Categories
AI

The Arithmetic of the Sold-Out Warehouse

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
Aging Living

The Manufacture of Emergency

The screens at Visa never went dark. Somewhere in a data center, transactions were clearing at a rate that made the number itself feel abstract, and our job was to sit inside that river and notice the one drop moving wrong. A fraud ring testing stolen numbers in ascending increments. A merchant category code that didn’t match the geography. A velocity spike that looked, if you squinted, like nothing at all, until it didn’t. The work had a shape to it: quiet, then a pattern surfaces, then the chase, then the catch or the near-miss, then quiet again. I didn’t think of it as drama at the time. I thought of it as Tuesday.

It took me a long time to notice that the shape of that work was also the shape of something in me, and that I’d built a career, without quite meaning to, out of environments that manufactured the same cycle over and over. Fraud detection was one. A Piper Cherokee is another — not because flying is reckless, it isn’t, competent flying is the opposite of reckless, but because every cross-country trip contains a small structured emergency built into the planning itself: the weather that might close in, the fuel math that has to work, the decision point where you commit or you divert. You solve it. You land. The relief is real and it is, if you’re honest with yourself, part of what you came for.

Retirement removes all of that. Nobody hands you a fraud queue. There is no fuel gauge counting down over Ohio. What retirement hands you instead is something much harder to sit inside: an undifferentiated stretch of days with no built-in shape, no crisis with a clock on it, no adversary to defeat by five o’clock. I did not expect this to be difficult. I had spent decades, if you’d asked me, wanting exactly this — quiet, unstructured time, the absence of alarms. And for the first while it was wonderful. Then I noticed I had opened a second screen.

The options positions started small and reasonable, the way these things do. A covered call here, a defined-risk spread there, sized so that nothing could actually hurt. But I noticed the pull on days when nothing needed attention. I’d be three-quarters through a walk around the pond, the morning doing exactly what a morning is supposed to do, and I’d feel for my phone anyway, not because a position needed managing but because the quiet had started to feel like something unresolved. I would open the app looking, if I am being precise about it, less for information than for a small manufactured stake — something with a clock on it, something I could watch move against me and then, with luck, defeat.

This is not a confession about trading, which when done with defined risk and a plan is just a hobby with numbers attached. It’s an admission about what I was actually shopping for on those walks, which was the old shape. Tension, then resolution. Fear, then relief. The problem with the shape is that it doesn’t ask whether the underlying situation warrants it. It just asks to be fed. A market that is genuinely quiet gets treated the same as a fraud queue with a live threat in it, because the appetite doesn’t distinguish between real stakes and stakes you went looking for. You can tell yourself you’re staying sharp. Sometimes you’re just bored, and bored, for a person built the way I apparently am, does not feel like rest. It feels like a low hum of unfinished business, and unfinished business is a thing I have spent a working lifetime being paid to resolve.

I don’t think this gets solved so much as it gets noticed, and noticing changes the ratio a little. The phone is always in my pocket; that isn’t the part I can negotiate with. What I’ve started doing instead is naming the moment my hand goes for it on a walk that doesn’t need interrupting — catching the reach itself, mid-motion, and asking what exactly I’m expecting to find. Most mornings I still open the app. But I open it now as someone watching himself do it, which is a smaller thing than stopping, and also, it turns out, not nothing.

Categories
AI Business Technology

The Diffusion of Ordinary Work

A recent O’Reilly Radar piece has stayed with me longer than most: Jeff Ding’s diffusion theory of great-power competition applies just as well to AI adoption, and it suggests that companies chasing the frontier might be optimizing for the wrong thing.

Ding, a political scientist at George Washington University, pushes back on the standard story of technological power — that the country or company which first invents or dominates a glamorous new sector locks in lasting advantage. The historical record says otherwise. General-purpose technologies like steam, electricity, and computing produced durable national advantage not through invention but through diffusion: the slow, unglamorous work of embedding a technology into ordinary productive work across an entire economy. The infrastructure that mattered was never the breakthrough lab. It was the education and training systems that produced large numbers of competent, ordinary engineers who could put the technology to work. Ordinary engineers, in Ding’s framing, matter more than heroic inventors.

The same logic holds inside a company. Frontier models turn over every few months. Organizational know-how compounds.

Palantir makes the abstraction concrete. The company doesn’t train frontier models — it builds the layer underneath them: a live, machine-readable model of how a specific organization actually works, a data integration fabric, and a platform that connects whatever model a customer chooses to real operational decisions. It is deliberately model-agnostic. The value proposition is governance, context, and the accumulation of reusable logic rather than access to the newest weights. Practitioners embed with the customer, learn the domain, and configure the system against the customer’s own data and processes — diffusion as a job description.

Leadership has been unusually blunt about what this implies: frontier labs, they argue, are optimizing for benchmarks while under-delivering on what enterprises actually need. The clearest evidence for the argument is also the most citable one — there have been production cases where an unmodified open-weight model, running inside Palantir’s platform with customer-specific context, outperformed frontier models on the actual task. If true, and it appears to be, the implication is uncomfortable for anyone selling model quality as the whole story: the ground underneath the model — the ontology, the data, the accumulated rules — often determines outcomes more than the model itself.

Electrification is the closest historical analogue. Factories didn’t get more productive the day they installed electric motors. The gains showed up years later, once entire production systems had been redesigned around decentralized power. The lag was organizational, not technical. AI diffusion looks likely to follow the same shape — the bottleneck was never going to be model capability, it was going to be the patient, unglamorous work of redesigning how people actually work.

I don’t know who’s training the ordinary engineers right now — the ones who will spend the next decade doing the diffusion work rather than the invention work. I don’t think anyone’s tracking their names.

Categories
Living Memories

The Depth of the Dimes

The lifeguard came out with a canvas bag, and that was the signal. Saturday morning, Kettering, Ohio, the municipal pool not yet warmed by the day, and forty kids arranging themselves along the edge like something had been rehearsed, though nobody had rehearsed it. We knew the shape of the thing without being told. He would walk to about the middle of the deck, and he would start throwing coins into the water.

Not pennies, mostly. I remember that distinctly, the way you remember the specifics of a thing you can’t explain the pull of. Nickels and dimes, a scattering of quarters, no half dollars that I ever saw hit the surface. Somebody in the recreation department had made a decision about denominations, a budget, maybe, or a sense of what a Saturday morning was worth to forty kids in Ohio in the summer, and none of us thought about that then. We watched the coins fall instead. That’s the part I keep returning to — not the diving, the falling. Coins don’t drop through water the way they drop through air. They wobble. They catch light and lose it. They take their time getting to the bottom, and you’d track one the way you’d track a hawk, losing it, finding it again against the pool floor, memorizing the spot before it disappeared into the general scatter of everything else he’d thrown.

We tried to angle ourselves near the shallow end, obviously. That was the whole strategy, such as it was — get positioned somewhere you could stand, somewhere your ears wouldn’t need to fight the pressure, somewhere the breath-holding would be a formality instead of a genuine risk. But the lifeguard wasn’t interested in making it easy. He’d throw a few coins shallow, just enough to keep the little kids occupied, and then he’d load up the deep end, and that’s where the real money went down. If you wanted more than a couple of nickels, you had to go where you couldn’t stand.

Then the countdown, and then all of us at once, forty kids hitting the water in the same three seconds, which meant the water itself turned against you before you’d even started looking — churned, clouded, chlorine stinging worse than usual because now there were elbows in it, knees, someone’s foot near your face. I wasn’t especially good at it. I want to say that plainly, because I don’t think I was; I’d come up with a handful, four or five coins, sometimes a quarter if I’d gotten lucky with the angle, and that felt like enough. Enough to matter. Enough to walk back to the locker room afterward with my trunks pocket sagging and clinking, enough to spend the rest of the morning transferring nickels and dimes into my actual pants pocket like a kid who’d earned something.

There was a boy down the street whose father worked at NCR, and some Saturdays he’d disappear to Old River instead — a real pool, he made sure we knew, an enormous one, with none of this scrounging-for-change business. I remember being aware of that difference without being able to name what it meant. He had the better pool. We had the lifeguard with the canvas bag.

I came across a piece online the other day, something about swimming and what it might mean, the kind of essay that circles a simple physical act until it turns into a meditation on something larger, and it knocked this memory loose after forty years of sitting untouched. I don’t know why that particular Saturday ritual is the one that stayed. Not the swimming lessons, not the diving board, not anything a person would call a memory on purpose. Just the coins going down, and all of us watching them go, already planning where we’d be standing when the countdown ended.

Categories
Creativity Inversion

Inversion

I came across a post on X this morning by Aakash Gupta that cut through the noise around SpaceX’s mobile ambitions with unusual clarity.

SpaceX spent $19.6 billion on spectrum everyone said was too small to matter. 65 MHz—a fraction of what any one of the big three carriers controls. Then Gwynne Shotwell explained where the cell towers go, and AT&T, Verizon, and T-Mobile fell 2 to 4% in an afternoon.

The plan skips towers entirely. Starlink has 12 million customers with dishes bolted to their roofs, and Shotwell wants to attach a small cellular base station to the same mount. Every rooftop becomes a cell site.

That breaks the carrier cost model in a specific way. The radio was always the cheap part of a cell site. The money goes to land leases, permits, and trenching fiber backhaul to feed each antenna, which is why small cell buildouts have crawled for a decade.

Starlink’s version deletes all three. The site is a customer’s roof. The backhaul is the dish sitting next to the base station, already talking to satellites. And instead of paying rent to host the equipment, the host pays SpaceX $120 a month for internet.

Carriers pay landlords to carry their network. SpaceX’s landlords pay SpaceX.

The satellites cover everything the rooftops miss. Next-gen direct-to-cell satellites launch in 2027, and Shotwell says the upgraded service will be 100x better than the version that already texts your phone in dead zones today.

The strangest seat in the room belongs to T-Mobile. They leased SpaceX the 5 MHz that proved direct to cell works, marketed it as T-Satellite, and taught their own customers to trust the signal. In 2027 the partner files in as the fourth carrier.

What struck me most was the inversion itself. We’ve spent decades treating cell towers as fixed, expensive, hard-to-site infrastructure. The real costs were never the radios. They were the land, the permits, the fiber runs, and the ongoing rent checks. SpaceX is proposing to treat the customer’s roof as the site, the existing dish as the backhaul, and the monthly internet bill as the economic relationship—flipping who pays whom.

It is the kind of elegant deletion that looks obvious only after someone points it out. Most industries get stuck defending the expensive pieces of their stack long after those pieces stop being necessary. Here the expensive pieces are simply removed from the equation.

I don’t know yet whether the density will work in dense urban cores, how indoor coverage will actually feel, or how the regulators will treat residential rooftops as licensed transmitters. Those questions remain open. But the cost-model insight feels durable. Once you see that the landlords can be made to pay the network owner instead of the other way around, the old architecture starts to look like a historical artifact.

Worth sitting with for a while.

Categories
Living Walking

The Heron at Sharon Park

The heron did not fly.

Herons stand in water every morning of the world. Most don’t fly when a man walks past on a gravel path. But this one was close — close enough to see the yellow ring around its eye like a struck coin, the black cap combed back into two sharp feathers, a heron dressed for church. It considered me, or the water, or nothing at all, holding a stillness that costs a heron nothing and would cost me everything.

Sharon Park keeps a rotating cast of herons the way other places keep pigeons — gray, ill-tempered looking, small monks set down among the ducks. Usually they see me first and go up off the water in that ungainly unfolding, all elbows, and I’m left with the consolation of having glimpsed something before it left. This one let me stay. Let me get close enough to think the word friendly, though I know better — what I was seeing was a fish, or the absence of one, some interior calculus that had nothing to do with me. I was not a guest. I was weather.

It stood on one leg in green water, a green wall of reflected trees behind it, the low light turning that whole ceiling of water the color of a bottle. Its double stood beneath it, upside down, exact, patient as the original. Two herons for the price of one — one of feather, one of light, and I couldn’t have told you which was more real. The one in the water bent its neck into a shape the standing bird hadn’t made yet. It knew something the standing bird didn’t know, or had already forgotten.

Bubbles rose between them, a seam breaking the skin of the pond in a line, as though something down there were breathing, or as though the pond had decided, just this once, to show its work. Gas, probably, some decaying business at the bottom. But it looked like punctuation — the pond trying to say something and getting only as far as a string of commas before losing the thought.

I stood there longer than the moment required. This is what a heron does, if it lets you close and doesn’t leave: it makes you overstay. I thought about how little the bird was doing and how much I was doing to it — assigning it moods, welcoming it into my morning, when the truth was likely that it was simply waiting, the way I wait for coffee to cool, without company, without me. And yet it let me stay. Whatever arithmetic decided that, I was on the right side of it, for once, on an ordinary morning, my shoes going damp at the toe from standing too close to the edge.

Eventually I walked on, the way the heron surely expected I would, since staying is not a thing men do at ponds. I looked back once. It hadn’t moved. Its reflection hadn’t moved. The bubbles were still rising in their careful, aimless line — saying nothing, saying everything, the pond continuing to be a pond after I’d stopped watching it be one.

Categories
AI Aviation

Buffer Overflow

There is a moment in a stall, before the airplane actually stalls, when the controls go soft. The yoke stops talking back. You can still pull it toward you, and the nose will still come up, but the airplane is no longer answering in the language it used thirty seconds earlier, and if you do not recognize the change in dialect you will keep asking questions in a tongue the airplane has stopped speaking. Pilots have a phrase for the general condition this belongs to, which is broader than stalls and covers weather, traffic, radio calls, checklists, an unfamiliar airport with three runways and no tower: getting behind the airplane. The airplane is still flying. It is you who have stopped keeping pace with what it is doing.

I flew a Cherokee 235 for years, a airplane with enough useful load to make it forgiving and enough control weight to make it honest, and I only got behind it twice that I can remember with any precision, both times on approach, both times because I let a secondary task — a frequency change, a passenger question, a glance at a chart — eat the attention that the airplane needed at exactly the moment it needed it most. What is strange, looking back, is that the airplane never sped up. The airplane was doing what it always does on a three-degree glide path. I was the one who fell behind a constant.

I have started to notice the same falling-behind, unrelated to constants, in conversations with a language model.

It happens on the good days, which is the part that took me a while to understand. It is not the model being slow or confused. It is the model being unusually generative — pulling a thread from something I said four exchanges ago, connecting it to a domain I had not mentioned, offering three candidate framings where I had expected one — and somewhere in the second or third of these, I notice that I have stopped actually absorbing and started merely receiving. The words are still arriving. I have quietly stopped being the kind of reader who can do anything with them.

The name I have for this, mostly because I spent some years around fraud systems and payments infrastructure and the vocabulary never entirely leaves you, is buffer overflow. In a computer, a buffer is a fixed patch of memory set aside to hold data until a program is ready to process it — a loading dock, essentially, sized for a delivery truck of a known dimension. A buffer overflow is what happens when the truck backs in and keeps unloading past the edge of the dock. The classic and dangerous version of this is not that the extra data spills onto the floor and is lost. It is that the extra data lands on the memory sitting just past the dock, and overwrites whatever was stored there — a return address, a variable, something the program needed intact to know where to go next. The failure is not loss. It is corruption. The fifth insight does not politely fall away; it lands on top of the second insight and changes what the second insight was.

This is, I think, the more accurate complaint than “overwhelm,” which is the word I would have reached for a few years ago and which suggests simple excess, more water than the glass can hold. What I am describing is not excess. It is a rate mismatch between generation and integration, and the damage happens specifically at the boundary — not in the ideas that never arrived, but in the ones that arrived and were still being turned over when the next one came in and knocked them loose.

Aviation, as it turns out, has more than one name for this family of failure, and the names are not redundant, because they describe different mechanisms. Task saturation is the CRM term — Crew Resource Management, the discipline built in the seventies and eighties largely in response to accidents where a competent, rested, well-trained crew flew a functioning airplane into terrain because attention had been consumed by something lower priority than staying alive. Task saturation is measured, in training, less by how much is happening and more by whether the pilot can still prioritize — whether they know which thing to drop. Channelized attention is the adjacent and opposite failure: not too many things competing for a narrow channel, but one thing filling it entirely, a fixation on the landing gear light while the airplane, unflown, descends into the Everglades. And John Boyd’s OODA loop, developed for fighter pilots and stolen since by nearly every field that has ever needed a name for out-thinking someone under time pressure, describes what it feels like structurally to fall behind: you are not reacting to what the situation is, you are reacting to what the situation was, one iteration back, and every loop after that the gap does not close on its own.

I suspect what I am calling buffer overflow is closest to task saturation, with the wrinkle that in a cockpit the incoming data is at least all real-time and load-bearing — the runway is where the runway is — whereas a model in full flow is producing a mix of load-bearing insight and elaboration that only sounds load-bearing, and no light comes on to tell you which is which. Sweller’s cognitive load theory gives this a cleaner anatomy than aviation does: intrinsic load, which is the actual difficulty of the idea; extraneous load, which is how badly or well the idea is presented; and germane load, which is the effort of building the new idea into the structure of what you already know. My buffer does not overflow on intrinsic load — the ideas themselves are usually not hard. It overflows on germane load. The model can generate connections faster than I can lay the track that would let each new connection actually attach to something.

None of the aviation solutions to task saturation involve asking the airplane to slow down, and this is the part I keep returning to, because slowing down is the intervention that occurs to me first and is also, I think, the least aviation-like response available. A pilot who is task-saturated on approach does not usually ask the tower to widen the pattern. He drops something. He un-couples the autopilot from one axis and flies it by hand so the workload becomes tactile instead of cognitive, or he tells the passenger the question will have to wait, or he reads back only the clearance and lets the weather advisory go unacknowledged for ninety seconds because the weather advisory is not what is going to kill him in the next ninety seconds. The skill is not deceleration. It is triage performed at full speed, which looks, from outside the cockpit, indistinguishable from calm.

I do not yet know what the triage move is for a conversation with a model that is generating faster than I can integrate. I have a guess, which is that it looks less like asking the model to slow down and more like periodically stepping outside the exchange entirely — not to catch up on what was said, but to write down, in my own words, the one thing from the last five minutes I actually want to keep, before asking it to continue. That would make the move not deceleration but discard: choosing, the way the saturated pilot chooses, which incoming data does not get processed at all, on the theory that a buffer with something deliberately thrown out of it still holds its shape, and a buffer that tries to keep everything is the one that overflows.

Or maybe the real answer is the one the checkride examiner gave me in Springfield, on a September morning in 1978, when I came in too fast and too high and asked, afterward, what I should have done differently. He said the airplane had told me everything I needed to know about forty seconds before I noticed, and that the only skill that mattered was noticing forty seconds earlier next time. Not slower. Earlier.

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
Business Startups

They Love Having Meetings

Paul Graham posted on X this morning:

The danger of selling to big companies, if you’re a startup, is that they don’t say no outright. They have months of meetings with you first. Since you hate meetings, that seems to you a sign of commitment. But it’s not. They love having meetings! It’s almost all they do.

The founder walks out of the third or fourth session feeling hopeful. The room was full. People took notes. Someone said “interesting” more than once. A follow-up got scheduled. In the founder’s world, that much calendar time is expensive. It feels like proof that something is moving.

It isn’t. And this is easier to see from the other side of the table than from the founder’s.

Inside the big company, the meeting isn’t a delay before the work — it is the work. It’s how progress gets demonstrated, how risk gets spread thin enough that no one owns the outcome alone. A series of meetings can continue for months without anyone deciding yes or no, and nobody in the room experiences this as failure. The process is functioning as designed. No one has to kill the idea, because no one was ever positioned to fully own it. The calendar keeps filling because filling the calendar was most of the job.

The founder, who hates meetings for good reason, reads the big company’s willingness to keep talking as commitment. It’s a natural misreading, because outside a large organization, sustained attention almost always signals intent. Inside one, it can just as easily signal the opposite: an idea comfortable enough to keep discussing precisely because no one has been asked to stake anything on it.

The cost isn’t only the founder’s calendar. Energy that could have gone into shipping for actual buyers goes instead into decks, talking points, and the ongoing work of interpreting vague enthusiasm — real work, spent guessing at a decision someone else was never going to make. When the process ends — a polite “not at this time,” or more often just silence — the damage isn’t only the lost months. It’s the false signal that kept the founder from spending those months elsewhere.

The people on the other side of the table who move faster are the rare ones still able to say yes without assembling a committee to say it with them. Those conversations are shorter. They produce actual outcomes instead of follow-ups. They’re rare precisely because they require someone willing to own a decision alone — and most large organizations are built, deliberately or not, to make that as uncomfortable as possible.

Graham’s observation is simple and sharp because it names a quiet trap. The absence of a no is not the presence of a yes. Sometimes the most expensive thing a large company can offer a startup isn’t money, or even time. It’s the appearance of being taken seriously.

Categories
Computers

My First Computer

I noticed Elon Musk posted this morning on X that his first computer was a Commodore VIC-20. That same machine was part of our kids’ education in the Montgomery County, Maryland public schools back in the early 1980s.

At home, our first computer was an Atari 400 — the one with the flat membrane keyboard. Not exactly suited for touch typing.

Somewhere along the way I got a modem that let me get online, which led to joining CompuServe and getting active in HamNet, the amateur radio forum. One of the reasons I subscribed to CompuServe, if I’m honest, was to read the San Francisco Chronicle — and in particular, Herb Caen’s column.

At the time I worked for IBM, so when the first IBM PC launched, I was one of the early purchasers. The Atari 400 was retired soon after. But I sure had a lot of fun (and wasted a lot of time) on that first computer.