Tag: investing

Categories
AI

The Arithmetic of the Sold-Out Warehouse

Sold Out

In the spring of 2026, Nvidia reported a quarter in which it sold $81.6 billion worth of chips, wrote it up at a gross margin of nearly 75 percent, and casually mentioned that cloud GPUs were sold out. Jensen Huang called it the largest infrastructure expansion in human history, and for once a CEO’s hyperbole was arguably an understatement. Revenue was up 85 percent from a year earlier. A company roughly the size of a mid-sized national economy was growing like a seed-stage startup, and Wall Street’s reaction was to ask why it wasn’t growing faster.

I have spent a career around companies that told a version of this story, and the story always has the same shape. Something becomes scarce. Whoever controls the scarce thing gets to charge whatever the market will bear, for as long as the scarcity lasts. The interesting question was never whether Nvidia’s chips were good. Everyone agreed they were good. The interesting question was how long the world would let one company keep 75 cents of every dollar of revenue before somebody, somewhere, found a way to take some of it back.

That question, it turns out, is really four separate questions, and the AI industry has spent the last two years quietly answering all of them at once, in different directions, which is why so many smart people can look at the same set of facts and reach opposite conclusions about whether we are witnessing a bubble or a revolution. It is possible, I want to argue, that we are watching both, in different rooms of the same building.

Start with the money. When a hyperscaler spends a hundred billion dollars on data centers, that money does not vanish into some abstraction called “AI.” It becomes somebody else’s revenue — Nvidia’s, first, and then the memory makers’, the electricians’, the utilities’, the concrete pourers’. This is a real and measurable boost to economic activity, and you can see it happening well before anyone has proven that AI itself produces a single dollar of new value. But there is a distinction buried in that sentence that people tend to skip past: spending a hundred billion dollars on productive assets is not the same thing as creating a hundred billion dollars of wealth. The assets still have to earn their keep. Somebody has to use them for something worth more than they cost.

Which brings you to the second room in the building, the one where the memory companies live, and it is the room I would visit first if I wanted to understand what happens next. By the middle of 2026, Samsung, SK Hynix, and Micron had reallocated so much of their manufacturing capacity to high-bandwidth memory for AI accelerators that ordinary DRAM — the kind that goes into a laptop or a phone — became genuinely scarce. Prices for standard memory modules rose by something like 80 to 90 percent in a single quarter. SK Hynix posted an operating margin north of 70 percent. Micron’s profit rose more than sevenfold year over year. Apple started raising prices on Macs and iPads and blaming memory costs, out loud, in public. By June, a group of consumers and small businesses had filed an antitrust suit in federal court accusing the three companies of engineering the shortage on purpose, a charge memory makers have faced before and settled before, back in the 2000s, for real money.

I don’t know whether that lawsuit has merit. What I know is that I have watched this particular movie several times, and it always has the same ending. Scarcity produces extraordinary margins. Extraordinary margins summon capital. Capital builds capacity. Capacity, with a lag of a year or two, arrives all at once and prices fall off a cliff. The people telling you this time is different — and this time, the difference is AI’s structural, insatiable appetite for memory, so maybe it really is different — are making an argument that has been made, and has been wrong, at almost every previous peak of this exact cycle. Building a new fab takes eighteen to twenty-four months. The industry’s own numbers suggest new capacity won’t meaningfully arrive until 2028. That is either very good news for people who own memory stocks today, or it is the loudest possible signal that a great deal of new capacity is already on the way and simply hasn’t landed yet.

Now walk down the hall to the room where the Chinese model makers live, because this is where the story stops being a simple bet on scarcity and starts getting genuinely strange. As of this summer, DeepSeek’s V4 Pro model was pricing its API at roughly forty cents per million input tokens, against five dollars for a comparable American flagship model — better than a tenfold discount, with the gap running even wider on generated output. Alibaba’s Qwen and Moonshot’s Kimi were sitting in a similar band. Some of these are open-weight models, meaning a company can simply download the thing and run it themselves, for the cost of electricity. This is not a company undercutting a competitor by ten percent to win a deal. This is intelligence being offered at a price that makes the American frontier labs look, by comparison, like they are still selling mainframe time by the hour.

If you take that seriously, it forces an uncomfortable question. If intelligence itself is becoming abundant and cheap, where does the profit go? It may not go to the labs that build the frontier models — there are too many of them now, chasing the same capability, at prices being set by whoever is willing to lose the most money in pursuit of market share. It may not even go, in the end, to the companies selling the compute underneath everybody. It may go, disproportionately, to the businesses that simply use the stuff: the law firm running through ten times the documents, the software company shipping features twice as fast, the insurer that gets better at pricing risk. Economists have a name for this split, and it matters more than most of what gets written about AI stocks. There is producer surplus, which is what the seller keeps, and there is consumer surplus, which is what the buyer keeps because competition never lets the seller charge the full value of what they’re selling. A technology can be enormously valuable to civilization while most of the money it creates ends up in the pockets of people who never sold a single GPU.

Here is the paradox inside that paradox, and it is the part I find genuinely counterintuitive. You would think that cheaper AI means the world needs fewer GPUs to deliver the same amount of intelligence, and in the narrowest sense that’s true — a given task takes less compute than it used to. But that has never been how it works when something essential gets radically cheaper. Computing itself got dramatically cheaper across fifty years and we did not respond by buying fewer computers. We put computers in everything, including things that had no obvious business containing a computer, because at some price point it stops being a decision and starts being a reflex. The same thing may be happening with intelligence right now. Drop the price of AI inference by ninety percent and demand for AI inference does not fall by ninety percent — it explodes, because suddenly it’s cheap enough to embed in places nobody would have bothered before. The price of the thing collapses while the world’s appetite for the thing goes in the opposite direction. Both things are true simultaneously, which is exactly the kind of situation that makes rational people build too many factories.

Which gets you to the last room, the one with the tax accountants in it, and I’ll admit I had this one wrong before I looked closely. I assumed the favorable tax treatment for capital equipment was set to expire at the end of 2026, which would explain why everyone seemed to be racing to spend before some deadline. It isn’t expiring. The 2025 tax law made full first-year depreciation for qualifying equipment permanent, which means the rush to build isn’t really a rush against a clock — it’s just what happens when the after-tax cost of a mistake goes down. Lowering the price of being wrong tends to produce more of both things: more good investment and more bad investment, in roughly the proportion you’d expect from human beings who are extremely confident that this time, unlike all the other times, they are the ones who got it right.

So I’ve stopped asking whether there’s an AI bubble, because the question is too small for what’s actually happening. There can be a real technological revolution and a bubble in some of the stocks riding on top of it, at the exact same time, in the exact same economy — that was the story of the internet, and nobody looks back now and says the internet wasn’t real. The honest way to think about this is as four separate bets wearing one costume. Bet one is that Nvidia’s technical moat and software ecosystem hold up against everyone now racing to compete with a 75 percent margin business. Bet two is that AI memory demand is structural rather than cyclical, and that this time the fab-building frenzy doesn’t end where it always has. Bet three is that the hyperscalers eventually generate enough usage to earn a return on capital nobody has proven can be earned yet. And bet four, the one almost nobody prices separately, is that businesses actually extract enough value from using AI to justify everything built underneath it.

Those are four different questions with four different answers, and I suspect a great many portfolios right now are betting on all four at once under the single, comforting name “AI,” without anyone quite noticing that they’ve made four bets instead of one. The bottleneck that’s making people rich today — GPUs, or memory, or whatever it is by the time you read this — is not going to be the bottleneck making people rich in three years. It never is. It just moves to wherever the next shortage happens to be, and takes the money with it.

I keep coming back to that sold-out warehouse. Somewhere out there is the shipment that finally isn’t sold out. Nobody rings a bell when it arrives.

Categories
AI

Claude as Walter Cronkite

Gavin Baker said something this week that stuck with me.

In his latest conversation with Patrick O’Shaughnessy, he described a quiet shift happening across public markets. Nearly everyone he knows in the equity business—retail and institutional—now feeds every piece of news straight into Claude. Sometimes Claude Code. Sometimes a Claude agent. The model is probabilistic, he noted, and he was speaking from what he sees in his own network rather than from a measured study. But his impression was that the variation in how it interprets the same information is surprisingly small. A huge chunk of the market ends up trading on a shared reading of events.

Baker reached for an old analogy: Claude has become Walter Cronkite for the stock market. The single trusted voice. Everyone just believes what it says.

He tied the observation to Michael Mauboussin’s work on how a breakdown in diversity of thought helps create the conditions for bubbles and crashes. When independent judgment collapses into a narrower set of interpretations, the system becomes more brittle. Moves get sharper. Errors get amplified.

I spent the back half of my career inside fraud detection systems at Visa, watching correlated failure up close. The lesson that never left me: the dangerous moment isn’t when a single model is wrong. Individual errors wash out. It’s when every model in the ecosystem is wrong in the same direction, because they were trained on the same data, tuned against the same benchmarks, built by people reading the same journals and hiring from the same three schools. A fraud ring doesn’t need to beat your model. It needs to find the blind spot every model in the industry shares. That’s not a tail risk. That’s the whole risk.

Which is what made me sit up a few weeks ago, watching a position reprice in a straight line and catching myself, mid-scroll, about to ask Claude what it thought was happening before I’d looked at a single primary source myself. The tool hadn’t done anything wrong. I had reached for the shared interpretive layer before reaching for my own judgment, out of habit, the way you reach for a light switch in a dark room you’ve walked through a thousand times.

Dan Geer wrote about this two decades earlier, from a different angle entirely. Geer and colleagues argued that Microsoft’s dominance had created a software monoculture: nearly identical systems sharing the same vulnerabilities. In biology, monocultures are efficient until a pathogen finds the common flaw. Then the failure is systemic rather than local. Diversity limits the blast radius. Geer’s point was never that the dominant platform was worse in isolation. It was that identicality itself becomes the risk multiplier.

Baker is describing a cognitive version of the same phenomenon.

The platform is no longer Windows. It is a frontier model that a large fraction of market participants now use as their primary interpretive layer. The shared vulnerability is not a buffer overflow. It is a common set of priors, training data, reasoning patterns, and prompt conventions. Slight probabilistic differences still exist. But the center of gravity of interpretation has tightened.

The result is correlated positioning. Feedback loops that reinforce themselves. A market that can reprice more violently than the underlying fundamentals alone would justify. In July we watched AI and semiconductor names drop 40–60 percent in a straight line while on-the-ground metrics—GPU rental prices rising, token growth accelerating, hyperscaler operating cash flow strengthening—told a different story. One plausible contributor to that gap is an AI-mediated consensus that overweighted certain narratives relative to the harder data.

There is an important difference in degree. Software monocultures create technical cascade risk you can patch. Interpretive monocultures create cognitive cascade risk you can’t—there’s no CVE number for a shared blind spot in judgment. The latter is softer and harder to measure. But the mechanism is familiar: reduced diversity of independent judgment.

I use these models constantly. They compress research, surface patterns I’d have missed, and force clearer thinking when I use them well—Claude caught an inconsistency in a cash flow assumption last month that I’d read past twice on my own. That’s real. The danger isn’t the tool. The danger is treating the tool as the authoritative voice rather than one input among many. The edge increasingly belongs to people who combine the model’s speed with proprietary data, primary research, domain experience, and a willingness to hold non-consensus views. Those who simply outsource the interpretation may find themselves more correlated than they realize, and won’t know it until the moment it matters.

Diversity of thought was never free. It was always work.

I noticed myself skipping the work, just for a second, on an ordinary Tuesday. That’s usually how it starts.

Categories
AI

The Encyclopedia and the Reasoner

I was standing in the cereal aisle a few weeks ago, doing the thing I always do — flipping the box over, scanning the fine print, comparing fiber grams like it mattered more than it probably does — when I thought about the model I’d been testing that morning. Sharp. Fast. Occasionally, confidently, wrong about something I could have looked up in ten seconds.

There was no label for that. No panel telling me what was inside, what it was good at, what it might get wrong, what it cost to run. Just a chat window and a kind of blind trust.

That’s the itch behind this post. What would it look like if AI models came with something like a Nutrition Facts label — the kind the FDA forced onto every box in your pantry back in 1994? Not as a gimmick, but as a real answer to a real problem: we are feeding these things into our decisions, our writing, our portfolios, our kids’ homework, largely on faith.

The IQ Number That Isn’t Quite an IQ Number

I keep running into a shorthand in investing circles — Jordi Visser and others talking about frontier models as “140 IQ” systems, reasoning at a level that outpaces most humans on the kinds of puzzles we associate with fluid intelligence. Pattern recognition. Logic chains. Novel deduction under pressure.

It’s a useful number. It’s also a bit of a trick.

Human IQ tests were built to measure something narrow and specific — not wisdom, not knowledge, not judgment, but the raw machinery of reasoning. When we borrow that language for AI, we inherit the same narrowness, which is fine as long as we remember it. A model that aces abstract reasoning benchmarks isn’t necessarily the model that knows the correct dosage, the right case law, or what actually happened in 1932. Reasoning and knowledge are cousins, not twins.

Two Kinds of Smart

Here’s an old-fashioned way to think about the split: Britannica versus World Book.

Britannica was the encyclopedia my father would have trusted — dense, expert-written, unapologetically deep, assuming you could keep up. World Book was the one actually sitting on the shelf in most houses I knew growing up, mine included: friendlier, broader, built for a general reader, a little shallower in exchange for being a little more useful on a Tuesday night with a homework assignment due.

Neither is wrong. They’re optimized for different things. And training data does the same kind of sorting. A model fed heavily on curated, scholarly, expert-vetted sources leans Britannica — deep, careful, occasionally slow to update. A model trained on the sprawl of the open web leans World Book — broad, current, occasionally sloppy, sometimes brilliant at the edges precisely because it’s seen everything.

Any honest label for a model needs a section on this. Call it “Knowledge Sourcing.” Not just how big the training set was, but what kind of encyclopedia it’s pretending to be.

Sketching the Label

If I could design the box myself, it might read something like this:

Serving Size: 1 query, ~500 tokens

Reasoning Score: 138 (fluid problem-solving, logic, abstraction) Knowledge Depth: Moderate–High (cutoff: [date]; strongest in [domains]; weakest in [domains])
Ingredients: Curated scholarly corpora, licensed news archives, public web crawl, synthetic reasoning data, human feedback Allergens: Confident hallucination under ambiguous prompts; recency gaps beyond training cutoff; known weakness in [specific domain]
Cost per Serving: $X per million tokens; Y watt-hours per query Best Paired With: Retrieval tools, human review for high-stakes decisions

It’s a little tongue-in-cheek written out like that. But underneath the joke is something I actually want — the same instinct that made me read cereal boxes as a kid. Not to be scared of what’s inside, just to know.

The Part That Actually Excites Me

Here’s where the scaling laws get interesting, and where I think the real opportunity sits.

World knowledge is expensive. It’s greedy for data and parameters — you need to have practically read the internet to know the boiling point of tungsten, the plot of a minor Victorian novel, and the org chart of a mid-cap company all at once. Reasoning, it turns out, is a different kind of animal. It can be distilled, compressed, taught through synthetic problems and careful post-training, and squeezed into something far smaller than you’d expect.

Which means a genuinely thrilling possibility is already taking shape: sharp, high-reasoning models small enough to run on a phone or a laptop, entirely offline, because they’ve shed the encyclopedia and kept the mind. Pair one of those with a personal index — your own notes, your own documents, a retrieval layer built around your actual life — and you get something closer to a personal thinking partner than a general-purpose oracle. Private. Fast. Always available. Tuned to you rather than to everyone. Apple may be on to something with this kind of strategy?

I think about this constantly in my own workflow — the daily scans, the little agents I’ve built to help sort signal from noise, the genealogy digging, the investment frameworks I keep refining. What I usually want isn’t more encyclopedia. It’s a clear-headed reasoner sitting next to my own carefully kept knowledge, not buried under someone else’s version of the whole internet.

Why the Label Matters More Than the Score

None of this works, though, without honesty about what’s inside the box. A 140 on a reasoning benchmark tells you almost nothing about whether a model will quietly misremember a fact it was never that confident about in the first place. And a model can be extraordinarily knowledgeable while being a mediocre reasoner — plenty capable of reciting the right ingredients and still getting the recipe wrong.

The nutrition label movement in food didn’t eliminate junk food. It just made it possible to choose junk food on purpose, with your eyes open, instead of by accident. I’d like the same deal with AI. Not a demand that every model be a genius generalist, but a demand that I get to know what I’m actually consuming — and choose the lean local thinker over the bloated encyclopedia when that’s what the moment calls for, or the other way around when it isn’t.

Curiosity got me into that cereal aisle habit decades ago, and it’s the same instinct pulling me toward this idea now — not suspicion of the box, just a wish to read it clearly before I decide how much of it to trust.

What would you want on your label?

Categories
AI Semiconductors

The Margin of the Weather

A company that has sold memory chips for forty years — memory, one of the most humiliatingly commoditized products in capitalism, a business that has bankrupted entire Korean and Japanese conglomerates teaching each other lessons about discipline — is about to make more money in twelve months than in the previous four decades combined.

Samsung’s chip chief told a room of his own employees: this year’s profit will exceed everything the division has earned since the 1970s. Forty years of grinding, erased by one fiscal year. You’d think they’d invented something.

They hadn’t. Everyone building an AI data center needs memory. Nobody built enough factories. Samsung was one of three companies on earth able to supply the shortfall, and the price of a chip that costs what it always cost went up fifty percent. Samsung kept the difference. Not innovation. What happens to a farmer when the drought hits every field but his.

We don’t credit the lucky farmer with genius. We say: good year. And we don’t expect the good year to repeat. Rain comes back. The price falls. Scarcity is weather, not a personality trait.

There’s a real achievement in this story too, and it has nothing to do with the weather. A year ago Samsung failed to qualify its most advanced memory for Nvidia’s systems — performance problems, a rival getting the business instead. The engineers went back and fixed it. That’s the actual skill in this company’s year: unglamorous, uncelebrated at the town hall, worth nothing next to the number that got the confetti. The competence arrived quietly, on a different chip, in a different meeting, and nobody’s putting that on a plaque.

The stock market didn’t put it on one either, but it seemed to know the difference. Best quarter in Samsung’s history — profit nineteen times the year before — and the shares fell seven percent. Not despite the earnings. The gain had already been priced in, the shares having run up a hundred and fifty percent on the expectation of exactly this number, so the number’s arrival became a ceiling instead of a floor. A market rewards discovery. It does not reward weather. Had investors believed Samsung built something durable — the Nvidia qualification, the years of engineering behind it — the stock would have ripped, the way See’s Candies or Apple gets rewarded quarter after quarter, because everyone agrees the thing generating the money isn’t going anywhere. Instead the market glanced at the record harvest and asked, politely, whether it would rain again next year.

Analysts insist the shortage holds through next year. Someone always insists that, right before it doesn’t. Fabs get built. Capacity catches the demand that summoned it, the way it always has, and the cycle ends the way memory cycles end — too much supply chasing too little demand, margins reverting toward the number they were always going to revert toward. Nobody knows if this time is different. A company just posted the best year of its life, on a windfall it didn’t earn and a fix it did, and the market — which has seen droughts end before — hasn’t decided yet which one it’s watching.

Categories
AI

The Taste Beneath the Summary

The real work of staying informed has never been volume. It has been the quiet, repeated acts of judgment: does this matter, to whom, why now, what is the signal beneath the noise.

A recent piece from Bridgewater’s AIA Labs and Thinking Machines Lab, “Learning to Replicate Expert Judgment in Financial Tasks,” describes training models to do the triage investors actually do—filtering news, research, central bank documents, internal notes, for relevance. Frontier models struggled with judgments that looked simple and weren’t. The fix wasn’t a bigger model. It was Qwen, fine-tuned on labeled examples from practitioners, and it beat the frontier leaders while costing a fraction to run.

The bottleneck was never model size. It was taste. And taste, it turns out, can be taught to something small and cheap, if you’re precise enough about what you’re teaching it—a market’s worth of Mercors is already proving the same thing at scale.

The researchers were clear that expert judgment doesn’t reduce to rules or prompts. It took high-quality, domain-specific labels from people doing the actual work. The most powerful systems will be built in partnership with practitioners who can say, and keep saying, what “good” looks like in their own context.

Which raises the question I haven’t answered yet: what would I actually put in the labels, if someone asked me to teach my own taste to a cheap model.

Categories
AI China Youth

The Arithmetic of Youth

The first meeting was at one of the banks on a high floor somewhere in Shanghai, the kind of view that turns a city into an abstraction. It was 2005, and I was there the way American investors were there that year — curious, a little jet-lagged, trying to read a country that was rewriting itself faster than anyone could print the new edition. Across the table sat a management team, and what struck me wasn’t anything they said. It was how young they were. Not junior-young. Running-the-company young.

Afterward — in the hallway or the car, early in the trip, when I still had the confidence of someone who thought he could just ask — I put the question to one of our local colleagues. Casually, expecting a casual answer. Something about a young country, a young economy, energy meeting opportunity.

The answer I got instead was the Cultural Revolution.

There was a generation, she explained, that simply wasn’t there. Sent to the countryside, pulled out of universities, handed shovels instead of textbooks. By the time China opened back up, that cohort had a hole in it — a rung missing from the ladder. So the young people I’d just watched run that meeting weren’t there because anyone had bet on youth. They were there because there was no one older left to put in the chair. Youth, in that boardroom, wasn’t a strategy. It was a vacancy dressed up as one.

I have thought about that answer, off and on, for twenty years, without knowing what to do with it. Then a few weeks ago I read a summary of a conversation with Nathan Lambert — an AI researcher who’d just spent time visiting the frontier labs in Beijing and Hangzhou — and I found myself back in that room, except everything about the youth in it had flipped.

He describes teams at places like Moonshot AI as almost absurdly young, tight-knit, close to giddy about the work — “the best vibes,” he calls it. Zhipu AI, he says, has built something close to an AGI showroom, a physical space engineered to perform confidence for whoever walks through the door. These aren’t companies with a hole where the experienced people should be. These are companies that went looking for twenty-five-year-olds because twenty-five-year-olds move at the speed frontier AI research demands, and installed them at the center of the room. The showroom isn’t hiding a vacancy. It’s staging a choice. That’s panel two.

Same demographic. Same first city — Beijing both times — with a high-speed rail line now running to Hangzhou instead of whatever second city I’d have named twenty years ago. Opposite cause. In 2005, youth in the room meant a generation had been taken from the labor force involuntarily. In 2026, youth in the room means a generation has been selected for it, deliberately, competitively, because being young is now the qualification rather than the disqualifier. The Cultural Revolution left a gap that youth filled by default. The AI boom left a door that youth is filling by design.

I would have stopped there, satisfied with the irony, except for a number I couldn’t get out of my head once I went looking for it: 15.6 percent. That’s China’s urban youth unemployment rate — ages sixteen to twenty-four, university students excluded — as of May 2026, and it counts as good news, down from 16.3 percent in April. A year earlier it had spiked to nearly nineteen percent in a single August, the month twelve million university graduates walked out of commencement and into a labor market that had no idea what to do with them. Some will sit for civil service exams, chasing what people there still call the iron rice bowl — the illusion of permanence a state job used to guarantee, back when your grandparents didn’t choose their careers so much as get assigned them. Others will enroll in another degree, not because they want one, but because a classroom is a more dignified place to wait than an unemployment line.

So there is a third panel now, and it doesn’t fit neatly next to the other two. It isn’t a vacancy, and it isn’t a showroom. It’s just a very large number of young Chinese people who did everything they were told to do — studied hard, got the degree — and are standing outside a door that isn’t opening. And somewhere behind that door, in a much smaller room with much better lighting, another group of young Chinese people, maybe the same graduating class, are building the technology that a Silicon Valley researcher travels overseas to admire for its vibes.

I don’t think those two rooms are as separate as they look. I think the showroom is real, and I think the twelve million are real, and I think the mistake — my mistake, sitting here in Menlo Park two decades removed from that conference table — is letting either one stand in for “Chinese youth” as if it were a single sentence instead of a population. The Moonshot AI team is not a representative sample. It’s the visible sliver of a generation, selected with a precision that turns the unemployment numbers into part of the same mechanism — one sorting process, not two unrelated stories. The best vibes in that lab and the worst numbers in that economy might just be describing the two ends of the same funnel.

I keep coming back to that hallway in 2005, and to how confident I was in the question I asked — as if a generation’s youth could only ever be telling one story. It couldn’t then, and it can’t now. I got a true answer that day and thought I understood something. I understood one panel of a triptych I hadn’t seen the rest of yet — and I’m still not sure I’ve seen all of it.

Categories
Aging AI Business Living

The Being Phase

There is a metric making the rounds in technology investing circles that is, on its face, about market share and revenue concentration. Alex Sacerdote of Whale Rock Capital calls it the New Rule of 40 for AI. The formula is simple: take the percentage of a company’s sales derived from AI, add its percentage market share in that AI category, and if the sum reaches 40, you have a winner. Celestica, a company most people have never heard of, scores extraordinarily well. It owns somewhere between half and sixty percent of the cloud Ethernet white-box switch market. NVIDIA doesn’t need a formula. It simply is what it is.

Sacerdote designed the metric to cut through a specific kind of noise — the companies claiming AI exposure they don’t actually have, the giants whose AI revenue hovers at one or two percent of their base while their press releases suggest otherwise. The framework is a detector. It finds the companies that have stopped becoming AI infrastructure and started simply being it.

I found myself less interested in the companies than in that distinction.

I spent years at Visa watching a network that had long since crossed that threshold. By the time I arrived, Visa wasn’t becoming the global payments infrastructure. It was the global payments infrastructure. The work was real — fraud detection, modeling, the daily labor of keeping something enormous running — but the existential question had been settled before I got there. The network existed. Merchants accepted it because cardholders carried it. Cardholders carried it because merchants accepted it. That loop had been closing for decades. We were custodians of a fait accompli.

There’s a particular feeling to working inside something that has already won. It’s not complacency exactly. The problems are genuine and the stakes are high. But the uncertainty has a different quality — it’s operational uncertainty, not existential uncertainty. You’re not asking whether the thing will survive. You’re asking how to run it well.

I didn’t have language for that distinction then. Sacerdote’s metric gives me some. The companies that score highest on his New Rule of 40 have resolved their existential question. They’re not fighting for position. They’re administering a position already held.

The question that has followed me out of that career, and out of several decades of watching technology cycles turn, is simpler and more personal than any investment framework.

When did I cross that line myself?

I have been writing at sjl.us since 2001. That’s not a boast — it’s a data point. Twenty-five years of thinking out loud, of ideas arriving rather than being argued, of the specific memory as structural anchor. The blog is not becoming anything. It is what it is: a record of a mind moving through time, accumulated into something that has its own weight and shape.

The book on payments systems exists. The career at Visa exists. The photographs exist. The train journeys exist. The years in Dayton exist, and the years on the Peninsula, and the particular way the light falls on the California coast at Pescadero in the late afternoon — when the fog is still offshore and the hills are improbably green and everything goes briefly, completely quiet, as if the world is deciding whether to continue.

These are not things I am building toward. They are things I am.

Sacerdote would say I have high market share in a specific category. The category is small — one person, one particular configuration of experience and attention and accumulated knowing — but the share is essentially total. There is no competitor for the position of having lived this particular life. The moat is absolute. The switching costs are infinite.

I used to find that thought melancholy. The narrowing as loss. The aperture closing on what remains.

I’m not sure I find it melancholy anymore.

The L-Curve, Sacerdote says, is a long flatline followed by a vertical explosion. The tinkering phase, then the moment of lift. He means it as a description of demand curves for technology infrastructure. But I recognize the shape from somewhere closer. The long middle of a life, building and becoming, and then the morning you wake up and realize the building is substantially done. What remains is the being.

That’s not an ending. It’s a different kind of beginning.

Sacerdote’s metric will eventually stop working. All frameworks do. The AI infrastructure cycle will mature, the L-Curves will flatten, and some new measure will emerge to find the next thing that is just beginning to become what it will be. That’s the nature of markets. The detector has to change as the signal changes.

But there’s a complication worth naming. Analysts at Citadel Securities published a note recently observing that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints, and marginal returns. Token bills are arriving unexpectedly. Compute is scarce. The vision of AI as ubiquitous, frictionless, and immediate is colliding with physical reality. Their conclusion: asset prices will periodically be forced to reconcile ambition with physical constraint.

That’s not a refutation of Sacerdote. It’s a reminder that feeling like you’ve arrived and having actually arrived are different things. The being phase has to be load-tested. The position has to hold under pressure.

I think about the fiber optics Corning is laying into the massive data center clusters — ultra-thin, bendable, carrying more light than anything that came before. The cable doesn’t know it’s infrastructure. It just carries what it’s given, at the speed it’s capable of, across whatever distance is required. It doesn’t matter what the cable believes about itself. What matters is whether the light actually moves.

That seems right to me. You become what you are over a long time, largely without noticing. And then one day someone builds a metric that accidentally describes your life, and you recognize yourself in it, and you think: yes. That’s the shape of it. High concentration. High share. A moat that deepened while you were looking elsewhere.

But the moat still has to hold.

The being phase, it turns out, is not the end of something. It’s the proof that something was built. And the daily question — for companies, for infrastructure, for a person in his late seventies still writing, still paying attention — is whether what was built is actually load-bearing.

You don’t get to stop finding out.

Categories
Friends Gratitude Kindness Living

The One Thing Money Doesn’t Buy

Somewhere there is a couch that launched a hedge fund.

It belonged to a man named Carter, and for the better part of a year it was where Dan Loeb slept while he figured out what came next. No office. No fund. No Third Point. Just a friend’s apartment and the specific grace of someone who didn’t need you to have already become something before they let you in the door.

When Loeb finally landed at Jefferies, Carter gave him a few hundred thousand dollars to manage. That became a million. The million became seed capital. Third Point was built on top of it — thirty years of it, billions of dollars of it — and all of it traces back, in some straight unbroken line, to a couch and a person who said yes before the evidence was in.

Patrick O’Shaughnessy asked him about it near the end of a long conversation. The kindest thing anyone has ever done for you — it’s the question O’Shaughnessy always asks, and it always cuts through. Loeb had just finished making a case for kindness as a serious value, not a soft one. Something that belongs at the top of the hierarchy, he said, next to honesty and intelligence. The mechanism that unlocks empathy. He noted, almost reluctantly, that it also compounds in business — before adding that the moment you start treating it as an investment, you’ve already lost the thread.

Then he quoted Palmer Luckey.

The one thing money doesn’t buy you is friends that believed in you when you had nothing.

Luckey built Oculus in his parents’ garage. Sold it for two billion. Founded Anduril. He has spent his adult life proving that if you are relentless and strange and right, you can make almost anything happen with money. And what he noticed, somewhere in all of that, is where money stops. Not at luxury. Not at access. It stops at loyalty that predates your success. You cannot purchase the memory of Carter’s couch. You cannot acquire, at any price, the specific knowledge that someone held you when you were nothing yet.

I have been thinking about the people in my own life who did some version of this. Not always with money. A call made on your behalf before you knew you needed it. A door held open to a room you couldn’t see. These moments are nearly invisible when they happen. They only become legible later, once the room turns out to matter — once you can look back and trace the line.

The line is always shorter than you think. And it always ends at a person.

Categories
AI

The Coach Who Wouldn’t Change

In 1975, a twenty-four-year-old Kodak engineer named Steve Sasson built the first digital camera. It was the size of a toaster, captured a black-and-white image at 0.01 megapixels, and took twenty-three seconds to record a single photograph to a cassette tape. Sasson showed it to his managers. Their response, as he later recalled, was essentially: that’s cute, but don’t tell anyone about it.

Kodak was not a stupid company. It was a dominant one. At its peak it held 90 percent of the American film market and 85 percent of camera sales. Film was not just a product line — it was the entire economic architecture of the company. Processing fees, paper, chemicals, the retail relationships built around the assumption that photographs needed to be developed. Digital threatened all of it simultaneously. So Kodak did what dominant companies do when confronted with a threat they can’t absorb into the existing model: they managed it. They ran studies. They filed patents. They made incremental moves. They protected the thing that was working rather than building the thing that would work next.

Kodak filed for bankruptcy in 2012. The digital camera had been sitting in their own archives for thirty-seven years.

Nokia’s version of the same story has a different texture. Where Kodak’s failure was about protecting a margin, Nokia’s was about identity. Through the 1990s and into the early 2000s, Nokia was mobile phones — not a major player, but the category itself. At its peak it held over 40 percent of the global handset market. The company had navigated a remarkable transformation earlier in its history, shedding paper mills and rubber boots to become a pure technology company. It knew how to change. It had done it before.

What it couldn’t do was change from a hardware company into a software one. When the iPhone arrived in 2007, Nokia’s internal assessments were, by most accounts, accurate. They understood the threat. They had touchscreen prototypes in development. What they couldn’t manage was the cultural distance between building phones that were superb physical objects — durable, reliable, made to exacting standards — and building phones that were primarily platforms for software that other people would write. The excellence that had made Nokia great was manufacturing excellence. The game was becoming something else, and manufacturing excellence was not only insufficient for the new game; it was actively in the way, because it oriented every decision toward the object rather than the experience.

Nokia’s market share collapsed from over 40 percent in 2007 to under 5 percent by 2013.

Andy Grove, who built Intel into the dominant force in semiconductors, called it plainly: only the paranoid survive. He meant it as a prescription. His successors treated it as a trophy.

Both stories have the clean shape of settled history. We know how they end. The verdict is in, the lesson is available, and it’s easy to read them now as cautionary tales about obvious mistakes made by people who should have known better.

This is the wrong way to read them.

Kodak and Nokia didn’t fail because they were blind. They failed because they were standing on a fulcrum — a moment when the old game and the new game were both plausibly real — and they chose the wrong side. At the time, that choice was not obviously wrong. Film was still enormously profitable. Nokia’s hardware was genuinely superior. The rational case for staying the course was real, and the people making it were not fools.

The reason the Kodak story is still told fifty years later is not that the mistake was obvious. It’s that it wasn’t — and they made it anyway.

Which brings us to now. Because there is a fulcrum in front of the enterprise software industry, and nobody knows yet which way it tips.

The companies in question — Salesforce, ServiceNow, and most of the SaaS category built over the last twenty years — were constructed on a simple and powerful premise: that businesses would pay recurring subscription fees for software that managed their customer relationships, their workflows, their data. The premise was correct. It produced some of the most durable businesses in the history of technology.

The threat AI poses to this model is not subtle. If an AI agent can handle a customer service interaction, manage a workflow, or synthesize a CRM record without a human touching licensed software to do it, then the per-seat subscription model — the economic engine underneath all of it — starts to look like film processing in 2003. Theoretically intact. Quietly at risk.

The responses of these companies have been instructive, and they’ve diverged.

Here is the honest position: we don’t know yet. The fulcrum is still in motion.

It’s possible that Salesforce’s Agentforce is the Kodak digital camera — the real thing, built by the right company, that gets buried under the weight of protecting what already works. It’s possible that the SaaS model is more durable than the threat suggests, that enterprises will pay for trusted platforms regardless of the underlying labor model, and that the companies racing hardest to cannibalize their own revenue streams are making a different kind of mistake. It’s possible that ServiceNow’s consistency is discipline, or that it’s the Nokia instinct to keep building the best version of the thing that used to win.

What the Kodak and Nokia stories actually teach — not the simplified version, but the harder one — is that the mistake is never visible in the moment it’s made. It only becomes visible later, when the fulcrum has tipped and the choice that was once defensible has become permanent.

The coach who wins five championships holds the philosophy and rotates the players. The coach who wins one holds the players and calls it philosophy.

The enterprise software companies standing at this moment have a version of the same decision. The ones who make it correctly will, in twenty years, be the ones we cite as examples of adaptation. The ones who don’t will be the ones we cite as examples of something else.

We just don’t know yet which is which. That’s not a comfortable place to stand. It is, however, exactly where we are.

Categories
AI Business

The Topography of a Face

I found myself staring at the physical geometry of a conversation the other day—not the words, but the topography of the faces delivering them.

Elad Gil recently shared a fascinating experiment during a conversation with Tim Ferriss. He’s been uploading photos of startup founders into AI models and asking the machines to predict if they’d be successful, purely based on their “micro-features.”

“Because if you think about it, we do this all the time when we meet people, right? We quickly try to create an assessment of that person, their personality, and what they’re like. There are all these micro-features—like, do you have crow’s feet by your eyes, which suggests that your smiles are genuine? […] So, I have this whole set of prompts that I’ve been messing around with, just for fun, around: ‘Can you extrapolate a person’s personality based off of a few images?'”

He notes the model breaks down the crow’s feet and the furrowed brows, extrapolating a personality from a static frame. It’s a parlor trick, perhaps. But it works because it holds a mirror to our oldest, most unexamined instinct.

We are all amateur phrenologists of the human face. We sit across a table, measure the crinkle of an eye or the tightness of a jaw, and we build a rapid, invisible architecture of trust or suspicion. Over decades of investing and making career choices, I’ve often leaned heavily on this silent language. I’ve backed founders because their intensity felt genuine, and I’ve passed on others because something in their posture felt misaligned.

But if I am brutally honest, that intuition has sometimes been a mask for my own blind spots. I’ve held on to failing investments for far too long because I trusted a reassuring smile. We like to think our gut instinct is a sophisticated instrument. Often, it is just a pattern-matching engine running on deeply flawed historical data.

Now, we are handing that very human habit over to a machine. We prompt the AI to become a “cold reader,” and it obliges, predicting who will be the quiet observer and who will deliver the dry wit.

The unsettling part isn’t that the machine might get it wrong. The unsettling part is that it might get it exactly right—by mimicking the very same rapid, superficial judgments we make every day, just at a terrifying scale.

We are teaching silicon to read the human code. The future will belong to those who realize the code was always written in our own biases.