Rolex’s crown-like logo), which since the early 2000s has been laser-etched discreetly into the 6 o’clock position of the sapphire crystal. A couple of millimeters wide and only visible under bright light, the supposed anti-counterfeit measure was present in our replica Submariner. On some fakes the coronet is fudged with a sticker or crudely scratched into the back of the sapphire. Other times it seems genuine, but fails to match the original, which is made up of tiny stars of varying sizes, not just the etched dots of the counterfeit.

Opening the watch revealed a replica movement that looked, at first glance, very much like the real deal. It appeared to be “reasonably good,” but then Hailwood pointed out how there’s a balance regulating arm to help fine-tune how quickly the watch runs—a type of movement that Rolex hasn’t used since 1957. Rolex now uses movements with free-sprung balances, so, in a bid to fool authenticators, counterfeiters have been known to swap the whole assembly around, obscuring the balance components. “That caught a lot of people out,” Hailwood says.

Since WIRED’s watches were examined, however, the counterfeiters have already upped their game. Hailwood claims free-sprung balances have now been introduced across clone movements imitating those by Rolex, Patek Philippe, Richard Mille, and Audemars Piguet. Once a relatively small-volume upgrade, these are now “industrialized features” that eliminate one of the tells he identified in our replica Submariner.

“The investment to produce this kind of clone movement … this is not back-alley sweatshop stuff,” says Hailwood, “this is big factories investing millions to produce full-on clones. It’s said by some watchmakers that these are now close enough that they can be serviced with Rolex parts, which does raise the specter of someone taking a genuine Rolex balance out and swapping it in [to a fake].”…

…According to a May 2025 report from the Organization for Economic Cooperation and Development, the global fake goods trade was worth $467 billion in 2021—the most recent data available—and accounts for 2.3 percent of all global trade, rising to 4.7 percent in the European Union. Clothing, footwear, and leather goods are the most commonly seized counterfeit goods, while watches rank fifth by quantity, and first, comfortably, by value.

A second OECD report focused on Switzerland found that, also in 2021, around $4.7 billion worth of counterfeit goods infringed Swiss trademarks, leading to a $3 billion loss in domestic sales. More than 40 percent of those goods, by value, are watches, with roughly 65 percent of seizures originating in China and Hong Kong. Overall, the global fake Swiss watch market equates to about 7.7 percent of the value of Switzerland’s legitimate watch exports, but with far lower production costs and sales prices per unit.

But remember, these figures are based on seized goods, not those that slip through the postal system unnoticed…

…Ultimately, what’s most striking isn’t merely the scale of this barely hiding counterfeit industry, but the misplaced enthusiasm driving it. These are real watch lovers who have built a genuine online community—people who are fluent in reference numbers, obsessing over index alignment and lume color. They are thrilled to share their knowledge and time, for free, with curious newcomers. It’s very much a genuine passion for watchmaking, but like the crooked hands of a red-lighted replica, it’s pointing the wrong way.

5. Let the Bond Market Speak – Stanley Druckenmiller

The Treasury Department announced on Aug. 19 that it would double the size of its long-dated bond buybacks, from $2 billion to at least $4 billion per operation, aimed at the 10- to 30-year sector and running from Sept. 9 through Nov. 4. The announcement came after the 30-year yield touched a 19-year high. Yields fell within minutes. By the next afternoon they had round-tripped to levels above where they started. The market’s verdict was swift and correct: This wasn’t liquidity management, it was price management—and a mistake far larger than $4 billion suggests…

… Inflation is 3% to 4% and has been above the Fed’s target since 2021. Unemployment is 4.1%, full employment by any definition. The deficit is running near 6% of gross domestic product, a number America has never before produced in peacetime at full employment. The national debt crossed $40 trillion the same week Treasury intervened. Net interest will exceed $1.1 trillion this fiscal year, more than the defense budget. The 10-year yield, even after the summer selloff, sits at or below the economy’s nominal growth rate. That means a borrower (federal government) running 6% deficits at full employment, with above-target inflation, still funds itself at roughly the rate its economy grows….

…The long-term Treasury yield is the most important price in the world. It is also the only fiscal disciplinarian the U.S. has left. Neither party will run on entitlement reform. Both have spent the past decade expanding commitments while ignoring arithmetic. Democracies don’t repair their finances because a budget office publishes a table. They repair them only when the cost of inaction becomes visible and immediate, when mortgage rates bite, when auctions tail, when the political price of a rising long bond finally exceeds the political price of touching spending…

…Every basis point of artificial yield suppression is a subsidy to procrastination. Suppressed long rates sugarcoat the interest-cost projections, shrink the apparent urgency, and let incumbents assure voters the debt is someone else’s problem. If Congress and the administration are unlikely to touch entitlements even with the market’s signal, they are certain not to touch them without one. Whatever this operation saves in basis points, it will cost multiples in delay…

…The defense of the buybacks writes itself: It is a routine tool, introduced in 2024 for liquidity and cash management, trivial against a marketable debt stock approaching $30 trillion. All true but beside the point. Routine operations aren’t announced off-cycle, at double size, on the heels of the long bond’s hitting a two-decade high, with a signal that they can grow without limit…

…What should happen instead is straightforward. Return buybacks to their stated purpose: small, scheduled, off-the-run liquidity operations announced at quarterly refundings, never off-cycle responses to yield levels. Term out the debt honestly and pay the price the market sets. If the 30-year must trade at 5.5% to clear, that isn’t a crisis. It is an invoice. Then do the only thing that durably lowers long-term yields: address the primary deficit. Reform entitlements gradually and honestly, through means testing, indexing changes, eligibility adjustments phased in over decades—so that the burden is shared across generations instead of dumped on the youngest…

…Governments defending prices against fundamentals always lose. The only variable is how much they spend before conceding.

What We’re Reading (Week Ending 23 August 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 23 August 2026:

1. AI Is Dead. Organoids Are Alive – Claire L. Evans

I mean that if lab-coated biologists took a sample of your skin and very carefully manipulated the cells inside it, they could actually make a brain. They do it all the time.

Not a brain as complex as the one behind your eyes, of course, but a glob of gray matter nonetheless, with a few-million-odd neurons that can send and receive electrical signals. Biologists call these strange creations human brain organoids. Kept at a womblike 98.6 degrees Fahrenheit for eight months, they’ll produce repetitive oscillations—brain waves—nearly indistinguishable from those made by a premature baby…

…To make a brain organoid, all you need is that sample of skin I mentioned before. (Samples of blood, hair, or teeth work too.) You take the adult cells and introduce them to some special proteins that revert them to their embryonic state. Given a second chance to mature, these so-called induced pluripotent stem cells can become anything: tear gland organoids that cry, heart organoids that beat, or brain organoids that … well, that’s the question.

In utero brain development is, as one bioethicist told me, “a black box” of scientific knowledge. Historically, a lot of what we know about it is inferred from studies with mice. But with an organoid, the transformation of stem cells into neurons into brain tissue happens in full view. In theory, scientists could one day study how a colony of dividing cells comes together to create a mind—to make, from 86 billion neurons, a person…

…Visually, organoids are not compelling; they’re opaque, snot-colored, the approximate size and shape of a chia seed. Muotri’s organoids contain 5 million cells, of which 2.5 million are neurons. (The rest are non-neural glial cells, which serve as scaffolding.) This, he reassures me, is the size of a bee’s brain…

…In Melbourne, Cortical Labs cultivates flat neural cultures—the stem cells were donated by the company’s own founder—and loads them into sleek white biological “computers” called CL-1s. Each is the size of an elongated toaster and boasts an onboard life-support system capable of keeping a culture of up to a million neurons alive for six months. With the CL-1, Cortical Labs is aiming to become the Nvidia of neural computing, providing hardware and, let’s say, “neurons as a service” with a sub-millisecond delay.

For now, these neural computers are mostly of interest to researchers who want to work with neurons without taking on the tedious wet-lab husbandry themselves. Eventually, however, the company hopes that neurons will prove themselves to be an energy-efficient, resilient substrate for more general computing applications—including some tasks currently handled by AI, like image recognition and classification…

…In 2022, using a system similar to what currently powers the Cortical Cloud, Kagan grew a neural culture on a microchip and trained it to play the 1972 Atari game Pong, rewarding the neurons with predictable electrical pulses when they made correct decisions and punishing them with chaotic bursts when they made mistakes.

The technique served as a minimal proof of concept for a theory, proposed by the neuroscientist Karl Friston, that self-organizing biological systems tend to minimize surprise whenever possible. By showing that neurons will reorganize themselves to avoid chaotic stimulus, Cortical Labs demonstrated one possible approach for programming living matter. But the experiment also signaled that the CL-1 could be considered hardware for testing theories of cognition…

…WHEN I SET out to report this story, I was sure it was about consciousness: the eerie moment a quarter-peanut of flesh sparks with self-knowledge, and what that precipice means for the researchers responsible. I imagined long dark nights of the cell and tiny funerals for spent neurons. What I found, however, was that nearly all scientists who keep organoids see the consciousness question as a distraction.

Most bristle when asked. They gesture to the organoids themselves—tiny balls bobbing in liquid solution like droplets of olive oil in vinegar—as if to say, give me a break. “Consciousness is so qualitative,” complained Annie Kathuria, an organoid researcher, when I visited her lab at Johns Hopkins. “How am I supposed to measure something qualitative on a tissue that’s floating in a dish? Someone has to define it. That’s what I say to everyone: Define to me what consciousness is, in quantitative terms.”…

…Conveniently, organoids can’t grow much bigger than 5 millimeters anyway. Once they reach a certain size, without a vascular system to pump in oxygen and pump out metabolic wastes, they develop a “necrotic core” and suffocate. This has created an upper ceiling for the organoid debate, but it won’t hold much longer. At Johns Hopkins, the nanotechnologist David Gracias is developing biomimetic artificial arteries; across town, at the Medical School, Kathuria creates rough blood vessel networks from endothelial organoids. Researchers are keeping organoids alive longer and longer. Muotri’s record, of three years, might’ve gone longer had someone in his lab not dropped the dish.

“THE GOAL IS to get to a 1-centimeter brain organoid,” said Thomas Hartung, as he walked me around the Center for Alternatives to Animal Testing, or CAAT, at Johns Hopkins, where he’s experimenting with new perfusion systems to get around the bloodlessness problem. One centimeter is roughly the size of a mouse brain—“a completely different beast,” Hartung said, than the 500-micrometer organoids he’s accustomed to…

…Nearly every organoid researcher I met reporting this piece told me the same fact: that in human clinical trials, the failure rate for neuropsychiatric drugs is close to 95 percent. The pipeline for new medications for conditions like depression, Alzheimer’s, and epilepsy is long, and often dry. That’s because drugs are ordinarily tested on animals, not people, and animal testing has never been the most physiologically relevant way to ensure the efficacy of drugs—it’s just been the most practical one. Organoids have changed that calculus…

…In July 2025, the National Institutes of Health announced that it will no longer award grant funding to research that relies exclusively on animal testing, encouraging consideration of “new approach models,” like organoids, instead; in September, the NIH announced an $87 million investment in building a Standardized Organoid Modeling Center. Fortunately for the NIH, since organoids are made with induced pluripotent stem cells from consenting adult donors, they don’t inspire the same religious ire as embryonic stem cell research did in the 1990s. And unlike lab rats or primates, organoid testing doesn’t irk the animal rights crowd.

Recent studies suggest that ordinary people, by and large, aren’t much bothered by organoids. When they do lodge an objection, it’s not because globs of neural cells might someday achieve human-level sentience. It’s because brain organoids are creepy. They muddy the line between person and thing, which, like the difference between humans and animals, or between the dead and the living, is a fundamental distinction that transcends cultures…

…Somewhere between the few million neurons in a brain organoid and the billions more that make up our minds, selfhood emerges—and absolutely nobody knows when…

…For the AI people, machine intelligence seems achievable, even inevitable. They “feel the AGI.” Part of this is hype; part of it is a willingness to take intelligence for what it does, as opposed to what it is. “If it quacks like a duck, it is a duck,” said John Evans, the UCSD sociologist. “The Silicon Valley types are super pragmatic. If AI is capable of doing what a human with consciousness does, they’ll call it consciousness.”

Biologists who work with neural cultures, however, are far less likely to use such a word without plenty of agonizing caveats. These biologists cultivate the raw meat of mind every day; they’re in the privileged position to understand how colossally complex it really is. As biology and AI converge, the culture shock between these worldviews is likely to be bracing.

For now, it seems that in the race toward conscious machines, there will be two lanes. One will be paved with rare earth minerals and silicon—forced, at enormous financial and energy cost, to model the brain from the top-down, in the form of artificial neural networks…

…Which leaves the other path. Admittedly, this one will be slippery and meandering—adapting, as all living things do, to the journey as it goes. It might not be the most direct route, but in the evolutionary history of life on Earth, it’s the only thing that has ever led to intelligence.

2. GEN-1.5 – Generalist Team

The ability to learn closed-loop physical skills from just one or a few demonstrations, and to do so across a broad range of tasks without such restrictions, has predominantly been considered out of reach. Such an ability may also likely be underpinned by a foundation that enables other broad generalization capabilities…

…We’ve created GEN-1.5, our latest robot foundation model that exhibits broad one-shot and few-shot learning from demonstration capabilities, as well as zero-shot generalization, e.g. improvisation and novel tool use (e.g. brush, dustpan, etc.). GEN-1.5 is a large multimodal model that processes video input (30 seconds of memory, alongside other sensor, language, and proprioceptive inputs) and produces 100 Hz action trajectories. Its capabilities include:

One-shot learning via in-context prompting. The model learns new tasks in seconds when prompted with 3 to 12 seconds of a single demonstration, no training required. We refer to the use of sensorimotor examples in the context window as “physical prompting.”…
…Few-shot adaptation via gradient descent. The model can be fine-tuned to a new task in 1–10 gradient steps on 1–5 minutes of data (~10–50 demonstrations).
Improvising new strategies and tool use. The model generalizes at the level of behavioral strategies: forming entirely new trajectories to reach a goal, using unseen tools (e.g. brush, dustpan, etc.) to create new solutions to tasks demonstrated with other tools, and working ambidextrously even when prompted or fine-tuned to perform the task with a specific hand.

These capabilities appear to emerge directly from pretraining on large amounts of physical interaction data. We did not explicitly train for any of them: no architectural changes to promote in-context learning, no inner or outer meta-learning loop pressuring the model to adapt from minimal data, no auxiliary objectives encouraging improvisation…

…Although the tasks are simple and short-horizon, and the success rates are modest, this is the first model we know of that has demonstrated the general ability to learn a wide range of dexterous closed-loop physical tasks from just one-shot or few-shot demonstrations…

…GEN-1.5’s initial pretraining began in parallel — it has now been training continuously for over eight months. We left it running because every metric we tracked kept improving with the engine: absorbing more data, scaling more efficiently with compute, and achieving step-change gains with successive surgical architectural and algorithmic changes. It was clear that the model was getting better, and the trend was consistent: new tasks were becoming more data-efficient, more compute-efficient, and more general.

As the model continued to train, we began experimenting with how few finetuning steps we could use to adapt to new tasks, finding the model could learn new tasks from 100s, then 10s, then eventually, 1 gradient step on just one minute of data. As far as we know, the ability to learn skills with such few gradient steps had not been observed before. We then asked, can this model learn new tasks without training, purely in-context and with zero gradient steps? That this works at all changes how we think about how these models can be used, about their potential impact, and the road ahead for building general physical intelligence…

…GEN-1.5 can be prompted with a single demonstration inserted into its 30-second context window, and the remainder holds rolling observations. Physical prompts are sensorimotor examples (i.e. sensor data plus action trajectories), recorded either as human data (with a pair of handheld grippers) or as rollouts from the robot itself. Once the prompt is in context, the model performs the task immediately, with no training steps. The performance of one-shot learning in-context is modest (59% average success across diverse tasks including handling zippers, opening jars, grabbing money out of wallets, etc.), but the fact that inserting a single demonstration in the context buffer, without ever training for it, yields any measurable competence at all was unexpected…

…We did not explicitly train GEN-1.5 for in-context learning, and the tasks we tested were not engineered into the pretraining data beforehand. This is a general model which we are prompting without regard to the pretraining data distribution.

Why this capability emerges from pretraining is difficult to pinpoint. One hypothesis, by analogy to language, is that the distribution of physical observations and actions may exhibit “burstiness” and Zipfian structure of the kind that has been linked to in-context learning in language models.17 It is also possible that physical work contains naturally repetitive cycles, and the model may have learned to detect and extend such patterns, as language models do with general sequences.18 The model was pretrained on randomly sampled continuous spans from our data engine (activities captured in homes, warehouses, factories, and elsewhere) with no bespoke infrastructure for packing examples into context — physical prompts introduce discontinuous jumps in time that the model never saw in training.

Robotics is inherently multimodal; and as in human learning, there are many ways to teach a robot something new — the two options of either (a) demonstrations or (b) language instructions are perhaps the most natural for having humans specify tasks.19 While language suffices for some task specifications, many physical actions are difficult to precisely describe in language20 (e.g. it is far easier to show exactly how to seat two Lego bricks than to say it). Prompting a task in native observations and actions is also a more comprehensive test of sensorimotor understanding: the model must infer the goal from the demonstration, repurpose existing knowledge, and improvise under new initial conditions…

…In some cases, in-context learning transfers across the embodiment gap entirely: a human demonstrates a task with their own hands, observable through the robot’s cameras, and the robot can reproduce it immediately afterward….

…In one example, we demonstrated using a brush to sweep a block into a bowl, and fine-tuned the model on 5 minutes of human demonstrations. The model was able to figure out how to use a variety of other tool options besides the brush in order to accomplish the task. When presented with a banana, it used the banana as a makeshift brush. When presented with a dustpan, however, the model exhibited a larger strategic departure from its demonstrations, and through a variety of means would use the dustpan to lift up the block and dump it into the bowl. Neither the fine-tuning data nor, to the best of our knowledge, the pretraining data contains a dustpan used this way, and the nearest pretraining examples bear little resemblance to the task. Handed a dustpan, the model composed an entirely new contact sequence to complete the task out of the box, with no language guidance…

…Although the model was only fine-tuned to put the block into a bowl, it appears to be able to remove obstacles (like a piece of paper covering the bowl) to complete the task, and sometimes place the paper back on top of the bowl. There was no paper covering the bowl in the 5 minutes of task-specific data (with which the model was fine-tuned for only 1 gradient step), and no such task in this setting (to the best of our knowledge) was in the pretraining data…

…GEN-1.5 is a milestone we believe to be profound scientifically, not because of higher success rates, but because it represents a new frontier of generality — one that challenges our own understanding of how these models behave when pretrained at a scale of physical interaction data few thought possible without shortcuts…

…What is clear now, and perhaps obvious in hindsight, is that past a certain threshold of pretraining, the cost of adaptation becomes negligible. Emergent in-context learning from a few seconds of data, or one gradient step on one minute of demonstrations, is no longer task-specific training in the conventional sense. It is closer to reminding the model of something it nearly knows, with a tiny amount of compute. That this works at all, changes how we think about how these models can be used, about their potential impact, and the road ahead for building general physical intelligence.

3. Apple forced to restructure ATT – Eric Benjamin Seufert

Yesterday, the German competition authority, the Bundeskartellamt, concluded its antitrust investigation into Apple’s App Tracking Transparency (ATT) framework after Apple agreed to legally binding changes to the ATT prompt and related consent design…

…Apple now has four months to implement these changes, and the commitments will remain binding for seven years…

…I believe the agreed-upon changes to the ATT prompt, as well as the ability for developers to bundle other data-use consent requests with the ATT prompt, will nudge opt-in rates upward by a non-trivial amount. Apple has stated that the changes will apply in almost all EU countries, and I expect the remaining European investigations to resolve similarly.

But I’m skeptical that the US will adopt similar measures, and for that reason, the broader impact on the digital advertising market of this restructuring of ATT will be muted: without any changes in the US, and given that historical ATT opt-out decisions will not be automatically reversed en masse, my sense is that the principal impact of the concessions extracted from Apple by the Bundeskartellamt will be precedential rather than immediately economic…

…So ATT will survive, but Apple’s ability to impose asymmetric rules on third parties under the banner of privacy without meaningful constraint will not. The immediate economic impact of these concessions may be modest, particularly if the changes stop in Europe, as I believe they will (for a more extensive argument on why I think that’s the case, see Could ATT be rolled back?). But their precedential impact is consequential. ATT was never simply a privacy policy; it was an exercise of platform power, and that power is now circumscribed.

4. A $21 Billion ‘Kids in Chips’ Startup Is Scooping Up Nvidia Talent – Robbie Whelan

Etched doesn’t make software but semiconductors, a product category whose high capital costs and long development cycles have relegated it to a corner of Silicon Valley where experience still trumps youthful ambition…

…Etched has reached a key milestone: signing up its first customer—Jane Street, the secretive Wall Street quantitative-trading giant—for its product, a server rack filled with AI processors optimized for rapid inference computing. The startup has booked more than $1 billion in orders and already started shipping chips…

…The startup says it took just 44 days after getting its test chips back from Taiwan Semiconductor Manufacturing to have them up and running inference workloads—the computing processes that allow AI models to respond to user queries—a process that usually takes six months or more.

“We may be the only AI chip that was built by a startup that was successful on the first try,” said Robert Wachen, co-founder and president of Etched…

…Around 15% of Etched’s roughly 400 employees previously worked at Nvidia, and it has recruited aggressively from the market-leader, as well as from other established semiconductor firms…

…Then there is the 2-megawatt in-office data center. Lined with refrigerator-sized server systems, the room emits a low roar from its cooling systems and allows potential customers to remotely access and try out Etched’s chips.

Another way in which Etched is atypical is in how much of its supply chain it owns and controls. The company has a team of 20 in Taiwan, where it owns a factory that builds server components. Most chip startups outsource much of the testing of their finished products, while Etched does almost all of it in-house.

Company executives say they design their chips using a distinctive design approach called “cluster-scale memory,” a way of allowing multiple chips inside a server to communicate more quickly and thus act as one processor. They say communications tasks that take about 4,000 nanoseconds for an Nvidia Blackwell processor to perform can be done by Etched’s chips in 700 nanoseconds, thanks to the chip’s architecture and custom interconnections.

5. The Future Of AI Compute Won’t Run On Just One Kind Of Chip – Liz Allan, Satadal Bhattacharjee, Ashish Darbari, Moshiko Emmer, Sharad Chole, Cameron Brunner, and Sumit Vishwakarma

Bhattacharjee: That’s right. One of the trends we are seeing, which Nvidia started, is disaggregating the inference pipeline. In inference, there are a few stages. One is called prefill, where you enter a prompt and it’s just trying to figure out what you’re asking so that it can take action. The prefill stage is extremely compute-intensive. Recently, Nvidia announced that its Groq 3 LPU (language processing unit) will be used in the prefill cluster, showing more than a GPU is required to do some of these tasks. Then there’s decode, where it actually does the task, or creates the response that will be generated and shown. Then with the agents coming in, there is the tool calling or executing the task — for example, booking the Uber ride or making a hotel reservation.

We are seeing that with inference disaggregated, you have a cluster with a specific set of hardware and software to do the pre-filled stage. Then, you have a cluster to do the decode stage, and then you have another fully compute cluster to do the execution by the agents. And these are all stitched together. They’re all communicating with each other, most likely through Ethernet right now, but each of the clusters has a different mix of software and hardware to do the function that they do best. That’s going to be more of the norm going forward, because until now every AI problem was solved with a GPU, and the industry is recognizing that every nail doesn’t have one hammer…

… There are some companies that have come up, such as Gimlet Labs and Together AI. Their pitch is that they provide the software layer to run on top of a heterogeneous hardware environment, even on disaggregated inference, and make sure the prefill cluster is run optimally, the decode cluster is run up to value, and the compute cluster is run optimally to do each of the workloads. They are taking care of the software orchestration, and they are working with different companies to do that. This is a very critical part when you go beyond the top hyperscalers like Google, which are invested in creating these custom TPU clusters. Not many companies can do that. Especially if you go to the neocloud guys, which are the next level of cloud providers like DigitalOcean, CoreWeave, Lambda Labs [now Lambda AI], Verda, and others, they’re all going to have this same challenge. There is a desire to bring more hardware diversity. There’s a desire to bring token costs down — of course, without sacrificing performance — and do it without having a single company’s hand holding you. This is a big challenge that we are facing right now. We are at the early stages. It will take some time before this problem is solved, but there’s a lot of concentrated effort going into building this heterogeneous cluster with optimized software coming and solving this big problem that we have, where everybody is waiting in line for Nvidia systems because that’s the only system that is proven to be working at scale right now.

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, and TSMC. Holdings are subject to change at any time.

What We’re Reading (Week Ending 16 August 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 16 August 2026:

1. Amazon 2004 shareholder letter – Jeff Bezos

Though some may find it counterintuitive, a company can actually impair shareholder value in certain circumstances by growing earnings. This happens when the capital investments required for growth exceed the present value of the cash flow derived from those investments.

To illustrate with a hypothetical and very simplified example, imagine that an entrepreneur invents a machine that can quickly transport people from one location to another. The machine is expensive—$160 million with an annual capacity of 100,000 passenger trips and a four year useful life. Each trip sells for $1,000 and requires $450 in cost of goods for energy and materials and $50 in labor and other costs.

Continue to imagine that business is booming, with 100,000 trips in Year 1, completely and perfectly utilizing the capacity of one machine. This leads to earnings of $10 million after deducting operating expenses including depreciation—a 10% net margin.

The company’s primary focus is on earnings; so based on initial results the entrepreneur decides to invest more capital to fuel sales and earnings growth, adding additional machines in Years 2 through 4…

…It’s impressive: 100% compound earnings growth and $150 million of cumulative earnings. Investors considering only the above income statement would be delighted. However, looking at cash flows tells a different story. Over the same four years, the transportation business generates cumulative negative free cash flow of $530 million…

…Notice, too, that a focus on EBITDA—Earnings Before Interest, Taxes, Depreciation and Amortization—would lead to the same faulty conclusion about the health of the business. Sequential annual EBITDA would have been $50, $100, $200 and $400 million— 100% growth for three straight years. But without taking into account the $1.28 billion in capital expenditures necessary to generate this ‘cash flow,’ we’re getting only part of the story—EBITDA isn’t cash flow…

…Unfortunately our transportation business is fundamentally flawed. There is no growth rate at which it makes sense to invest initial or subsequent capital to operate the business. In fact, our example is so simple and clear as to be obvious. Investors would run a net present value analysis on the economics and quickly determine it doesn’t pencil out. Though it’s more subtle and complex in the real world, this issue—the duality between earnings and cash flows—comes up all the time.

2. Nvidia’s Risky Business – Ben Thompson

Today corporate executives and financial engineers don’t need to control newspapers; thanks to his new X account, Nvidia CEO Jensen Huang can go straight to the public. From an X Article posted last night:

NVIDIA AI Factory Compute Is Becoming an Investable Asset Class

Today, we announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time.

This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue.

AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world’s most productive assets. In AI, compute is revenue.

Huang argues that Nvidia-based AI factories are fungible, protecting residual value, and that CUDA makes AI factories better over time, extending their economic value; according to Huang:

These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed.

Thus the attempted formalization of a new investment structure:

The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly. That is why we are partnering with the world’s leading long-term capital providers.

Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world’s leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs.

What Apollo et al. are, are new sources of capital beyond the investment grade debt markets. In that sense this proposed structure is somewhat akin to Google’s equity issuance: a way to secure funding beyond bonds. The difference, however, is stark: whereas equity dilutes the upside for investors without adding risk to the company, this structure preserves Nvidia’s margins by finding new pools of capital willing to bear risk.

It’s not a total free ride for Nvidia: the company is backstopping opportunities with up to 25% residual-value based financing, suggesting that Huang believes his “investable asset class” pitch much more than the market does. That is, in a certain sense, a price cut, as the goal is to reduce the cost of capital for entities building data centers with Nvidia chips, by putting Nvidia’s profits on the line for uncertain investments. That guarantee is downstream from Google’s (and soon Amazon’s) aggressiveness: why build a data center with Nvidia chips if you can buy TPUs or Trainiums (Nvidia chips are likely better, but if the constraint on new data centers is capital, lower up-front prices may matter more than token efficiency)…

…This might not cost Nvidia anything in the end: if AI revenues truly take off, then the debt markets will open back up, and ultimately companies will go back to funding infrastructure investment through free cash flows. Right now, however, is the danger zone, as hyperscalers blow through the debt markets and Google at least starts to tap equity. To the extent Nvidia competes through novel funding mechanisms that, at the end of the day, draw on things like insurance floats and pension funds and other long-run liabilities that are the bread and butter of the asset managers the company is partnering with, the risk — unmarked, unlike equity — is considerably higher.

That’s why I started with 1870 and Cooke’s ill-fated agreement with Northern Pacific. Yes, the upside the deal afforded Cooke was incredible, but it was incredible for a reason: it was very risky, and pioneering new funding mechanisms only served to spread the pain when it all blew up. It’s one thing to spend all of your free cash flow; it’s another thing to tap the debt markets. And, beyond that, it’s a completely new nerve-racking thing to bring safety-seeking assets to bear. AI better deliver before it’s too late.

3. NVIDIA AI Factory Compute Is Becoming an Investable Asset Class – Jensen Huang

…NVIDIA A100 is a powerful example. NVIDIA introduced the Ampere-based A100 in 2020, and six years later, it remains in active commercial use for AI training, fine-tuning, inference and high-performance computing. Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.

The market is also demonstrating the durability of NVIDIA compute economics. One-year H100 rental pricing rose from about $1.70 per GPU-hour in October 2025 to about $2.35 per GPU-hour in March 2026. Cross-provider on-demand median pricing rose from roughly $2.00 per GPU-hour in October 2025 to $2.70 in June 2026. Blackwell capacity commands a premium, with reported B200 cloud rates spanning approximately $5.30 to $7.05 per GPU-hour…

…The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly.

That is why we are partnering with the world’s leading long-term capital providers…

…Why would NVIDIA support financing?

In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.

This is substantially lower than other compute-financing arrangements. NVIDIA can provide support because NVIDIA compute is unique: it is fungible, universally adopted, software-upgradable and redeployable across a large ecosystem of customers…

…Every industrial revolution has been built on infrastructure: electricity, transportation, communications and computing, with every buildout enabled by external financing.

4. The Future is for Everyone – Mark Zuckerberg

We are fortunate to live at an incredible moment in history. In the next few years, people will be able to use superintelligence beyond human capacity to create and discover extraordinary new things, build new businesses, express new ideas, learn new concepts, and advance our health and quality of life. As we get closer to this moment, it is important to develop a philosophy for how we can best use superintelligence to ensure it improves all of our lives, work, communities, freedom and safety.

The defining questions of our age are who will have access to superintelligence and what will we direct it towards. Will it be centralized and restricted to a few institutions, or will it be a tool that empowers everyone?

We propose a philosophy based on individual empowerment as the source of prosperity, invention as the primary purpose of superintelligence, and balance of power as the foundation of safety…

…At the same time, new technologies also bring new risks. There are many important concerns we are focused on addressing — from concerns about job displacement and ensuring local communities benefit from data center builds, to safety concerns around AI misuse related to cybersecurity and biorisk, to avoiding government tyranny and surveillance, ensuring the US and democratic countries lead, and ultimately making sure humanity maintains control over superintelligence so it serves rather than endangers us.

The conventional view is that these concerns are about technology, and that if we take enough time then we can perfect or align the technology to produce a single benevolent superintelligence. I think this view of alignment is fundamentally flawed.

Humanity is not a monoculture. People’s diverse values represent different tradeoffs they would make on important issues. There is no technological solution that can align with everyone’s opposing interests and values at once. Any singular superintelligence would have to prioritize some values over others and in the process would be incapable of being benevolent to everyone.

Instead, we propose that it is more productive to view each of these concerns through the lens of achieving the right balance of power, as western society has in democratic governing institutions. People and institutions with competing interests naturally check and balance each other to lead towards positive outcomes. The best and most realistic path to building a positive AI future is by delivering superintelligence to everyone.

As a thought experiment, imagine only one person had a superintelligent lawyer. They would have an unfair advantage in court — even if they were wrong on the merits. That would lead to a worse society. But now imagine everyone has a superintelligent lawyer. In this case, justice would be carried out much more fairly and efficiently than it is today when there is often an imbalance in skills and resources in litigation.

Similarly, if one person alone had a cybersecurity superintelligence, they could likely break into almost any technical system and the world would be much less secure than today. But if everyone has access to cybersecurity superintelligence, then all of our technical systems would become more secure than today since the widely deployed superintelligence would help harden and update every system.

If only one business had superintelligence, that business would outcompete all others and lead to a less dynamic and broadly prosperous market than we have today. But if everyone has access to superintelligence, then everyone will have the tools to create new things beyond what is possible today, and the economy will be more dynamic and generate more broad-based prosperity.

When people are empowered, they naturally compete and check each other economically, socially, politically, and in all other planes of human interaction. People also check and balance the power of institutions including businesses and governments.

But if the power of superintelligence is held by a small number of individuals, businesses, governments, or AI itself, then that will naturally lead to outcomes that are less favorable for everyone else. This is not a technological principle. It is about the balance of power. There is no such thing as a singular benevolent superintelligence.

Therefore, the key to a positive future for everyone is achieving a balance of power that favors individuals. The solution is to ensure that superintelligence is broadly distributed to empower people…

…Before the industrial revolution, 90% of people were farmers growing food to survive. Advances in technology steadily freed much of humanity to focus less on subsistence and more on the pursuits we choose. At each step, people used our newfound productivity to achieve more than was previously possible, as well as spending more time on creativity, culture, relationships, and enjoying life.

Of course some aspects of the way we work will change — just as it did with computers, the internet, and any new technology. This means people will have to adapt, and this will be challenging. But the more that everyone has a personal agent that is superintelligent at teaching us new skills and helping us adapt to change, the smoother this will be.

Company sizes may shrink — just as they did in the transition from industrial giants to tech companies. But this doesn’t mean fewer jobs overall. It implies a larger number of companies with fewer people each. There are many more valuable companies and services to build than people are able to build today. I expect we will start seeing small numbers of people with personal superintelligence agents able to run companies at significant scale. In the future, small businesses will continue to be the backbone of the economy, but each small business will be able to have a much larger impact…

…For example, in Richland Parish, Louisiana, where Meta is building a large data center, teachers received a $50,000 bonus this year because of the increased tax revenue from our investment. The superintendent told us that teachers are now moving there from across the country and he believes it will become one of the nation’s best school districts…

…We help keep electricity prices low by building our own energy-generating infrastructure wherever we invest. This ensures that not only are we not consuming energy that could have gone to the local communities, but in some cases we even supply a surplus of low-cost energy back to the communities. We think this is an important investment principle for sustainability.

Our data centers are also designed to be among the most water-efficient in the world. We are committed to being water-positive, meaning that we’ll restore more water than we use in the watersheds where we operate by 2030. In areas with high water stress, our goal is to restore 200% of the water we use…

…Some argue that the best way to reduce risk is to restrict the capabilities individuals can access. But giving people cybersecurity capabilities is also how we secure the long tail of systems, and giving people scientific capabilities is how we advance science in ways that should reduce the risks of harm over the long term. Restricting capabilities leads us down the path of centralization and lack of checks and balances, so we should be extremely careful about this — especially if other nations pursue less restrictive paths.

On cybersecurity, widely deployed open source systems have proven more secure because more people can identify vulnerabilities, harden the systems, and easily upgrade to the latest most secure versions. Even in recent weeks, we have seen companies handling security incidents like HuggingFace rely on widely available open models to patch vulnerabilities. Over time, I expect that widely deployed AI models with strong cybersecurity capabilities will lead to systems that are more secure, not less. This will be definitively true once superintelligence enables most of the world’s code to be verifiably secure. The long term answer isn’t to withhold capabilities but to establish a balance of power where superintelligence is broadly distributed.

5. AIndicators Hint at Doubts in Credit Markets – Richard Abbey and John Authers

Investors have awakened to the financial risks of the artificial intelligence buildout, and they have a deep well of precedent to draw on. Like all transformational technologies, it must be financed with borrowed money long before it can generate a return. More than 150 years ago, financier Jay Cooke’s ambitious campaign to fund the Northern Pacific Railroad began to unravel not because railroads were a bad idea, but because the bonds financing it traded at steep discounts for months. His firm’s collapse triggered the Panic of 1873. As Alberto Gallo of Andromeda Capital Management points out, history is full of worthy projects that weren’t worthy investments:

These projects benefited the wider population later on but ended up bringing insufficient financial rewards to their initial capital providers, especially when capital was in the form of credit.

Today’s debt-financed AI buildout isn’t necessarily destined for the same reckoning. But credit markets have a habit of registering doubt before broader markets and should be heeded. Spreads revealed mounting strain long before newspapers declared a financial panic in 1873. They are performing much the same function today. As Barclays shows, main AI players’ credit spreads have risen sharply in recent weeks…

…Spreads aren’t signaling a crisis just yet. Their recent widening nevertheless suggests investors are demanding greater compensation for financing the AI buildout…

…The bottom line is whether people use the product once it’s ready, and what they pay for it. In Field of Dreams terms, the industry is building it; will they come? A Federal Reserve study published in April estimates that AI adoption among US businesses reached 18% by the end of 2025, pointing to substantial room for further expansion. Demand tells a similar story. Data from OpenRouter, which tracks consumption of tokens (the basic units of text processed by large language models), show usage has more than tripled since January…

…Does soaring token usage necessarily translate into soaring revenues for providers of foundational models? Not quite. The LLM Token Expenditure Index, which measures the effective expenditure on large language models by combining token prices and usage, reveals the disconnect. As token volumes surge, falling prices and migration toward cheaper models suggest monetization has not kept pace with adoption…

…JPMorgan estimates that $4.1 trillion of the $5.5 trillion in AI capex will be debt-financed, so rate hikes would essentially pile on the costs of this buildout, putting a spoke in the wheels of the massive investment cycle.

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 Meta Platforms. Holdings are subject to change at any time.

What We’re Reading (Week Ending 09 August 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 09 August 2026:

1. The Disaggregation of Inference Compute – Eugene Ng

Recurrent Neural Networks (RNNs) process and convert a sequential data input into a specific sequential data output, one step at a time. Because RNNs read one word at a time, training is slow and cannot be parallelized. Worse, as sequences grow long, the learning signal either explodes into instability or vanishes towards zero, and the model increasingly struggles to connect distant words. In short, RNNs are slow, inefficient, and expensive to train, and become increasingly forgetful over distance.

Transformers ended the waiting and unlocked parallelism. Instead of reading one word at a time and passing notes down a slow chain, self-attention lets every word look at every other word at once, and positional encoding keeps track of the order the words came in. Attention decides what matters. Positional encoding preserves order…

…Attention has one non-negotiable rule. Every token must compare itself with every other token. It is how each word figures out which others give it meaning…

…Ten tokens, one hundred comparisons. One thousand tokens, a million. A hundred thousand tokens, ten billion. The work grows with the text squared.

As the context size increases, the attention weight matrix grows quadratically, meaning the number of scores that must be computed and stored grows significantly faster. This leads to higher memory usage and computational cost, making it challenging to scale transformers to very long sequences efficiently…

…Fixes have come in three variations. Compute it smarter. Compute less of it. Or replace it. The first two focus on treating the symptoms. The third goes after the disease…

…The quadratic wall is real, and no fix has removed it cleanly. So, the frontier settled on hybrids for now. Cheap linear layers do most of the work. A few real attention layers stay to catch what the cheap layers miss. The frontier has settled on hybrids, a truce, not a cure.

Because the quadratic problem was never fully solved at the software architectural level, the industry has been forced to keep compensating with more expensive HBM and more GPU FLOPs at the problem, plus incremental software fixes, especially as workloads shift from training to inference and agentic use cases…

…CPUs are built to finish one task fast. GPUs are built to finish many tasks per second. CPUs are great for serial computations, but are architecturally poorly suited for matrix multiplications, which involve numerous parallel calculations.

LLMs are mostly matrix multiplications by arithmetic. NVIDIA did not make GPUs good at matrix multiplication by accident. Matrix multiplication turned out to look exactly like the problem GPUs were already built to solve.

GPUs, which are excellent at parallel computations, have rapidly displaced CPUs in this initial AI infrastructure buildout phase…

…AI LLM workloads started primarily with training, as models are trained, and are increasingly shifting to inference and agentic workloads…

…Inference workloads can be split into prefill and decode.

Prefill (prompt processing) is compute-bound. It processes the entire input prompt in parallel, builds the KV cache for attention, and is dominated by large matrix multiplications.

Decode (token generation) is memory-bound. It generates one output token at a time sequentially and reuses and grows the KV cache on every step autoregressively. It is dominated by memory reads of model weights and KV state. The bottleneck is memory bandwidth.

Each token requires reading the entire model’s weights and the full KV cache from memory, then performing relatively light arithmetic on them. The bottleneck is bandwidth, specifically the latency of getting weights and cache to the compute unit.

The GPU’s FLOPs sit idle waiting for memory. Using a GPU to decode at a batch size of 1 is a Ferrari in a parking lot: expensive silicon and memory doing almost nothing.

Continuous batching can improve throughput by interleaving many independent sequences, but it cannot eliminate the fundamentally low arithmetic intensity of decode, especially for latency-sensitive or single-stream workloads.

Agentic workloads compound this issue further. The industry’s software response is to run both phases on the same chip and optimize for throughput. But the issue remains. One cannot batch your way out of a workload that is inherently sequential. Decode looks like a compute problem but behaves like a memory problem.

AI inference is no longer a single workload that can be served efficiently by a single type of accelerator or memory. AI inference is a dual-stage workload, with very different resource demands for each. The same knife should not be used to cut everything…

…GPUs address this hardware architecture flaw with fast, expensive HBM stacked next to the compute die. Stacking to increase capacity causes thermal, yield, and capacity issues. DRAM chips generate heat, and it is becoming more difficult to remove it effectively…

…There are physical limits to how much thinner DRAM can be shaved and how much higher the shaved layers can be stacked vertically with TSVs (Through-Silicon Vias) and micro-bumps to make HBM.

Yield is also becoming difficult to achieve as layer counts increase. For a given stack yield, total yield decreases as layer count increases…

…Inference compute remains memory-bound on decode and batch size one. While HBM is a lousy memory, unfortunately, it is the best solution we have right now that can address this structural memory wall problem.

In short, HBM is bit inefficient, power inefficient, and bandwidth inefficient. The memory makers likely know it too and recognize that HBM is not the final answer and will not solve the memory wall problem…

…The next best solution is to redesign the hardware to better address the inherent software flaws. One way that is increasingly being adopted by the industry is to disaggregate the inference compute workload of prefill, decode, and orchestration/execution…

…While GPUs have been great for training, they are increasingly poorly suited for inference, especially on decode. If inference hardware becomes increasingly disaggregated into prefill and decode and is even less suited for agentic AI, where workloads are dominated by orchestration, GPUs could become less dominant.

Prefill on compute-dense silicon (i.e., NVIDIA and AMD GPUs, custom ASICs). Decode on bandwidth-dense silicon (i.e., Groq LPU, Cerebras WSE, SambaNova RDU, etc). Then orchestrate both from a CPU (i.e., NVIDIA, Intel, AMD, Graviton CPUs) and carry the KV cache on the fabric.

It makes sense to further disaggregate the AI inference hardware stack. Numerous launches to disaggregate prefill and decode have been announced over the last few months by the majors, including NVIDIA, AWS, Intel, AMD, with Groq, Cerebras, and SambaNova for decode-specialized chips.

2. Inside Google’s $200bn Wall Street finance machine for Anthropic – Ryan McMorrow

Google has assembled one of the largest infrastructure financing programmes in history to supply more than $150bn of artificial intelligence chips to Anthropic…

…To support the relentless surge in demand for the AI chips, Google, Broadcom and Wall Street investors have each taken on different pieces of the financial risk.

Google guarantees the data centres. Broadcom commits to buying the chips and helps finance them. Apollo and Blackstone provide much of the private-credit capital that purchases the hardware before leasing it to Anthropic…

…In June, the first tranche of TPU hardware passed from Google through Broadcom into this financing blender. A special-purpose vehicle known as Compute SPV paid $35bn for roughly 1GW of the AI hardware, representing around 1mn TPUs, according to people familiar with the matter.

The SPV’s cash came from three tranches of debt anchored by Apollo and Blackstone. Broadcom, in effect, guaranteed the two senior tranches by agreeing to cover any shortfall if Anthropic stopped paying and the SPV could not sell the hardware for enough to make the senior investors whole.

The arrangement, known as residual value support, covers about $30bn of the $35bn financing, with Broadcom’s exposure declining as Anthropic makes its lease payments…

…Financing the chips solved only half of Google’s problem. The company also needed enough powered data centres to house them. “We have a schedule and we’re looking for capacity that will fit the schedule,” the Google executive said. “Crypto miners with excess capacity were helpful.”

It has helped transform several crypto miners with secured power into a new breed of AI infrastructure developers, with a small outfit called TeraWulf the first to land a Google backstop to add a 360MW data centre on its campus in upstate New York.

Google guaranteed the lease payments on the Anthropic-bound site, which Morgan Stanley packaged into a construction bond that in October raised $3.2bn to get it built…

…People familiar with the matter said the Big Tech company had so far backstopped 10 developments with 2.4GW of power for TPUs. Google’s guarantees put it on the hook for as much as $44bn if all the leases go bad, though it marks the liability at $815mn on its balance sheet. It could also step into the leases itself.

3. Drug Discovery Has No Magic Wands – Daphne Koller

To understand where AI fits, it helps to decompose drug discovery into its three essential stages:

1. Disease-to-mechanism: Identifying a biological mechanism — a pathway, a target, a molecular interaction — where therapeutic intervention will alter the course of disease in humans.

2. Mechanism-to-drug: Creating a molecular intervention in the right therapeutic modality — a small molecule, antibody, siRNA, gene therapy — that achieves the desired mechanistic effect with acceptable safety and pharmacological properties.

3. Drug-to-patient: Designing a clinical development program that identifies the right patients and assesses the molecule’s effects — beneficial as well as adverse.

The vast majority of AI work in drug discovery has focused on stage 2…

…More than 90% of drugs that enter clinical trials fail — a dismal statistic that has barely improved in several decades. In the large majority of cases, the molecule was engineered just fine. The mechanism it targeted was wrong. We are doing a pretty good job at manufacturing keys, but they are generally for the wrong locks. Even if AI lets us make better keys at an accelerating pace, that won’t improve our ability to identify the right locks. The real bottleneck in making a novel medicine is disease understanding: identifying a biological mechanism whose modification actually changes the course of disease in patients. That, far more than molecular design, is where drug discovery succeeds or fails.

This mechanistic understanding is a rare commodity. And because no-one likes to fail in the clinic, we are seeing industry trends that are truly destructive. There are currently 38 targets that have over 50 programs against each of them — slightly better keys for those few locks where we have strong conviction. How many variants of GLP-1 do we really need? Even worse than this misallocation of capital is the disservice to patients: the number of novel targets the industry advances each year fell from ~100 in 2015 to about 30 in 2024. That collapse is the far bigger cost: the inability to help the hundreds of millions of people for whom medicine currently offers nothing…

…The challenge is that human biology is incredibly complex, spanning multiple interconnected biological layers — DNA, protein, cells, multi-cellular environments, entire organisms. Individual components respond dynamically to even subtle changes in related components or in the environment. Moreover, biology wasn’t engineered; it is the result of billions of years of messy, stochastic evolution, which produced staggering variation — countless genes, cell types, states, and contexts, each behaving in its own way. There is too much of it, too idiosyncratic, to reason about in the abstract. You have to measure it…

…Which brings up the greatest data challenge. While some processes are conserved across all forms of life, others are far more specific. The folding of a single protein is a self-contained process, highly conserved — closer to physics than to biology; this allows protein folding models to be trained on sequences collected across thousands of species. Metabolism involves at least a dozen distinct cell types and might be conserved across mammals. Brain function and dysfunction involves dozens of distinct cellular identities; and these processes are exquisitely specialized to humans: rodents do not get Alzheimer’s disease; non-human primates do not recapitulate ALS. The diseases where we have made the least progress tend to be precisely those that are most human-specific, and therefore those for which the data is most expensive to collect, least available, and most fraught with ethical constraints…

…But agentic iterated optimization relies on a fundamental attribute: agents thrive when there is a fast, accurate, and cheap scorecard to evaluate progress. If you give a sufficiently smart model an instant feedback loop, it will grind against that benchmark until it wins. This is why coding assistants and molecular design tools advanced so rapidly — the feedback is cheap, accurate, and fast…

…Drug development is the exact opposite. The ultimate scorecard — whether a drug actually provides therapeutic benefit to a patient — cannot be captured well by computational models or high-throughput assays. The only true ground truth is a human clinical trial. This feedback loop currently takes years, costs millions, and is strictly bound by human ethics and living biology. It is the ultimate slow feedback loop, and no amount of compute or process optimization can change this…

…Some have argued that the most important AI unlock in drug discovery is in the third stage — drug-to-patient — taking a drug candidate through preclinical testing and clinical trials. This is the fourth AI Magic Wand: reduce the time and cost of this very expensive phase, and drug discovery becomes faster and cheaper. Sadly, if you accelerate a pipeline full of drugs aimed at the wrong mechanisms, all you get is faster failures…

… An AI-enabled, deep mechanistic understanding of a disease enables the identification of novel clinical readouts that serve three distinct purposes: selecting the patients most likely to respond, confirming that the drug is hitting its intended target, and detecting early and reliable signals that it is actually modifying disease biology. Together, these allow trials to enroll the right patients, read out faster, and catch failures earlier — changes that transcend clinical trial operations, transforming the trial design itself. This capability is inseparable from solving the disease-understanding problem; they are one and the same. Better trials, in the end, are downstream of better biology…

…To fulfill the promise of AI for the millions of patients lacking any meaningful treatment, we must direct our efforts toward the problem that really matters: the identification of biological mechanisms with disease-transforming clinical benefit. This is arguably the hardest problem in drug discovery, because the only conclusive test of whether we have correctly identified a novel biological mechanism is a human clinical trial. There are multiple other paths in this space with shorter timelines and clearer near-term proof points. Those paths are shorter because the problems are more tractable: the feedback loops are faster and the benchmarks are cleaner. But a shorter path to a smaller destination is still a smaller destination — process improvements for problems we already know how to solve.

4. The 1970s: Warren Buffett’s Defining Decade – Dirtcheapstocks

In the early 1970’s the Nifty Fifty were all the rage. Investors would seemingly pay any multiple for blue chip growth stocks (funny how history repeats itself).

Polaroid was selling for 91x earnings.

McDonald’s sold for 86x earnings…

…Then the music stopped.

The market was down nearly 50% from its highs in 1972.

That’s when Buffett got busy buying…

…Buffett’s most important purchase in the early part of the 1970’s was Blue Chip Stamps as the zero cost float provided leverage for other investments. He added to his position throughout the decade. Some of the prices paid were absurdly low…

…Buffett bought O&M throughout 1973 and 1974.

His basis valued the business at $29mm.

Ogilvy’s enterprise value was roughly the same as its market cap. So, Buffett was buying the shares at ~3x EBIT.

O&M grew its operating profit at a 23% CAGR from 1970 to 1974.

Buffett’s shares doubled in value within 2 years of his purchase…

…Interpublic fell 73% from its 1972 highs when Buffett began buying.

Buffett’s basis valued the business at a $25mm market cap. The enterprise value was only $19mm. Interpublic earned $14.8mm of operating income in 1973.

Buffett’s investment was up 10x in 10 years!..

…Berkshire’s book value compounded at a 30% CAGR from 1973 to 1985…

…The 1970’s made Buffett, but he was also perfectly prepared for the opportunity.

He didn’t stretch to buy businesses at lofty valuations. He waited for the prices to come to him.

This was a difficult time for American business, but it was hardly unprecedented. I believe there is a reasonable probability that businesses get this cheap again.

5. Ways to think about token pricing – Benedict Evans

There are only two things you can say with certainty about token prices: we’re in a supply crunch, and this is unstable. All of the variables are in play, and the market will get shaken out over the next few years to arrive at a new equilibrium. Right now we have a lot of frantic analysis of ‘time to power’, but the question at the end of that remains whether the foundation models have sustainable pricing power, strategic leverage and value capture, or whether they become low-margin commodity infrastructure providers. At the moment, I think every dynamic we can see points to the latter…

…First, how many people will pay to be at the top right of the curve – to be at the frontier? At one extreme there are already use cases that already work just fine with a small, old, perhaps open source model that runs for ‘free’ on-prem or on your phone; at the other extreme there will be some that get better results from the latest, most expensive frontier model, consuming lots of tokens for lots of money; and then there will be many that are somewhere in between…

…Second, does the frontier keep moving significantly? This is obviously the most basic science question in AI: how long does the frontier keep getting better, how long does that keep needing more and more compute, and does that continue to happen at a rate that keeps it ahead of downward pricing pressure from efficiency and capacity gains?…

…Third, will there still be fierce competition between frontier models? Does the field shrink to fewer and fewer frontier models, perhaps with network effects emerging? Do frontier models diverge, with different models having much clearer leads in different fields? That could be another path to sustainable pricing power…

…Fourth, how much of the value from those high-end use cases is captured by the frontier model itself? How much needs to be wrapped in tooling, process, proprietary data, go-to-market, networks, support, and everything else associated with a traditional software company, even if you do need the big expensive frontier model underneath? Can that model do the whole thing, or is the model, no matter how good, still a piece of infrastructure that you use to make the actual product?…

…Meanwhile, there is structural uncertainty at the early stages of every big new technology, but the uncertainty now is different, because we don’t have a good theoretical understanding of why these models work so well and so we don’t know how much better they can get. In 1995, we didn’t know how the internet would evolve but we knew that there were less than 100m PCs on earth (and they were expensive) and that telcos couldn’t give everyone FTTH next year; in 2010 we didn’t know what the next iPhone would be but we knew it wouldn’t have retinal projection. We knew the physical limits in ways we don’t really know with LLMs. Next month a new approach could cut inference compute needs by 90%, or double demand, or both…

…That makes mobile data a more fruitful comparison here. Mobile networks have marginal cost for capacity, and like AI they had an enormous surge in usage 15 years ago, that overwhelmed capacity and had carriers scrambling to add capacity and rebalance their pricing. Meanwhile, selling bits looks superficially similar to selling tokens: it’s an opaque measure of marginal cost that doesn’t map in any transparent or intuitive way to use cases or value, and needs to be replaced with bundles of some kind. But most importantly, in the last 20 years cellular data traffic has risen by several orders of magnitude, and this has become an enormous industry, with annual revenue of a trillion dollars and capex of $200 billion, but the stocks have gone nowhere, and all the value was captured by other people further up the stack. This, of course, is one of the core questions for AI: is this going to be low-margin commodity infrastructure with all the value captured by other people further up the stack?…

…However, these examples do tell us, empirically, that something can be very important, very expensive, change the world, and be full of very sophisticated science and engineering, and yet have a wide range of possible outcomes. There isn’t one inevitable path here: you can have price equilibrium at high margins and at low margins, and with and without market concentration, and you can’t hand-wave that away by talking about AGI and saying “you don’t understand exponentials!”

However, if one thread in everything I’ve written above is how much we don’t yet know, the other thread is that every path to foundation models having market dominance, strategic leverage, value capture, winner-takes-all effects, or anything else other than becoming commodity infrastructure, requires something to change.

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

What We’re Reading (Week Ending 02 August 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 02 August 2026:

1. Cyprus – “weird” opportunities galore – Swen Lorenz

In 1999, the main index of the Cyprus Stock Exchange rose nearly tenfold, making it the world’s best-performing stock market.

It was one of history’s greatest investment manias:

Brokers had such a backlog of orders that it took them up to three weeks to clear a trade.
Some Cypriot farmers reportedly sold their sheep to invest the proceeds in the stock market.
Hastily arranged IPOs were oversubscribed by a factor of 50-100x.
One IPO by a small cruise company attracted subscriptions equivalent to 10% of Cyprus’ GDP.
A small shopping mall suddenly reached a valuation of EUR 2bn, roughly equal to the entire market cap of the Cyprus stock market before the boom began.

The business section of Britain’s The Guardian reported at the time:

“Everyone, housewives, cleaning ladies, ministers and businessmen are in on the act … The mania has reached such heights that from Cyprus’ taverna-terraced beaches to its remote mountain villages few now talk of anything else.”

It ended much like almost every investment mania ends: in tears.

After breaking records on the way up, the Cypriot stock market broke records on the way down, too. The market index ultimately lost 99%, which, according to some, remains the largest decline ever suffered by a national stock market index. By comparison, Greece “only” lost 92.5% during its sovereign debt crisis, while Germany’s hyperinflation between 1918-1922 saw the market fall by 97%.

2. You just hired a million bad employees – George Sivulka

For the first time in history, humans are cheaper than software.

And AI is creating more jobs than it eliminates…

…Managing AI is harder than managing people, because AI scales dysfunction instantly. Fortunately, we can learn from the past:

Agent workforces and human workforces fail in the same way.

Understanding the 7 major parallels between the two will unlock the next trillion dollars of AI value creation…

…1. Tokenmaxxing is throwing bodies at the problem…

…People are spending so much on tokens because they don’t know how to use them.

Maybe 1 in 100 employees knows how to give AI context. It’s a rare breed of person that can articulate a process clearly, that has the patience to empathize with a polluted context window, or even understands what that means.

Give an agent harness to the other 99 people and they will produce “loops”.

2. Loops are meetings about meetings…

…3. Wasted tokens are the new headcount bloat…

…Just like 80% of employees do nothing, 80% of tokens today do nothing.

People create more people. Tokens create more tokens. Looping is the new empire building.

4. 100X tokens are the new 10X engineers…

…In the same way a handful of employees make others 10X as productive, for any given job some amount of token context can cut AI effort down by orders of magnitude. There exist tokens that will give you 100X as much leverage.

Humans are cheaper than tokens on average, but good tokens are cheaper at scale…

…5. Context hoarding is the latest job security tactic.

There’s a massive political problem with AI inside the firm, and it will only get worse.

Employees don’t want to teach AI systems their secret sauce….

…6. Evals are the new OKRs.

The best way to manage a token workforce is the same as the best way to manage humans: by defining what good looks like.

The one breakout AI use case that escaped politics is coding. It expanded the pie and made every engineer better.

The mechanism is evals. 99% of AI revenue today is coding because coding has built-in evals. Code runs or it doesn’t…

…7. The next trillion-dollar opportunity is the transformation company.

Enterprises have been buying foundation model commits, the application layer, and internal builds for years now. All of it conceals a brutal truth about the economics:

Nobody has AI working reliably yet…

…In fact, the next biggest businesses won’t be eating existing services spend. They will sell a net-new type of service to existing players:

“AI transformation companies” will be 10X larger than any neofirm.

Transformation sounds like a one-off project. But there’s a Jevons paradox at work: every use case an organization adopts surfaces ten more. The more AI-enabled a firm becomes, the more transformation it consumes, while the frontier of what’s possible advances daily. Ongoing AI transformation efforts will become the only way to compete.

3. Data-Center Builders Are Racing to Offload Stakes Worth Billions – Anissa Gardizy

Data-center builders and operators across the U.S. are working with bankers to sell majority equity stakes worth tens of billions of dollars in their companies this summer, according to people familiar with the efforts…

…Sales of data-center operators are on the rise as owners of these firms seek exits and investor interest in owning the physical infrastructure behind advanced artificial intelligence grows. A massive backlog of demand for server capacity pushed companies to pursue novel strategies to secure more of it, from renting chips from direct competitors to launching data centers into orbit…

…Whether there are enough buyers with the means to absorb so many large deals at once this summer remains an open question. Ravi Purohit, the co-head of infrastructure at Paul Weiss, said that while plenty of investors want exposure to data centers, only a handful of firms can afford multibillion-dollar deals…

…But as these megadeals scale up, developers are increasingly running into fierce local opposition, driven by residents’ anxieties over rising utility bills, noise and advanced AI services in general. In some cases, it has pushed developers to pause or walk away from projects. This all makes investing in data centers riskier than it was in the past, investors said.

“I think people are downplaying the significance of Nimbyism in this country right now,” Purohit said, adding that companies looking to sell this summer will be scrutinized for how they plan to handle it.

“The more they can demonstrate to buyers that they have a constructive relationship with these communities…that actually goes a really long way,” he said.

4. How Terrorist Groups Are Using A.I. to Gain an Edge in Battle – Dustin Volz and Eric Schmitt

When a gang of motorcycle-riding members of Boko Haram attacked a military base in eastern Nigeria a couple of years ago, they were stymied by a defensive trench surrounding the complex.The extremists regrouped. Before launching another assault, they asked A.I. for help.

“We saw in a movie how motorcycles can jump over bridges,” a former Boko Haram commander told Antonia Juelich, a terrorism and technology researcher at Cambridge University. “We used A.I. to learn how to do this. We gave it information, like what motorcycles we use and the distance we need to jump and so on, and it gave us steps on what we have to do.”

Using tips from chatbots, mechanics modified the motorcycles to allow for faster acceleration and top speed. The riders dug their own holes, filled them with broken glass and fire, and practiced jumps — sometimes with fatal outcomes — until they achieved enough aerial liftoff to mount a successful attack, defectors said…

…Until recently, the Islamic State, Al Qaeda and other extremists primarily used A.I. in the information-operations realm — propaganda production, translation, recruitment and security tradecraft. But that has evolved as jihadists have turned to A.I. for tactical on-the-ground advantages, according to current and former U.S. military and counterterrorism officials and independent researchers…

…Daniel Byman, a terrorism expert at Georgetown University and co-author of a report about A.I. and the future of terrorism released on Friday by the Center for Strategic and International Studies, said terrorist groups were “mixing and matching” from different A.I. systems, seeking to avoid technical guardrails established by the A.I. companies. Dr. Juelich’s research also found that Boko Haram was platform agnostic, interchangeably working with OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini and xAI’s Grok, as well as the Chinese firm DeepSeek…

…Defectors recounted attending organized training sessions focused on how to best leverage the powers of generative A.I. models to inform or enhance their uses of the technology.

The trainings, in which laptops were equipped with virtual private networks and encryption software, were delivered via transnational jihadist networks often led by members of the Islamic State, interviewees said. Common topics included managing an account on an A.I. platform, suggestions on generating useful answers and tips on evading safety restrictions.

5. Are We Repeating the Biggest Mistake of 1873? (Transcript here) – Merryn Somerset Webb and Liaquat Ahamed

Liaquat Ahamed: What started it was the gold rush. Europe, which was the centre of the world, had gone through a terrible depression in the 1840s, fuelled by bad harvests and by revolution. They actually thought every government in Europe was going to fall and that we’d have the equivalent of the Russian Revolution across Europe. That didn’t happen — but at the same time, they discovered gold in the United States, and that provided the fuel to get the global economy going.

You had bankers like the Rothschilds, who had made a ton of money in the early part of the 19th century lending to governments, jumping on the bandwagon and starting to lend for infrastructure — particularly the railroads — but to the private sector. You got a massive boom in lending, and it was a boom based on the bond market. Everyone thinks the bond market is a sleepy place where people who don’t want to take risk put their money, but it was essentially the bond market that provided the impetus to growth. It grew by five times in the two decades from 1850 to 1870…

…Merryn Somerset Webb: That’s interesting, and we’ll come on to it — whether economies are incredibly resilient and whether the best thing to do is just leave them alone. Because when we get to 1873, we find out that everyone decided not to leave it alone. They succumbed to what we’d call “something must be done”-ism, and that’s when things started to get nasty.

But the pivotal moment is this war between Prussia and France that starts in 1870. It’s a short war, but it has enormous repercussions, because France is obliged to pay enormous reparations to Germany — to the tune of how much?

Liaquat Ahamed: Around a billion dollars.

Merryn Somerset Webb: A billion dollars, which would be the equivalent of $1.2 to $1.3 trillion today. And Prussia thought this was fine, because so much money would take France forever to pay off. This would keep them down and out of the way — the enemy dealt with indefinitely. Instead, France somehow managed to go out and get the bond markets to give them a billion bucks over two years. And it was the Rothschilds who were at the centre of that, because they had incredible reach.

Liaquat Ahamed: They raised two bond issues which together raised a billion dollars. But the most important thing was that in one case the issue was three times oversubscribed, and in the other, twelve times…

…Merryn Somerset Webb: And the London Stock Exchange went crazy.

Liaquat Ahamed: Because everyone who didn’t get into that bond issue still had their money.

Merryn Somerset Webb: Exactly.

Liaquat Ahamed: And in the US, the railroads had been a perfectly rational boom until then — and then suddenly railroad bond issues doubled. We went up to $500 million a year in an economy where that was roughly 5% of GDP.

Merryn Somerset Webb: Which today would be $1.5 trillion in the US. So that $1.5 trillion — the sort of money that goes into a SpaceX today — is what went into the railroads then. Fascinating. And then of course that billion dollars went to Germany over a two-year period.

Liaquat Ahamed: Yes, and they then had 25% of GDP to play with in cash.

Merryn Somerset Webb: And that was totally mismanaged.

Liaquat Ahamed: They could have fed it into the economy at a slower pace. But injecting 25% of GDP in liquid cash into an economy that was relatively unsophisticated — where everyone who owned government bonds suddenly found their bonds paid off —

Merryn Somerset Webb: Paid off meaning redeemed.

Liaquat Ahamed: Yes. They looked around and said, “What do I do with this money?” and started hunting for opportunities. And lo and behold, a whole lot of charlatans appeared on the scene to relieve them of it. It was not only a stock market boom but a giant IPO boom. The stock market, which had maybe 30 or 40 companies listed, suddenly ballooned to 500 or 600. A lot of them were banks, a lot were real estate, a lot were railroad companies — but there were also all sorts of things, like companies set up to explore the northern regions of Europe, or to look for banana plantations in West Africa. It was a crazy time…

…Merryn Somerset Webb: So the crash starts in Vienna. But because there had been a global boom and a global bubble, it spreads. There’s contagion across the world.

Liaquat Ahamed: Yes. You actually get a false period of calm. It crashes in Vienna, everyone says there’s going to be a global disaster — and then nothing happens for three or four months. So people said, maybe Vienna was just overpriced. It was a local incident. Everywhere else, earnings will rise to match prices and it’ll be fine.

But meanwhile, Jay Cooke — the premier investment banker, who had raised $2 billion for the Union government during the Civil War — suddenly found that because of the disruption in Europe he couldn’t raise capital. He ran out of money in the middle of constructing the second transcontinental railroad. It was a little like the Lehman Brothers story: he started injecting his own bank’s money into the project, and still couldn’t complete it. When he announced that he couldn’t raise the capital, there was total panic — the same sort of psychological panic that happened after Lehman. People said, “If Jay Cooke, a friend of the president and the most well-connected banker in the United States, can’t raise $100 million, what hope do we have?”

Every railroad stopped construction. There were 500 railroad companies in the United States; by the end of the year a third of them had stopped paying dividends, and within five years half of them had defaulted.

Merryn Somerset Webb: Everything comes crashing down.

Liaquat Ahamed: Everything comes crashing down.

Merryn Somerset Webb: And at the same time everything’s still going horribly in Europe — 70% of the banks in Vienna have gone bust. It’s global carnage…

…Liaquat Ahamed: What caused the boom to end? I think two things. One is that everyone tried to build railroads at the same time, so they started competing against each other and railroad profitability began to decline. The equivalent today is all of the hyperscalers trying to build AI infrastructure at once, and the price of tokens starting to collapse — which, by the way, has already started.

Merryn Somerset Webb: It’s already happening.

Liaquat Ahamed: It changes the economics of their investment. And at the same time, because of the disruption in Europe and the war, the price of capital started rising — and there are some signs of that happening now. Until recently we reassured ourselves that we could finance this boom out of the profits of the giant technology companies. But even that has proved inadequate, and they’re now going out and borrowing.

So the combination of declining profitability and a rising cost of capital at some point causes a crunch. In the US case, when Jay Cooke announced he couldn’t complete his railroad — the equivalent today would be OpenAI declaring, “Actually, we think we’ve miscalculated, we’re not going to be able to complete a whole model, and we’re going to have to sell to Microsoft.” Can you imagine the panic that would occur in the market for AI infrastructure?

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.

What We’re Reading (Week Ending 26 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 26 July 2026:

1. Keynote speech by Chinese President Xi Jinping at opening ceremony of 2026 World AI Conference

We human beings must answer the questions posed by our times: How to get along with thinking machines? How to ensure security when algorithm is part of decision making? How to tackle ethical challenges by technologies through adaptive governance? How to realize AI for all when the divide keeps widening? These questions demand serious consideration and real answers from the whole international community.

In China’s view, all countries should take a people-centered approach and develop AI for the positive and for good. We should ensure that AI is an important driver for shared prosperity and common security. We should join hands to build a just and equitable system for global AI governance. To this end, I wish to share four observations.

First, we should adhere to the principle of openness and win-win and boost innovation-driven development. As a new engine of world economic growth and an accelerator for the shift of growth drivers, AI is moving from the digital world into the physical world. We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing. We should facilitate technological innovation, industrial development and scenario-based application of AI. We should make coordinated advances in the transformation and upgrade of traditional industries, the cultivation and growth of emerging industries and forward-looking planning for future industries, so that all sectors and businesses can benefit from AI.

Second, we should strengthen risk-awareness and ensure that AI is secure and controllable. AI should be a trusted tool for humanity. We should take seriously the various types of inherent and secondary risks that AI may trigger. We should put in place laws and regulations, technological monitoring, early warning and emergency response systems in order to strengthen the line of security, prevent abuses and malicious use, and ensure that AI is always under human control. In the meantime, we should jointly oppose overstretching the national security concept in the field of AI and placing one country’s security over that of others.

2. The AI Bubble? – Nothing Linear?

Five very different numbers get quoted in this debate, and they blur together constantly:

$725B — 2026 AI capex from the big four hyperscalers alone (Microsoft, Google, Amazon, Meta).
~$1.5T — estimated total AI infrastructure spend across the whole industry in 2026.
~$3T — Sequoia’s David Cahn’s estimate of the annual revenue AI must eventually earn to justify that build-out.
~$50–100B — roughly what the largest AI firms actually earn today: OpenAI ~$25B run-rate, Anthropic ~$30–47B ARR, plus Google/Microsoft AI revenue.
The gap — the distance between the last two is the entire debate. It is the widest it has ever been, and widening…

…The three genuine choke points:

Advanced AI chips. Nvidia holds roughly 80% of the AI-accelerator market, and TSMC is effectively the sole advanced-node foundry able to fabricate the leading chips — virtually every leading chip, Nvidia’s, AMD’s, and hyperscalers’ custom silicon alike, passes through it. TSMC’s advanced-node capacity and CoWoS packaging are reportedly booked out for years, with Nvidia taking a large share. A single geopolitical or manufacturing disruption in Taiwan would immediately choke the entire industry’s ability to train or serve frontier models. Lesson: sovereign AI ultimately needs sovereign or allied access to advanced chip fabrication — software alone cannot route around this bottleneck.

Electricity & data-centre capacity. The IEA projects data-centre electricity demand roughly doubling from ~415 TWh (2024) to ~945 TWh by 2030, with AI the dominant driver. In some US states and countries (Ireland, Virginia) data centres already draw a fifth or more of total electricity. Power availability, not chip supply, is increasingly the binding constraint on how fast new capacity can be built. AI’s physical footprint — power, water, land — is becoming as strategically important as its digital one, and draws the same local political resistance as any heavy industry.

Frontier model training. Roughly six labs — OpenAI, Google, Anthropic, xAI, DeepSeek, Zhipu — can credibly compete at the very top, because training a genuinely frontier model costs tens to hundreds of millions in compute alone. But this choke point compresses faster than the physical ones: open-weight releases (Llama, DeepSeek, Qwen, GLM) have shown near-frontier capability at a fraction of the presumed cost. Algorithmic efficiency keeps lowering the compute needed to reach yesterday’s frontier.

A fourth layer, high-quality training data, is a contested rather than settled choke point: the freely-scrapeable internet text that trained the first LLMs is finite and non-renewing, and a fast-growing share of new web content is itself AI-generated — raising the risk of models training on their own degraded output (”model collapse”). Licensing disputes and lawsuits are actively reshaping what data can legally be used, pushing labs toward paid licensing, synthetic data and proprietary enterprise data. Data is shifting from an abundant free input to a scarce, litigated, paid-for one — a structural change in the industry’s cost base most forecasts do not yet price in…

…Part 5 · The bull case — why this may not be a bubble

The technology genuinely works. Unlike some past manias, the product is real and used daily by hundreds of millions. Generative AI reached an estimated 53% adoption among the general population within about three years — faster than the PC or the internet.

Revenue is growing very fast. The leading labs are scaling revenue at rates rarely seen: Anthropic went from roughly $1B to a ~$30–47B run-rate inside 18 months, and OpenAI reached a ~$25B run-rate. Fast growth can, in principle, close even a very wide gap.

The spenders are the strongest companies in history. Unlike the debt-laden telecom builders of 2000, the biggest AI spenders are among the most profitable firms ever. Nvidia earned roughly $120B in net income last year; Microsoft, Google and Amazon are cash machines. They will not simply collapse if returns are slow.

Valuations are rich, but not insane. The NASDAQ-100’s forward P/E is around 26x today, versus roughly 60x at the 2000 dot-com peak. Expensive, and concentrated in a few names — but not the pure fantasy of the last great tech bubble.

Individuals are extracting real value now. Even where enterprise ROI is unclear, the estimated value of generative-AI tools to US consumers reached about $172B annually by early 2026, with median value per user roughly tripling year over year.

Part 6 · The bear case — why it may be a bubble…

…Big-four AI capex has gone from ~$90B (2020) to $147B (2023) to $410B (2025) to $725B (2026). The gap itself has widened from a $200B question (2023) to $600B (2024) to an estimated ~$800B+ annual gap in 2026 — Allianz pegs the capex-vs-revenue divergence at ~46%, exceeding the ~32% seen in the 2001 telecom bubble.The cash-flow squeeze. PIMCO estimates Big Tech capex will consume ~94% of operating cash flow over the next two years, up from ~40% in 2023 — meaning for every $100 earned, only ~$6 is left for dividends, buybacks, salaries and everything else. For the first time, the hyperscalers are also leaning on debt: the big five raised roughly $108B in new debt in 2025.

Enterprise ROI — the crack in the revenue side. The core bull rebuttal is “enterprises will pay, because AI makes them radically more efficient.” The data from the industry’s own consultants is, so far, harsher. MIT Project NANDA found 95% of enterprise GenAI pilots showed zero measurable P&L impact (2025, 300 deployments). BCG’s “AI at Scale” survey of 1,800 executives found only ~26% of companies are generating meaningful financial value from AI. S&P Global found 42% of companies abandoned most AI projects in 2025 — more than double the prior year. McKinsey’s 2026 State of AI found fewer than 20% of pilots reach enterprise-scale production.

Most failures are not the technology’s fault — MIT found ~70% of the work to make AI pay off is process and workflow redesign, not the model. But from a market standpoint the cause barely matters: if the ROI is not showing up in the P&L, the revenue needed to justify the capex is not showing up either…

…The technology can be completely real and the bubble can still burst. That is not a contradiction — it is the normal pattern of every industrial build-out in history. The pattern runs four steps: high returns attract capital; capital keeps flowing until overcapacity is built; overcapacity triggers collapse; survivors inherit the wreckage cheaply and, when demand finally catches up, make fortunes on assets others paid to build.

3.Maybe Intelligence Ain’t All That – Clifford Sosin

Superintelligence arrived. You probably didn’t notice, because it turned out to be kind of incremental…

…We hold a thin scatter of facts about the world, and intelligence or reasoning is whatever fills the space between them. It extrapolates between things we already know, and LLMs are incredible at that. In studying our words, they learned the structure of our thoughts. We will soon have it in unlimited supply, at 200 IQ, for close to nothing.

Where the space between the facts behaves well, this is close to godlike. Real estate law isn’t hard. We made it up, it’s internally consistent, and a model holding every statute and ruling should beat any lawyer for free. Coding, math and most administrative work are similarly benign. What makes them easy is that they have relatively smooth solution spaces and are tractably verifiable.

Most of what matters doesn’t behave like that. The universe is mostly the emergent behavior of complex systems. Stir cream into coffee. Watch a storm build out of nothing but atmospheric temperature differences and water vapor. The local rules are simple and the tornado is not. In systems like these, no amount of reasoning delivers the answer, because there’s no shortcut hiding in the gaps. You have to run the thing. And running it has a ceiling of its own, since small errors compound, which is why no supercomputer will ever give you a clean two-week forecast. The smallest object that can perfectly simulate the universe is the universe. Human systems are similarly complex…

…An AI will hand you a genuinely clever design for a jet turbine blade. It might be far more likely to work than anything your engineers came up with. It’ll still probably fail, because that’s the base rate at the edge of what anyone knows. The only way to find out is to build the blade and try to break it.

That’s the real limit on learning, and it doesn’t care how smart you are. Coming up with ideas was never the hard part. The hard part is how fast reality answers them.

4. A Stock Certificate From 1941 Taught Me More About AI Than Anyone from OpenAI – Francis Huang

But I think the railroad story, if you take it seriously, tells you six things about what happens next.

First, the technology will work. That part isn’t in doubt. Railroads worked. AI works. The question was never “will trains move faster than horses?” or “will language models generate useful output?” The answer to both was always yes. The technology question is settled. Everything that follows is a finance question and an ownership question, and those are much harder.

Second, the buildout will be larger than anyone currently projects. In 1850, America had 8,879 miles of track. By 1860, it had 30,626. Nobody in 1850 would have believed that number. AI infrastructure spending has quadrupled since 2022 and shows no sign of slowing. McKinsey’s $6.7 trillion projection for 2030 might end up being low. When a technology changes the cost structure of everything, the capital required to build it out has a way of exceeding every estimate, including the ones that already seemed crazy.

Third, the financial system will change in ways we can’t anticipate. Railroads didn’t just use the existing capital markets; they created new ones. Bond markets, equity markets, underwriting syndicates, credit analysis, bankruptcy law, corporate governance, even the concept of the limited liability corporation, all got reshaped or invented to handle railroad finance. AI will do the same. We don’t know what the new financial instruments will look like yet. Maybe compute futures. Maybe revenue-sharing tokens tied to model performance. Maybe something nobody has named yet. The railroad precedent says the instruments themselves will be part of the story.

Fourth, a crisis will come, and its trigger will be something nobody is watching right now. In 1873, it was a Viennese real estate bubble. In 1893, it was the cumulative effect of rate wars nobody thought would last. Whatever hits AI won’t be “AI doesn’t work.” It’ll come from some adjacent system that’s quietly entangled with the AI buildout in ways nobody has mapped. A chip supply disruption. A sovereign debt crisis in a country that’s heavily invested in AI infrastructure. An energy bottleneck. Something nobody is talking about at Davos or on the All-In Podcast. The black swan, by definition, is the one you’re not looking for.

Fifth, the people who build it and the people who own it long-term will be different. The merchants of Boston who funded the Boston & Worcester Railroad in 1831 were not the Vanderbilt family that controlled it in 1900, and neither of them were CSX, which runs it today. OpenAI’s current investors, Anthropic’s current investors, the hyperscalers currently spending $700 billion a year: these may or may not be the entities that own and profit from AI infrastructure in 2050. Morgan got rich not by building railroads but by picking up the pieces after other people’s railroads collapsed. Someone will play that role in AI. We don’t know their name yet.

Sixth, and this is the one I keep coming back to: the infrastructure will outlast everyone’s financial projections. The tracks the Boston & Albany laid in the 1840s are still carrying freight in 2026. Data centers being built today will run computation, in some form, for decades after the companies that built them have been restructured, merged, acquired, or dissolved. The physical layer endures. The capital layer above it churns.

5. Why Are Berries Everywhere, in Every Season? Driscoll’s – Julia Moskin

In just the last decade, berries have completed the journey from fragile, local, seasonal treat to worldwide refrigerator staple and marketing juggernaut. Global production has tripled since 2000, according to research from the U.N.’s Food and Agriculture Organization, and still cannot keep up with demand. In sales and volume, berries are the fastest-growing category in American produce, according to data from the U.S. Department of Agriculture.

Most of that growth has been driven by Driscoll’s, a $7 billion California company that began as a multifamily farm in 1904, patented its first strain of strawberries in 1958 and is still controlled by family members. In 1989, its board made what the company calls the Meadowood Declaration, a resolution that seemed preposterous at the time: to make all four berries available, in every season, in every part of the world.

Today the company is the undisputed global market leader, shipping four billion containers of highly perishable fruit across 60 countries each year…

…According to Circana, a market research firm, Driscoll’s is now the second-highest-earning brand in American supermarkets, behind only Coca-Cola…

…Instead of owning land, the company owns the genetic material of its berries and the knowledge of how best to plant, pick and transport them. It subcontracts with farmers around the world to grow those breeds according to its specifications, then handles sales and distribution after harvest.

But global access to berries has a cost, measured in metrics like water consumption, pollution, pesticides and labor practices. Driscoll’s has come under fire on all four fronts…

…Inside a nearby laboratory, where two full-time sensory scientists make their assessments, 210 raspberry varieties were laid out in a grid of plastic pints. Some had been bred for visual appeal, with more shapely shoulders, uniform drupelets and less “hair” (the thin red styles that sprout where the berry is pollinated). Others were developed to maximize yield, with fewer thorns and better “plant architecture” — tall, fluffy stalks that make the berries easy to pick. Each cultivar is tested for qualities like P.S.I., the interior pressure that determines whether a berry will yield to the teeth with an explosive, juicy pop.

Out of those 210 strains, said Kyle Rak, the company’s chief raspberry scientist, perhaps two will make it to market…

…Many Driscoll’s berries are no longer planted in soil, but grown in pots filled with carefully balanced mixtures of organic materials like coconut fiber and moss. This system of substrate farming was developed over centuries in the Netherlands to produce maximum yields from minimal land.

It requires a substantial start-up investment by Driscoll’s growers, who also absorb the costs of ever-shifting factors like labor, weather, equipment and rent. The company provides seed plants and “inputs” like soil treatments, along with technical support and marketing dollars. After harvest, the company retrieves the filled clamshells, then compensates the grower according to the price those berries command. According to Driscoll’s, growers receive 75 to 80 percent of the revenue…

…Demand for berries has exploded in the United States because of overlapping recent trends: more snacking and the rise of “functional” foods that promise specific health benefits…

…Berries are shifting entire economies.

In 2023, they became Mexico’s most lucrative agricultural export, surpassing avocados, beer and tequila. On Moldovan plantations and in Andean highlands, growers of low-margin crops like sugar cane and corn have switched to berries, which command premium prices.

In 2025, China overtook the United States as the world’s largest blueberry producer. Driscoll’s, the first foreign berry company allowed to operate there, now has about 8,000 acres under cultivation.

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

What We’re Reading (Week Ending 19 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 19 July 2026:

1. The Great Wave Has Arrived – Tang Jie

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

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

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

…We have a simple but demanding definition of AGI:

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

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

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

The First Mountain: Long-Horizon Task Capability

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

…The Second Mountain: Fully Autonomous Agent Systems

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

…The Third Mountain: Self-Evolution

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

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

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

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

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

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

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

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

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

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

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

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

From the very beginning, Zhipu established a guiding principle:

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

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

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

2. The Reverse Information Paradox – Satya Nadella

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

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

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

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

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

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

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

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

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

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

Meet Jean-Joseph D’Ieteren.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Consider D’Ieteren.

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

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

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

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

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

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

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

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

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

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

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

What We’re Reading (Week Ending 12 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

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

1. AI’s Value Capture problem – Jaya Gupta

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why?

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

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

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

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

So HBF is for storing model weights.

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

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

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

But HBF can provide 512 GB of capacity per stack!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It inadvertently created an anchor around which the vouchers traded…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

What We’re Reading (Week Ending 05 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

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

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

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

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

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

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

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

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

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

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

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

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

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

Their original underwriting assumed:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5. How funerals keep Africa poor – David Oks

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

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

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

So what are kinship societies?

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

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

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

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

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

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

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

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

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

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

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.