That trick is the whole of artificial intelligence. AI is not a brain. It is the marble jar, industrialised — a very, very good averaging machine, trained on roughly everything humanity has ever written down, handing you the middle of it on demand.

And the middle, it turns out, is excellent. Well-organised, grammatically immaculate, and almost always fine. That is exactly the problem. AI isn’t making us wrong. It is making us all the same shade of plausibly right — and it is doing it to everyone at once.

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But the machine plays a beautiful move

You are about to object, and you are right to. In 2016 a machine called AlphaGo played a move in a game of Go that no human would have played, a stone so strange the commentators assumed a glitch. It was not a glitch. It was beautiful, and it won. A few years later AlphaFold solved the shapes of nearly every protein known to science, a fifty-year problem, correct, at a scale no laboratory could touch. If your complaint about the averaging machine is that it only ever hands back the mushy middle, these are your counter-examples, and they are good ones. Drop the lazy version of the argument.

But look at what kind of new this is. Go is a closed world: fixed rules, fixed board, a fixed way to win, all of it handed to the machine before it starts. Inside that world there are more possible games than atoms in the universe, so a good enough search will always turn up a beautiful place no human has stood. AlphaGo found the move. It did not invent Go, and it could never tell you whether Go was worth playing. There is finding a new move, and there is drawing the board. The machine does the first better than we ever will. It has not once done the second.

That is the averaging, refined. The machine is not too timid to be brilliant inside a frame you have drawn for it. What it cannot do is draw the frame, choose which game is worth the candle, or feel that the whole board is wrong. The averaging that bites is not of the answer. It is of the judgment one layer up, the taste that picks the question, and that is the layer the world is now quietly outsourcing.

The jar only works if nobody can see the other guesses

Read the fine print on that marble jar. The magic depends entirely on the guesses being independent. The wild over-counters and the timid under-counters cancel out, and the truth shines through the noise. Let the guessers see each other’s answers first, and the whole thing collapses: everyone shuffles toward the loudest number in the room, and the “average” stops measuring the marbles and starts measuring the crowd’s confidence in itself.

There is even a formula for the magic, written down by the social scientist Scott Page: the crowd's error equals the average person's error minus the diversity of the guesses [9]. Read it twice, because the second term is the whole game. What makes the jar work is not that anyone is right — it is that everyone is differently wrong. And the formula carries a bomb in its pocket. Shrink the diversity, and the crowd gets stupider even while every guesser in it gets smarter. The market can get dumber while every investor in it gets sharper. Nothing else in this piece matters more than that sentence.

Cliff Asness of AQR made the obvious, devastating point: markets are not a sealed jar [2]. Investors can all see each other. They read the same headlines, hold the same positions, and repeat the same story until the story moves the price and the price seems to prove the story. The wisdom of crowds needs the crowd to think independently.

It is worth being precise here, because averaging is not the villain. Done right, it is close to magic. Philip Tetlock spent a decade proving it. His Good Judgment Project recruited teams of hobbyists — a retired programmer, a pharmacist, a ballroom-dancing instructor — and set them against professional intelligence analysts who had access to classified intercepts. The hobbyists won, comfortably [3]. Not because any one of them was a genius, but because the project pooled their genuinely independent, genuinely different views. A good jar, kept carefully sealed.

Now feed that crowd a tool trained on its own back catalogue. We all prompt similar models, trained on similar data, parsing the same feeds — a healthy slice of which were themselves written by AI. The answers converge. This is measured, not suspected. Researchers put the same open-ended prompts to 102 people and 22 different AI models: the models mirrored each other far more than humans mirror humans, even after controlling for the obvious confounders [10]. Another team ran 26,000 real user queries across dozens of models and won a best-paper award for documenting the same collapse — different companies' machines, strikingly similar answers [11]. And the material the machines learn from is already partly their own: by late 2024, up to a quarter of corporate press-release text was machine-written, a figure the study's authors call a lower bound [12]. Dozens of sources say the identical thing, each one apparently confirming the others, and we mistake the echo for a chorus. We have automated groupthink and given it a warm, confident, faintly human tone of voice.

Everyone’s a genius now, which is the giveaway

You may remember the Dunning-Kruger effect: the least competent people are the most confident, because they don’t know enough to know what they’re missing. It was a comforting law. It meant overconfidence was at least a signal — a red flag you could watch for.

AI broke the flag. A 2026 study in Computers in Human Behavior, flagged by Baillie Gifford’s Tom Slater, found that AI use produces uniform overconfidence across every skill level [4]. Novice and expert walk away equally certain they understand more than they do. The cruel twist: the people with the highest AI literacy were the worst calibrated of the lot. They had mistaken fluency with the tool for mastery of the subject.

If that sounds familiar, it should. Tetlock had already catalogued the species in the wild. Tracking tens of thousands of predictions by celebrated experts, he found the average one forecast about as well as “a dart-throwing chimpanzee” [5]. Worse: the more famous and quotable the expert — the ones with the book deals and the green-room makeup — the lessaccurate they tended to be. Confidence and television charisma were inversely related to being right. AI, in this light, is simply the dart-throwing chimpanzee finally handed a publishing contract and a soothing voice. Always available, always certain, reliably average.

We have, in other words, all become the chap at the dinner party who skimmed one article and now has Opinions — except the article is beautifully written, we have twelve of them, and they all agree. As Slater puts it, the winners “won’t be those who use AI most, but those who can still think without it” [4].

So what is actually scarce?

Whatever cannot be averaged.

The philosopher Michael Polanyi gave us the phrase sixty years ago: we know more than we can tell [6]. The surgeon’s hands, the trader’s bad feeling about a deal that looks flawless on paper, the way a great manager reads a room before anyone has spoken — none of it was ever written down, which means none of it is in the machine. It is tacit knowledge, and it is the one asset that does not depreciate the moment it is shared, because it cannot be shared. You had to be there. For years.

My favourite illustration comes from the investor Carson Block, who described his proudly Luddite father on a recent podcast: up 70% in a year holding Nvidia and CrowdStrike, without — by his son’s account — knowing what either company actually does [7]. The punchline: “If our algorithm ever broke down, I’d just ask my father what to buy.” A lifetime of pattern recognition, sitting quietly above the spreadsheet, waiting for the day the spreadsheet fails.

That is the part no model can reach. AI is built to produce an answer. It cannot sit comfortably in uncertainty, and it certainly cannot laugh at the gap between what the data says and what experience knows.

The people who beat the jar

Here is the part that should change how you think about your own edge. What made Tetlock’s superforecasters exceptional was not raw brainpower, and it was not a secret data feed. It was a method — and every ingredient of it is the precise opposite of how a machine produces an answer.

They are comfortable being uncertain. They think in fine-grained probabilities — not “yes” or “no” but “sixty-eight per cent, and here’s what would change my mind.” They treat every forecast as a draft, nudging it in small steps as new evidence trickles in, a habit Tetlock calls being in “perpetual beta.” And they deliberately gather clashing viewpoints and hold them in tension before deciding — the “dragonfly eye”: many lenses, one synthesis [3].

Read that list again and notice what it is. It is a human being doing, slowly and on purpose, the very thing the averaging machine does instantly and for free — except the machine skips the part that matters. It synthesises, but it cannot doubt. It aggregates, but it cannot hold two ideas in tension and feel which one is wrong. It will never tell you “sixty-eight per cent, and here’s what would change my mind,” because it was built to sound sure. The superforecaster’s edge was never information. It is judgment under uncertainty — and that is exactly what grows scarcer, and dearer, the more the world floods with confident, average answers.

The consensus engine cannot sell you the unpopular truth

Here is the structural catch, and it is fatal to the dream that AI will hand you an edge. AI is, by design, a machine for surfacing what most people already believe. Contrarian insight — the kind that spots the turn before the crowd — cannot come out of a system trained on the crowd. You cannot average your way to a non-consensus view. That is what “non-consensus” means.

In August 1979, BusinessWeek ran a cover declaring “The Death of Equities” [8]. It was the perfect distillation of everything sensible people believed, beautifully argued, and it landed almost exactly at the start of the greatest bull market in living memory. A 1979 AI would have agreed with that cover enthusiastically, in flawless prose, with footnotes. The median view is most comfortable precisely when it is most expensively wrong.

Where this leaves your money, and your mind

The investment translation is short, because the principle is. The average is now free. The edge is whatever can’t be averaged.

And the market was rigging itself for sameness long before the machines arrived. In the late 1950s the average American share changed hands roughly every eight years; by 2020, measured the same way, every five and a half months [13]. Roughly two thirds of the money in US stock funds is now passive — it buys everything, weighted by size, and asks nothing [13]. After Europe changed how research is paid for, 334 listed companies lost analyst coverage entirely; nobody is even looking [13]. You can see the sameness on the tape. Ten names are 37 per cent of the S&P 500 [14]. In 2025 alone, 79 per cent of active large-cap managers underperformed the index; over twenty years, 93 per cent have [15]. Everyone expresses their personality in the small positions and hugs the same ten giants with the real money. And sit with the loop inside that fact: the ten giants are the AI trade. The largest concentration in market history is a machine-averaged consensus about the averaging machines themselves.

So the premium shifts to the un-averageable: judgment built over cycles, relationships that took a decade to earn, the on-the-ground pattern recognition that still decides outcomes in real assets, private markets, and physical things — domains where being in the room beats reading about the room. Call it the cognitive-purity premium. It accrues to the people and the businesses that can still think without the machine, in a world where almost nobody bothers to try.

There is a warning tucked in here for every organisation merrily automating. It is gloriously easy to cut the explicit layer — the drafting, the modelling, the codifiable work — and bank the savings. It is much harder to notice you have also quietly stopped producing judgment, because the junior who used to earn it by doing the boring work now just prompts for it. Automate the explicit, by all means. Just don’t strip-mine the tacit, or in twenty years you will run a beautifully efficient company full of people who have never had an original thought, all agreeing with each other, fluently.

One last thing about the middle

When Tetlock’s team built their forecasting machine, they ran into something with a name that belongs on the cover of this piece. The crowd’s averaged forecast, even a good crowd’s, came out chronically too timid — huddled near the middle, hedged half to death. So they did something counterintuitive. They took the average and shoved it back outward, toward the extremes, toward conviction. They called it “extremizing,” and it made the forecasts more accurate, not less [3].

Sit with that. The best forecasters alive found that the average pulls you too far to the middle, and that the fix is to deliberately lean back out.

AI will write your emails, draft your code, and summarise your meetings. Let it — hand over the whole commodity middle with my blessing. Just don’t outsource the one thing it was never able to do: disagree with everybody, sit with the discomfort of being maybe-wrong, and turn out to be right.

A note of thanks before the close. A day before this went out, Ahmed Husain arrived at much the same fear by a different road, with evidence I had not found and a phrase I wish I had coined: a weirdness premium [16]. Two people reaching the same conclusion independently is, of course, precisely the diversity this piece says is disappearing. Long may we disagree.

The future, as ever, belongs to the weirdos who still think for themselves.

This article is 100% free to read, so please share it to pay it forward.

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References

[1] Francis Galton, “Vox Populi,” Nature (1907) — the original “wisdom of crowds” experiment: at a 1906 country fair, the median of ~800 independent guesses of an ox’s weight came within ~1% of the true figure. Popularly retold as guessing marbles or jellybeans in a jar.

[2] Cliff Asness (AQR Capital Management) on the “wisdom of crowds” failing in markets — the “poll the audience” analogy and a narrative-to-flows-to-price feedback loop. Asness is a public commentator; argument deployed here in paraphrase.

[3] Philip E. Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction (2015). The Good Judgment Project — teams of amateur forecasters outperforming intelligence-community analysts with classified access; the superforecaster traits (”perpetual beta,” the “dragonfly eye,” probabilistic thinking); and the “extremizing” of aggregated forecasts to improve accuracy.

[4] Tom Slater, Investment Manager, Scottish Mortgage Investment Trust (Baillie Gifford), “AI Isn’t Coming For Your Job. It’s Coming For Your Mind,” 2026 — citing a 2026 study in Computers in Human Behavior finding AI use produces uniform overconfidence across skill levels, with the most AI-literate the least well-calibrated.

[5] Philip E. Tetlock, Expert Political Judgment: How Good Is It? How Can We Know? (2005). The roughly two-decade study in which the average expert’s forecasts proved “roughly as accurate as a dart-throwing chimpanzee,” and the most media-prominent experts were among the least accurate.

[6] Michael Polanyi, The Tacit Dimension (1966): “we know more than we can tell.” The foundational statement of tacit versus explicit knowledge.

[7] Carson Block (Muddy Waters Research), interview on the Financial Times Unhedged podcast, 2026.

[8] “The Death of Equities,” BusinessWeek cover, 13 August 1979 — the canonical example of consensus conviction arriving at a major market turning point.

[9] Scott E. Page, The Difference (Princeton University Press, 2007) — the Diversity Prediction Theorem: collective squared error = average individual squared error − prediction diversity. An algebraic identity, not an empirical claim.

[10] Emily Wenger and Yoed N. Kenett, “Large language models are homogeneously creative,” PNAS Nexus 5(3), March 2026 — 102 human participants vs 22 LLMs; model responses mirror other models far more than humans mirror humans.

[11] Liwei Jiang et al., “Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond),” NeurIPS 2025 (Best Paper Award) — 26,000 open-ended queries; pronounced inter-model homogeneity across 70+ models.

[12] Weixin Liang et al., “The Widespread Adoption of Large Language Model-Assisted Writing Across Society,” Patterns (2025) — 537,413 corporate press releases, Jan 2022–Sep 2024; up to 24% of text LLM-generated by September 2024; described by the authors as a lower bound.

[13] Market structure: Reuters/NYSE turnover data (holding periods, 2020 analysis); Investment Company Institute, “Active and Index Investing,” June 2026 (63.8% of US domestic equity fund assets indexed); Fang, Hope, Huang and Moldovan, “The Effects of MiFID II on Sell-Side Analysts, Buy-Side Analysts, and Firms,” Review of Accounting Studies (2020) — 334 EEA firms lost coverage entirely.

[14] S&P 500 constituent weights, official fund holdings file, 24 August 2026 (top-10 constituents = 37.0%).

[15] SPIVA U.S. Scorecard, Year-End 2025, S&P Dow Jones Indices — 79% of active large-cap funds underperformed the S&P 500 in 2025; 93% over 20 years.

[16] Ahmed Husain, “The Weirdness Premium,” The Curious Mind, Research Letter No. 2, 26 August 2026 — thecuriousmind.org/p/the-weirdness-premium.

And here is the audio podcast version (done with Gemini Notebook):

Everyone is hunting for the crash in data centers. Or in the hardware stocks. Or the debt markets.

They are looking in the wrong place entirely.

Saying that AI is a bubble has become the standard consensus view across Wall Street. But in finance, the exact moment an observation becomes consensus is the exact moment it loses all utility. The question is never whether we are in a bubble. The question is which kind of bubble it is, and which layer of the stack gets slaughtered when it pops.

Infrastructure booms throw off two fundamentally different kinds of bubbles. They behave nothing alike and they reward opposite investors:

• The Early-Cycle Bubble arrives early, as its name implies. It builds something magnificent that the next generation inherits for free. It crashes hard, but leaves the physical asset standing. Britain’s Railway Mania bankrupted its investors, but left six thousand miles of track. The dot-com buildout laid tens of millions of miles of fibre, lit barely ten percent of it, went bust, and quietly funded the next twenty years of the internet [1].

• The Late-Cycle Bubble arrives late. The technology has obviously won. Financing turns aggressive, and the cycle’s cheap input gets wildly over-consumed until the economics break. It feels gentler on the way up because real earnings sit underneath it. It is far nastier on the way down, because what it leaves behind is not a public commons but stranded capital.

We are in the second kind of bubble. AI is not a new industrial platform; it is a late-cycle efficiency input.

Inputs like that do not keep their profit margins. But the money isn’t vanishing into thin air either. It is migrating to the two extreme ends of the pipe: to the platforms that control customer distribution at the top, and to the physical bottlenecks that supply energy and land at the bottom.

What gets stranded is the layer in the middle. So you have to own the ends and be terrified of the middle.

LLMs Are Memory Chips, Not Processors

The right historical reference point for Large Language Models is not the railway or the internet. It is the microprocessor [2].

The microprocessor made the entire global economy exponentially more productive while the price of computation itself plummeted relentlessly toward cost.

Some inputs manage to keep their margins. Intel held fat rents on x86 architecture for decades, and Nvidia does the same today with CUDA. Why? Because a massive wall of software was built specifically for their design and could not easily move.

An input keeps its rent only when it locks its users in.

Large language models do the exact opposite. They are accessed through near-identical interfaces and can be swapped out in an afternoon. They are not the processor; they are the memory chip, a pure commodity where whichever supplier is cheapest and good enough wins.

The tell that a technology has entered its late cycle is who adopts it first. New paradigms start on the fringe as crude toys that serious executives ignore. This generation of AI was seized instantly by incumbent megacaps, because the enterprise rails were already laid. When the breakthroughs come from multi-billion-dollar corporate research budgets rather than a garage, you are looking at a mature cycle, not a wild new frontier [3].

The Great Price Split

The headline narrative says the price of machine intelligence is falling across the board but the data says otherwise.

The price didn’t fall, it split:

Since mid-2024, the cheapest capable models have anchored at roughly two and a half cents per million tokens. Meanwhile the most expensive flagship on the market today costs $465 per million tokens on a blend of 30% input and 70% output, against roughly $51 for the flagship of three years ago on the same blend [5]. The top of the market raised its prices roughly ninefold while the bottom collapsed to near-zero.

Today, roughly 43% of all available models cost under a dollar per million tokens. The gap between the most expensive and the cheapest is an absurd 19,000 times [5].

This structural split ruins the middle layer. A commodity layer has emerged at the bottom, where good-enough intelligence is practically free, while a hyper-specialised niche holds at the top. What is disappearing is the middle class of AI: the assumption that a very good proprietary model is a durable, broadly monetisable business.

And the proof keeps compounding. In July 2026 a Chinese open-weight model, Kimi K3, took the top spot on a public coding leaderboard ahead of every Western frontier model, and its creators released the weights for free download [4]. Days later, OpenAI cut prices on its smaller models, taking roughly 80% off the output price of one of them.

The repricing did not stop there. DeepSeek, the most watched of the budget providers, is reported to have raised its prices by roughly 205% over the same weeks. The reported gap between OpenAI’s cut model and DeepSeek’s flagship collapsed from 21 times to 1.4 times. The cheap end is no longer a race to zero; it is converging on a single commodity band.

A free or cheap model doesn’t need to defeat the frontier everywhere. It just needs to be good enough for ninety percent of daily tasks.

Data Centers Aren’t Warehouses of Obsolete Chips

If model prices are commoditising, doesn’t that mean the massive capital expenditure pouring into data centers is being incinerated?

Look at the operating numbers [6].

• Azure crossed $100 billion in annual revenue in Microsoft’s fiscal 2026, growing 43% year on year in the June quarter.

• Microsoft Cloud overall reached $214 billion.

• Google Cloud’s operating margin expanded from 20.7% to 35.6% in twelve months.

These are widening operating margins, not collapsing ones.

Paper valuation gains. Much of Big Tech’s net income spike stems from markups on private stakes in AI labs. The scale of it is visible in the index: FactSet’s blended second-quarter earnings growth for the S&P 500 was 50.4%, but 32.0% excluding Alphabet and Amazon [7]. That is why I look at cloud operating margins rather than net earnings.

Cash burn. Alphabet’s quarterly free cash flow turned negative for the first time since it listed, a $5.9 billion outflow. Amazon’s June quarter was a $7.7 billion outflow. Meta’s fell 91% to $784 million. Only Microsoft stayed firmly positive, at $19.6 billion. Together, on the companies’ own definitions, the four generated roughly $6.9 billion while capital expenditure surged, and their credit spreads widened.

So how to reconcile booming cloud operating margins with tightening cash? By understanding the two-clock option structure of data center construction.

Land, buildings and power grid connections require a twenty-four-month lead time. But the actual chips and accelerators, which are the majority of total cost, are ordered just three months before installation, only after real customer demand is visible.

As Amazon put it bluntly: “If the demand isn’t there, we won’t spend the capital.”

That flexibility is real, and it covers the chip orders. Only the chip orders. For the chips to be stranded, demand must vanish inside a single ordering window, and even then what stands empty is a building with a massive power link, not a warehouse full of decaying chips.

The other clock carries no such option. The filings already hold roughly $3 trillion of contracted commitments across the big spenders, around $2.3 trillion of it binding on the strictest reading [9]. A later piece in this series follows those signatures.

The Model is an App, Not the App Store

Wall Street committed a fundamental classification error. It priced AI models like two-sided platforms, assets that gain network effects and pricing power as more users join.

The model is not the App Store. The model is just an app. The hyperscaler is the App Store.

Consider what a distribution platform actually does

• It owns the end-customer relationship.

• It processes all telemetry and billing.

• It sees exactly which features get traction.

• And when a third-party app gets big enough, the platform builds its own version and defaults it onto the customer’s screen.

This playbook is currently being executed in AI with Hyperscalers owning the corporate billing accounts. They see which models are being called and at what price and when a third-party model charges a premium, Microsoft or Google simply deploys its own internal or open-weight alternative directly into the stack.

The rents do not belong to the model. They belong to whoever owns customer distribution at the top, and whoever owns scarce power and grid access at the bottom.

The $217 Billion Exposure: Where the Real Risk Lives

The strongest counterargument to this thesis is circular financing.

A chipmaker invests in an AI lab, which uses the funds to buy chips. A cloud platform invests billions in a lab, which turns around and commits those billions back to the platform in compute contracts. It echoes the circular accounting that unravelled telecom firms during the 2001 crash.

If the model labs are funded by the very platforms selling to them, what happens if the labs go under?

Microsoft disclosed two crucial numbers on the same earnings call [6]:

• Commercial contracted backlog grew +84% to $678 billion.

• Excluding OpenAI, backlog grew +25%.

Do the arithmetic: OpenAI alone accounts for at least $217 billion of Microsoft’s forward book. That is just under a third of it at the strictest floor, and plausibly closer to half. That figure is a floor, not an estimate: it assumes OpenAI had no backlog whatsoever a year ago, and any starting value above zero makes the share larger.

Current cloud revenue is highly diversified: nearly 90% of Microsoft Cloud revenue comes from customers outside the frontier laboratories. That measure also carries Office, LinkedIn and Dynamics, so the concentration inside the cloud infrastructure business alone is higher still. The forward contracted book is wildly concentrated.

If a laboratory fails to raise its next multi-billion-dollar equity round, it won’t trigger an immediate drop in today’s cloud computing usage. Instead it creates a severe credit and lease exposure on the forward books of the platforms. One rating agency has already acted on exactly this risk. In July 2026, S&P downgraded Oracle and wrote that “OpenAI remains a key credit risk”, estimating that OpenAI makes up “roughly half of the $638 billion” in Oracle’s contracted book [8]. Moody’s holds the same company on negative outlook, citing the counterparty risk of its largest AI infrastructure customer.

What Happens When the Buyer Changes?

If model laboratories stumble, won’t traditional enterprises and governments absorb the extra compute capacity?

Yes. But at a fraction of the price.

Every mile of dot-com fibre optic cable was eventually lit and used. But the equity owners who laid that fibre still went broke; the eventual returns went to distressed-asset buyers who bought the infrastructure for pennies on the dollar.

Why? Because the buyer changed:

• Laboratory demand is funded by capital markets. It demands peak hardware and maximum memory bandwidth at almost any price, to win the intelligence race.

• Enterprise demand is funded by corporate operating budgets. It demands good-enough inference performance at a cost that clears an internal committee.

When capital-market funding slows, the volume of compute demanded doesn’t disappear. What disappears is the buyer who would pay anything for the newest, most memory-dense configuration, replaced by one who will only pay for the cheapest arrangement that works.

Volume holds. Realised price per unit falls. That is the same bifurcation as the model-price chart, seen from the demand side.

Training wants maximum memory bandwidth per chip whatever it costs; inference wants the cheapest adequate configuration. So the memory content of each machine is not a constant. It is a function of the training-to-inference mix, and that mix is about to move.

The Investor’s Playbook

On its face this is not an absurd multiple bubble. Names like Nvidia, Broadcom and Meta trade at growth-adjusted multiples below 1.0. Keep in mind though that the earnings beneath those multiples carry the paper markups flagged above.

The risk is not that price-earnings multiples are crazy. The risk is that future earnings assumptions rely on middle-layer rents that cannot be sustained.

So that is where this Substack will spend its time. When I try to understand an AI related investment opportunity in the issues ahead, the work will sit at the two ends of the pipe: the platforms that own customer distribution at the top, and the physical bottlenecks that own power, land and memory at the bottom. The middle is what I will seek to avoid.

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Core takeaways

The moat is at the distribution layer. The platforms hold their position regardless of which model wins, because they own the customer relationship and the billing, they can see which models are actually consumed and at what price, and they can ship their own version of anything that works. That is not a forecast about technology. It is where the defences in this stack actually sit. One boundary belongs in the same breath: the rent is real, and the committed capacity is the cost of defending it, signed years ahead of the revenue it serves. The moat and the bill come together.

The second moat is physical, and narrower than it looks. Power, land, memory and storage are genuinely scarce and cannot be conjured. But memory content per machine is not a constant; it is a function of the training-to-inference mix. Anyone holding a bottleneck because “AI needs it” is really betting that the mix holds.

Be sceptical of the middle. Most of it is unlistable, be sceptical of anything priced as though it will one day charge a platform’s toll. Its margins will likely converge toward cost as open-weight models improve.

And note where the exposure actually sits. When a laboratory cannot raise its next round, today’s cloud usage does not stop. What moves is the forward book, and that is concentrated in a way current revenue is not. It arrives as marks and leases on platform balance sheets. It is a credit exposure, not a demand collapse. That is a different risk from the one most people are watching for, and it is the one already being acted on.

So here is the position and what would change my mind. The bubble sits in the model layer, not the infrastructure layer: model economics converge toward zero profit while the platforms above them and the bottlenecks below them keep the rent.

Two things would tell me I have this wrong. The first is a proprietary frontier model rebuilding real pricing power. I would see it in the same price capture behind [5], re-pulled each quarter: the top flagship’s premium over the cheapest capable tier widening again, and holding for some time. The second is a platform’s cloud growth stalling on ordinary enterprise demand rather than on laboratory commitments. Microsoft discloses that itself every quarter: the non-frontier share of Microsoft Cloud, and commercial remaining performance obligations excluding OpenAI. I am keeping a close eye on that.

Neither is happening yet and the price of intelligence has split rather than fallen. The floor has been flat since mid-2024 while the frontier costs around nine times what it did three years ago on a like-for-like blend. And the reported gap between OpenAI’s cheap tier and DeepSeek’s has collapsed from twenty-one times to 1.4. Remember that open source models are free in the sense that they are free to own, but the expenses to run them remain. Open-weight models lead at least one public capability board. Cloud revenue is growing above forty per cent, with nearly ninety per cent of Microsoft Cloud sold to non-frontier customers. The committed backlog is growing eighty-four per cent, or twenty-five excluding a single frontier customer.

Knowing that AI is in a late-cycle input bubble won’t tell you the exact date of the top. But knowing which layer the bubble sits in tells you exactly what to hold while you wait.

References

[1] Carlota Perez, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages (Edward Elgar, 2002). The frenzy and maturity phases are Perez’s framework; reading the two as leaving a usable commons versus stranded capital is Singh’s extension of it (see [2]).

[2] Sameer Singh, “AI: The Wrong Kind of Bubble,” Breadcrumb, 2026. The microprocessor reference class and the input-not-platform reframe draw on this essay. This piece departs from it on where the stranding lands.

[3] Nicolas Colin, “Late-Cycle Investment Theory” (Drift Signal, 2025) and “The Beginning of the End for Big AI” (Drift Signal, 2026), for the tell that a paradigm has matured.

[4] Arena.ai Frontend Code leaderboard, July 2026, and Moonshot AI release materials for Kimi K3. Standings re-verified at publication.

[5] Model pricing from a full capture of a public model marketplace listing, 19 August 2026. Blended price defined as 30% input plus 70% output per million tokens; batch and free tiers excluded. The 2023 comparison is on the same blend: the then flagship, at $30 input and $60 output per million tokens, blends to $51. Prices move weekly; the capture is re-verified at publication and re-pulled quarterly thereafter to score the falsifier.

[6] Microsoft fiscal 2026 fourth-quarter results and earnings call, 29 July 2026, for Azure and Microsoft Cloud revenue, the share of cloud revenue from customers outside frontier model companies, and commercial remaining performance obligations. Alphabet, Amazon and Meta second-quarter 2026 results and calls for cloud margins, capital-spending commentary and free cash flow; free-cash-flow figures are the companies’ own reported definitions for the June quarter.

[7] FactSet Earnings Insight, August 2026, for blended S&P 500 second-quarter earnings growth of 50.4% and 32.0% excluding Alphabet and Amazon.

[8] S&P Global Ratings, “Oracle Corp. Downgraded To ‘BBB-/A-3’ From ‘BBB/A-2’ On Rising Business Risk And Weaker Cash Flow; Outlook Stable”, 9 July 2026, including the estimate that OpenAI makes up roughly half of Oracle’s $638 billion in remaining performance obligations. Moody’s Investors Service, affirmation of Oracle at Baa2 with negative outlook, February 2026, citing counterparty risk associated with its largest AI infrastructure customer.

[9] Purchase-commitment, lease and power-agreement disclosures in the largest AI spenders’ most recent annual and quarterly filings, at SEC EDGAR. The roughly $3 trillion contracted and roughly $2.3 trillion binding aggregates are my own reading of those notes; a later piece in this series shows the work.

[10] National Bureau of Economic Research, Business Cycle Dating Committee chronology; the NBER-based recession indicator is FRED series USREC. The February to April 2020 contraction is the only recession in the fifteen-year window.

Start with a desk.

Every computer has two places to put data, and they do completely different jobs.

The filing cabinet in the corner is the hard drive. It holds everything you own, cheaply, and it is painfully slow. You have to walk over and find the folder.

The desk in front of you is the memory. It holds only what you are working on right now. It costs vastly more per square inch, and it is instant.

The entire point of the desk is immediacy. When you buy memory, you are not buying storage. You are buying time.

For forty years the desk was the boring part of the technology industry. Then artificial intelligence arrived and demanded a desk the size of a football field.

Why AI is starving for memory

An AI model is not a search engine looking things up. It is a vast grid of numbers, hundreds of billions of them, which are the weights it learned during training. To answer a single question, the whole model has to be run through the processor.

This is the bottleneck almost everyone missed. The entire model has to be sitting on the desk at once. Not in the filing cabinet. Every single time anyone asks it anything.

A data centre packed with the fastest processors on earth is useless if there is nowhere to put the numbers those processors need. The industry spent three years working out that the binding shortage was not the graphics chips. It was the desk space next to them.

The answer is High Bandwidth Memory, or HBM: ordinary memory chips stacked twelve or sixteen layers high, drilled through, bonded, and placed hard against the processor. Data travels millimetres instead of centimetres. It is extremely difficult to manufacture, and it is now the single component holding up the global AI build.

The catch: an oligopoly that has never stopped overbuilding

Only three companies make HBM at scale: Samsung, SK Hynix and Micron. SK Hynix is currently the best at the hardest part of it.

In any normal industry, three suppliers controlling a bottleneck for a generational technology would mean you buy the shares, close your eyes, and collect the rent.

Memory has never been a normal industry. It has been a ruthless commodity:

• Buyers choose on price. Historically there has been no software lock-in and no ecosystem loyalty.

• When memory is scarce, prices triple and the makers print money.

• When it is plentiful, prices collapse and they lose money on every chip.

And here is the flaw that has defined the industry for four decades: they have never once stopped themselves from overbuilding. They do not even need new factories to flood the market. They convert existing lines and shrink the circuitry on the chips they already make. Samsung raised its output of memory by about 40% in a year, measured in bits rather than chips, and spent almost nothing extra doing it. Supply arrives without anyone having to fund it. That is why the good times end faster here than in copper, oil or shipping.

So the question is not whether AI needs memory. It plainly does. The question is whether this time the industry finally behaves differently.

There is now real evidence that it is, and that is what most of the market is still not pricing.

Why the demand panic is wrong

SK Hynix shares peaked at 2,987,000 won on 25 June and closed at 1,504,000. They are down 49.6% in seven weeks. The market is pricing the end of the AI build. The evidence does not support it.

1. Customers are paying up, and saying so out loud.

Amazon raised its 2026 capital spending from $200bn to $220bn and its chief executive named rising memory prices as the reason. In the same results it disclosed a contracted cloud backlog of $496bn, up $132bn in one quarter, and said it will not have enough capacity to meet demand in 2026 or 2027. A customer who raises his budget to meet your price is not a customer with weakening demand.

2. The product is now genuinely contracted, and this is new.

On 28 July the company said it has concluded long-term agreements with around ten customers, including its key customers, with further discussions running. On the same call it set out the terms. They run normally around five years. They carry long-term volume commitments. And they carry deposits, which the company says are there “to support contract fulfillment and enhance visibility” of demand. Pricing is deliberately not fixed: the company uses “a range of pricing mechanisms that can better respond to price volatility.”

Read what that structure actually is. The volume is committed and the price is not. For thirty years this industry sold neither. A business with contracted volumes and open pricing is not a commodity producer, it is a toll. That is the single most important change in this company’s history and it happened three weeks ago.

And the deposits matter more than they sound. A customer who puts cash down in advance is part-funding the capacity that will serve him. The oldest question to ask of any supplier is who pays for the next factory. When the answer is the customer, the supplier keeps the rent. That answer has never been available in memory before.

3. It is not just the frontier labs.

The loudest bear case is that the big model labs run out of money. But Microsoft has disclosed that about 90% of Microsoft Cloud revenue comes from ordinary corporate computing rather than frontier AI. That measure includes Office 365 and LinkedIn, so treat it as a direction rather than a precise number, but the direction is clear and it is growing on its own.

4. The supplier is not releasing supply. It is contracting it.

Conventional DRAM wafer capacity is flat in 2026 and shrinks 4.0% in 2027. The share of wafers going to AI memory is forecast to rise from 18% at the end of 2025 to 22% in 2026 and 30% in 2027. Nobody is flooding anything.

Management describes its expansion in terms this industry has never used. Equipment investment and production ramps will run “in phases while considering demand visibility, investment efficiency”, staying ”flexible in alignment with confirmed customer demand.” The historical failure was building against forecasts. This is building against orders.

5. Prices have not turned.

A benchmark chip traded at $42.61 on 12 August, having risen from $12.76 last November. Contract prices rose 93% to 98% in the first quarter and 58% to 63% in the second, with 13% to 18% forecast for the third. The rate of increase is collapsing. A slowing rate of increase is not a falling price.

6. Even the bears’ best evidence points the other way.

Nvidia cut the memory in its Rubin Ultra design from about 1TB to 192-256GB, which was widely read as weak demand. The industry’s own price reporters state the cause directly: the 2027 shortage means suppliers cannot deliver the volume. That is a buyer rationing itself in a shortage, not a buyer that needs less.

So why did the shares halve? A single fund running about $45bn, heavily concentrated in exactly these names, took margin calls at the end of July and was forced to sell most of its public book to Citadel, ending with roughly $10bn. The KOSPI fell about 33% from its June peak. That is a positioning event, not a demand event.

The real pressure point, and how it actually resolves

There is one genuine mechanism that could break this, and it deserves to be understood properly rather than waved at.

A memory factory makes both kinds of chip from the same silicon wafers. Ordinary memory and stacked AI memory. Same buildings, same wafers, a choice about what to run. AI memory yields roughly a third as many gigabytes per wafer because it is so much harder to make.

So AI memory has to sell at a large enough premium to justify giving up the ordinary memory those wafers could have made. For two years that was easy, which is exactly why ordinary memory went short and its price tripled. Not because anyone stopped wanting ordinary memory, but because the wafers that would have made it were making something else.

And the premium has been closing, because ordinary memory has risen far faster than AI memory. If it closes far enough, the wafers should flow back, ordinary supply would rise, and the price of the product that earns most of the profit would fall.

How violent would that be? The industry’s own history gives exact answers, and they are brutal:

• 1.38 points of surplus was enough to turn the price.

• 5.8 points of surplus broke it.

• One to two points of shortage is what tripled it.

Small moves in volume, violent moves in price. That is the whole industry in three numbers.

But the mechanism has now been tested in public for nine months and has not fired. The price reporters flagged it last October and reported capacity beginning to shift two months later. Since then the share of wafers going to AI memory has gone up, not down. Ordinary memory set a record price. Nobody blinked.

Here is why, and it is the part the bears have wrong:

• Leaving AI memory is easy. Coming back is not. Ordinary memory needs a wafer and nothing else. AI memory needs a wafer plus a second step that happens somewhere else: stacking, drilling, bonding, and a control chip bought in from TSMC. The wafers are shared between the two products. That stacking capacity is not, and it had to be built on purpose.

• So a maker who moves wafers out to chase today’s ordinary price may not get them back in when it matters, and would surrender its position in the most important hardware of the decade to do it.

• And the shortage itself is now the brake. The expected 2027 shortage limits how much capacity can go to AI memory at all, because ordinary memory is too short and too profitable to starve any further.

Which leaves one resolution, and it is the bullish one. If the premium is too thin and no wafers can move, then the premium has to widen from the other end. The industry’s price reporters expect exactly that: AI memory contract prices rising several-fold in 2027. The gap closes by AI memory repricing upward, not by ordinary memory collapsing. Nobody has to switch, and the shortage holds.

That is the reasonable base case, and the market is not paying for it.

What management actually did, and what it does not mean

In July SK Hynix sold $26.5bn of new shares on the American market. Within twenty-four days it approved roughly $41bn of new factory building.

This is usually presented as a warning sign. Read the actual filing and it is mostly not.

The prospectus states what the money is for, and it is specific: 45.5 trillion won for Fab 1 at the Yongin complex and the P&T7 advanced packaging plant at Cheongju, both completing at the end of 2030, plus 11.9 trillion won of EUV lithography scanners for delivery by December 2027.

And it says the raise does not cover it. That is a 57.4 trillion won programme against net proceeds of about 26.2 billion dollars, roughly 37 trillion won. The company writes that it expects to fund the difference from operating cash flow, borrowings and debt. So the common claim that they did not need the money is simply wrong on their own document. It is also worth knowing that quarterly free cash flow is about $12.5bn, not the $40bn of quarterly operating profit that gets quoted as though it were cash.

There are four reasons on the record, and three of them are ordinary:

1. A Nasdaq listing requires a primary raise. This was the largest first-time US share sale by a foreign company ever, seven times oversubscribed, priced at $149, up 13% on debut. You cannot list without selling something.

2. There is a published building programme, and the money does not cover it.

3. Washington is applying pressure. The US Commerce Secretary has been openly lobbying both Korean makers to build American fabs, at the same moment the two of them pledged over $550bn to Korean plants. A US listing and a US shareholder base is a cheap opening move.

4. Management may have thought the price was good. This is the fourth answer, not the best one.

But there is one genuinely interesting fact in the sequence, and it is not the raise. The prospectus filed on 10 July names Fab 1 and P&T7 and nothing else. The terms “M17” and “Fab 2” do not appear in it once. Eighteen days later the company’s own results release named M17, a second Yongin phase, and a new cluster. So they raised against one program and then approved a larger and different one almost immediately.

That is a company responding to demand it did not have visibility on three weeks earlier. It is not a company calling the top.

Valuation: pricing the cycle

Last quarter the company earned 60.5 trillion won of operating profit on 79.3 trillion won of revenue. At 1,418 won to the dollar, that annualises to about $224bn of revenue and $171bn of operating profit, on a 76% margin, against a company worth about $773bn.

So the whole company costs about 4.5 times one year of current operating profit. That is not a cheap business. It is a peak one. And note the basis: that is a multiple of pre-tax operating profit, not earnings, so the true price-to-earnings figure is roughly a third higher again.

One number has to be set aside, and it is not the one usually named. Last quarter’s bottom-line profit was 93.9 trillion won, half as much again as the 60.5 trillion the factories earned. The difference was not made by making memory. Most of it is a gain on the company’s convertible bond in the Bain-led vehicle that owns Kioxia, the Japanese memory maker, which converts into a stake in that vehicle rather than into Kioxia shares directly. Only about a third of the holding was actually sold. The rest is carried at what the market says it is worth, and Kioxia’s share price roughly halved in July. Every number in this piece uses the factory profit and ignores that gain entirely.

The three cases

How each case is built:

• The bear respects the company’s own history. Revenue fell 33% in 2019, with the margin going from 51.5% to 10.1%. It fell 27% in 2023, with the margin at minus 23.6%. Volume growth has never once prevented a revenue decline. A 25% fall is therefore milder than both precedents, deliberately.

• The bear margin is 25% rather than the 20% the industry averages through a cycle, and the contracts are the reason. The catastrophic margins of 2023 happened because price *and* volume collapsed together and the fabs ran half empty. Committed volumes with deposits are precisely what prevents that. A price-only downturn does far less damage than a price-and-volume one.

• The base assumes today’s 76% margin is the anomaly it obviously is and halves it, while volume growth offsets falling prices to hold revenue flat.

• The bull needs the multiple to change, and now has a reason. Thirteen times instead of ten is what you pay for a business with contracted volumes rather than a pure cyclical. Before those agreements existed that was a hope. It is now a mechanism.

• The multiple is a judgement, not a fact. At 8x instead of 10x, every number falls by a fifth.

The risk that trades at nothing

Two of the company’s plants sit in China: Wuxi, which makes legacy DRAM, and Dalian, the former Intel NAND plant. Their US export waivers were revoked at the end of 2025 and replaced by an annual site-license review. The company’s access to American equipment for a meaningful part of its capacity is now renewed once a year, at Washington’s discretion. That is currently priced at nothing.

The bottom line

Demand for high bandwidth memory is not weakening, and the 50% fall was a forced seller in a crowded trade rather than a change in the business. What has changed, and what the market has not absorbed, is that this company now sells contracted volume at open prices. That is a different business from the one whose forty-year range everyone is quoting at it. The pressure point on wafers is real, and on the evidence it resolves through AI memory repricing upward rather than through ordinary memory collapsing.

I am treating this as a tactical position with a rerating case attached rather than a buy-and-forget.

Wrong if:

1. The share of wafers going to AI memory starts falling while prices hold. That would mean the supply response of the past forty years is finally arriving.

2. The long-term agreements turn out to carry no deposits and no real volume commitment. The structure is the whole rerating case. If the terms are soft, the case is soft.

3. A benchmark contract price prints a quarter-on-quarter fall. Not a slower rise. A fall.

Status:

1. On its last filed accounts, 99.87% of 2025 revenue and 99.94% of first-quarter revenue was recognised at a point in time, with no backlog and no take-or-pay. The agreements announced on 28 July are not yet in any filing.

2. The share of wafers going to AI memory is still rising, not falling.

3. The most recent benchmark spot price is a new high, not a fall.

None of the three conditions is met.

References

1. SK hynix Inc., Prospectus (Form 424B4), filed 10 July 2026. SEC CIK 0002120882, accession 0001193125-26-299963. Use of proceeds, capital expenditure table, revenue recognition by timing, and China facility risk factors.

2. SK hynix Inc., “SK hynix Announces 2Q26 Financial Results”, 28 July 2026. Revenue, operating profit, net profit, long-term agreements, and mid-to-long-term investment plans including P&T7 and M17.

3. Amazon.com Inc., second-quarter 2026 results and earnings call, 30 July 2026. Capital expenditure guidance and AWS backlog.

4. Microsoft Corporation, quarterly disclosure of Microsoft Cloud revenue composition.

5. TrendForce, Memory Spot Price Update, weekly, November 2025 to August 2026. DDR4 1Gx8 3200MT/s spot prices.

6. TrendForce, DRAM contract price forecasts, quarter on quarter, 2026, published 3 July 2026.

7. TrendForce, “DRAM Supply to Remain Tight in 2027, Prompting NVIDIA to Lower HBM Configurations for Rubin Ultra”, 4 August 2026.

8. TrendForce, HBM wafer allocation and 2027 contract price outlook, 2 June 2026.

9. US Bureau of Industry and Security, “Revocation of Validated End-User Authorizations in the People’s Republic of China”, Federal Register, 2 September 2025, effective 31 December 2025.

10. EODHD market data. SK hynix (000660.KO) closing prices and USD/KRW, to 12 August 2026.

Click here for : Part1 - This is Part 2

Most market commentators spend their lives playing a trivial game: guessing where the S&P 500 will close in December or predicting when a recession will occur. The track record of point forecasting is atrocious, recessions also. The people who sound the most certain are almost always the ones who have forgotten their long history of being wrong.

I take a different approach. I focus on locating where we sit inside a much longer macro rhythm. If you understand the tide, you do not need to predict every individual wave. You simply need to know what assets are worth holding, which risks matter, and what noise you can safely ignore.

The market cycle everyone used to watch is the business cycle, a brief oscillation of expansion and contraction lasting a few years. But even that cycle is breaking down. Historically, the United States suffered an economic contraction roughly every six years. Over the last fifteen years, we have seen exactly one recession, and it lasted all of two months during the 2020 lockdown.

Beneath the business cycle runs a far more powerful engine: the debt and inflation super-cycle. It is a generational tide where the cost of money falls for thirty to forty years, and then rises for the next thirty to forty.

The Arithmetic of the Tides

You can date the turn in 1981 with precision when Volcker raised rates. The recent trough sits somewhere between 2012 and 2020, depending on how you account for emergency pandemic intervention. But the broader pattern holds regardless of the exact dating. The current rising leg is young and could still look like a cyclical bounce, but the fiscal mechanics behind it suggest the secular tide has officially turned.

These patterns persist because the underlying forces move at generational speed. Debt loads accumulate over decades. Demographics shift over half-centuries. Inflation psychology, the quiet assumptions that workers and companies carry into salary negotiations and corporate contracts, takes an entire generation to learn and another to unlearn.

We only have two and a half full regime transitions in modern US history:

• The post-war inflation run

• The Volcker reversal of 1981

• The structural turn unfolding right now

When you have a sample size that small, you cannot rely on statistical overfitting. You have to look at the current, real-world evidence: valuations, credit conditions, and of course fiscal policy.

What Wins When the Tide Flips

Where you sit in this cycle dictates which assets outperform.

Era 1: Falling Cost of Money

When money gets cheaper across a multi-decade span, financial assets dominate. Equities surge because a lower discount rate makes future earnings worth more today. Growth stocks perform best of all because their projected earnings sit furthest out in time. Bonds rally alongside stocks. Gold, energy, and real assets lag behind.

Era 2: Rising Cost of Money

When money gets dearer, the order completely inverts. Commodities, tangible assets, and gold lead the board. Bonds lose real money as yields climb. The most expensive growth stocks get crushed as the same discount-rate arithmetic that helped them begins to hurt them.

The transition between these two regimes is where we sit today. The assets that led for forty years are now concentrated inside passive market indices and institutional portfolios to an extreme degree (I will be writing in coming issues about passive and how it is impacting market stability). That concentration is precisely where the most capital will be surrendered as the regime shifts.

How We Got Here: The Anatomy of a Late-Stage Bull

The secular bull market that most investors view as “normal” was built on a single tailwind: relentless disinflation.

From the early 1980s onward, the cost of money fell almost continuously. Volcker broke 1970s inflation, globalization injected a billion lower-cost workers into the global economy to suppress wages, and the discount rate applied to corporate earnings plummeted. A huge portion of what passed for investment genius over the last forty years was simply this discount-rate math at work, aggressively amplified by passive index flows and corporate stock buybacks.

Dated from the 2009 low, this bull market is about seventeen years old. It can be measured against post-war secular bulls that historically lasted sixteen to eighteen years, so if history is any guide, this one is pretty old.

The valuation metrics look even more stretched than the calendar count:

• Shiller CAPE Ratio: Sitting above 40, more than double its historical average.

• Index Concentration: The ten largest names make up roughly 40% of the entire index, double their weight from a decade ago.

• Profit Margins: Put simply, at historic highs.

• Retail Allocation: Household equity holdings sit near record territory, the five highest readings in the history of the series are all from the last eighteen months.

An expensive market, operating on peak profit margins, owned by almost everyone: this is the exact cocktail that has preceded every major secular top in history.

Every great secular bull relies on a narrative technology at its core. It was radio in the 1920s, the internet in the 1990s, and artificial intelligence today. That technology explains the enthusiasm fueling the move, but it tells you nothing about when you have to stop dancing and the music stops.

Crucial Distinction: A high valuation is not a sell signal on its own.

The conditions that historically end secular bull markets are a tightening credit environment and a central bank out of ammunition. Neither is present today. The yield curve is positively sloped, credit conditions are loosening, earnings are growing, and the Federal Reserve has ample room to cut rates. The massive AI capital expenditure boom continues to inflate corporate earnings while concentrating systemic fragility.

My positioning reflects this reality: I remain invested through this final leg. The returns during a final melt-up are massive, but the exit door will be narrow and violently crowded. This is the exact moment to quietly structure hedges, because the odds of timing the top are close to zero (but so exhilarating to try).

The Inflationary Aftermath

When this bull market eventually terminates, the regime that follows will be defined by recurring bouts of inflation.

The driving force is political dominance. When sovereign debt expands beyond what economic growth or taxation can service, governments historically inflate away the real burden rather than default or enforce fiscal austerity. Modern political incentives across both major US parties favor spending over consolidation. At the same time, the world is re-arming and re-shoring supply chains, swapping a supply-abundant world for a supply-constrained one. Persistent deficits meeting structural supply constraints is the classic recipe for volatile inflation.

Addressing the Three Main Objections

1. “Look at Japan”

Critics argue Japan ran massive deficits for thirty years and generated nothing but deflation. This comparison fails for three clear reasons:

• Balance Sheet Dynamics: Japan’s government spending was filling a crater created by a private sector aggressively paying down debt. US deficits today run alongside a private sector whose balance sheet repair is finished and whose debt service is well below 2007 levels. US deficits are net additive to demand.

• Demographic Isolation: Japan aged first and alone, offshoring production to cheap foreign labor. A world aging simultaneously across all major economies cannot use that trick.

• Creditor vs. Debtor: Japan ran its experiment as the world’s largest creditor nation, funded by captive domestic savings. The US is a twin-deficit debtor dependent on foreign capital. A debtor country engaging in fiscal excess sees the pressure hit its currency and domestic price levels.

2. “AI is Deflationary”

A technology driving the cost of intelligence toward zero is undeniably a supply-side disinflationary force, much like China entering the global trade system. But AI is a supply-side shock, while our macro regime is driven by demand.

Cheaper production does not stop fiscal deficits from flowing directly into economy-wide spending. In fact, if AI displaces workers on a large scale, the political pressure for government transfers and deficits will increase dramatically. The technology that makes goods cheaper to produce simultaneously strengthens the political imperative to print and spend.

3. “The Wage-Price Spiral is Dead”

Labour lacks the unionization and contractual indexing it held in the 1970s, so price shocks burn out faster rather than compounding automatically into higher wage rounds.

This mechanism is real, which is why inflation will arrive as a series of sharp, unpredictable steps rather than a smooth vertical climb. But a broken wage-price spiral only limits how fast inflation accelerates; it does not stop it from persisting. When the inflationary impulse is fiscal, driven by government transfers into a supply-constrained real economy during every downturn, the persistence comes from the deficits, not the wage negotiations.

The Trap: Expect the Decoy First

Here is where most macro thinkers get caught out: sequencing.

The transition to a high-inflation regime might not happen in a straight line. A secular bull market so heavily dependent on a concentrated capital-spending boom usually ends in a sudden deflationary scare:

1. Risk assets sell off sharply.

2. Credit spreads widen rapidly.

3. Capital flees into long-dated US Treasuries.

4. The Federal Reserve cuts interest rates aggressively.

This first phase will probably look and feel like 2008. It will fool a generation of investors into thinking the old deflationary playbook still works, tempting them to pile back into long-duration paper and growth tech.

That deflationary crash will be the decoy. The massive fiscal stimulus deployed to counter that shock will travel directly into the real economy rather than getting trapped on bank balance sheets. That is when the secular turn asserts itself, cementing a decade defined by volatile, step-function inflation.

Add to this a massive, completely untapped powder keg of demand: trapped home equity. Years of relentless real estate appreciation have handed households a gigantic pool of wealth, but it currently sits frozen behind high mortgage rates. Owners' equity in US real estate is $34.9 trillion as of Q1 2026, with the 30-year mortgage at 6.69%. That is larger than the entire US economy. That number is roughly the size of US GDP. When the Fed inevitably slashes rates to arrest that initial deflationary scare, they will inadvertently light the fuse on this dormant savings pool. Millions of homeowners will finally refinance, cash out, and spend. That newly unlocked wealth will flood directly into the real economy, throwing a massive secondary wave of fuel onto the recovery.

Where This Leaves Us

The long-cycle framework does not attempt to pinpoint the exact Tuesday the market tops out. It tells you where you are standing.

We are late in a forty-year decline in the cost of money, navigating the final leg of an equity bull market that is expensive, narrowly concentrated, and widely owned at peak corporate earnings.

I am actually creating a full “Regime Board” to help you, my readers, keep track of the indicators I am following and estimate how close we might be from the edge of the cliff. I will be sharing it with you when it is ready.

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The path ahead involves a likely sharp deflationary scare first, where capital runs to traditional safety, followed by a far more volatile inflationary era. Because those two phases reward completely different asset classes, getting the sequence right matters far more than guessing the exact date.

Play the final leg of the bull market, but stay close to the exit. The regime change is coming, and it will not announce itself nicely.

When I wrote about Magnum back in May, I left one big question open. I called it the load-bearing one: does the listed company structure put a permanent ceiling on Magnum’s margin compared to what Froneri can reach?

Froneri is the Nestlé and PAI joint venture making ice cream in the exact same countries, buying the exact same dairy, cocoa, and packaging. Everyone uses them as the yardstick so I did too.

But as I do for every name I discuss here, I went back to dig more through Froneri’s accounts and made a few interesting discoveries.

How Bad Math Becomes Gospel

The number floating around every trading desk is that Froneri earns 26%. Against Magnum’s mid-teens, so what is Magnum doing wrong? Add in the fact that Froneri owns Häagen-Dazs outright in North America, bought for cash a quarter of a century ago and royalty-free until 2110, which is a setup Magnum cannot replicate.

But Froneri’s audited accounts show 21.5%, and 21.6% the following year. The year before that, they earned 18.3% and actually lost money before tax. Even the rating agencies covering their debt only forecast a peak in the low twenties.

So where does the 26% come from? It is worth understanding, because this mechanism is incredibly common and it is exactly how garbage numbers travel around.

Somebody took a press-reported enterprise value, divided it by a press-reported transaction multiple to back out an EBITDA figure, and then divided that by revenue to get a margin. The multiple and the margin are the exact same data point and that phantom EBITDA sitting in the middle is a heavily adjusted figure prepared for a debt syndication pitch, running about a fifth higher than the audited number. The leverage statistic everyone blindly quotes for Froneri comes from that exact same denominator and understates the real debt load by more than a full turn.

The Math Works

So the open question I had in May has an answer and it is not the one implied. Magnum’s reachable range is 19% to 22% so the cap I could not rule out looks like it is not even actually there.

I want to be careful about what this does and does not settle. It does not make the cost-cutting program easy. Management is guiding to forty to sixty basis points a year, which is a glacial pace, and slow corporate turnarounds fail regularly. It also does not make Froneri a weak operator. Pulling 21.5% is a very strong margin in this category, and Magnum still has a steep hill to climb.

What it removes is the arithmetic impossibility of catching them.

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Taking the ‘L’ on the Overhang

There is one other thing I have changed my mind about, and this one cuts the other way.

In May, I described Unilever’s remaining stake as coming to market on an orderly, scheduled basis. That is not what the filings say.

Unilever still holds essentially all of it. There are absolutely no lock-up arrangements, and the whole position has been registered for an at-will sale since before I even bought the stock. We are talking about a block equivalent to roughly a quarter of the free float, just sitting there undated. The optimist stance is that Unilever trusts the business and wants to remain invested, the pessimist stance is that the can dump that stock at any time.

The Bottom Line

Despite the overhang, the position is up around 23% and I am keeping it at full size. It is still trading below the multiple its private peer commands so I will continue holding it as long as the margin continues to trend towards that number.

Globus Medical gets booted out from the medtech basket. The ETF remains, it is the simplest way to express this trade, this is only a small one-time adjustment to the basket.

The rationale here is painfully simple. Back in July 2024, the FDA sent Globus a warning letter regarding its flagship surgical robot. Fast forward two full years, and as of its most recent annual report, that letter was still open and I missed that. I could have left the basket as is but this is one of my highest conviction themes for the next 12 months so it deserves to be as “perfect” as I can make it.

To be fair, an FDA warning letter is not a product recall, nor is it a red flashing safety hazard. It is simply the regulator informing a company that its quality control system is lacking. Normally, management teams scramble and close these things out in a few months.

Globus has not. Worse, this lingering demerit sits squarely on the ExcelsiusGPS robot. That is the exact product carrying the company’s growth narrative, rather than some forgotten legacy side hustle. While that file stays open, management is busy negotiating with regulators instead of actually selling robots. You better believe any potential acquirer looking at the division is going to aggressively price in that exact headache.

I built this basket because medical device businesses are trading at irrationally cheap prices. That thesis is entirely intact but that argument relies on a rerating. A company nursing a two-year regulatory hangover on its flagship product does not get to be treated like a normal business until the FDA explicitly says so.

Then there is the sheer absurdity of the math. The robotics division accounts for less than 5% of their total sales. We are essentially dealing with a 24-month regulatory purgatory attached to a business line that was never going to radically move the needle anyway.

I apologize for missing that and I am correcting that omission by taking out the stock. On a positive note, you can witness that I continuously review existing holdings, so in theory, I hope to catch mistakes and adjust them before they cause too much damage.

I will be sharing a page with subscribers that lists all trades since inception (four months) and where they stand, I am just finalizing the layout.

In the meantime, here is the updated basket:

Four months ago we drew a map of the rates market. It held where it mattered and failed where we warned it might. Today we close the book: one large win, two small losses.

“The Wrong Map” went out in March on one admission, we did not know which tail would arrive, so we built three legs that would pay across different states of the world. Looking back, thank god we did not venture in predicting how the Hormuz crisis would evolve!

The scorecard

The tanker win dwarfs the two rates losses, and the premiums were small by design, so the book closes comfortably ahead.

Right direction, wrong vehicle

The risk we identified in that issue was a painful middle with oil high enough to keep core inflation sticky, not high enough to break growth. That is exactly what happened: the Fed held at 3.50 to 3.75%, the 10-year sits near 4.6%, and the disruption that froze our rates legs is the one that detonated the tankers.

Then the June reaction complicated the trade even more. US core inflation printed flat on the month, the friendliest bond number of the year and Treasuries shrugged. When the perfect input produces no move, it’s clear you are missing something: the private sector spending (i.e. AI infrastructure spending) is stronger than anticipated, and our read on the near-term path is unlikely to resolve in the timeframe of these options. Two multi-year theses, wrapped in early-2027 options, are not going to fly. So we close both and step back, rather than roll a decaying option into a view we can no longer fund with conviction.

We are not done with US rates

We will likely express a view on US rates in the coming quarters. The scenario we fear is an AI-capex unwind: risk assets down, credit wider, the Fed cutting hard, capital stampeding into long Treasuries. Against that, a long bond hedges the entire portfolio. But if the hedge has a negative carry you can’t risk being way too early. The tells are not on the tape, and a hedge bought early is theta paid to wait, the exact bill we are settling today.

The trade moves to Britain

The surviving view goes where the mispricing is cleaner and needs no American recession, the front of the UK curve. And the case is not just that the Bank is mispriced. The economy underneath it is weak enough to force the cut.

Britain is not America here. Its mortgages do not lock away for thirty years; they refix every two to five. So the rate the Bank set in 2023 is still biting, household by household, as cheap pandemic fixes roll off and reset several points higher, a slow tightening still under way, long after the last hike. To a British homeowner the policy rate is not a screen abstraction; it is a bigger number on next month’s direct debit. It shows. Households have pushed their saving rate to nearly 9% of income, against about 6% before the pandemic with a straight subtraction from demand. And the labour market is loosening the way that leads disinflation, not lags it: vacancies a tenth below pre-pandemic, and pay, which trails the labour market by about a year, already down to 2.9%, its weakest since 2020. The second-round wage spiral the Bank keeps invoking is nowhere in the wage data.

Yet the market is braced the other way. Bank Rate is 3.75%; in June two members voted to hike and none to cut; Britain now carries the highest 10-year yield in the G7, on the weakest growth in it.

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The vehicle, and the risk in it

We are long the front of the gilt curve through the short-gilt future. It fixes the flaw that just cost us, no premium bleeding while we wait and hands us another: a leveraged future can be stopped out before the thesis pays. So we are starting with a small position and a stop loss.

The stop is a weekly close above 4.577%, the 2-year’s 2026 high, the line “The Wrong Map” has fought since March, barely 10 basis points higher than now. That stop is not very far and that’s what we like it because the front end is not the safe end of the curve. In 2022 the 2-year sold off hardest of all, and the Bank’s rescue bought long gilts, not short. We own the 2-year because it is the cleanest expression of a cut, not because it protects us from a fiscal scare. That risk we manage with a small sized position and the stop loss.

We started at half size, and may add after the Bank meets on 30 July. Half, because the case is not clean yet with core still stuck at 2.6%, and July’s 13% jump in the energy cap will lift headline over the next prints.

The shape is convex. Base case, the 2-year drifts to 3.75–4% as the Bank cuts late. Growth scare, it rallies hard as the hawkish curve unwinds. Core stays hot, the stop caps it. Gilts and sterling break together, the co-selloff pulls us out. Downside known; upside not.

• Our call: the UK front end is priced for a hike an economy this weak cannot sustain; the next Bank move is a cut, and 2-year gilts rally.

• Wrong if: core and services stay hot through the energy-cap pass-through and the Bank holds or hikes; or gilts and sterling sell off together, the fiscal tail the front end is most exposed to.

• Status: long UK 2-year via the short-gilt future, half size from 23rd of July; hard stop on a weekly close above 4.577%; may add after the 30th of July meeting.

References

[1] UK June CPI — Office for National Statistics: headline 2.6%, services 3.6%, core 2.6%. Private-sector regular pay growth 2.9% (3 months to May, lowest since 2020, ONS AWE); job vacancies 707,000, 10.4% below the pre-pandemic Jan–Mar 2020 level (ONS Vacancy Survey); household saving ratio 8.9% in Q1 2026 versus ~6% in 2019 (ONS series DGD8). Ofgem default tariff cap +13% to £1,862 from 1 July (Ofgem). UK mortgages predominantly short fixed-rate terms that refix every 2–5 years, versus the US 30-year fix; sub-3% pandemic fixes are rolling onto materially higher rates through 2028 (Bank of England, July 2026 Financial Stability Report; UK Finance).

[2] Bank of England — Bank Rate 3.75%; June MPC vote 7-2, two members preferring a hike, none a cut (Monetary Policy Summary). UK 10-year yield the highest in the G7 (sovereign yields, market data). OIS-implied policy path is market pricing, not a Bank forecast.

[3] The 2022 UK gilt-market episode, in which the 2-year yield rose furthest across the curve and Bank of England intervention purchased long-dated gilts (Bank of England; market data). For context, UK whole-economy investment was the lowest in the G7 at 18.6% of GDP in Q3 2025 (ONS).

[4] US rates context — FOMC target 3.50–3.75%; US 10-year ~4.6% (Federal Reserve H.15); June US core inflation flat on the month (BLS). Position marks and the tanker close are our tracked book; the tanker basket closed at roughly +30% total return in Issue 17 (22 June).

[5] “The Wrong Map,” Stray Narratives Issue 03 (23 March 2026) — the painful-middle call and the argument that a Gulf disruption is borne by Europe and Asia, not the US.

We are opening a position in a Dutch company that builds the largest man-made objects that move, parks them in the deep ocean, and then does the single most boring, most profitable thing in energy: it collects the rent. The company is SBM Offshore. It trades in Amsterdam, most professional investors have never opened its filings, and it is the closest thing the oil industry has to a toll booth — sold to us, right now, at the price of a cyclical.

Let us start with the machine, because the machine is the whole story and hardly anyone outside the industry has seen one.

Imagine an oil tanker. Now imagine that instead of a hold full of crude, its deck carries a chemical plant — separators, compressors, gas-treatment trains, a power station, accommodation for a couple of hundred people — the entire apparatus of an onshore refinery, welded onto a hull the length of three football pitches and moored in two kilometres of water a hundred miles from land. Pipes run down to wells on the seabed. The raw mixture of oil, gas and water comes up, gets separated on deck, the oil is stored in the hull, and every week or so a shuttle tanker pulls alongside and carries it away. It is called an FPSO — Floating Production, Storage and Offloading vessel — and it is how the world now produces oil in places too deep for a pipeline to shore: offshore Brazil, offshore Guyana, off West Africa. One of these things can cost three to five billion dollars and will sit on station, pumping, for twenty years.

Source: SBM Offshore

https://media.sbmoffshore.com/en/asset-details-page/3328:image:3dc3e75dafc34c08b263d44f8251a982#/search-id=1784027290282_107140893/

Here is the part that matters. The oil major — Petrobras, ExxonMobil — does not, as a rule, want to own the machine. It wants the oil. Owning a floating factory is a headache: you have to build it, finance it, crew it, maintain it, keep it running through hurricanes and equipment failures for two decades. So the major does what any sensible company does with an expensive, specialised asset it needs but would rather not run. It rents.

That is SBM’s business. It builds the FPSO, and then it leases and operates it for the field’s life — ten, fifteen, twenty years — for a contracted day-rate, paid whether the oil price is forty dollars or a hundred. The major is contractually obliged to pay for the machine to be available, in the same way you pay your mortgage whether or not you enjoyed living in the house that month. SBM is not selling oil. It is selling uptime. It is a landlord.

By User:WikiDon - From en: Ther uploaded with GFDL- licence by User:WikiDon, CC BY-SA 3.0,

https://commons.wikimedia.org/w/index.php?curid=696897

The magic trick everyone watches instead

The reason nobody talks about the landlord is that offshore energy is a genuinely thrilling casino, and the landlord is the one person in the building not gambling.

When investors say “offshore is back” — and it is; deepwater now breaks even around forty-three dollars a barrel, which is marginally cheaper than American shale at forty-five, a quiet reversal of the last decade’s received wisdom [6] — they reach for the exciting names. They buy the drillers: Transocean, Valaris, the companies that own the rigs that drill the wells. These are magnificent, terrifying instruments. A drillship is rented by the day, and the day-rate swings from a hundred and fifty thousand dollars at the bottom of the cycle to six hundred thousand at the top and back again, tracking the oil price like a needle on a seismograph. Own one at the right moment and you triple your money; own one at the wrong moment and you go through Chapter 11, which most of them did in 2020. It is a wonderful trade and a terrible annuity.

Or they buy the plumbers: TechnipFMC, Subsea7, Saipem — the engineering firms that lay the pipe and install the hardware on the seabed. Better businesses, real backlogs, and right now the market’s darling: TechnipFMC trades at roughly fifteen times EBITDA, near an all-time high, because it is the quality name in a hot sector. There is even a merger to gossip about — Subsea7 and Saipem are combining into a contractor with a backlog north of forty billion euros, antitrust regulators permitting, which is its own soap opera.

So the whole room is watching the drillers gamble and the plumbers merge. And in the corner, unwatched, is the one company that already signed the twenty-year lease and is simply collecting.

A toll, not a bet

This is the distinction the market is not pricing, and it is the entire idea: a day-rate is a bet on the oil cycle; a twenty-year lease is a toll on production. The driller gets re-priced every few months by the crude curve. SBM’s tenant is locked in until the 2040s at a rate agreed before the first barrel flowed.

Put a number on it. SBM’s order backlog — the contracted, already-signed future revenue — is $31 billion, with cash-flow visibility running to 2050. Eighty per cent of that backlog, and roughly seventy per cent of the profit, is the lease-and-operate book: the rent. Not the oil price. The rent. The margin on that rent is fifty-four per cent, because once the machine is built and paid for, keeping it running is cheap and the cheque arrives every month regardless.

And the obvious objection — that a twenty-year fixed cheque is not an annuity but a bond, quietly halved by two decades of inflation — is one the company has already answered. The backlog is, in SBM’s own words, “backed by firm contracts from premium clients with inflation protection” [5]. The rent escalates. The landlord thought of it first.

We have spent a good deal of this publication's ink on the difference between businesses paid on volume and businesses paid on price — the toll booth versus the commodity. SBM is the cleanest toll in the entire energy complex, and it is hiding in the one corner of the market everyone has decided is a pure commodity play.

“But look at the debt”

Here is where the professional investor, glancing at the screen for the first time, recoils. SBM carries around eight billion dollars of net debt on the IFRS measure, against a market value near six. For a company that gambles, that is a death sentence. It is the reason the stock screens as a scary levered cyclical and gets a scary levered-cyclical multiple.

It is also the wrong number — and this is the crux of the trade.

There are two debt figures here, and the gap between them is the entire argument. The IFRS measure consolidates every financed vessel in full. The company’s own preferred measure, Directional, tracks what the parent is actually on the hook for, and it stands at US$3.2 billion. Most of the difference is non-recourse project finance. It does not sit at the parent. It sits inside each individual floating factory, secured against — and repaid by — that specific lease. The bank lent against the contracted cash flow of one machine and can only ever look to that machine. The debt is a mortgage on a building, and the building comes with a tenant who has signed for twenty years and cannot leave.

You do not have to take our word for it, because SBM ran the experiment for us. In February it sold one FPSO — a unit called ONE GUYANA — to ExxonMobil Guyana for about US$2.32 billion in cash, and used the proceeds to repay, in full, the US$1.74 billion of project financing secured against that vessel [2]. Read that again: one machine, sold for a third more than the debt tied to it. The mortgage was not a millstone. It was comfortably covered by the asset it was lent against. Directional net debt is now US$3.2 billion, down forty-three per cent year on year, primarily on that sale [3]. And the landlord did not even lose the tenant — SBM continues to operate and maintain the vessel until 2035 [2]. It sold the building and kept the management contract.

A levered cyclical cannot do that. A portfolio of financed annuities can, because the leverage was never fragility. It was the plumbing of the toll.

The price

So what does the market charge for the cleanest annuity in energy, run by a company that just proved its debt is a mortgage and not a millstone?

The whole enterprise — equity and debt together — costs about five times forward EBITDA: earnings before interest, tax, depreciation and amortisation. For context, the plumbers you are being encouraged to buy instead trade at six to fifteen times the same measure, with more oil-price exposure and more project risk. On the crude price-to-earnings line the stock looks like value-trap wallpaper — roughly seven times — but the sharper figure is the cash it hands back. SBM returned $440 million to shareholders last year, up fifty-seven per cent, a mix of dividend and buyback worth about seven per cent of the market value, and it has committed to a minimum of $2.1 billion of returns over the next six years. You are paid roughly seven per cent a year, in cash, to wait — while a book of twenty-year leases quietly amortises the debt for you and a deleveraging balance sheet does the re-rating in the background. The analysts who cover it — the few who do — carry an average target around thirty-five per cent above today’s price.

Where we could be wrong

We try never to fall in love, so here is the case against, stated as strongly as we can make it.

First, SBM is a hybrid, not a pure landlord — and the impure half is not a half. The Turnkey division, which builds the machines, booked US$2.9 billion of first-quarter revenue against Lease & Operate’s US$0.6 billion. Turnkey is most of the revenue and little of the profit; the rent is little of the revenue and most of the profit. That asymmetry is the company — and it means the construction arm can hurt you long before the annuity does.

Second, this is a concentrated toll, not a diversified perpetuity. Strip the backlog down and it is overwhelmingly two counterparties: Petrobras in Brazil, and ExxonMobil in Guyana. A Brazilian political lurch or a Guyanese disruption would hit the “annuity” harder than the word annuity implies. Two tenants is not a bond ladder.

Third, the rent roll does not grow in a straight line. The backlog actually fell by four billion dollars last year, because new mega-contracts are lumpy — a handful of awards drive the whole book — and the pace of new leases still rides the deepwater sanctioning cycle. If oil sags into the fifties and the majors defer their next round of projects, the existing rent keeps arriving on schedule, but the growth stalls and the stock waits longer for its re-rating.

Notice, though, what none of those objections touch: the cash flow already contracted to 2050. The bear case is an argument about SBM’s growth and its construction arm. It is not an argument against the rent. You are being asked to underwrite whether the landlord finds new tenants, not whether the current ones pay — and while you wait for the answer, you collect seven per cent.

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The trade

We are adding SBM Offshore to our list of positions, and we are underwriting it over three years.

The shape of the return is unusually legible for an energy name. In the sour case, growth stalls and the multiple sits where it is, and we are left clipping a seven-per-cent yield for something in the low teens a year — a poor outcome that is still a positive one. In the base case, the backlog replenishes, the balance sheet keeps shedding debt as the leases amortise, the stock drifts toward the analysts’ thirty-five-per-cent-higher target, and the yield rides on top, for high teens to low twenties a year. In the good case, the market finally notices that a twenty-five-year contracted cash-flow stream is an infrastructure asset and not an oilfield-services stock, re-rates it toward the multiple that pipelines and toll roads command rather than the one that drillers get, and the number has a three in front of it — a year.

The market is paying fifteen times for the company that installs the plumbing, and five times for the company that owns the factory and collects the rent until 2050 — and it calls the second one risky because it appears to have debt, when the debt is simply the mortgage on a building that someone else is contractually obliged to pay off.

We will happily be the landlord.

References

[1] SBM Offshore — Full-Year 2025 Earnings and Annual Report. Directional backlog US$31.1bn with cash-flow visibility to 2050; Lease & Operate backlog US$25.0bn of US$31.1bn (~80%); Lease & Operate Directional EBITDA US$1,235m of US$1,709m total (~72%) on revenue of US$2,295m (~54% margin); the ~US$4bn year-on-year backlog decline; IFRS net debt US$8.1bn; Directional net debt US$5,651m at 31 December 2025; capital returns US$440m (+57%) and the US$2.1bn six-year minimum distribution commitment.

[2] SBM Offshore — FPSO ONE GUYANA purchase by ExxonMobil Guyana completed (4 February 2026). Total cash consideration of c. US$2.32 billion; the net cash proceeds primarily used for the full repayment of the US$1.74 billion project financing; SBM Offshore continues to operate and maintain the FPSO up to 2035.

[3] SBM Offshore — First Quarter 2026 Trading Update. Directional net debt US$3.2 billion at 1Q 2026, a 43% decrease year on year, primarily reflecting the ONE GUYANA sale.

[4] TechnipFMC, Subsea7 and Saipem — company filings and the Subsea7/Saipem merger announcement. Comparator EV/EBITDA multiples (~6–15×) and the combined entity’s backlog.

[5] SBM Offshore — First Quarter 2025 Trading Update: “Our pro-forma Directional backlog … is backed by firm contracts from premium clients with inflation protection.”

[6] Rystad Energy — published upstream breakeven analysis: deepwater breakeven ~US$43/bbl against North American shale ~US$45/bbl.

[7] EODHD. Share prices, forward EV/EBITDA (~5×) and P/E (~7×), and the sell-side consensus target (€43.20, ~+35%).

The most important question for the next five years for your portfolio has absolutely nothing to do with whether artificial intelligence is “real.”

AI is real. So were the railroads, the telegraph, and the fibre-optic cable. Each of them was still real on the morning after its bubble burst.

The question that will actually dictate the next decade of returns is much drier: What holds the American economy up when the spending on AI slows down? And the slightly more awkward follow-up: Does anything?

The United States is sitting in the final stage of a forty-year disinflationary bull market. The technology currently credited with extending that bull market is being dangerously misread. AI is not a new paradigm. It is a late-cycle efficiency input arriving at the maturity phase of a long technology cycle, not the dawn of a fresh one.

Sameer Singh’s reference class is exactly right: AI is the new microprocessor.

Here is the problem with microprocessors. An efficiency input makes the entire economy more productive, while its own profit margins are ruthlessly beaten to death in a commodity price war. The economic surplus flows downstream to the users, not to the companies supplying the hardware.

The rent does not disappear so much as relocate. It gathers at the one or two chokepoints no one can design around, the instruction set and the single foundry that can etch it, and drains away from the vast, undifferentiated capacity the hyperscalers are buying and renting back out.

A two-sided platform (like an App Store or a search engine) does the exact opposite. It traps users, extracts rent, and keeps its pricing power for a generation.

Right now, the market is paying the platform’s price for the microprocessor’s economics.

Stray Narratives has so far published individual investment ideas and a few intellectual articles on how we are understanding AI. This piece opens a series that steps back to the picture they sit inside: where the market stands in its long cycle, what this AI boom actually is, how it rhymes with the booms before it and where it turns worse, what lies underneath it, and what to do about it. The pieces that follow take it up one question at a time.

The 200-Year Loop

Every infrastructure build-out in the history of capitalism runs the exact same loop. Capital violently crowds into the “new thing.” The few companies who can afford the scale overbuild it. The financing strains. The bubble bursts, leaving a pile of stranded assets when the assumed cash flows mysteriously fail to appear.

Canals, railroads, rural electrification, fibre optics, and shale oil each completed this exact, predictable circuit.

To be a structural bull today, you have to make a spectacular assumption. You have to look at two hundred years of financial history and say with a straight face: “Yes, but this time the guys burning the cash are really smart.” You must believe that this AI build-out will be the very first to magically escape the loop, and that the firms currently spending the money are acting rationally against revenues that literally none of them can forecast.

Hyperscaler capital expenditure is now running above 2% of GDP. This is among the largest peacetime build-outs on record. But this specific cycle is uniquely dangerous in three ways:

The Illusion of Earnings: Silicon Valley has leaned ever harder on the ancient, magical art of depreciation accounting. The excess is hiding in reported earnings rather than the multiple, as hyperscalers mysteriously decide their hardware will last much longer than anyone thought. Meanwhile the revenue, though real, still covers only a fraction of the cumulative bill, which the BIS now puts above a trillion dollars across 2025 and 2026, and is increasingly financed with debt, and free cash flow is evaporating: at Amazon alone from a filed $26 billion to barely $1 billion in a single year. It is a spectacular business model if you don’t like money.

The Duration Mismatch: We are currently funding thirty-year data-centre debt to produce a deflationary token whose cost, for any given level of capability, drops 70% to 90% a year. The entire structure is running on chips with the effective lifespan of a flagship phone (18 to 36 months). And just to keep things interesting, a state-backed Chinese competitor is actively trying to drive the marginal cost of that token to absolute zero.

The Stranded Asset Trap: When the railroad bubble burst, it politely left behind tracks that built the modern economy. When this bubble bursts, the buildings and the power lines will find new owners at a fraction of their financed cost, while the returns that were meant to justify them simply never appear, and the loss lands where the money did, on repriced debt syndicated through the insurers and pension funds of middle America.

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The Economy Beneath the Bubble

The American economy that will eventually have to absorb this bust is, to put it politely, exhausted.

Once you strip out the AI capital spending, the foundation looks incredibly weak. Capex categories supplied roughly three-quarters of Q1 GDP growth. Beneath that capex sits a frozen labour market, housing rolling over, and a personal savings rate slashed to 3%, nearing its pre-crisis lows.

Consumption is holding up in large part because the equity bubble itself is sustaining a wealth effect for the top decile of earners.

One asset price is currently working triple shifts: it is flattering the index’s earnings, supplying the marginal GDP growth, and propping up the consumption beneath both. Aswath Damodaran’s caution about pricing versus value applies to this entire house of cards, not merely the semiconductor leaders.

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The Asymmetry

This is the asymmetry of the current market, and it is the reason we are laying this groundwork now rather than writing a witty autopsy later.

The market looks undeniably safe right now precisely because the thing inflating it is so fragile. The wealth effect, the earnings, and the growth all rest on a single price, with nothing independent beneath any of them. John Templeton famously noted that bull markets die on euphoria. That euphoria is always strongest at the point of maximum dependence on one single input.

We are positioning our capital for that specific shape, rather than trying to guess the exact timing. Because if the history of these cycles teaches us one thing, it is that the timing of a break is the one secret the market flatly refuses to tell you in advance.

Next in the series: before you can say what this cycle will do, you have to know where you are standing in it. The next piece takes two hundred years to answer.

Every desk has a version of the complaint: right on the thesis, wrong on the P&L. Uranium is currently turning that complaint into an exhibit. In April we put the case for owning physical uranium through the Sprott Physical Uranium Trust and added it to our list of trades at C$27.83 [1][8]. We set out three conditions that would show whether the thesis was actually working — conditions, not vibes. At the end of June the third one fired: the long-term contract price reached US$95.50 a pound, its highest since 2008, having risen in five of the last six months and fallen in none [2].

And yet. The units mark around C$27 — a shade below our entry — and the trust has slid from net asset value to an 8 per cent discount [7][8]. The metal is making new highs while the cheapest way to own it has gone on sale. That is the paradox, and it has a perfectly good cause: the marginal financial buyer has wandered off at precisely the moment the world’s governments are taking the metal off the table. Which is why we hold the position with more conviction than we opened it.

The Invisible Bull Market (by design)

In Issue 08 we drew the line between utilities signing contracts in volume at $90 and the spot price merely drifting there because nobody is signing anything [1]. The term price is where that distinction lives. It is assessed off live offers and settled contracts rather than screen trades, which is why it moves at the pace of a barge. The market, meanwhile, spends its days glued to a spot ticker on which almost no real pound trades, while the contracts that actually keep reactors fuelled are struck quietly, off screen, by people with no interest in entertaining anyone.*

The monthly series since December reads [2]:

• December: $86.50

• January: $89.00

• February: $90.00

• March and April: $91.50

• May: $94.00

• June: $95.50 (spot, for comparison, sits at $85.00)

Five rises, one flat month, nothing down. A price built from real, signed contracts does not do that on noise. So where does that leave our three conditions? The second — the run-down of Kazatomprom’s inventory buffer — is still advancing. The first, and the one that actually drives price, is still missing: Western utilities contracting at or above the replacement rate of roughly 180 million pounds a year [1]. Two of three, with the decisive one outstanding. For now.

Utility fuel buyers are many things; a studio audience is not one of them.

Sharpening the Maths

We owe readers two corrections to the numbers we published in April.

First, Issue 08 put Kazakhstan at 45 per cent of global primary supply. The World Nuclear Association’s 2024 data has it at 23,270 tonnes of a 60,213-tonne global total, which is 39 per cent [3]. Our figure sat above the primary record; this publication will cite 39 per cent from here.

Second, we described Kazatomprom’s guidance cuts as evidence it could not rescue the market. The cuts are real but relative. Kazakhstan’s absolute output rose 11 per cent in 2025, to 25,839 tonnes, and the 2026 guidance of 27,500 to 29,000 tonnes is a cut only against the levels its subsoil permits allow — not against what it actually produced [3]. Readers should have had both halves.

Neither correction changes the trade. The inventory buffer that matters still sits at about four months, and rising output in Kazakhstan is worth very little to a Western utility that is not permitted to buy it.

The Iron Curtain

Since April the pool of uranium the West will eventually have to bid for has shrunk, and not gently. Follow the Kazakh pounds.

To the East. In February Kazatomprom signed a long-term supply agreement with India’s Department of Atomic Energy worth roughly US$4bn; stack that on its contracts with China’s CNNC group and more than half the company’s booked asset value is now spoken for before a Western utility gets a look [4].

To Russia. Budenovskoye — Kazakhstan’s largest growth project — has 100 per cent of its output through 2026 reserved for Russia’s civil nuclear industry [6].

Into law. A code signed in December now requires Kazatomprom to hold more than 75 per cent of any new uranium joint venture, and 90 per cent or more of licence extensions, unless the foreign partner transfers conversion or enrichment technology [5]. Western developers can read: Laramide Resources terminated its Chu-Sarysu Basin option in January, on the view that an economic case for foreign investment in Kazakh uranium exploration no longer exists [5]. When a junior walks away from cheap optionality, believe the junior.

The Financialised Blind Spot

In April the Sprott trust traded at net asset value. It now sits at a 6.06 per cent discount, holding 81.4 million pounds of uranium oxide against US$7.05bn of net assets [7].

The flywheel we described in Issue 08 is real — and capped. Sprott can buy at most 9 million pounds a year in the spot market, and it bought 8.57 million in 2025, hard up against the ceiling [7]. When the units trade below NAV the trust stops issuing new ones, and its spot buying stops with them. The one leg of the thesis that used to run on autopilot has, for now, switched itself off.

That is the bearish reading, and it is true. The other reading is equally true: a 6 per cent discount means the units cost less than the uranium sitting inside them. The asymmetry we bought in April is on offer again — wider than it was at entry.

The position

A rising term price on thin volume is exactly how this market behaves when a handful of utilities move early, and the documented failure mode of the sector is the rest of the tape mistaking those early movers for noise, getting bored, and selling the week before the herd shows up [1]. We know that script. We decline the part.

Read together, the evidence points one way. The term price is climbing. The inventory buffer is draining. The East has first call on the supply. And the premier physical trust can be bought for less than the metal it holds. The one thing missing is the decisive one — Western contracting in volume. What has moved since April confirms the structure without yet delivering the outcome [8].

We hold the position, with more conviction than we bought it. The metal has heard the news; the units have not. That gap is the trade.

References

[1] Stray Narratives Issue 08, Is Now the Time to Invest in Nuclear?, published 27 April 2026 (straynarratives.com). The three conditions; the ~$90 long-term price at publication; the Sprott mechanism description; the trust at a +0.56% premium to NAV per [8] therein.

[2] UxC and TradeTech long-term price indicators as published by Cameco (cameco.com/invest/markets/uranium-price): month-end long-term price US$86.50 (Dec 2025), US$89.00 (Jan 2026), $90.00 (Feb), $91.50 (Mar), $91.50 (Apr), $94.00 (May), $95.50 (Jun); spot $85.00 (Jun). June level the highest since 2008.

[3] World Nuclear Association, world uranium mining production data: Kazakhstan 23,270 tU of 60,213 tU global in 2024 (39%). Kazatomprom 4Q 2025 Operations and Trading Update, 2 February 2026 (kazatomprom.kz): 2025 output 25,839 tU, up 11%, within guidance of 25.0 to 26.5 ktU; 2026 guidance 27,500 to 29,000 tU.

[4] Kazatomprom Investor Relations (kazatomprom.kz): long-term U3O8 supply agreement with India’s Department of Atomic Energy (Directorate of Purchase & Stores), announced February 2026, value exceeding 50% of Kazatomprom’s booked asset value (no dollar value disclosed; press estimates near US$4bn), approved by shareholders April 2026; prior CNNC/CNUC agreements, which together with earlier transactions exceeded 50% of booked asset value by November 2024 (single Nov 2024 contract ~KZT1.25tn, ~US$2.5bn, per Interfax); 2026 production guidance and inventory disclosures per the 4Q 2025 operations update and FY25 results.

[5] Kazatomprom announcement on amendments to the Subsoil Use Code, signed into law 26 December 2025: new uranium exploration/mining joint ventures require Kazatomprom above 75%; subsoil-use agreement extensions or production/reserve increases require Kazatomprom at 90% or above, or transfer of conversion/enrichment technology by the foreign partner. Laramide Resources announcement, January 2026, on exiting its Kazakh project (Chu-Sarysu Basin option, terminated 20 January 2026).

[6] Kazatomprom 4Q 2025 Operations and Trading Update (2 February 2026): 100% of Budenovskoye output for 2024 to 2026 contracted to Russia’s civil nuclear industry at market-related terms; Rosatom-linked ownership of 49% per Interfax.

[7] Sprott Asset Management, Physical Uranium Trust (sprott.com), 3 July 2026: NAV US$20.47 per unit, market price US$19.23, discount to NAV 6.06%, net assets US$7.05bn, holdings 81,447,348 lbs U3O8. Spot-purchase cap of 9 million lbs per year per the trust’s base shelf prospectus undertakings and Annual Information Form; calendar-2025 purchases 8.57 million lbs.

[8] U-UN.TO daily closes, EODHD: entry C$27.83 (24 April 2026); close C$27.35 (3 July 2026). Move from entry: minus 1.7%. [Verify-before-publish: re-quote at the open on publish day.]

In May I published the case for Xunlei (NASDAQ: XNET) at $6.33: a 78% discount to net asset value, anchored by a 7.8% stake in Insta360, the Shanghai-listed camera maker, worth two and a half times Xunlei’s own market capitalisation, and by a hard date, the 11 June expiry of the lock-up on that stake. The title promised four weeks to close. The date has passed, the shares mark $5.64, the position is down 10.9%, and the discount has not closed [1][6]. The position stays on our list of trades, on humbler terms than the ones I published.

The discount itself behaved: 78% at publication, roughly 75% today. What fell was the net asset value. Insta360 has dropped 36% since the May mark, from 214 yuan to 135.93 [2], and Xunlei’s NAV per share has fallen by about a fifth [7]. The trade lost money the one way the piece spent least time pricing: the catalyst arrived and damaged its own collateral.

What happened

The 11 June expiry freed Xunlei to sell, and it freed everyone else: 56.5% of Insta360’s share register became tradable that day, roughly 37 billion yuan of stock across 28 pre-IPO investors, of which Xunlei’s holding is one line among many [3]. Insta360 was already de-rating from its September peak of 377.77 yuan, a price war with DJI gave the de-rating a fundamental engine through June [4], and the unlock supplied the paper. The stock set a 52-week low of 130.88 yuan on 29 June and closed at 135.93 on 3 July [2]. The May piece named 130 yuan as the level that changes the arithmetic. The mark sits two bad sessions above it.

Management, handed the moment the thesis required, chose patience. On the first-quarter call, two weeks before the unlock, there was no dividend, no timetable for the stake, and one load-bearing adverb: Xunlei would “gradually seek to adjust” its holdings toward a stated ceiling of investment securities at 45% of total assets, excluding cash and government securities [5]. No sale of Insta360 shares has been disclosed since the lock-up lifted. What has been disclosed is a repurchase: on 26 June the board approved a programme to buy back up to $20m of Xunlei’s own shares over the twelve months from 1 July, funded from the cash balance [8].

What I got wrong

The timing failed on its own stated deadline. The body of the piece described an 18-to-24-month monetisation window, but the title said four weeks, and the calendar was wrong: the discount-narrowing flow I expected into the date never appeared, and the shares closed as low as $4.66 before the unlock even arrived [6]. (The title, in hindsight, was the most falsifiable sentence in the piece.)

I priced what the unlock permitted our company to do, and missed what it permitted the other 27 unlocked holders to do. The event that made Xunlei’s exit possible cut the value of the thing being exited by a third, a scenario the published piece did not carry.

The catalyst rested on willingness. The Investment Company Act gives this management a direction and no timetable. The lesson filed in our mistake log: a catalyst date earns the name only when someone is obliged to act on it.

The published return arithmetic inherited the flaw. A base case of 55% to 75% a year was that willingness with a spreadsheet attached; the scenario that assumed the least of management, the bear case, is the one the market delivered.

The position, remarked

On the piece’s own methodology, at the 3 July close [7]: the Insta360 stake, 31.28m shares at 135.93 yuan and 6.77 to the dollar, is worth $628m, against $986m at publication. Net cash is roughly $234m, slightly higher than published. The operating business keeps its trough range of $410m to $745m, midpoint $580m: first-quarter revenue grew 54.1% and subscriptions 26.2%, though management guided to a modest slowdown ahead [5]. Net asset value is therefore about $1.44bn, or $22.65 per ADS, against $5.64 on the screen. The discount is 75%.

Two published facts survive the markdown. Net cash plus the trough value of the operating business comes to about $814m, 2.3 times the $359m market capitalisation, so the camera stake still comes free even marked a third below where I first called it free. And the regulatory pressure has not moved: at current marks investment securities sit near 74% of the relevant asset base against management’s stated 45% ceiling, and the sale required to reach that ceiling is roughly $446m, one and a quarter times the market value of the entire company [5][7].

Where I stand

The trade I published is dead, and pretending the horizon was always longer would be worse than the loss. What remains is a different position: a deep-discount holding with no clock, worth keeping only while that arithmetic holds, and re-examined in public whenever it is tested.

The case for closing is real: book NAV fell 25% in the two quarters to March, Insta360 is fighting a price war at its 52-week low, management’s pace is glacial, and wide discounts persist for years when nothing forces them shut. The case for holding is that at $5.64 the market pays less than the cash and the subscription business alone, the supply event has now happened rather than loomed, Insta360 sits at the May piece’s own bear-case mark, and the Investment Company Act still points in one direction. Selling at the bear-case mark would mean selling the cash and the subscription business below their own worth to be rid of a stake the price already writes off. The honest cost of staying is that the unlock supply has not cleared; it is now permanent.

I am holding, on those re-underwritten terms, with one mechanical exit: a next capital-allocation move of size that is an acquisition rather than a sale or a distribution. A decisive break below 130 yuan on Insta360 is a tripwire of a different kind. It sends the position back to the workbench for a fresh re-underwrite, published here, rather than forcing a sale on its own; a lower mark changes the arithmetic, and it is the arithmetic that decides. The first disclosed sale of Insta360 shares is what upgrades this from a discount we hope closes to one being closed. The watch items posted under the article in June stand, and the first has partially fired: the $20m repurchase is a real, if modest, step toward returning capital, while the pace of the 45% unwind and the next mark on the stake remain open.

References

[1] Stray Narratives Issue 11, An 80% Discount to NAV, Four Weeks to Close, published 14 May 2026 (straynarratives.com/p/an-80-discount-to-nav-four-weeks). Entry $6.33; NAV $28.57 per ADS on 62.86m ADS; net cash $230m; Insta360 stake $986m at the published 13 May mark of 214 yuan; operating business $410m to $745m at trough multiples, midpoint $580m.

[2] Arashi Vision Inc. (Insta360, SHA: 688775) closed at 135.93 yuan on 3 July 2026; 52-week low 130.88 yuan set 29 June 2026; 52-week high 377.77 yuan (September 2025). Stock Analysis and FT market data. Issue 11 published mark: 214 yuan, 13 May 2026, the basis on which the published $986m stake value was computed; the official 13 May close was 220.44 yuan.

[3] Pandaily, Insta360 at a Crossroads: 37 Billion Yuan Lock-Up Expiry Collides with DJI Rivalry, June 2026. 56.5% of shares, worth approximately 37 billion yuan, tradable from 11 June 2026; 28 pre-IPO financial investors; Xunlei at 7.84%.

[4] Caixin Global, DJI, Insta360 Lock Horns in Camera Pricing Standoff, 4 June 2026.

[5] Xunlei Limited, Unaudited Financial Results for the First Quarter Ended March 31, 2026, Form 6-K Exhibit 99.1, filed 28 May 2026, and the accompanying earnings call. Revenue +54.1% year on year to $98.6m; subscriptions +26.2%; cash, equivalents and short-term investments $303.6m; long-term investments $888.6m; total assets $1,415m. Management commentary on holding investment securities below 45% of total assets, excluding cash and government securities, and on adjusting holdings “gradually”; US Investment Company Act of 1940, s3(a)(1)(C), sets the statutory 40% test.

[6] XNET.US daily closes, EODHD: $6.33 on 13 May 2026 (entry); low close $4.66 on 5 June; $5.64 on 2 July 2026, the last session before the US holiday. Move from entry: minus 10.9%.

[7] Refreshed NAV on the Issue 11 methodology: 31.28m Arashi Vision shares × 135.93 yuan ÷ 6.7702 USDCNY = $628m. Net cash $234m ($303.6m cash, equivalents and short-term investments less $69.6m of bank borrowings, Q1 2026 balance sheet). Operating business at the published trough midpoint, $580m. NAV $1,442m ÷ 63.65m ADSs = $22.65 per ADS; market capitalisation $359m at $5.64 on 63.65m ADSs (318,268,921 shares outstanding at 31 March 2026; one ADS is five shares). Investment-securities ratio: $628m ÷ ($1,154m remarked total assets, the Q1 total of $1,415m with the $888.6m long-term investments line remarked to $628m, less $304m cash items) ≈ 74%; sale required to reach the 45% ceiling ≈ $446m.

[8] Xunlei Limited, 2026 Share Repurchase Program, Form 6-K filed 26 June 2026: up to US$20m of ADSs or common shares over the twelve months from 1 July 2026, funded from the cash balance.

If you prefer to listen rather than read, here is a podcast interview version of this same subject:

The American medical-device sector trades at its smallest premium to the wider market since the 2008 financial crisis, and on next year’s earnings it has slipped to an outright discount for the first time in living memory [1]. This is a group of companies that grew revenue from roughly seventy billion dollars to a hundred and seventy-six billion over the past decade, earns operating margins near twenty percent, and sells into the most dependable demand curve in the economy, which is the ageing of the rich world [8]. We are adding a basket of its cheapest quality names, equal-weighted, to the list of trades, because the market has priced a collapse that the businesses themselves refuse to deliver.

Two fears did the damage. The first is that weight-loss drugs will hollow out demand for everything from artificial knees to heart valves to glucose monitors. The second is that the sector’s profitability has quietly broken. Both are checkable against the operating numbers, and the numbers disagree with the price: the drugs are expanding several of these markets rather than shrinking them, and the margin dip everyone extrapolated was a post-pandemic inventory hangover that has already reversed.

How a good business got cheap

The de-rating is real and worth respecting. Large-cap medical devices spent most of the past decade carrying a four-to-six turn premium to the S&P 500 on forward earnings, the going rate for steady growth and recession resistance. That premium has compressed to roughly nothing, and the median name now trades near fifteen times forward earnings against an index above twenty-two [1]. The last time the sector was this cheap relative to the market, the world was busy recapitalising its banks.

Two things drove it. Through the pandemic, hospitals over-ordered devices and consumables, then spent 2022 and 2023 working that inventory back down, so reported revenue growth stalled to under two percent before recovering [2]. And from 2023 onward, every earnings call carried the same question, in one form or another: what does Ozempic do to your volumes. The market has spent two years concluding that weight-loss drugs will empty the operating theatres, and has priced the sector accordingly, which would be the correct response if the operating theatres were emptying.

The engine did not break

Cheap is only interesting if the business behind it is sound, and the ten-year record says it is. Set the pandemic distortions aside and the profitability picture is a cyclical dip that has already recovered: operating and free-cash-flow margins now sit at or above where they were a decade ago, EBITDA margins are at a ten-year high near twenty-six percent, cash conversion has improved, and research spending has held steady at about seven and a half percent of revenue the entire time [2]. The one genuine soft spot is gross margin, which has slipped two to three points over the decade on mix and input costs, an erosion the sector has more than recovered further down the income statement. A business that keeps investing the same share of revenue in its own future while its cash margins climb has not weakened. It has stopped commanding the market’s attention.

What the weight-loss trade got wrong

The GLP-1 fear deserves a real answer rather than a wave of the hand, because the logic is not absurd. Thinner, healthier people should, in time, need fewer of the interventions that obesity drives. The difficulty for the bears is that the operating data has begun to arrive, and for most of the sector it points the other way.

Sleep apnoea is the purest expression of the bear case, and its leading device-maker was punished hardest. The company ran its own analysis across roughly six hundred and sixty thousand patients and found that those prescribed a weight-loss drug were about eleven percentage points more likely to begin therapy, and its device volumes grew double digits straight through the scare [3]. The clinical trial that was supposed to prove the drugs cure apnoea instead found that most patients still had disease serious enough to treat [4]. The drugs are pulling people into the system, getting them diagnosed, and leaving the majority still needing the device.

The pattern repeats across the basket. Continuous glucose monitors were meant to lose their diabetic customers; instead more than two in five users of the leading monitor are now also taking a weight-loss drug and using the sensor to manage the transition [3]. Orthopaedics was meant to lose its knee and hip replacements; in practice surgeons routinely require patients to lose weight before they will operate, so a drug that reliably reduces body mass widens the pool of people eligible for surgery.

There is one place the bears are right. Bariatric surgery, the operations designed specifically to treat obesity, faces a genuine and management-acknowledged decline as the drugs substitute for the scalpel. For the diversified surgical-robotics franchise in our basket that is a small and recoverable share of a far larger procedure base, but it is the one unambiguous loss in the group, and pretending otherwise would be the sort of analysis that gets quoted back to you in eighteen months.

The asset nobody is pricing

There is a longer-dated reason to own these companies that the multiple ignores completely. The device-makers have spent two decades accumulating the one thing the artificial-intelligence boom has made suddenly precious: vast, proprietary, regulated clinical datasets that cannot be scraped from the open web or generated synthetically. The surgical-robotics leader holds the logged record of more than twenty million procedures. The connected-breathing-device maker has billions of nights of physiological data with the contractual right to use it. The diabetes and diagnostics franchises hold continuous, outcome-linked readings at a scale no model-builder can assemble, because a regulator will not accept invented data and a hospital cannot hand its patients’ records to a chatbot. Several of these firms already sell medical AI products the regulator has cleared, built on exactly this data [5]. A market paying extraordinary multiples for the companies that own AI models is paying nothing for the companies that own the one input those models cannot manufacture. We treat this as the free option on top of the value rather than the reason to buy.

The moat has one genuine challenger, and it is not a model-builder. Chinese device-makers are taking share in the high-volume, lower-margin corner of the industry, and they compete on price rather than on data. State procurement has cut the price of routine in-vitro diagnostic tests by as much as three-quarters, and China’s domestic champion in patient monitoring and bench diagnostics is moving upstream into the reagents and instruments Western firms long treated as captive; in gene sequencing a Chinese pair has already overtaken the former Western leader inside China [6]. The pressure falls hardest on the commodity-diagnostics and imaging lines, where the product is a consumable sold to a price-driven state buyer, and lightest on the franchises whose advantage is a regulated, outcome-linked dataset locked inside an installed base: a robotic-surgery record, years of physiological monitoring, a continuous glucose feed. The honest split is that the diagnostics and imaging names in the basket carry a structural margin headwind that quality does not fully offset, while the device-data franchises that anchor the thesis are the ones the substitution reaches last. It argues for leaning on the moat names inside the basket, not against owning it.

Why it re-rates

Cheap and sound can stay cheap for a long time, so the question is what closes the gap. The most concrete answer is that the buyers with the most information and the most capital are already taking these assets off the market outright. 2025 was the busiest year for medical-device dealmaking in more than a decade, including a private-equity take-private of a diagnostics company at roughly eighteen billion dollars and a twenty-one billion dollar acquisition by one of our own basket members [7]. When trade buyers and private equity pay cash for whole companies above where the public market marks them, they set a floor under the equity that sentiment struggles to break through. The slower catalyst is the ordinary one: as the inventory hangover fully laps and earnings revisions turn up, a sector at a discount to the market with a thirty-year demographic tailwind tends to be repriced by the same flows that left it. Those flows are only now beginning to reach devices: the group has turned up sharply off its June low in the past week, led by the diabetes and diagnostics names, though a few days off a six-month bottom is not yet a trend, and that hesitation is the honest timing risk in the trade.

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The position

We are adding an equal-weighted basket of ten names to the list of trades at the open: ResMed, Medtronic, Abbott, GE HealthCare, Boston Scientific, Stryker, Zimmer Biomet, Becton Dickinson, Globus Medical and Dexcom. Six are quality compounders growing earnings and cash flow into the demographic tailwind; three are deep-value or special-situation names where the cheapness and a catalyst do the work; one, Dexcom, is the clearest single embodiment of the thesis, a proprietary-data franchise that the weight-loss drugs turn out to help. Equal-weighted, the basket trades near sixteen times forward earnings, well below both the market and the cap-weighted sector index, which is dragged up by paying thirty to forty times for the two most expensive names in the group. We hold the data moat through its cheaper carriers rather than overpay for the marquee one.

For readers who would rather own the theme in a single line, the iShares U.S. Medical Devices ETF (ticker IHI) delivers the same exposure, with the caveat that its cap weighting makes you pay up for exactly the expensive names the basket is built to avoid. The basket is the better expression of the view, the ETF the more convenient one. A two-line case for each holding sits at the foot of this note.

Stray Narratives is free and carries no advertising, so it travels only as far as its readers send it. If this was worth your time, a like, a restack, or a forward to one colleague who should see it is the whole of our distribution, and what keeps it free.

The position implication

You are buying a sector at a discount to the market for the first time since 2008, with margins at decade highs, a demand curve that compounds for thirty years, acquirers setting a cash floor beneath it, and a proprietary-data moat the AI trade is pricing at zero. What that pays, on a three-year view and entered at the basket’s sixteen-times multiple, is a base case of roughly seventeen percent a year: about half the discount to the market closes, earnings compound near nine percent, and the dividend adds a point. If the discount shuts to parity and earnings run faster, the basket returns about twenty-six percent a year. If it never closes at all and earnings only grind, the basket still returns about six, because the growth and the yield carry it without the re-rating. The re-rating is the option you are handed for free.

That asymmetry is the reason to own it now: the discount exists because the market paid in advance for a catastrophe, and the catastrophe is being refuted in the operating numbers one quarter at a time. The repricing does not require the businesses to surprise anyone. It only requires the absence of the disaster that is already in the price.

References

[1] Forward and historical relative-valuation work on US large-cap medical devices (median next-twelve-month P/E versus the S&P 500 and the healthcare sector), from a sell-side equity research desk, May 2026; cross-checked against a major asset manager’s published note placing healthcare at its lowest relative valuation since 2008. Our own basket multiples are computed from company filings.

[2] Stray Narratives analysis of the ten largest US medical-device companies’ annual filings, 2015 to 2025 (revenue, gross/operating/EBITDA/free-cash-flow margins, research and development, return on equity), aggregated sum-of-parts.

[3] ResMed published analyses of GLP-1 prescription data and CPAP initiation/adherence (IQVIA claims dataset, ~660,000 patients), and company device-volume disclosures, 2025-2026; continuous-glucose-monitor co-prescription figures from the leading manufacturer’s disclosures.

[4] Eli Lilly SURMOUNT-OSA trial results and the FDA approval of tirzepatide for obstructive sleep apnoea, December 2024.

[5] FDA clearances of medical AI/machine-learning device software across the named manufacturers (FDA AI/ML-enabled device list, public).

[6] Chinese medtech competition and pricing: China’s centralised “volume-based procurement” of in-vitro diagnostics (provincial tender data via the National Healthcare Security Administration), the domestic patient-monitoring and IVD leader’s vertical-integration into reagents/raw materials, and the displacement of the incumbent gene-sequencing supplier inside China by a domestic pair (company filings and trade-press deal data; surfaced by a sell-side note, underlying primaries cited).

[7] Medical-device M&A activity, 2025: aggregate deal value and the take-private of a diagnostics company and a large strategic acquisition cited as illustrative (public deal announcements).

[8] US Census Bureau and UN Department of Economic and Social Affairs population projections (US 65+ population toward 71 million by 2030; global 65+ toward ~1.5 billion by 2050).

The basket, name by name

• ResMed (RMD). The cleanest rebuttal to the GLP-1 bear: its own claims data shows patients on weight-loss drugs are more likely to start CPAP, not less, while device volumes still grew double digits. At 16x forward earnings with operating margins above 30 percent, the market is paying late-cycle prices for a structural grower.

• Medtronic (MDT). The lowest-multiple way to own the data moat: several FDA-cleared AI products already run on its proprietary device data, and it converts roughly five billion dollars of free cash flow a year. At 13x forward earnings it is priced as a no-growth utility while revenue has returned to mid-single-digit growth.

• Abbott (ABT). The diversified ballast, spanning glucose monitors, diagnostics and devices, with free cash flow up roughly half over three years to more than seven billion dollars. Its Libre franchise is a GLP-1 beneficiary, as drug users adopt monitoring rather than abandon it.

• GE HealthCare (GEHC). The cheapest large-cap imaging franchise at 13x forward earnings, and the most prolific filer of FDA-cleared AI in the group. Growth is slow, so the case rests on the re-rating of a misjudged spin-off rather than on accelerating fundamentals.

• Boston Scientific (BSX). The Farapulse cardiac-ablation cycle is still driving low-double-digit revenue growth and steady share gains, with free cash flow roughly doubled over three years. At around 13x forward earnings it is the cheapest of the growth names, partly because the stock has sold off sharply in recent weeks on a flagged stagnation in its Watchman left-atrial-appendage line, a setback in a different franchise from the Farapulse ablation business that underpins the growth case.

• Stryker (SYK). Orthopaedic robotics with a genuine data moat: its Mako system has logged more than a million cases a rival cannot replicate, and weight-loss drugs help by clearing the BMI thresholds that gate joint replacement. The 21x multiple is the one premium we pay for quality and moat together.

• Zimmer Biomet (ZBH). The deepest value at under 11x forward earnings, a mean-reversion bet on an orthopaedics recovery rather than a growth story. The same GLP-1 dynamic that worries the bears makes more patients eligible for the hips and knees Zimmer sells.

• Becton Dickinson (BDX). The special situation: 11x forward earnings against a company pursuing a separation that could surface value the conglomerate structure hides. It also carries the most debt in the basket, so this is the position underwritten on the catalyst rather than the margins.

• Globus Medical (GMED). The quality mid-cap: a net-cash balance sheet, a double-digit return on equity, and operating margins climbing back toward the high teens as the NuVasive merger integrates. Small enough to stay a credible acquisition target in a consolidating sector.

• Dexcom (DXCM). The thesis in a single name: a proprietary glucose-data moat, a verified GLP-1 tailwind as drug users adopt monitoring, and free cash flow more than doubled to over a billion dollars. The 27x multiple is the basket’s richest, and the risk to watch is the price war with Abbott, not the drugs.

A crude tanker spends most of its working life earning a return that would embarrass a savings account. Then, for a few months every decade, a chokepoint makes the evening news, crude reroutes the long way round, and the holder books years of profits in a single quarter.

That window is the trade I put on in March. I called it the Wrong Map: a basket of three tanker equities, DHT Holdings, Frontline and International Seaways, equal-weighted, bought as a direct wager on Middle Eastern disruption. I closed it this week with a nice profit:

The reason to own these names was the disruption, and the disruption is resolving. The United States and Iran have signed a memorandum of understanding, the Strait of Hormuz is reopening, and a reopened strait is, with a lag, poor news for ton-miles: crude takes the direct route again, voyages shorten, idle capacity returns, and freight reverts toward breakeven. The war premium I was renting is being handed back.

The arithmetic

When conflict in the Gulf forces crude onto longer routes, ton-miles, the volume of oil multiplied by the distance it travels, rise, and tanker spot rates rise with them. At its early-March peak the Baltic benchmark for the Gulf-to-China route reached roughly $424,000 a day against an operating breakeven near $22,000, the spread that produces the best quarter of a shipping cycle and the record dividends that came with it. It had eased toward $100,000 by mid-June, and the congestion of the reopening keeps it bid for now, but the direction is no longer higher.

Why I am not holding out for the last dollar

Tanker equities have the awkward property of looking cheapest at the exact moment they are about to earn the least. They are priced off the prevailing spot rate, so at the crest of a disruption their trailing earnings look spectacular and the shares screen as the cheapest assets on the market, a multiple usually quoted without the word “trailing” attached. That multiple is a mirage built from a single, unrepeatable quarter, and when freight reverts the earnings behind it leave with the peace. I would rather hand the optically cheap multiple to whoever wants to own the normalisation than sell it to them a month later at a lower price.

There is a genuine floor under freight. Sanctioned Russian barrels, an expanding shadow fleet and structurally longer average hauls should keep ton-miles above the pre-war normal even after the queue clears, which is why the exit may be too early. But a floor a few thousand dollars above breakeven is a rounding error against a rate that spiked to nearly twenty times breakeven. It argues for owning these names at a normalised rate on a quiet day, not for riding them through the risk of a round trip.

Where I stand

I have now closed the Wrong Map tanker basket and all three legs finished in the black, including DHT, which spent most of the trade as the laggard before the reopening congestion firmed rates into June and pulled it level with the others. The other leg of the Wrong Map, the position in rates, runs on a different mechanism and a much longer clock, and it stays on; I will however restructure that trade in the coming weeks to make sure it embeds enough time horizon to materialise.

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In the Central Valley of California, farmers are ripping more than 210,000 acres of almond trees out of the dirt.

They aren’t doing this to rotate crops. They are doing it because the maths of the last decade has completely broken. The University of California, Davis recently ran the numbers on planting a new almond orchard: factoring in land, trees, water, ten years of capex, and debt service, a debt-financed grower now needs roughly twenty years to break even at today’s price deck near US$2.70 a pound, and about twelve years even with no land debt to service [1].

Nobody invests with a twenty-year breakeven. Twenty years is not an investment horizon; it is a sentence. The margin of safety is gone, marginal growers are walking away, and the supply response that historically kills almond bull markets is dead.

An almond tree bears its first crop in year three, reaches full yield around year seven, and produces for roughly twenty-five years before the orchard is replaced. Supply is set the better part of a decade in advance, and it cuts both ways: the planting that should feed 2030 is not going into the ground, and the trees being pulled now will not come back at these economics.

But while California capitulates, the cycle break has opened an asymmetric mispricing 12,000 kilometres away.

I am adding Select Harvests (ASX:SHV) as a trade at the open, at roughly A$3.93. The trade is the global almond cycle break, expressed through the lowest-cost producer outside California, with a revenue tailwind from US-China tariff arbitrage and a share price trading at a steep discount to its hard assets.

Here is why the cycle is broken — and why the downside on this trade is bound by physics rather than by market sentiment.

The Ultimate Capitulation Signal

When an industry quietly stops publishing the data that historically hurt its prices, that is not a methodology change, it is a confession. It is the agricultural equivalent of a struggling tech company quietly retiring quarterly guidance.

In December 2025, the Almond Board of California voted to stop funding the USDA Objective Measurement Report — the rigorous July estimate, built on physical orchard sampling, that historically corrected the looser May grower survey [1]. The timing gives away the motive. The vote came months after the July 2025 estimate landed high and triggered the single largest one-day almond price drop in modern memory [1]. Confronted with the one number that reliably delivered downside surprises, the industry chose to stop publishing it rather than argue with it. Officially, the reason was accuracy. Read it however you like — but an industry quietly euthanising the data that keeps embarrassing its prices is not the behaviour of people who think supply is about to flood.

The data backs the tell. California controls 80% of global almond production, so what happens in the Central Valley dictates the cycle [2]. Three things are happening at once:

• Removals are unprecedented. Land IQ data show more than 210,000 acres — roughly 15% of bearing acreage — pulled since 2022, including about 67,000 in 2024 alone. In 2026, California’s bearing almond acreage fell for the first time since 1995 [2].

• The pipeline has collapsed. Non-bearing acreage, the leading indicator for production three-to-seven years out, fell 26% year-on-year. The supply that should feed the 2028–2032 window simply will not exist [2].

• The marginal acre is uneconomic. Westlands Water District, historically the largest destination for new plantings, planted essentially zero new acres in 2023 and 2024, as water costs sat structurally elevated under California’s Sustainable Groundwater Management Act (SGMA) [2].

Combine that with Australian output running broadly flat toward 2030 as growers replant rather than expand, and structural drought across Spain’s growing regions, and the global supply curve is locked. Meanwhile demand keeps climbing — on tariff-driven Chinese buying, Indian consumption, and the developed world’s determination to put almonds in everything that once contained milk.

Capital Allocation: Utility Over Signalling

The market is pricing SHV on cyclical fear, assuming we are near a top. Management is quietly arbitraging that behavioural error with a textbook capital-return playbook [3].

After a strong 1H FY2026 result, the board announced a 10% on-market buyback — around 14.2 million shares, 10% of the issued capital — and resumed the interim dividend. It also secured a new A$60 million debt facility on favourable terms, not for distressed working capital, but to shrink the float at a depressed valuation [3].

This is the unglamorous version of smart capital allocation: financial utility over market signalling. Buying a dollar of productive almond orchard for eighty cents is not a sophisticated manoeuvre — it is just an unfashionable one. And the mechanics are cycle-proof: a 10% reduction in the share count lifts earnings per share by roughly 11% on a flat earnings base, entirely independent of where almond prices go next.

The Asset-Backing Floor

Replacement-cost analysis is the discipline that stops a cyclical equity from becoming a cyclical trade. On a sum-of-the-parts replacement basis [3][4]:

That is an enterprise value of A$557–803 million. Strip out net debt of roughly A$80 million — down 51% to A$79 million at the last full year — and, across 142 million shares, the implied equity sits at A$3.35–5.10 per share [3][4].

At around A$4.00, the market is paying for the assets at below the midpoint of their own replacement value, with zero operating premium. You get the largest Australian almond producer — A$118–130 million of FY26 base-case EBITDA — thrown in for free on top of the dirt [3].

Unless the orchards literally burn down, the hard assets bind the downside around A$3.30–3.50. Below that, the assets are worth more than the equity, which makes a private buyout increasingly plausible — the kind of valuation that tends to attract people with cheque books and patience. Precedent: Costa Group went private at A$3.20 in 2024 on exactly this sub-replacement logic [4].

Reality Check: The Bear Cases

A thesis is only as strong as its weakest link. Here is what breaks this trade, in order of probability.

1. A Trump-China tariff thaw (~12–15%). Australian almonds have been winning share in China on the US-China tariff differential. A partial agricultural deal in late 2026 or 2027 narrows that gap and reroutes Chinese buyers back toward US origin. Impact: a 15–20% cut to the realised-price assumption.

2. A Murray-Darling water shock (~8–12%). This is the hidden leverage in the model. SHV owns the permanent rights to only about 18% of the water it needs and sources the rest from the allocation market — which is to say, from the spot market and the goodwill of the sky. Under El Niño conditions, Australian allocation prices spike viciously (A$300+/ML) [4]. In a concurrent drought, global almond prices rise but SHV’s unit margins compress as spot water costs bite. Impact:15–25% off EBITDA in the affected year — though the hard-asset floor still protects the equity base.

3. Tail shocks (~3–5%). August-September frost, or a resurgence of the varroa mite that hit Australian hives in 2022. Impact: a 20–30% drawdown.

The unifying point: in every realistic bear case, the asset floor binds. Outside the tail scenario, SHV does not go meaningfully below ~A$3.30 without the assets being worth more than the equity. The downside is capped by physics, not by sentiment.

Position and Expected Return

Adding SHV to the list at the open, ~A$3.93, for a 12-to-18-month window — with optionality to extend into the 2028–2030 production gap if the supply-destruction story fully materialises.

*Fair values recomputed on the corrected 142.1m share count (the 1H result confirmed 142.1m, not the 152m the original model used). Upside rows hold a ~7× EV/EBITDA backed out of the base case; bear and tail rows are anchored to the ~A$3.30–3.50 asset floor. These are total returns over the 12–18 month horizon, not annualised. Probability-weighted, the trade carries roughly +23% to +33%, with the downside floored by the assets — a sharper asymmetry than the original model showed, because fewer shares lift every per-share figure and the buyback is retiring stock below replacement value.*

What would make me add: California’s 2026 crop confirmed at ≤2.65 billion pounds; Murray-Darling allocation easing below A$120/ML; FY26 EBITDA tracking above A$130 million. What would make me trim: a US-China deal that narrows the tariff differential; spot water sustained above A$250/ML for two quarters; FY26 EBITDA tracking below A$95 million.

Bottom line. The cycle is not at its peak. The cycle has broken. New orchards do not get planted at twenty-year breakevens, the pipeline has collapsed, and the industry has stopped publishing the number that used to keep it honest. Select Harvests is the cleanest way to own that break: capped downside bound by physical assets, asymmetric upside, and a management team compounding capital while everyone else is reading the cycle backwards. There is, occasionally, a free lunch in markets. It is usually buried under something nobody wants to look at — in this case, more than 210,000 acres of dead trees.

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References

[1] University of California, Davis — 2024 Almond Cost Study (Sacramento Valley & Southern San Joaquin editions), for the new-orchard breakeven horizon; Almond Board of California position reports, including the December 2025 vote to discontinue funding the USDA Objective Measurement Report.

[2] Land IQ Standing Almond Acreage estimates (commissioned by the Almond Board of California; the USDA NASS standalone California almond acreage survey was discontinued), 2022–2026 — bearing removals (more than 210,000 acres / ~15% since 2022; first bearing-acreage decline since 1995, 1,401,097 → 1,385,870), the ~26% drop in non-bearing acreage to the 2025 final (142,306 → 104,900), and Westlands Water District / Land IQ planting data; California ≈ 80% of global production (International Nut & Dried Fruit Council). (SGMA — California’s Sustainable Groundwater Management Act — is the regime behind the elevated marginal-acre water costs.)

[3] Select Harvests 1H FY2026 results announcement and presentation, 28 May 2026 — FY26 EBITDA guidance, the 10% on-market buyback, A$60m debt facility, resumed interim dividend, and orchard / processing / water-entitlement asset disclosures.

[4] Asset-floor anchors — Australian agricultural land and water transaction benchmarks (incl. Murray-Darling Basin entitlement and allocation pricing) and the Costa Group / Paine Schwartz Partners take-private at A$3.20 (completed early 2024; asset-backed precedent).

When a technology concentrates wealth this completely in the hands of capital owners, the political system reacts, as it has at every comparable point in modern history. AI does not ask for stock options, does not unionise, and does not complain about the return-to-office mandate, which is precisely why the reaction will not come from inside the companies. The previous issues in this series traced where economic value migrates as the cost of cognition collapses: toward what AI cannot make — the human element, physical limits, and verification capacity.

The central investable question of the next decade is what the political system will do to the winners.

Morrow’s Diagnosis

In the years around the Great Financial Crisis, Robertson Morrow, a macro thinker and former CIO at Peter Thiel’s Clarium Capital, wrote a paper titled “The Bull Market in Politics” — the paper this issue takes its name from [2]. His argument was simple: going forward, government influence over trade, immigration, social policy, and the decisions of war and peace would matter more to investors than business fundamentals ever would.

He was exactly right, and his thesis perfectly describes the post-AI economy. When the economic engine stops working for the median voter, policy swings take over the wheel.

What is currently breaking that engine? We are watching the bimodal compression of cognitive labour begin [3]. It is driven by two opposing forces acting on the market simultaneously:

• The Pull of Commodification: Capital pushes anything codifiable toward zero cost. If a task can be modelled, AI does it for fractions of a cent. The cost curve for that work collapses.

• The Pull of Irreducibility: What AI cannot do — cumulative judgment, signature liability, primary-source relationships, taste, and verification authority — becomes aggressively more scarce and exponentially more expensive.

The middle will get eaten from both sides. The routinely competent professional — the junior associate, the mid-tier analyst, the junior radiologist — is structurally exposed: their commodifiable output is absorbable by the model, while they lack the accumulated reputation and liability-bearing capacity that defines the top tier. As AI capability matures, the two ends of the economic spectrum will pull violently apart, and the middle will vanish.

Roughly 13% of US cognitive jobs, over 21 million workers, sit directly in the path of that compression [4]. Wage data reveals polarisation of this kind only in retrospect, on a lag of years, and the deployment wave is barely three years old. What shows up so far is concentrated in the occupations where adoption ran first. But the real political danger isn’t happening at the bottom of the income ladder. It is happening in the upper-middle-class waiting room that this compression is emptying.

Elite Overproduction: The Holding Pen is on Fire

This brings us to Elite Overproduction, a concept pioneered by historical macro-sociologist Peter Turchin [5].

Turchin studies history through the lens of population dynamics, and his thesis is dangerously relevant today: late-cycle societies produce far more credentialed “elite aspirants” than there are elite slots to absorb them. The American numbers tell the story plainly. The number of US lawyers tripled from roughly 400,000 in the mid-1970s to 1.2 million by 2011, while the population grew only 45 percent. MBAs over the same window grew six-fold [6]. The supply of credentialed aspirants for elite positions has run miles ahead of the fixed supply of positions themselves.

For decades, the economy kept these surplus elites pacified with high-paying, middle-management cognitive jobs. It was a massive holding pen for ambition. AI is the match held to it.

Nothing radicalises a population faster than a newly minted professional who realises their expensive master’s degree merely qualifies them to supervise a chatbot. When you have too many ambitious, highly educated people fighting over a rapidly shrinking pool of status-bearing jobs, they don’t form a support group. They fracture the political system.

Historically — whether you are looking at the late Roman Republic, pre-revolution France, or the late Russian Empire — intra-elite competition destroys institutional trust. The frustrated elite aspirants turn populist, harnessing the anger of the broader working class to tear down the very structures that locked them out.

The Fujiwara Effect: When Labour Throws a Brick

If Elite Overproduction explains the anger of the professional class, the Fujiwara Effect explains the mechanics of how that anger rewrites the rules of the market.

Borrowed from meteorology, where it describes two or more cyclones merging into a single larger system, the Fujiwara Effect has been made the organising metaphor of Viktor Shvets’s recent work as Global Strategist at Macquarie Capital, most fully developed in The Twilight Before the Storm [7]. In Shvets’s reading, three cyclones are converging: three decades of neoliberal policy, deep financialisation, and the disruptive Information Age now turbo-charged by AI. The central observation is grim: as these forces compound, the “winner-takes-all” dynamic becomes absolute, and the marginal economic utility of human labour collapses. First they came for the blue-collar assembly lines; now they have come for the white-collar spreadsheets.

But Shvets makes a lethal political observation: the changing value of votes.

When a median worker realises their labour no longer gives them any leverage over capital, they remember they still possess one asset the market hasn’t fully depreciated: their vote. And they tend to throw it like a brick.

The democratic system’s traditional mechanism for absorbing inequality is to translate economic discontent into gradual policy shifts. But when labour’s leverage falls this fast, gradualism dies. Silicon Valley billionaires suddenly discovering a deep, philosophical interest in Universal Basic Income isn’t altruism [8]. It is fire insurance. The serious versions are already being drafted, from corporate “displacement taxes” to an AI dividend fund modelled on Alaska’s [9]. They can read the room. They understand that a society where capital owns everything AI cannot make, and labour fights over the shrinking pie of what it can, is a society begging for a wealth tax.

The Catalyst: The Sovereign Accounting Trap

The sociological anger is the fuel, but the spark that ignites the political snapback is pure accounting. Modern Western governments fund themselves primarily through taxing labour (income and payroll taxes). Capital is highly mobile and taxed at much lower effective rates.

Every point of GDP that migrates from salaries to capital income leaves the payroll-tax net with it. If the bimodal compression runs its course, the sovereign tax base hollows out at precisely the moment the political system requires trillions of dollars to pacify a fractured, jobless middle class (via UBI, bailouts, or aggressive stimulus).

How does a sovereign fund a massive redistribution with a shrinking tax base? It cannot do it through gradual policy. It is forced into draconian asset taxes, financial repression, and aggressive currency debasement. This is the mechanical proof for why the coming crisis resolves via inflation.

The Inevitable Snapback: History’s Rhythm

No serious analytical framework predicts that accelerating disparity at record levels resolves itself gracefully. The tension always breaks, and it breaks via state intervention.

Look at the resolution of the three prior US secular bear markets:

• The 1930s (Deflationary): Resolved through the New Deal. A massive, state-executed restructuring of financial regulation, forced by the undeniable political pressure of mass unemployment.

• The 1970s (Inflationary): Resolved through Volcker’s extreme disinflation, after the failure of Nixon’s wage-price controls [10].

• The 2000s (Deflationary): Resolved through Quantitative Easing and financial repression [11]. This was a massive wealth transfer to capital owners to solve a banking crisis. It saved the system, but the bill for that disparity is now coming due.

A forced redistribution is coming. The only question for an investor is how to position capital before the politicians finish drafting the legislation.

The Investable Playbook: What This Means for Capital

The political question is not if the state will intervene to redistribute wealth, but where it will aim when it does.

• Overweight the Diffuse Scarcities (Real Assets & The Human Element): Capital owners of what AI cannot make — relational-sector luxury brands and physical-input chokepoints — hold pricing power that is durable to inflation. More importantly, they possess political camouflage. A politician is highly unlikely to nationalise an ultra-luxury handbag boutique, and they cannot easily expropriate a fragmented network of prime agricultural land. The populist case against them is weak.

  *The Macro Caveat:* Do not mistake political camouflage for invincibility. During an inflationary bust, the state will not outright steal the copper mine, but it will absolutely levy windfall taxes on the excess margin or impose price caps. You own these assets to survive the currency debasement, but you must actively manage the margin risk.

• Overweight Verification Infrastructure: If AI drops the cost of cognitive output to near-zero, the economic value migrates entirely to the entity that stamps it as true. Think of the global testing, inspection, and certification giants — Swiss-based SGS, French-based Bureau Veritas, or British-based Intertek. An AI can optimise a global supply chain flawlessly, but it cannot legally certify that a shipment of medical supplies meets EU safety standards. The liability, and therefore the economic moat, remains human.

• Underweight the Coordination Layer (The Expropriation Target): This refers to the digital middlemen. These are the platforms that don’t actually produce the goods or provide the service, but extract rent by controlling the matchmaking — think Amazon’s marketplace, Uber, App Stores, and the digital advertising duopolies. They are massive, highly visible, centralised, and deeply unpopular. When an angry political system needs a scapegoat to appease a fracturing society, Big Tech has a giant bullseye painted on its back. Margin-crushing regulation or antitrust dismantling of these platforms is a populist dream.

• Hedge with Store-of-Value (With Cyclical Caution): When the political resolution inevitably demands wealth redistribution, the fiat currency bears the cost. Gold and structural currency diversification compound significantly when governments inflate their way out of a social crisis. However, this is not a one-way street; precious metals typically suffer brutal liquidity drawdowns during initial market shocks before ripping higher in the policy response.

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  What This Is Not

  To ensure we are trading on market realities and not political emotion, we must define what this framework does not claim:

  • It is not a prediction of exact timing. These are secular trends. In terms of sequencing, there are actually growing arguments — which I will cover in a coming issue — that we will likely see a cyclical, deflationary recession first. That recession will act as the catalyst, pushing the political system to panic and finally triggering the inflationary policy bust.

  • It is not a partisan political warning. State intervention historically comes from both sides of the aisle. Nixon (a Republican) imposed wage-price controls; FDR (a Democrat) built the New Deal. The math forces the state’s hand regardless of who occupies the office.

  • It is not a doomsday argument. The historical pattern is resolution, not total collapse. If you are familiar with Neil Howe and William Strauss’s The Fourth Turning [10], you recognise this rhythm. Late-cycle secular bear markets force an institutional reset, producing new social contracts and redirecting capital flows. Properly positioned capital does not just survive these transitions — historically, it multiplies.

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    The Bottom Line

    We are living in a late-cycle society where the marginal worker’s economic leverage is falling vastly faster than the political system can comfortably absorb. Bimodal compression and Elite Overproduction are the disease; populist redistribution is the historical cure.

    The most dangerous assumption an investor can make today is that the benign regulatory environment of the 2010s will survive the brutal economics of the late 2020s. Asset prices reprice the exact moment a political mechanism becomes visible — not when the bill is signed into law.

    The people who get this right will hold the assets the resolution rewards: the diffuse, inflation-resistant owners of the human element, physical limits, and verification moats. The people who get it wrong will hold the massive, highly visible coordination-layer platforms, watching their margins compress against regulatory caps while the wealth they thought they had concentrated quietly migrates away.

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    References

    [1] Chief Justice of India Surya Kant’s remarks in open court, 15 May 2026, and the rise of the satirical Cockroach Janta Party (founded 16 May 2026 by Abhijeet Dipke; roughly 19–22 million Instagram followers within a week, surpassing the ruling BJP’s official account): BBC News, CNN, The Guardian and Al Jazeera, 19 May – 8 June 2026. Graduate unemployment: nearly 40% of Indian graduates under 25 are jobless, per Azim Premji University research cited by CNN, 22 May 2026.

    [2] Robertson Morrow, “The Bull Market in Politics” (Clarium Capital, circulated mid-to-late 2000s). Morrow was CIO of Clarium Capital. Primary attestation in George Packer, “No Death, No Taxes: The Libertarian Futurism of a Silicon Valley Billionaire,” The New Yorker, 28 November 2011, which discusses the paper and the thesis within a profile of Peter Thiel. Morrow’s later by-line at The American Conservative (2002–2005) provides corroborating biographical detail.

    [3] Stray Narratives, Issue 12: “Who Gets Paid After AI,” May 2026. The four-mechanism framework and the bimodal compression that runs through each.

    [4] Stray Narratives, Issue 06: “The Noise Economy,” April 2026. The 13% / 77% workforce split.

    [5] Peter Turchin, End Times: Elites, Counter-Elites, and the Path of Political Disintegration, Penguin Press, 2023. The elite-overproduction framework is developed across the book; earlier statements appear in Ages of Discord (2016).

    [6] American lawyer counts ~400,000 (mid-1970s) to 1.2 million (2011), per the American Bar Association, cited in Peter Turchin, Ages of Discord: A Structural-Demographic Analysis of American History (Beresta Books, 2016) and End Times (Penguin Press, 2023). MBA six-fold expansion over the same window per the same Turchin sources. Secondary summary in Noah Smith, “Blame Rich, Overeducated Elites as Our Society Frays,” Bloomberg Opinion, 20 November 2013.

    [7] Viktor Shvets, The Twilight Before the Storm: From the Fractured 1930s to Today’s Crisis Culture (Boyle & Dalton, August 2024). Shvets is a global strategist at Macquarie Capital; the book is cited here as a public work. The Fujiwhara Effect itself is a 1921 meteorological term (named after Sakuhei Fujiwhara) describing the interaction of two or more cyclones; Shvets adopted it as the organising metaphor of his macro thesis, deployed across both The Twilight Before the Storm and his earlier The Great Rupture (2020). His three-cyclone reading (neoliberalism + financialisation + Information Age) is laid out at viktorshvets.com/the-fujiwara-effect and discussed with Alan Kohler in The New Daily, “The Fujiwara Effect — three cyclones of modern world,” 26 August 2024.

    [8] Silicon Valley figures advocating Universal Basic Income, including Sam Altman’s Y Combinator Research UBI experiment and public commentary, 2016 onward.

    [9] Alap Shah, “The Global Intelligence Crisis, Part Three: The Path Forward” (alapshah1.substack.com, March 2026) — the “American Prosperity Compact”: a corporate displacement tax funding income security and an American AI Dividend Fund modelled on the Alaska Permanent Fund.

    [10] Paul Volcker’s Federal Reserve tightening cycle, 1979–1982, taming inflation following the failure of Nixon’s 1971 wage-price controls. Federal Reserve archive and historical record.

    [11] Federal Reserve Quantitative Easing programmes commencing November 2008; the ensuing decade of financial repression as the macro-policy response to the Global Financial Crisis. Federal Reserve communications and successor literature.

    [12] William Strauss and Neil Howe, The Fourth Turning: An American Prophecy, Broadway Books, 1997. The generational-cycle framework.

• The Law of the Post-AI Economy: What an algorithm can codify drops to near-zero cost; what it cannot produce becomes exponentially more valuable. The wealth freed up by collapsing commodity costs doesn’t vanish—it migrates directly toward scarcity. The engine is psychological, not technological. Comin and Lashkari find that roughly three-quarters of the century-long shift in consumption is driven by income effects rather than Baumol’s cost disease: as people get richer, they stop paying for utility and start paying for human provenance and social signal.

• Three scarcities sit on the far side of the cost collapse, each scarce for a different reason and each compounding as AI improves: the Human Element (provenance, exclusivity, curation), the Physical Limit (copper, power, and land that cannot be synthesised), and Verification (the judgment and liability that underwrite cognitive output).

• The labour market splits in two: the junior doing commodity work loses leverage to the model, the senior holding the licence and the liability keeps the margin, and the middle is squeezed. One honest correction to our earlier work — the early hollowing-out of entry-level hiring tracked remote work more closely than AI. The AI displacement is additive, and still ahead of us.

The conclusion is to own the chokepoints, not the AI software winners: human-element brands with waitlists, physically constrained real-asset producers, and the verification moats of audit, certification, and named-principal liability. Late in the Kondratiev cycle, with the next secular bear likely inflationary, these hold pricing power while commoditized producers compress to zero real margin.

Here below is the AI Podcast version of this same article:

Starbucks tried to automate. A $112 billion company selling one of the most standardised products in the modern economy spent years systematically removing labour from its stores.

Then, this spring, CEO Brian Niccol reversed course. He brought back handwritten notes on cups. Ceramic mugs. The return of great seats. More baristas, not fewer. He reinvested in the hospitality, not the coffee. [1]

Read carefully, that decision points at the entire post-AI economy. Starbucks reached for what AI could not replace, because what AI could replace was no longer the thing customers were actually paying for.

The consensus story about artificial intelligence assumes that when machines can produce anything humans can produce, the jobs disappear and human economic value goes to zero. That view is fundamentally backward.

The principle of the new economy is entirely mechanical, and it fits into one sentence: What AI can produce gets cheap; what AI cannot produce gets exponentially more valuable.

AI is a production technology. It compresses the cost of producing the things it can codify. Anything fungible, anything modular—the cost curve collapses, and the price follows. But as the cost of these commodities drops to near-zero, the freed-up capital and consumer spending do not disappear. They migrate.

The most important investable question of the next decade is where that money flows. The data shows it flowing directly toward three distinct scarcities that possess moats an algorithm fundamentally cannot cross.

The Engine: The Psychology of Wealth

We are consistently told that services dominate the modern economy because they resist automation. That is mostly false. In 2021, economists Diego Comin and Danial Lashkari showed that this price effect—Baumol’s cost disease—only accounts for about a quarter of the macroeconomic shift. The other 75% is driven by income effects. [2]

When income rises, people stop paying for pure utility and start paying for social signals. You can see this clearly in US consumer data: the wealthiest 20% of households don’t just consume a larger volume of goods than the bottom 20%; they disproportionately shift their wealth toward categories with a heavy relational component—in-person dining, entertainment, and exclusive services. [3]

This is the macro engine of the post-AI economy. The structural reallocation of wealth runs on human psychology, not just technology. Even if AI can automate a luxury service for pennies, wealthy consumers will still shift their spending toward the human alternative—because paying for the human alternative is the point.

Which brings us to the first scarcity.

1. The Human Element (The Psychology of Scarcity)

Nobody who buys a $3,000 Armani suit is buying a more efficient way to stay warm. They are buying the brand, the social meaning, and the fact that other people know what it is but cannot have it.

As utility gets cheap, the premium on social signalling skyrockets. Demand shifts toward goods whose entire value is derived from human provenance, exclusivity, and taste.

Economists Alex Imas and Graelin Mandel recently ran an experiment using physical art prints that serves as the single most important data point for understanding consumer behaviour in the AI era. When human-made artwork was made exclusive (one physical copy versus many), its value jumped by 44%. But when AI-generated artwork was made exclusive, that premium collapsed to just 21%. [4]

Sit with that finding. The mere fact that an algorithm was involved in producing the object destroyed the exclusivity premium. AI involvement is inherently anti-exclusive. It cannot capture the premium that comes from human provenance because that premium is defined entirely against AI involvement.

Furthermore, as AI drops the cost of generating content and products to zero, supply becomes infinite. When supply is infinite, the ultimate scarcity is the Filter. The market will pay massive premiums for human curators, tastemakers, and editors who filter the deluge of AI-generated noise and dictate what is actually worth our attention.

2. The Physical Limit (Geological Scarcity)

AI moves at the speed of code. The physical world moves at the speed of geology.

You can prompt-engineer a million lines of code in seconds, but you cannot prompt-engineer a new copper mine, a gigawatt of baseload power, or a new water basin. We are currently watching the algorithmic, exponential growth of AI compute collide directly with a physical supply curve that refuses to budge.

Look at the agricultural equivalent: productive land and water rights. In California’s Central Valley, the breakeven on planting a new almond orchard has stretched to twenty years because water is so physically and regulatorily constrained. [5] The marginal acre is uneconomic. The supply curve is effectively dead. A coming issue will lay out the investment case on this specifically.

Just as AI destroys the exclusivity premium of luxury goods, it is completely powerless to synthesise base reality. Whether you are looking at copper miners with depleting reserves, LNG export terminals with physical chokepoints, or grid hardware manufacturers with multi-year backlogs, the thesis holds.

3. Verification (Reputational Scarcity & The Balance Sheet)

If the demand side of the new economy is driven by the human element, the supply side is driven by verification and risk.

AI is aggressively commoditising the raw materials of cognitive work: drafting, data analysis, calculation, and pattern recognition. When the cost of producing complex cognitive output drops to near-zero, the output itself is no longer the product. What stays valuable is the signature and the balance sheet.

An AI can draft a flawless 50-story architectural blueprint for a fraction of a cent. But an AI cannot assume liability if the building collapses. It cannot go to jail. It cannot hold a licence. Similarly, an AI can mathematically underwrite a massive commercial real estate loan, but the AI cannot take the financial loss if the borrower defaults. Risk capacity is strictly constrained by human or institutional capital.

The medical opinion, the audit letter, the legal interpretation, the structural stamp—the underlying work product gets cheap, but the cumulative human judgment and financial liability that underwrites it stays scarce.

This dynamic will produce a bimodal compression in the labour market as AI capability matures. The junior associate doing commodity drafting loses leverage to the model; the senior partner holding the liability licence captures the margin. The middle gets squeezed, the top compounds—and the speed of that compression will be the central question of the next five years.

Where the Capital Should Sit

The investment framework over time will not be about picking the AI software winners. It will be about owning the chokepoints of the post-commodity economy.

• The Human Element (Product as Signal & Filter): Businesses where the human element, curation, or exclusivity is the product itself. Handbags with multi-year waitlists. Concert venues with finite capacity. Family-controlled luxury houses with a century of brand equity.

• The Physical Limit (Dead Supply Curves): Businesses where the input is physically constrained. Copper miners with depleting reserves. Energy infrastructure and grid hardware manufacturers with multi-year backlogs.

• The Verification Moat (Irreducible Signature): Businesses on the supply side of cognitive production where the human signature and balance sheet are the units of value. Global testing, inspection, and certification (TIC) giants. Audit firms with senior-partner sign-off authority. Architecture firms with named-principal liability.

The Bottom Line

The consensus assumes that because the commodity form is completing itself—delivering products with zero human involvement at near-zero marginal cost—the value of human economic output goes to zero.

In reality, cheap commodities free up real income. That income flows directly toward comparative preferences, physical constraints, human curation, and liability-bearing signatures.

The macro environment makes positioning for this reallocation urgent. We are sitting on the late-stage side of the Kondratiev long cycle. [9] Historical patterns—moving through 1930s deflation, 1970s inflation, and 2000s deflation—suggest the next secular bear market will likely be inflationary. Real-asset businesses and luxury brands with pricing power survive inflationary regimes by definition. Commoditised producers compress against zero real margin.

But there is a looming, darker question here. If AI concentrates wealth into the hands of capital owners, and systematically compresses the wages and leverage of the middle class, the tax base of the sovereign state collapses exactly when society demands a bailout.

What happens politically when a highly educated, newly displaced class gets angry? That is the subject of a coming issue.

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References

[1] Starbucks “Back to Starbucks” turnaround under CEO Brian Niccol: the reversal of in-store automation in favour of hospitality — return of ceramic mugs, handwritten notes on cups, and condiment bars, alongside increased barista staffing (staffing raised at ~3,000 stores; a 700-store coverage pilot). Niccol’s remark that the chain had “ran like a manufacturing facility” per Fortune, 23 March 2026; programme detail per Starbucks corporate, “Back to Starbucks,” 2025, and Reuters reporting, May 2025.

[2] Diego Comin, Danièle Lashkari, and Martí Mestieri, “Structural Change with Long-Run Income and Price Effects,” Econometrica, Vol. 89, No. 1 (2021). Income effects account for roughly three-quarters of the long-run shift in consumption structure; the Baumol price / cost-disease channel for about a quarter.

[3] US Bureau of Labor Statistics, Consumer Expenditure Survey, 2022 — top-quintile versus bottom-quintile expenditure on relational categories (in-person dining, entertainment, personal services). Corroborated across the universe of US consumer spending by Joachim Hubmer, “The Race Between Preferences and Technology,” Econometrica, 2023.

[4] Alex Imas and Graelin Mandel, experimental findings on AI involvement and the exclusivity premium for human-made versus AI-generated artwork — a +44% exclusivity premium on human-made prints versus +21% on AI-generated prints. Summarised in Alex Imas, “What Will Be Scarce?”, Ghosts of Electricity (Substack), 2026, with companion technical note at aleximas.com.

[5] California’s Sustainable Groundwater Management Act (SGMA), now in enforcement across the San Joaquin Valley, with irrigation districts shutting deep-well agricultural pumps from 2026; the Public Policy Institute of California projects that pumping reductions could take up to ~20% of San Joaquin Valley farmland out of production by 2040. Almond orchards (3–4 acre-feet of water per acre, and non-fallowable once planted) are the marginal casualty.

[6] Peter John Lambert and Yannick Schindler, "The Broken Ladder: AI, Remote Work, and Early-Career Hiring," SSRN working paper, 2026 (analysis of ~243 million new hires and ~407 million job postings across the US, UK, Canada and Australia, 2017–2025); when work-from-home and generative-AI exposure are estimated jointly, the work-from-home effect on the junior-hire share holds while the AI coefficient attenuates toward zero. Analysis sharpened by Paul Kedrosky.

[7] A major global investment bank’s proprietary adoption survey (April 2026; ~800 companies across banking, software, tech hardware, semiconductors and professional services in the US, UK, Germany, Japan and Australia, all 12-month-plus AI adopters): a net ~5% reduction in roles over the prior year, with eliminations and non-backfilled positions concentrated in early-career staff — roughly six times the rate of the most experienced. Anonymised per house sell-side policy.

[8] US Census Bureau, Business Trends and Outlook Survey (BTOS), 2026: fewer than 20% of US firms report any AI use in a given two-week window; over 90% report no employment effect from AI over the prior six months; among firms reporting an effect, more cite increased than decreased employment. The most AI-exposed sectors account for roughly 15% of total US employment.

[9] Kondratiev long-cycle framework as documented across roughly a century of bond-yield and secular equity-cycle data; current late-cycle position per a long-cycle macro strategist’s May 2026 work (anonymised per house attribution policy). The three prior US secular bear markets — 1930s deflationary, 1970s inflationary, 2000s deflationary — bracket the pattern.

Markets anchor to trauma. When an asset class burns investors badly enough, as Chinese equities did through the tech crackdowns, zero-COVID lockdowns, and the property collapse, the consensus simply stops looking at the data. The narrative hardens into a single word: uninvestable.

But the market’s refusal to look is exactly where the alpha lives.

Right now, China is quietly handing us three massive, structural tailwinds firing simultaneously:

• Cloud and AI pricing power is expanding.

• The consumer is shifting from defensive hoarding to equity participation.

• The real estate drag is finally finding a floor.

The country has not seen these three pillars align in over a decade.

Yet, because the crowd is still trading the trauma of 2022, the biggest beneficiary of the global AI boom is being priced as if the AI revolution is passing it by entirely.

At the open, I am adding KWEB (China internet) and MCHI (broader China) to the model portfolio, sized for a 12-to-18-month window. MSCI China is currently trading at roughly 12.6x consensus 2026 earnings. Compare that to the S&P 500 at 21.4x. They are posting broadly comparable EPS growth, yet you are getting the Chinese tech-internet segment, which is actually growing faster than the US aggregate, at a 41% discount [1].

I am not playing for a dead-cat bounce but a potential structural rerating. Here is the case for why I am making this move.

Pillar 1: The new era of tech pricing power

To understand the Chinese tech opportunity, you have to look at the global compute market. Compute is structurally scarce. Through 2024 and 2025, the AI story was about land grabs and volume as companies subsidized access to build their user bases.

Today, that phase is over. Pricing power has taken the wheel. Anthropic stopped subsidizing enterprise plans this spring, and per-token prices have jumped [2]. Customer demand has vastly exceeded installed capacity globally, meaning the providers now hold the pen on price. The race-to-the-bottom on per-token cost has officially reversed.

The West operates under the assumption that China is locked out of this dynamic due to semiconductor sanctions, but the data says otherwise.

According to Stanford’s 2026 AI Index, the US-China frontier-model performance gap has collapsed from 17.5% in 2023 to just 2.7% today [3]. Chinese models are running roughly 12.6 trillion tokens per week (quadruple the US volume in recent tracking) [4], and they are doing it at 8% to 14% of the unit cost of leading US closed-frontier models [5]. China is running the AI economy on a vastly steeper, cheaper domestic demand curve.

When a scarce input meets immense demand, you get multiple expansion and profitability acceleration at the same time. Alibaba is the cleanest proof of concept. In their FY26 Q4, cloud and AI revenue grew 38% year-over-year. But more importantly, cloud and AI adjusted EBITDA grew 57% [6].

That 19-point spread between revenue growth and profit growth is the sound of operating leverage firing on all cylinders. The capex is already yielding cash. As Alibaba CEO Eddie Wu put it on the 13 May earnings call:

“There isn’t a single card on our service that is idle... we have a lot of customers still waiting to access the service.” [6]

Pillar 2: The saver-to-investor rotation

The persistent bear argument against China is the lack of a “bazooka” fiscal stimulus to revive the consumer. What this argument misses is that a second stimulus event isn’t necessary. The capital is already sitting on the household balance sheet; it just needs a reason to move.

Over the last few years, the Chinese consumer played aggressive defense. But the premium tier is turning. Alibaba’s 88VIP membership (their top-decile capture) hit 62 million in Q4, with growth accelerating to 24% [6]. Merchant ad spend on the platform is accelerating [6]. This is wallet-share capture among the knowledge-economy professionals in Hangzhou, Shenzhen, and Shanghai who are already reaping the AI windfall.

The mechanism that turns this premium-tier spending into broad market participation is financialization. There is a towering 50 trillion yuan wall of one-year-plus deposits maturing in 2026. In Q1 alone, 7.6 trillion yuan landed in personal deposits [7].

But savers are waking up to the reality that parking cash in defensive assets is a dead end. In January 2026, the China Securities Depository and Clearing Corporation reported 4.9 million new individual investor accounts, the highest monthly print since the stimulus peak in late 2024 [8]. The retail investor is beginning to speculate in equities again on a scale we haven’t seen since the 2014-2015 cycle. The cash is migrating to the stock market and the impact compounds.

Pillar 3: Property is clearing

I am not making a sweeping, optimistic call on Chinese real estate. The aggregate numbers across 70 cities are still in deflation [9]. But to use the aggregate as a reason to avoid China is lazy analysis.

What matters to the broader equity indices is that the drag is effectively capped, and in the areas that matter, the market is clearing.

First, Tier-1 resale has officially turned positive. In April, Shanghai, Beijing, Shenzhen, and Guangzhou all printed positive month-over-month price action [9]. Shanghai just recorded its highest secondary-home transaction volume for the month of April in a decade, surpassing the 2019 pre-crisis peak [10]. The recovery is highly specific to the AI-windfall cohort cities, and it is widening.

Second, and more importantly, the supply side has been obliterated. New construction starts are down more than 70% from their peak [9]. Developers are solely focused on completing existing projects.

This mirrors the exact mechanics of the Spanish and Irish real estate markets in 2014 and 2015. When you completely halt new supply, the residual inventory gets absorbed, prices flatten, and the sector stops being a black hole for GDP. The math points to a transition in 2027 where property actually shifts from a drag to a contributor. Until then, it is simply no longer the weight that sinks the ship.

The reality check: what could break this trade

I take the failure points seriously. Here is what can go wrong:

The AI capex air pocket. This is the most legitimate threat. The defense against a tech bust is the $1.9 trillion in aggregate Remaining Performance Obligations (RPO) logged by the Big Four hyperscalers. But that backlog is dangerously concentrated (Oracle’s $455B total RPO is roughly $300B in OpenAI commitments) [11]. Historically, suppliers re-rate the moment a major customer guides capex down, not when a bust is confirmed. If a US hyperscaler trims its 2027 capex guide by 10-15%, the Asian supplier base will compress 20-30% within a week. The Chinese internet leg is partially insulated by domestic demand, but it would not escape the gravity.

Geopolitics. A 41% multiple discount already prices in a heavy geopolitical premium. However, a Trump escalation (a blanket Section 301 tariff above 60% and an expanded Entity List) is the modal bear path. It would be worth a 10% to 15% net drawdown.

The policy regime. Central Huijin (the state fund) operates as a PBoC-backstopped floor, but it doesn’t buy for upside [12]. The implicit bet of this trade is that Beijing maintains its current pro-AI posture. If they revert to 2021-style platform crackdowns, the trade requires immediate liquidation.

The 90-day watchlist. I am actively monitoring the mid-June NBS release for any Tier-1 city resale flipping back to negative, alongside May PPI data and Alibaba cloud growth (a dip below 30% breaks the momentum thesis). I am also watching for any formal Beijing action constraining AI commercialization.

The playbook and expected returns

This is a timing-arbitrage trade, sized to capture the closure of the multiple gap rather than relying on heroic EPS revisions.

The Base case assumes a partial gap closure to roughly 15x forward earnings on MSCI China against a stable US multiple. The Bull case adds a re-rating to 17-18x plus upward EPS revisions on AI-cloud acceleration. The Bear case accounts for the AI capex air pocket intersecting with a regulatory shock.

JD.com is the multiple-recovery candidate at the defensive end of the KWEB basket. It trades at roughly 9 to 10 times consensus 2026 earnings against a balance sheet closer to a fixed-income instrument than to a Chinese e-commerce platform. The market is pricing it as if the franchise has permanently lost share to PDD and Meituan, which is not what the operating numbers say. JD bounds the downside of the basket if the broader AI rerating thesis takes longer than 12 months to play out.

Alibaba is the multiple-recovery candidate at the offensive end of the KWEB basket. The 57% cloud and AI EBITDA growth that anchored Pillar 1 is being priced at roughly 12 times forward earnings, against US peers trading at 25 to 30 times for materially less acceleration. The market still has Alibaba in the 2022-platform-crackdown frame even as the cloud segment compounds. If pricing power on compute holds, this is the name in the basket where EBITDA growth flows into the multiple first.

Bottom line

The financial media is treating Chinese equities as uninvestable value traps. But true value traps are defined by deteriorating fundamentals, shrinking margins, and capital flight. They do not exhibit 57% EBITDA growth in frontier technologies, a rapidly steepening domestic demand curve, and millions of retail investors rotating out of cash and into equities.

The dislocation between the dominant market narrative and the actual cash flows being generated on the ground is the widest it has been in ten years. You do not need a geopolitical miracle or a massive government bailout for this trade to work. You simply need the market to realize it is aggressively pricing in an apocalypse that is no longer happening.

References

[1] MSCI China and S&P 500 consensus forward P/E and 2026 EPS growth: MSCI factsheets (30 April 2026); S&P 500 consensus via FactSet aggregate.

[2] Anthropic enterprise pricing communications, spring 2026; OpenAI, Google Cloud, and AWS public API price lists through Q1-Q2 2026; NVIDIA earnings commentary on GPU pricing and inference wait-lists.

[3] Stanford Institute for Human-Centered AI (HAI), 2026 AI Index Report — frontier-model benchmark performance gap.

[4] OpenRouter weekly token-consumption analytics, late March / early April 2026; reporting via Bloomberg secondary coverage.

[5] DeepSeek V3.2 and GPT-5.2 published inference pricing per million tokens; benchmark comparisons from Inference.net independent compilation; Anthropic Claude Sonnet 4.5 published rate card.

[6] Alibaba Group Holding Limited, FY26 Q4 earnings release, supplemental financial information, and earnings call transcript (CEO Eddie Wu management commentary), 13 May 2026.

[7] People’s Bank of China, Quarterly Financial Statistics Q1 2026, released 13 April 2026.

[8] China Securities Depository and Clearing Corporation (CSDC), Monthly Investor Account Data, January 2026 release.

[9] National Bureau of Statistics of China releases, April 2026 cycle: 70-City Sales Prices of Residential Buildings (18 May), Producer Price Index (10 May), and new-construction-starts series.

[10] China Real Estate Information Corporation (CRIC), April 2026 monthly secondary-home transaction data.

[11] Hyperscaler RPO disclosures, Q1 2026: Microsoft, Alphabet (Q1 2026 Form 10-Q cloud RPO), Amazon, Oracle (FY26 Q1, $455bn total RPO). Aggregate roughly $1.9 trillion across the Big Four.

[12] Bloomberg reporting on Central Huijin Q1 2026 position adjustment and PBoC backstop commitment, April 2026; PBoC press communications on the Central Huijin lending facility.

Key Takeaways

• AI generates four observable mechanisms across the cognitive economy: recursive output proliferation, constant experimentation as time-sink, verification burden, and attention fragmentation. Each carries its own bimodal logic and its own compressed middle.

• The audit profession ran one of these mechanisms first. Between 2002 and 2026, audit work commodified entirely; the signature on Page 1 kept 100% of the pricing power. The cognitive economy is now running all four at once.

• The slice of the workforce this hits is roughly 13% of the US labour force, the proceduralised cognitive roles Issue 06 named [11]. The other 77% (food service, retail, construction, healthcare support, transport) is not running this trade.

• The unemployment question is a sequencing question, not a directional one. AI eliminates roles at machine speed; new economic categories crystallise at institutional speed. The gap is where the shock lives.

• The equilibrium across each mechanism is bimodal: a small top tier of cumulative-judgment-holders retains pricing power, a large bottom tier becomes interchangeable, the middle goes away.

Here below is the podcast version of this article produced with NotebookLM:

What an auditor sells, in 2002 and in 2026

In 2001, an auditor sold thoroughness. The audit itself was the product, the partner’s signature at the end was a procedural formality, and selling thoroughness is what you do when you have nothing else to sell.

Enron collapsed in late 2001. Arthur Andersen, the audit firm that had blessed its accounts, collapsed with it. A market eighty-nine years in the building evaporated in nine months because the signature had not done what the signature was supposed to do.

Sarbanes-Oxley followed in 2002. Buried inside the thousand pages of other reforms was the change that mattered: the audit partner whose name appeared on the opinion was now personally responsible for it. The signature became the unit of liability; the workpapers, which for ninety years had been the product the audit firm sold, stopped being the product. Signed accountability did.

Twenty-four years later, the audit field work is largely automated. AI handles anomaly detection, transaction sampling, reconciliation, control testing, journal-entry analysis. The audit teams of 2026 are smaller, more senior, supervisory. What they sell is the same thing they sold in 2003: the partner’s name on Page 1. The room got smaller. The signatures got more expensive.

If you have ever signed an audit letter, or sat across a table from someone who has, this is the transition you have already lived through. The audit profession ran the cleanest of four mechanisms first, because verification was the dominant binding constraint and Sarbanes-Oxley named it in legislation rather than letting the market discover it slowly. The cognitive economy is not so lucky. It is running all four at once, with no Sarbanes-Oxley to clarify any of them.

The slice of the cognitive economy this transition will run through is roughly 13% of the US workforce but that 13% is going to create havoc and the market is still in denial.

As Ken Griffin of Citadel appears to have just noticed, finance will be hit hard. Financial and macro research, probably does not even need the audited signature approval on the content created. Its forecasting ability has always been poor not from lack of work or effort, but simply because forecasting complex adaptive systems is more art than science. However plentiful the hallucinations of the AI model, they will likely be no less error prone than the approximations and assumptions of the analysts. As he said, “there is no theoretical position to retreat to”, especially in finance.

The data is starting to show up where the framework predicts it should. The unemployment rate for workers under thirty-five holding a Master's degree has reached its highest level in nearly twenty years. Business-school applications are dropping fast enough that specialised degrees are now openly discounted; the higher-education credential factory boom that defined the 1990-to-2020 period is ending in real time.

The four mechanisms

A previous piece [10] named the four mechanisms AI produces in cognitive labour markets. Each runs in parallel; each creates its own bimodal compression; each carries its own scarce role; each is at a different stage of legibility.

1. Recursive output proliferation. AI outputs become inputs become outputs. The half-life of any specific knowledge advantage collapses, because what one analyst learns this quarter is in everybody’s training set the next.

2. Constant experimentation as time-sink. Every team running parallel trials on tools, workflows, agents, and models. Time spent evaluating what to use displaces time spent on the work itself.

3. Verification burden. When an AI can produce anything, verifying becomes the bottleneck. Citations may be hallucinated, code may compile and still be wrong, drafts may pass spell-check and still be incoherent on a re-read.

4. Attention fragmentation. The bottleneck is no longer capability but choice: what to attend to, in a stream that has lost most of the curatorial gates that used to do that work for us.

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Recursive output proliferation

The premium on having access to a piece of analysis falls because the cost of producing equivalent analysis falls. Each output becomes training data for the next model release, and each release lowers the floor under what a generic AI summary can produce on the same topic. Volume rises; average per-unit value falls, perhaps a long way.

Sell-side equity research is the cleanest live case. The morning notes that used to anchor a buy-side institution’s intraday positioning are now, within a quarter, ingested, paraphrased, and surfaced as AI-summarised feeds to subscribers who never paid the issuing firm [15]. The note still exists; the informational asymmetry it used to carry does not. The half-life of an analyst insight has compressed from quarters to days.

Academic working papers run the same loop. A preprint posted on arXiv on Tuesday is summarised, cross-referenced, and embedded in literature-review queries by Thursday. Authorship is preserved; what has changed is the duration over which the paper functions as the reader’s first encounter with its argument.

Inside the cognitive workplace the loop runs faster. An internal research memo, once written, is fed into the firm’s AI tooling within a week and surfaced as context to every junior analyst’s queries thereafter. The analyst who wrote it has handed her firm a permanent productivity asset and lost the ability to be paid for being the only person who could have written it. Two quarters later, she is competing with a tool trained on her own outputs.

What remains scarce is what the training set cannot reproduce. A radiologist with a million scans has calibration the model cannot have until it is trained on a comparable distribution, and the distribution itself is the constraint. A senior credit analyst with thirty years of cycle memory holds judgement compressed from outcomes the model has no access to. A primary-source researcher with cultivated relationships at central banks, regulators, or operating-company management produces information the model has never seen because it was never written down [1][3]. The conference-room conversation that never makes it to email, the off-record deal-room exchange, the boardroom briefing that exists only in the partner’s memory, the dinner with a CFO whose remarks are not minuted: these are the channels that stay scarce by structurally avoiding the recursive loop. The professional whose value sits in those channels remains paid for being trusted; the professional whose value is fully digitised has handed it to the next model release.

The bimodal: signal-extractors and primary-source-holders keep their pricing power; output-producers compress; the middle, the analyst whose work the next model release will absorb at no cost to the customer, goes away.

Constant experimentation as time-sink

There is a tax on attention, the AI experimentation tax, and it takes the form of keeping up with platform churn. The tax compounds quietly because the comparison to the prior tooling regime fades from memory. People remember being busy; they do not always remember whether the busyness was productive.

An engineering organisation ran on a stable IDE in 2022. In 2023, GitHub Copilot was the new shared assumption. In 2024, Cursor displaced Copilot for many teams. In 2025, Claude Code displaced Cursor for several of those same teams. Each marginal model release shifts the relative ranking enough to justify a re-evaluation. Each evaluation costs a team a week of attention; each team runs roughly four per year. The aggregate looks like a tax of one month per engineer per year, paid in deferred work, before any productivity benefit from the chosen tool is counted.

The same dynamic runs one layer up. Agentic frameworks, MCP servers, orchestration layers, retrieval-augmented stacks: each is now under live evaluation at most cognitively intensive firms, and the cost of the evaluation is the senior engineer-week that is not spent on the work itself. The output of these meetings is rarely a new tool; it is usually the firm’s collective decision to keep using roughly the same tool, slightly upgraded. The tax was paid for an answer the firm already had.

What remains scarce is the meta-decision-maker. The senior practitioner who can credibly say “we are not re-running this comparison; the gain is below the cost of evaluating it” removes the tax from the teams underneath. Standard-setters who pick a stack and force the firm to live with it for two years rather than two months recover the attention the experimentation tax was draining. These are not popular roles. They are paid because the alternative is the entire firm in a permanent state of evaluation.

The bimodal: meta-decision-makers and standard-setters win. The busy-but-not-deciders, running the comparison for the fifth time, lose the attention that used to do the work itself. The middle, the team-lead who keeps comparing because no one more senior will stop the comparison, is the one whose week vanishes into the tax.

Verification burden

When an AI can produce anything, verifying becomes the bottleneck. Citations may be hallucinated, code may compile and still be wrong, drafts may pass spell-check and still be incoherent on a re-read. This is the mechanism the audit profession lived through first, and the one Issue 01 named as the Trust Economy quadrant of the Verification-Substitution Matrix [7]. It is now running across the rest of the proceduralised cognitive economy with no Sarbanes-Oxley to organise it.

If you have ever spent a week waiting on a single senior signature while the underlying work was done in an hour, you have already met this bottleneck.

• Legal. Bottleneck: a senior partner has to verify the AI-drafted brief before it can be filed. Lawyers in Texas, New York, and California have been sanctioned for filing briefs with AI-fabricated citations [4]. A senior partner described the new workflow as “a whole second associate-level workforce we did not have to hire two years ago, doing nothing but verifying what the first one produced.” A second associate-level workforce, except non-billable, and somehow this is filed under productivity gains.

• Medical. Bottleneck: the AI cannot be the named clinician of record on the diagnosis. Diagnostic AI has reached or passed expert-level accuracy on benchmark datasets in radiology, dermatology, and pathology. The radiologist who signs off is still the named accountable clinician; the signature is the unit the legal and insurance systems can act on. AI produces more candidate findings per patient that must be ruled in or out, and the rate at which a radiologist can rule findings in or out has not changed. The AI is faster, the radiologist is the same speed, and the patient queue gets longer.

• Software. Bottleneck: a senior engineer has to credibly approve the pull request. Code volume is up; the engineer count is down; senior time in code review is up sharply. Engineering leaders describe the new bottleneck as “the rate at which a senior engineer can credibly approve a pull request,” and that rate is the same as it was in 2022. The career path that produced senior engineers now runs through the tier the AI is eating, which is the sort of thing the strategy memo never quite gets to.

The 2025 macro numbers fit. US GDP grew 2.7% with 181,000 net jobs added, the worst non-recession year for net employment since 2003 [5].

The bimodal: senior verifiers retain pricing power; junior producers compress, because the bottom rung of the cumulative-judgment ladder is now performed by the model.

Attention fragmentation

Email becomes agent-to-agent. Search becomes AI-mediated. Content becomes infinite. The bottleneck is no longer capability but choice: what to attend to, in a stream that has lost most of the curatorial gates that used to do that work for us.

Google’s AI Overviews now displace a meaningful fraction of the organic-search referral that used to send users to underlying publishers [14]. The user gets an answer; the publisher gets nothing. The previous bargain (the publisher writes, search indexes, the user clicks through, the publisher monetises the click) has been quietly rewritten so that the click is no longer needed. The gate (the search-results page with its ten organic links) has been replaced by a single curated answer in which no publisher’s URL needs to appear. This is the channel-controller dynamic Issue 04 named [9], one layer further down the stack.

Inbox email is running the same compression. Agent-to-agent message flow (the AI assistant that triages, summarises, and replies on behalf of its user) reduces the inbox from a stream of human signals to a stream of pre-processed summaries. The pre-processing removes senders the user no longer needs to hear from, which sounds like an upgrade; it also removes the curation-by-attention that used to make the inbox a signal of who was paying attention to whom. The signal degrades; the time saved feels real; the relationships do not survive.

Long-form content is the most obviously infinite. Every newsletter, podcast, and video channel the user might subscribe to now produces at the rate the user used to consume across all of them combined. The cost-of-production constraint has fallen; consumption has not expanded to meet the new floor. What used to be a curation problem is now a triage problem.

The scarce role is the tasteful curator operating at scale. The Substack writer whose subscribers trust their selection enough to read what is recommended and skip what is not, the podcast host whose guest selection is itself the product, the editor whose name on a piece of content is the reason it was opened: these roles function as the new choice-architecture. The user delegates attention to them because the alternative is to drown in the unfiltered stream [2].

The dynamic mirrors music after the phonograph: a small star tier whose names were the asset, a great middle buried. Network effects in content distribution concentrate attention on a handful of writers, podcasters, and curators whose distribution is near-universal; the majority is statistically invisible not because the work is worse but because the volume of competing output makes any individual title disappear against the average.

The bimodal: tasteful curators with cumulative reader trust retain pricing power; generic distributors lose. The middle, the title whose curation is now indistinguishable from any other generic gate, goes away. The Google homepage of 2012 is the cognitive economy’s church-choir-and-dance-hall of 2026: the middle distribution layer, eaten by the platforms above and the algorithmic curation below.

Pieces like this one are fighting the same fight, against the same deluge. The fact that the reader has made it this far means the curatorial gate worked at least once today, which writer and reader (?) can perhaps both notice with some relief.

The gap between elimination and emergence

The unemployment question is a sequencing question, not a directional one. AI eliminates roles at machine speed across all four mechanisms. New economic categories crystallise at institutional speed, which is slow, and in forms nobody currently knows how to describe.

The audit profession is the reminder that the gap can be long. The transition from Sarbanes-Oxley in 2002 to the senior-and-supervisory 2026 audit firm took twenty-four years, with workforce contraction front-loaded and the new equilibrium back-loaded. Junior auditors absorbed the loss; senior partners absorbed the rebalanced rent; the audit-manager tier in between, whose function had been to verify juniors who were no longer being hired, was the slowest-resolving casualty.

The four mechanisms now run in parallel without the legislative cue Sarbanes-Oxley supplied. There is no act of Congress arriving to clarify that the AI summary is not the named author of record; no statute defining the verification ratio at which an AI-generated brief becomes a sanctionable filing; no licensing body declaring the meta-decision-maker the new partner-track role. Institutions form around the gap slowly, and the gap is the shock.

I do not try to predict the length of the gap. I am naming the mechanism by which the unemployment outcome is determined: elimination runs faster than emergence; the unemployed are the working-age population in the gap; the gap is set by the slower of the four institutional clocks. Whichever mechanism resolves slowest sets the duration of the dislocation.

Bottom Line

Two forces, opposite directions. Capital pushes work toward commodification, the way the phonograph pushed singing: the singing stayed, the leverage dissolved, the great middle of working singers in church choirs and dance halls mostly went away [8]. Accountability pushes the surviving slice toward irreducibility, the way Sarbanes-Oxley pushed the audit partner’s signature toward irreducibility. AI accelerates the first and sharpens the asymmetry; the mechanism is the same in each of the four domains, the costume different.

The equilibrium is bimodal. A small top tier (cumulative-judgment-holders, primary-source-holders, meta-decision-makers, verifiers, tasteful curators) keeps its pricing power. A large bottom tier (the analyst the next model release absorbs, the team-lead running the fifth comparison, the junior producer whose verification ladder has been kicked away, the generic distributor) becomes interchangeable. The middle hollows out across all four. What would falsify this is a stable verification ratio across firms over time, or a regulatory shift commodifying accountability the way limited liability commodified ownership in the nineteenth century. Neither has happened yet.

The luxury sector of cognitive labour compresses into a single tier of Hermès at the top and a long tail of interchangeable at the bottom [12]. The middle goes away.

If you are reading this from inside the affected 13%, the question I keep returning to is which fields defend themselves and which do not. No checklist gets this fully right (too many variables, too long a horizon), but three tests do most of the work for me, and I think they are the right starting point:

1. Does the field reward judgment that takes decades and cannot be compressed?

2. Is the junior-to-senior ladder still being funded? No juniors means no future seniors.

3. Does credentialing intensity or regulatory accountability prevent the AI shortcut at the entry tier?

The defensible fields by all three: medicine and surgery, senior engineering disciplines where firms still invest in junior training, specialty research, track-record-based investment management, skilled trades outside the AI-cognitive frontier. The cautious fields are the ones the shock is hitting hardest at the junior end: financial compliance, legal junior-associate work, junior software engineering, content production, white-collar BPO. The ladder breaks at the bottom rung; the field shrinks even where the senior tier remains scarce.

Paul Kedrosky is right that “unprecedented in human history” is a small-sample argument; the compression dynamic does not need precedent to operate. This is the elite-overproduction half of Peter Turchin’s framework [13]: a credentialed professional class larger than the elite positions available to absorb it, alongside popular immiseration. A cognitive class compressed into a star tier and an interchangeable tier across four mechanisms at once is what elite overproduction looks like in real time. Turchin’s evidence base does not predict revolution; it predicts volatility, delegitimisation, and a search for scapegoats. A generation of credentialed professionals is walking into that configuration without the vocabulary to recognise it. Some of them are reading this. I am one of them, having run the three filters above on my own field and chosen not to elaborate on which is missing.

The signature is what is being bought. The draft is now cheap. The audit profession ran one of the four mechanisms first, between 2002 and 2026; three more are running through the cognitive economy in parallel, in different costume, without the legislative cue that organised the first. When verification and cumulative judgement are the scarce inputs across all four, a new economic class forms around their certification, and a market emerges priced around it. Each of these four mechanisms warrants its own deep treatment, and future notes will take them on individually, starting with recursive output proliferation. I named the four mechanisms I observe today; further mechanisms may surface as the AI impact on the economy continues to take shape, and my framework will be extended as they do.

References

[1] Sangeet Paul Choudary, Reshuffle, platformthinkinglabs.com, 2024-2026. The coordination-layer thesis.

[2] Alex Imas, “What Will Be Scarce?”, Ghosts of Electricity, 2026. The relational-sector reframe of post-commodity scarcity.

[3] Arvind Narayanan and Sayash Kapoor, AI as Normal Technology, Knight First Amendment Institute at Columbia, 15 April 2025. The three speed limits of invention, application, and diffusion.

[4] Mata v. Avianca, Inc., 22-cv-1461 (S.D.N.Y. 2023). Canonical first sanctions case; subsequent sanctions through 2024-2026 in jurisdictions including Texas, New York, and California are documented in legal trade press and federal court orders.

[5] “Unprecedented ‘Jobless Boom’ Tests Limits of US GDP Expansion”, Bloomberg, 18 February 2026. 2025 GDP growth and the 181,000 jobs added per BLS Employment Situation and BEA NIPA tables.

[6] Anthropic ARR of ~$30bn and headcount ~3,500 per Reuters / Bloomberg, April 2026. Salesforce fiscal-2024 revenue $34.86bn / 72,682 employees per Salesforce 10-K, February 2024.

[7] Stray Narratives, Issue 01: “The Verification-Substitution Matrix”, February 2026. The Trust Economy quadrant.

[8] Stray Narratives, Issue 02: “Adoption Asymmetry”, March 2026. The bargaining-power-shift mechanism: wage suppression arrives first through eroded leverage, not direct displacement. The phonograph case study is set out in the body.

[9] Stray Narratives, Issue 04: “The Channel Controllers”, March 2026. AI capability absorbed by channel controllers as toll collectors and platform operators.

[10] Stray Narratives, Issue 10: “What We Got Wrong About the Internet (and May Get Wrong Again)”, May 2026. The four-mechanism passage on which this piece’s framework is anchored.

[11] Stray Narratives, Issue 06: “The Noise Economy”, April 2026. The 13% / 77% workforce split.

[12] Alex Imas and Kristóf Madarász, Mimetic Dominance and the Economics of Exclusion, Quarterly Journal of Economics, 2024. Willingness-to-pay doubles with exclusion.

[13] Peter Turchin, End Times: Elites, Counter-Elites, and the Path of Political Disintegration, Penguin Press, 2023. The two-precondition framework (popular immiseration plus elite overproduction) is developed across the book; the empirical base across 20+ societies and 5 centuries is set out in the introductory chapters.

[14] Search referrer trend data for AI Overviews displacement of organic traffic to news and reference publishers, 2024-2026, as reported by Similarweb, Press Gazette, and publisher-side disclosures (BBC, Mail Online, News Corp earnings calls).

[15] Salesforce / Bloomberg / consumer AI-search ingestion of third-party sell-side research: industry trade reporting in Institutional Investor and Risk.net, 2025-2026, documenting analyst-research summary surfacing in chatbot products and the resulting compression of research half-life.

If you have no time to read and prefer to sit back, relax, and listen to this article in a podcast format by Notebook LLM (only 5 minutes long!):

The trade

Buy Xunlei (NASDAQ: XNET) for 6 US dollars before the 11 June 2026 lock-up unlocks on its Insta360 stake [1]. You are paying $398m for a Chinese internet operator whose Shanghai-listed equity stake is worth $986m at the 13 May close [2], whose net cash is $230m after netting bank debt [1], and whose operating business is worth another $410m–$745m on current Chinese-internet trough multiples.

That is a 78% discount to net asset value with a hard catalyst date and a published lock-up schedule.

Twenty-two years in the attic

Xunlei was a download manager in 2003, the first to make peer-to-peer file transfer fast enough to matter in China. It IPO’d on Nasdaq in 2014, lost the founder in 2017, pivoted into cloud, then live streaming, then short-form video, then accelerator subscriptions. Each pivot left a residual asset on the balance sheet.

One residual was a strategic equity position in Insta360, taken in the early 2010s when Xunlei was still a venture-portfolio business. It compounded silently while management chased four other things. Insta360 IPO’d on Shanghai’s STAR Market on 11 June 2025. Xunlei held 8.73% pre-offering, 7.84% after dilution, and approximately 7.8% at year-end 2025 per the audited filing [1].

This is what fifteen years of Chinese-internet pivoting looks like. It looks like an attic full of strategic equity stakes nobody is paying attention to. Most of them are worth nothing. One, by accident, is worth more than two and a half times the entire company holding it.

What you are buying, before the camera stake

Skip the camera stake for a moment. Pretend it does not exist.

For $398m you get a Chinese internet operator with $462m of revenue growing 42.5% in 2025 [1], $230m of net cash on the balance sheet, and a real operating business in three pieces.

Subscriptions. Xunlei Accelerator. $155m revenue, +16% growth, mid-twenties operating margins. Closest comps in the current trough are Tencent Music at 3.2× P/S, Bilibili at 2.2× P/S, iQIYI at 0.3× P/S [3]. At 1.5–2.5× revenue today, the subscription cash cow is worth $230m–$390m. Through-cycle (when the comps re-rate to 2020–2022 levels) the same business is worth $385m–$545m. Either way, it alone clears more than half the market cap.

Live streaming and IVAS. $170m revenue, +97.5% growth, including the inorganic Hupu contribution from Q2 2025. Hupu is the leading Chinese sports media platform, acquired May 2025 for RMB 500m, which sets a $69m floor under that piece. Closest comp: JOYY at 1.4× P/S [3]. At 1.0–2.0× revenue today this segment is worth $170m–$340m. Through-cycle, $425m–$680m.

Cloud. Twenty per cent residual in Onething, after Xunlei sold the controlling 50% to Kingsoft Cloud for RMB 125m in March 2026 [1]. Pro-rata value: $10m–$15m. The cleanest piece of capital allocation management has done in years was deciding they were not the right operator. They walked.

Operating business at current trough multiples: $410m–$745m, midpoint $580m. Through-cycle: $820m–$1,240m. We anchor the headline visual to the trough-midpoint because we want a number a sceptical reader cannot dismantle on a Bloomberg comp screen. The honest disclosure is that the trade improves substantially if Chinese-internet multiples re-rate.

The operating business and net cash exceed the market cap before Insta360 contributes a dollar. Even at the trough-low end ($410m of ops biz), net cash plus operating business clears $640m, against a $398m market cap. The camera company is technically free. It is also actually worth most of the value.

The Insta360 stake, the kicker

Insta360 holds 67% of consumer 360-degree cameras and is now joint leader in action cameras alongside DJI [4]. GoPro, the once-dominant incumbent, is the third name and falling. Q2 2025 revenue grew 41% to $190m. Trailing-twelve-month growth: 77%. The stock closed at 214 yuan on 13 May 2026, down 43% from its September peak of 378 yuan, well above the IPO offer price of 47 [5]. The chart is messy but the business is fine. Check out this video if, like me, you are too old to know what Insta360 is.

Xunlei’s stake: approximately 7.8% per the FY2025 20-F, equivalent to 31.28m Arashi Vision shares [1][2]. Audited carrying value at 31 December 2025: $1,051m. Mark at 13 May 2026: $986m.

The lock-up bucket is the standard 12 months under STAR Market rules. The 36-month rule applies only to controlling shareholders (Shenzhen Yiwei at 29.94%); the 24-month rule applies only to the underwriter’s investment arm. Xunlei sits in the 12-month bucket per the prospectus shareholder-commitment table [6]. The lock-up unlocks on 11 June 2026, less than a month away.

After the lock-up, Xunlei can sell. PRC corporate income tax (25%) plus Cayman repatriation withholding (10%) plus capital-control timing means roughly fifty cents of every gross dollar reaches ADR holders in the central case [7]. Insta360 is the kicker on a position the operating business already underwrites. You get the cake and the icing.

The risks honestly

Five concerns. They are also, mostly, the discount decomposition.

The lock-up could be voluntarily extended. It is not, per primary text. Xunlei is in the standard 12-month bucket. Management’s Q3 2025 commentary, “no plans to sell Arashi Vision shares due to regulatory requirements,” is consistent with the active 12-month restriction. Q4 commentary shifted to “determine the pace based on capital market conditions,”which is the right vocabulary for a board preparing to sell.

Insta360’s share price could correct further. It could. The doomsday case stress-tests against an IPO-band retracement to 70 yuan. There the trade loses money. I am not pretending otherwise.

Management never returns the cash. This is the structural risk. Twenty-two years without a dividend. Cumulative buyback through end-2025: $6.5m on a $20m authorisation [8]. At that pace, the authorisation will be exhausted some time in 2031. The market is pricing this management like the founder is still in the chair. He has not been since 2017. Current CEO Jinbo Li, in role since April 2020, launched the first two buyback programmes in company history (2023 and 2024). The pace is glacial. The direction is right. The Sohu/Changyou going-private at an 82% premium in April 2020 [9] is the comparable success precedent for a Chinese-ADR-holdco-to-portfolio monetisation. It is one data point, not a frequency.

Capital controls bite. They do. I model them in each scenario. The bear case (20% reach-through) is already heavy.

HFCAA delisting. Currently dormant. The PCAOB removed mainland China and Hong Kong from its inspection blacklist in December 2022 [10]; Xunlei does not expect to be identified as a Commission-Identified Issuer. The base case does not require this to remain dormant. Reactivation is the high-asymmetry tail.

The discount sits at 78% of net asset value because three reasons unwind mechanically (lock-up inertia, dormant HFCAA cohort drag, small-cap liquidity friction) and two are structurally priced (capital controls, management-distribution discipline). The market may be right that current management will inherit founder-era inertia. It may also be looking at the wrong twenty-two years.

How to express the trade

Stock: Linear, lower-leverage, suitable for a multi-month hold through the monetization window.

Call options: Higher leverage, time-bounded, binary downside. June, July, October, and January 2027 expiries each capture a different stretch of the catalyst window. Out-of-the-money calls magnify the stock returns 3–5×, with proportional risk of going to zero. Calls magnify everything, including being wrong. Reader picks the variance.

Expected return

Today’s $6.33 close is the entry. Hold through the 18–24 month monetisation window after 11 June 2026.

The visual headline of 4.5× is the gap between current price and net asset value. The realised return is the gap times the friction stack: tax leakage (25% PRC CIT + 10% Cayman withholding ≈ 50% reach-through), partial monetisation (30–40% of the stake sold over 18 months in our central case), and the residual unmonetised stake remaining exposed to whatever Insta360 trades at after the window closes. The visual depicts NAV. The IRR depicts what survives the friction stack.

Bull (Insta360 to ~300 yuan, 60% of stake monetised in 18 months, 50% reach-through, ops biz re-rates toward through-cycle): 125–160% per annum. Calls magnify 3–5×.

Base (Insta360 holds ~214 yuan, 35% monetised over 18 months, 50% reach-through, ops biz partially re-rates): 55–75% per annum.

Bear (Insta360 to 130 yuan, 20% monetised, 30% reach-through, no ops re-rating): even the bear case has a positive 15–25% per annum.

Doomsday (Insta360 to 70 yuan plus HFCAA escalates plus management distribution stalls plus operating margin compression): −15% to −30% per annum. This trade can lose money.

The path is staged: the first four weeks carry a discount-narrowing flow as the market reads the catalyst date; the next twelve carry the actual monetization; the eighteen to twenty-four months carry the operating-business compounding alongside.

Re-read points: Arashi Vision below 130 yuan or above 250 yuan changes the math; the first disclosed Insta360 sale by Xunlei confirms management willingness; Q1 and Q2 2026 earnings.

Bottom Line

A Chinese-domiciled internet operator trading at less than a third of audited net asset value. A Shanghai-listed equity asset on the balance sheet worth almost two and a half times the entire market cap. A subscription cash cow alone worth more than half the company is selling for. A four-week catalyst published in a prospectus.

Not a free lunch, there are none, but worth a closer look don’t you think?

Position disclosure

At time of publication, I hold a long position in XNET expressed via stock and call options.

References

[1] Xunlei Limited, Form 20-F for FY ending 31 December 2025, filed with the SEC on 28 April 2026. Consolidated Balance Sheets (cash $157.0m + short-term investments $148.2m + restricted cash $0.8m = $306.0m liquid; bank borrowings current $37.2m + non-current $38.4m = $75.6m). Note 6 (long-term investment in Arashi Vision: $1,050.6m fair value at 31 December 2025; approximately 7.8% equity interest). Note 12 and segment-revenue disclosure for Subscriptions, Cloud Computing, Live Streaming.

[2] Mark-to-market 13 May 2026: 31.28m Arashi Vision shares (= 401m total shares × 7.8% per Stock Analysis / Yahoo Finance public records) × CNY 214 closing price ÷ USD/CNY 6.791 (EODHD) = US$985.5m.

[3] EODHD fundamentals pull, 2026-05-13 close. Tencent Music P/S 3.24×, Bilibili P/S 2.21×, iQIYI P/S 0.30×, JOYY P/S 1.38×, NetEase P/S 4.83×.

[4] Mordor Intelligence, 360-Degree Camera Market; Grand View Research, Action Camera Market. Market-share commentary on Insta360 / DJI / GoPro.

[5] Stock Analysis (stockanalysis.com), Arashi Vision Inc. (SHA: 688775); Xunlei Limited (NASDAQ: XNET). Share price history and trading metrics.

[6] Lexology, In review: governing rules for IPOs in China. Hankun Law and KPMG STAR Market lock-up framework summaries; Arashi Vision STAR Market listing announcement (上市公告书), 11 June 2025; 36氪, “影石创新科创板上市”, 11 June 2025.

[7] PwC, China Tax Summaries: Corporate Income Tax, capital-gains treatment of listed-equity sales by PRC corporate sellers; Cayman / Hong Kong dividend-withholding treatment.

[8] Investing.com, Xunlei sets new $20 million share repurchase program, 4 June 2024; subsequent quarterly buyback disclosures in 20-F.

[9] Sohu.com Limited, Sohu.com Announces Completion of Changyou Going-Private Transaction, PR Newswire, 17 April 2020. Comparable Chinese-ADR-holdco-to-portfolio monetisation precedent.

[10] U.S. Public Company Accounting Oversight Board, Statement on PCAOB inspection access in mainland China and Hong Kong, 15 December 2022.

Research, not investment advice. Stray Narratives is the publishing arm of an investment-research operation; positions are disclosed alongside the analysis where they exist. Past performance does not guarantee future returns. Full disclaimer at straynarratives.com/p/disclaimer.

• The historical record on technology shocks shows a recurring pattern: the first-order impact people argue about turns out to be smaller than the second-order impact almost nobody saw coming.

• Three precedents (the talkies of 1930, the IT consensus of 1973, and the internet of 1995) all show the pattern, and the AI debate of 2026 already has the look of a fourth iteration.

• Issue 06 The Noise Economy introduced friction elimination as one form of unforeseen second-order effect; this issue argues that noise itself, the recursive proliferation of AI-generated outputs, is plausibly another, and there will be more.

• The contemporary anchor is the 2025 divergence: US GDP grew 2.2% with only 181,000 jobs added all year, the worst figure for any non-recession year of expansion since 2003, and Anthropic in 2026 is producing nearly the same revenue as Salesforce did in 2024 with around five per cent of the headcount.

• The political response is already visible in New York’s vote for Mamdani, where voters experienced the divergence directly: GDP rising, markets soaring, material lives stuck.

Here below is a podcast version of this article produced with Notebook LLM, hope you enjoy it!

Twenty-four thousand musicians

In 1927, The Jazz Singer arrived in cinemas and the live-music industry began to evaporate. Roughly twenty-four thousand musicians worked in theatres across the United States and Canada at the time, accompanying silent films night after night with whatever combination of piano, violin, drums, and brass the local theatre could afford. By 1930, the American Federation of Musicians estimated that around twenty-two thousand of those jobs had vanished [1]. Almost the entire trade, in three years. In some markets the destruction was even faster: New York and Cincinnati both lost between half and three-quarters of their working musicians inside the first two years of sound.

They did not go quietly. In 1930 the American Federation of Musicians spent the equivalent of ten million dollars (in today’s money) on a national campaign begging audiences to refuse “canned music” and to keep going to theatres that still employed live performers. Joseph N. Weber, the union’s president, predicted that the public would never accept “like-less, soulless, synthetic music.” Edward More, music critic of the Chicago Herald Tribune, agreed: the films, he said, “have a long way to go before they can duplicate living musicians.” The whole defence rested on a single load-bearing assumption, which is that the new technology would have to match the old one to displace it. The films had to play violin.

They never did. The films did not have to. As Dror Poleg put it in his essay on this episode, disruptive technology rarely seeks to replicate. It sidesteps and makes the old standards redundant [2]. Recorded sound made music cheap and accessible to a vastly larger audience, and most of that larger audience turned out to be perfectly content with what Weber had dismissed as soulless. Weber was, in a narrow sense, proved right: the public never did warm to soulless synthetic music. They simply turned out to be indifferent to the difference, which proved entirely sufficient. The threat to the trade was never a robot violinist. It was a different technology entirely, one that bypassed the contest the musicians thought they were in.

The same script ran a generation later in print encyclopedias. Britannica had been the authoritative reference work for two hundred and forty-four years when, in 1993, Microsoft launched Encarta, a digital encyclopedia replicating Britannica with multimedia and a CD-ROM price point. The threat looked clear, and the threat was the wrong one. Eight years on, when the actual challenger arrived, it was Wikipedia: free, crowd-sourced, infinitely updatable, and not trying to be Britannica at all. Encarta died in 2009. Britannica stopped its print edition in 2012. The threat to two and a half centuries of authoritative reference publishing came from a different category of object entirely, one that had no interest in being authoritative in the old sense.

This is the pattern of unforeseen second-order effects, and the reason the talkie episode and the encyclopedia episode keep repeating themselves in the historical record is that they are a feature of how technological transitions actually work, not a one-off. A useful shorthand for what’s happening: call X the predicted impact of a new technology, the thing the consensus debate of the time fixates on; call Y the actual dominant impact, the thing that turns out to matter most in retrospect. For the talkies of 1930, X was “machines that play music well enough to replace the live ones” (which never had to come), and Y was “recorded sound, sidestepping live performance entirely.” X and Y rarely coincide. Y is usually bigger. And Y is almost always something nobody at the time of X was writing about. The argument that follows is that we are now in the next iteration of the same pattern, with AI in 2026 standing in for the talkies in 1927, and the internet in 1995 standing in as the most recent precedent of how badly the X-versus-Y misalignment can go.

The 1973 mirror

Forty-three years later, the consensus was the symmetric opposite, and it was wrong in the same way.

Hobijn and Jovanovic, in their 2000 paper on the IT revolution and the stock market, record the 1973 expectation as a stylised assumption in their model: information technology, the consensus held, would substitute for white-collar cognitive labour the way mechanisation had substituted for blue-collar physical labour [3]. The subsequent record inverted it. IT complemented cognitive workers rather than displacing them; the cognitive-skill premium widened through the 1980s and 1990s; the wage gini diverged. The bigger story, the one nobody was writing about in 1973, turned out to be the personal computer, the web, social media, the attention economy, the wholesale reorganisation of how information moves through a society. The first-order story (white-collar substitution) was the consensus debate. The second-order story (the reshaping of attention itself) was the one that mattered, and it was almost entirely unforeseen.

This is the pattern of unforeseen second-order effects, and the reason the talkie episode and the encyclopedia episode keep repeating themselves in the historical record is that they are a feature of how technological transitions actually work, not a one-off. A useful shorthand for what’s happening: call X the predicted impact of a new technology, the thing the consensus debate of the time fixates on; call Y the actual dominant impact, the thing that turns out to matter most in retrospect. For the talkies of 1930, X was “machines that play music well enough to replace the live ones” (which never had to come), and Y was “recorded sound, sidestepping live performance entirely.” X and Y rarely coincide. Y is usually bigger. And Y is almost always something nobody at the time of X was writing about. The argument that follows is that we are now in the next iteration of the same pattern, with AI in 2026 standing in for the talkies in 1927, and the internet in 1995 standing in as the most recent precedent of how badly the X-versus-Y misalignment can go.

A note on what Hobijn and Jovanovic actually do here. Their paper records the 1973 expectation as an empirical fact about what the consensus of the time held. The point of citing them is the observation that the consensus of the time was directionally wrong, and that being directionally wrong about a technology shock has been the historical baseline.

The scope, and what Issue 06 already mapped

A scope limit before going further, because the temptation to over-extrapolate from one’s own corner of the economy is almost the defining vice of writing about AI from inside the knowledge professions.

Issue 06 The Noise Economy established the relevant numbers [9]. Around thirteen per cent of the US workforce sits in roles with high friction-elimination exposure, the proceduralised cognitive layer that the AI debate fixates on. Seventy-seven per cent sits outside that frame entirely: food service, retail, manufacturing, transport, healthcare support, government, education. The mechanisms I’ll develop below are about the knowledge economy. Many sectors will be little affected by what feels, from inside the affected slice, like a massive threatening revolution. I’m inside that slice. You probably are too. That fact, on its own, ought to make both of us a degree more cautious about the breadth of any claim that follows.

Issue 06 also did half the analytical work this issue extends. Friction elimination was the argument that a whole layer of professional activity exists not because it creates value at the point of transaction but because it navigates an opacity AI is now liquidating. Take the opacity away and the activity disappears, with no displaced worker because there was nothing left to displace. Noise, the candidate this issue offers, is a different mechanism in the same family: friction elimination removes activity; noise multiplies it. Both are shapes of unforeseen second-order effect, both bypass the displacement debate, and both belong to the same affected slice. The working assumption is that there are more shapes still ahead.

The unforeseen Y, applied to AI

The first-order story for AI is the displacement-at-speed-X debate that fills the column inches. Will agents replace lawyers in three years or fifteen? How many call-centre seats will be gone by 2027? The debate is real, the numbers matter, and Issues 01 through 06 of this series have been engaged in it. The question this piece sets down is whether, as in 1930 and 1973, the dominant effect turns out to be something quite different and quite a lot bigger.

The candidate this issue offers is noise, and the closest precedent for what noise actually does to a working life is something every reader has lived with for thirty years.

Email is the precedent. Email was sold as a productivity tool, and it was a productivity tool, for a few months. Then the volume became unconstrained. Anyone could send anyone a message at zero marginal cost, and the cost of that asymmetry has accumulated into a working day in which knowledge workers spend a third or more of their hours triaging messages they did not ask for. Email did not replicate paper memos; it made the entire structure of paper-memo norms obsolete. The unforeseen Y of email, in 1995, was not the productivity gain we expected. It was the productivity tax we now live with. AI noise is the next iteration of the same dynamic, run at a generation’s worth of speed.

A few mechanisms underneath that label, observable now:

• Recursive output proliferation. AI outputs become inputs become outputs. The half-life of any specific knowledge advantage collapses, because what one analyst learns this quarter is in everybody’s training set the next. The premium on having access to a piece of analysis falls because the cost of producing equivalent analysis falls. There is a great deal more of everything, and a great deal less to distinguish any one piece from any other. The shape of the resulting market is one in which volume rises and average per-unit value falls, perhaps a long way.

• Constant experimentation as time-sink. Every team running parallel trials on tools, workflows, agents, and models. Time spent evaluating what to use displaces time spent on the work itself. There is a tax on attention that takes the form of having to keep up with the platform churn, and the tax compounds quietly because the comparison to the prior tooling regime fades from memory. People remember being busy; they do not always remember whether the busyness was productive.

• Verification burden. When an AI can produce anything, verifying becomes the bottleneck. Citations may be hallucinated, code may compile and still be wrong, drafts may pass spell-check and still be incoherent on a re-read. Concrete cases are now a regular feature of professional practice. Lawyers in Texas, New York, and California have been sanctioned in the past eighteen months for filing briefs containing AI-fabricated citations, including, in one case, a complete fictitious appellate decision the firm had not noticed before submission. Federal judges now routinely spend several paragraphs of their sanction orders explaining, for the benefit of the bar, that the cited authorities do not exist, a sentence that until 2023 would have been a parody of itself. Software teams report a steady stream of AI-generated code that compiles and ships and is later discovered to have invented an API call that does not exist or a function whose stated behaviour does not match its actual logic. Hiring managers describe an arms race of AI-generated CVs and cover letters tuned to defeat applicant-tracking systems, where the marginal cost of producing an apparently-qualified candidate has fallen close to zero. In each case, signal-discrimination becomes the scarce capability, and it is exactly the capability that was previously externalised onto the production process: the editor, the legal reviewer, the senior analyst whose job was to know which numbers to trust. Strip out the production friction and the verification cost shifts up the chain, where it is harder and more expensive to apply at scale.

• Attention fragmentation. Email becomes agent-to-agent. Search becomes AI-mediated. Content becomes infinite. The bottleneck is no longer capability but choice: what to attend to, in a stream that has lost most of the curatorial gates that used to do that work for us.

The recent precedent for what this dynamic does at cultural scale is social media, and the version of that precedent worth getting right is one the consensus often blurs. Social media as a mass cultural phenomenon was not, on its own, an internet story. The internet was the substrate. The carrier turned out to be the iPhone, twelve years later: a device nobody in 1995 had thought about, which made always-on access universal and turned social media from a niche curiosity into the dominant capture mechanism for human attention. The unforeseen Y, in retrospect, was the smartphone, not the internet. AI’s equivalent carrier is not yet named.

What the four mechanisms above describe is what current AI noise looks like in working life. They are diagnostic, not prophetic. The harder claim, and the one this piece commits to, is that the cultural and economic centre of gravity of the AI era will form somewhere none of the four mechanisms describes.

The pattern of which technologies actually scale into mass cultural phenomena is consistent enough to be falsifiable. A technology becomes culturally dominant when it satisfies three conditions simultaneously: an engagement loop that captures attention, network effects that compound value as adoption rises, and a clear path to monetisation. The internet on its own satisfied two of the three. The internet on the iPhone satisfied all three, and the cultural revolution that followed was the result.

AI in its current form, foundation models accessed through chat interfaces, satisfies one or two of the three. Foundation-model companies have (still to be proven at scale) monetization , weak network effects, and largely utilitarian rather than addictive use. Anthropic and OpenAI are fundamentally building infrastructure businesses, with the engagement loops as a secondary consideration. That may become a profitable category but not the category that becomes the dominant cultural force. Where AI satisfies all three is the application layer that Tristan Harris has begun calling the attachment economy: AI companions, AI relationships, embedded AI tutors, AI confidants [8]. Engagement loops, in their most direct form, the relationship-attachment kind, run a subscription clock that does not stop. Network effects emerge as the AI accumulates context about its user and the user invites the relationship into shared spaces. Monetisation is the subscription itself, plus the data, plus the ecosystem lock-in. All three conditions, in the same product category. Replika is the early version. Character.ai is another. Most of the specific companies in this space will not survive in their current form. The category they sit inside almost certainly will.

The practical test follows from the framework. When you read about a new AI product in 2026, ask three questions: does it create an engagement loop, does it benefit from network effects, does it have a clear monetisation path. If the answer is yes to all three, you are looking at a candidate Y. If the answer is yes to one or two, you are looking at infrastructure. The current investment debate is almost entirely focused on infrastructure. The category that almost nobody is arguing about, because it still sounds small from inside the 2026 vocabulary, is where the cultural and economic centre of the AI era almost certainly forms. The closest a careful reader can get to the actual unforeseen Y is to look at which technology is starting to satisfy all three conditions, and to take that category more seriously than the infrastructure debate currently does.

The 2025 divergence

The unforeseen Y is already partly observable, and the data are starting to fill in.

US GDP grew 2.7% in 2025. Net employment over the same year was 181,000 jobs added, the worst figure for any non-recession year of expansion since 2003 [4]. Bloomberg published a piece in February 2026 under the headline Unprecedented “Jobless Boom” Tests Limits of US GDP Expansion, and the accompanying chart showed real GDP and nonfarm payrolls (two lines that moved in near-lockstep for over three decades) diverging in a way that, in Bloomberg’s words, “has never happened this far into expansion” [4]. What makes the configuration anomalous is that this is not a recession picture. There is no economic contraction holding payrolls down. The expansion is real, and it is happening without the headcount that used to come with it.

The killer specific is the per-employee revenue stat. In fiscal year 2024, Salesforce, by some distance the most successful pure-software company of the last decade, generated nearly thirty-five billion dollars of revenue with around seventy-three thousand employees, which is roughly half a million dollars of revenue per worker, strong by any pre-AI software standard. Two years later, Anthropic is producing roughly thirty billion dollars of revenue at an annualised run rate, with around three and a half thousand employees, which is closer to eight and a half million dollars of revenue per worker. Same software industry, same broad addressable market, two years apart. Salesforce has roughly twenty times the headcount producing nearly the same revenue, and the per-worker output at the AI-native firm is around eighteen times what Salesforce achieved at peak [5]. Put another way: if Salesforce in 2024 had operated at Anthropic’s 2026 per-worker productivity, it would have employed about four thousand people. The remaining sixty-nine thousand jobs were not, by any 2024 measure, redundant. They were the standard cost of running a company. The economy underneath that comparison is one in which output and employment are decoupling at the company level for any firm willing to build around the new tooling.

Beyond the macro charts, the pattern is showing up at named-CEO level. Klarna disclosed in early 2024 that an AI assistant was doing the work of around seven hundred customer-service agents, and the company’s headcount fell from roughly five thousand to three and a half thousand over the following year while revenue rose. Duolingo’s chief executive said in January 2024 that around ten per cent of the company’s contractors had been let go because AI was now doing their work. Goldman Sachs has visibly compressed junior banking-analyst hiring through 2025, with multiple reports of cohort sizes shrinking and some entry-level functions being routed through AI tooling instead [6]. None of these is a layoff event in the conventional sense. They are quiet adjustments to the production function, and they accumulate.

What macroeconomists have been arguing should not happen this far into an expansion is happening. The production function underneath the headline numbers has changed, and the change is showing up in the GDP-versus-payrolls chart, in the per-employee revenue gap, and in a steady drumbeat of named-CEO disclosures that AI is now doing work the company used to staff for. The first-order story (AI substitutes for cognitive labour at speed X) is one frame. The second-order story (the relationship between economic output and human employment is being rewritten in real time) is plausibly bigger, and is happening regardless of whether you believe the first-order story or not.

The political response to the divergence is already visible. New York elected Zohran Mamdani as mayor in November 2025 in part because, as Dror Poleg observed, voters experienced the divergence firsthand: the economy was growing, the stock market was soaring, and yet their material lives were not getting better [7]. San Francisco, in the same season, elected a different sort of mayor and started liberalising zoning. Two cities, two different responses, both confirming that the structural break is real and that voters can feel it before economists are willing to call it.

A short personal note

I’ve spent thirty years in finance, which is one of the sectors most exposed to the noise mechanism I’ve just described, and I read for a living the kind of analysis that AI now produces in volume. The best calibration of any thesis I write on AI’s impact is to remember that I’m squarely inside the affected slice and probably overweighting my own experience by a margin I cannot quite measure.

I have tried to write this article under a new format where it should be easier to capture the subject of the post from the very beginning and also provide a summary at the end. If you like this new format please “like” the post, it means a lot to me.

Bottom Line

In 1995, the case for the internet was that it would let us work and consume more efficiently. It did, eventually, in that narrow sense. The bigger story took twelve more years to arrive, and arrived through a device nobody in 1995 had thought about: the iPhone, which made the always-on internet into a pocket-sized reality and turned social media from a niche curiosity into the dominant capture mechanism for human attention. The unforeseen Y, in retrospect, was not the internet at all. It was the smartphone.

The case for AI today reads almost identically to the 1995 case for the internet. The first-order story is well-understood and being argued about in every research note and every Substack. It is also, probably, not the story that will matter most in retrospect. Whatever the dominant impact of AI turns out to be, it is almost certainly not on anyone’s spreadsheet right now. Watch for what is being built with all this capability that nobody designed for. The next piece in this AI series asks the question that follows: when noise dominates, where does signal go, and who pays for it?

References

[1] American Federation of Musicians, Music Defense League records, 1930. The 22,000 jobs lost figure is the AFM’s own contemporaneous estimate of theatre-musician unemployment by 1930, and is the canonical historical record cited across the labour-history literature on the talkies transition. The campaign and its underlying figures were widely covered at the time, including in Smithsonian Magazine‘s retrospective on the Music Defense League.

[2] Dror Poleg, Who Will Get Canned in 2026?, drorpoleg.com, 30 December 2025. The “disruptive technology rarely seeks to replicate; it sidesteps and makes the old standards redundant” framing in Section 2 is Poleg’s, and the article’s reading of the talkies episode owes its shape to his piece.

[3] Bart Hobijn and Boyan Jovanovic, The Information Technology Revolution and the Stock Market: Evidence, NBER Working Paper No. 7684, May 2000. Assumption 2 of the model records the 1973 consensus that IT would substitute for white-collar cognitive labour the way mechanisation had substituted for blue-collar.

[4] Unprecedented “Jobless Boom” Tests Limits of US GDP Expansion, Bloomberg, 18 February 2026. The chart of real GDP versus nonfarm payrolls and the “never happened this far into expansion” framing are drawn from this piece. The 2025 GDP growth of 2.7% and the 181,000 jobs added (worst non-recession year since 2003) are from US Bureau of Labor Statistics Employment Situation reports and Bureau of Economic Analysis NIPA tables, both publicly available.

[5] Anthropic annualised revenue run-rate of approximately thirty billion dollars as of April 2026, and headcount of approximately three and a half thousand, reported by Reuters and Bloomberg, April 2026. Salesforce fiscal-year 2024 revenue of $34.86 billion and year-end employee count of 72,682, per Salesforce’s audited 10-K filing, February 2024.

[6] Klarna headcount and AI-customer-service disclosure: public statements by CEO Sebastian Siemiatkowski via X and Bloomberg interviews, 2024-2025. Duolingo contractor reduction: public statement by CEO Luis von Ahn, January 2024 earnings communications. Goldman Sachs junior banking-analyst hiring compression: multiple Bloomberg and Financial Times reports through 2025.

[7] Dror Poleg, Welcome to the Jobless Boom, drorpoleg.com, 18 February 2026. The Mamdani-divergence interpretation cited in the 2025 divergence section is from this essay.

[8] Tristan Harris, Center for Humane Technology. The “attachment economy” framing extends the engagement-economy critique of social media to AI specifically, with AI companions and AI-mediated relationships as the central category. From Harris’s recent public appearances and writings.

[9] Stray Narratives, Issue 06: The Noise Economy, 12 April 2026. The friction-elimination argument with the 13% / 77% workforce split is developed in this issue.