Categories
AI San Francisco/California

Tsunami

The trucks are what I remember. Not the houses, not yet — the trucks.

This was 2012, Atherton, a Tuesday probably, and I was driving through on some errand that doesn’t matter anymore. What matters is that the street had rearranged itself. Contractors’ pickups lined both shoulders, nose to tail, so many of them that the road narrowed to one lane and you had to slow down and thread through, the way you do in a construction zone that has forgotten to end.

White trucks, mostly. Ladders racked on top. A generator humming behind a hedge somewhere I couldn’t see.

Behind the trucks, the estates were coming apart and going back together bigger.

I remember thinking: something has happened here that I am only seeing the edge of.

What had happened was Facebook.

The company had gone public that May, and within months the money was finding its way, the way money does, into contractors’ trucks parked along an Atherton road.

I didn’t call it a wave at the time. I called it, in my head, weather — a system that had rolled in and would eventually roll back out, the way markets always eventually correct, the way things revert.

I was an investor. I’d seen booms before.

I believed in the mean.

I was wrong.

The prices didn’t stay at their old level. They didn’t return to the world I’d known. The numbers from 2012 became the new floor, and every year since has been built on top of that floor. Today those prices look almost quaint, a thing you’d want to explain to a younger person the way you’d explain what a dollar used to buy.

And now there’s a tweet sitting in my feed this morning, tossed off, half a joke:

Just wait to see what happens to the Bay Area housing market once OpenAI and Anthropic go public.

I read it twice.

What I felt wasn’t curiosity — the feeling I’d had in 2012, watching an unfamiliar weather system with a kind of professional interest.

It was closer to dread.

Because I’ve already seen the after-photo.

And I know how to run the comparison forward.

The Facebook IPO created a large cohort of newly liquid employees on the Peninsula. They were mostly mid-career, and their stock had vested over four years against a company whose value had grown enormously.

The frontier labs are different.

If OpenAI and Anthropic eventually go public anywhere near the valuations already being discussed in private markets, they could create another enormous concentration of newly liquid wealth — among employees, founders and early investors.

I don’t know how large that wave will actually be. Maybe I’m overstating it. Not every employee will buy a house. Some will already own one. Some will move away. Much of the wealth will remain on paper for years.

And housing doesn’t respond mechanically to stock-market wealth.

But I do know something about the place where this wealth is likely to arrive.

There isn’t much of it.

Land is the constraint.

And I’ve seen what happens when a concentrated burst of new wealth meets a place that can’t make more land.

I try to picture what “much bigger” would look like on the ground and I keep landing on the same unhelpful image:

More trucks.

Longer lines of them.

People get ready..

Categories
Music Radio San Francisco/California

Remembering San Francisco’s KKSF

At midnight on July 31, 1987, KKSF came alive at 103.7 FM.

The first thing anyone heard was Steve Winwood, “Back in the High Life Again,” which is either a coincidence or the most on-the-nose piece of programming in the history of San Francisco radio. Because that is more or less what the station promised everybody who found it in the weeks after: a higher life, a smoother one, arriving at 103.7 on your dial like a room you didn’t know you’d been missing.

I found it not long after, in my kitchen, which is where I found most things back then. The radio sat on the counter, tuned low enough that it lived beneath the sound of dishes and the refrigerator door and whatever else was happening in a life that was in the middle of being built.

This was after 8-tracks and before anyone had figured out you could carry a hundred thousand songs in your pocket, which meant the radio still had a kind of authority it doesn’t have now. You didn’t choose the next song. Somebody chose it for you, and you trusted them, the way you trust a bartender who’s been reading a room for twenty years.

On KKSF, the person doing the choosing had impeccable taste and a voice like a hand on your shoulder telling you it was going to be fine.

I think about how strange that arrangement sounds now, handing your afternoon over to a stranger’s judgment, and how completely I didn’t question it then. Roger Coryell had the mornings, or near enough to them, and he had that particular gift — rarer than people think — of sounding as though he was talking to exactly one person, and that the person happened to be you, standing at your own counter in your own kitchen a hundred miles from the studio.

Miranda Wilson took the middle of the day and kept it there for years, eventually becoming the last live voice on a station that had long since become an institution.

I didn’t know, listening in 1988 or 1991 or whenever it was, that I was living inside something that had an expiration date.

You never do.

You think the kitchen and the counter and the voice on the radio are just Tuesday, just the texture of an ordinary life. It takes twenty years and a different city outside the window before you understand that you were actually inside an era, capital E, and that eras, unlike Tuesdays, do not come back.

There was a name for what KKSF was doing: New Adult Contemporary, or NAC. But nobody listening in the kitchen cared about the name. The idea was simple enough — give the Bay Area something between the rock stations shouting at it and the classical stations lecturing it, light jazz easing into soft rock easing into something with a saxophone in it that nobody could quite name. Holding it together, day after day, song after song, was a program director named Steve Feinstein, chasing down lesser-known imports and out-of-print oddities that gave the station its particular ear, until he died in September of 1996, still on the job.

What they created was a kind of ambient permission.

You didn’t have to hurry.

You didn’t have to shout.

You didn’t even have to know what you were listening to.

The music simply arrived, and you let it stay.

There was a version of San Francisco in those years that felt, at least to me, as though it had room for that kind of unhurried, unbranded taste. This wasn’t necessarily a gentler city in any objective sense. San Francisco had plenty of its own troubles then, as it always has. But it was the city I was living in, and KKSF somehow belonged to its frequency.

Real estate hadn’t yet eaten everything. The tech money was already there, but it hadn’t yet rearranged the furniture of the entire region around itself. There were still corners of the Bay Area where nobody seemed to be optimizing anything.

KKSF didn’t optimize.

It curated.

And there is a difference.

The station lasted twenty-two years, which sounds like nothing until you try to think of anything else in your life that lasted twenty-two years without you noticing it was a relationship.

Then, in May 2009, it stopped being itself.

By then Clear Channel owned the station, and economics won. The format disappeared, the music changed, and Miranda’s voice — which had been finding its way into kitchens across the Bay Area for years — went quiet on that frequency for good.

I didn’t hear it happen.

I was somewhere else by then, doing something else, the way you are when the things that shaped you quietly stop existing without asking your permission first.

I only found out later, the way you find out most things now: long after the fact, scrolling, discovering that a piece of your own furniture had been sold at auction while you weren’t looking.

What I keep coming back to isn’t the music, exactly, though I could still hum half of it.

It’s the kitchen.

And the particular quality of trust required to let somebody else choose what you’d hear next — to be doing stuff and suddenly hear a song you’d never have chosen on your own, and follow it somewhere you wouldn’t have gone. I miss the feeling that the world could still surprise me without first asking what I wanted. Old world radio curation.

Sometimes I think about the city outside that kitchen window, too. It doesn’t sound like that anymore. It doesn’t have quite the same room, at least to my ears, for anything that isn’t trying to be the loudest thing in the room.

Sometimes when music is playing in the background and my mind is wandering I think of KKSF and how a radio station is just a number until somebody fills it with twenty-two years of somebody else’s kindness and wonderful taste.

Categories
AI Anthropic Apple Google OpenAI Spacexai

It’s the Harness, Stupid!

I’ve been wondering whether we’ve been looking at the AI stack from the wrong end.

Recently Kris Patel on X laid out a set of excellent questions that he’s looking to have answered as Anthropic and OpenAI move toward going public:

  1. Do you really need frontier-scale intelligence for every task?
  2. Can open-weight models provide an effective alternative to frontier models at a significant discount?
  3. Is the ultimate moat the intelligence or the harness?
  4. What other business models will the frontier labs have to adopt to make the unit economics work long term?
  5. How are you going to prevent distillation from capturing your IP and releasing it?

I’m going to explore only the third question here — moat versus harness. The other four deserve their own consideration, particularly once we have the Anthropic and OpenAI S-1s in hand, revealing for the first time the unit economics of the two largest frontier labs and how much runway they have to support the capacity they’ve contracted.

By “harness” we mean everything surrounding the model: the interface, context, memory, tools, orchestration, evaluation, permissions, and increasingly the user’s accumulated habits and data. The model supplies intelligence. The harness turns intelligence into a product.

Listening to Gavin Baker on the recent All-In episode sharpened this line of thought into something more concrete. He referenced a thought experiment from Eric Vishria: even if OpenAI or Anthropic lost their edge at the pure model layer, they would still retain significant value because of the product harness—the interface, the surrounding tooling, the orchestration—and the user familiarity and habits that have already formed around those platforms. Baker said there is a strong element of truth to it. I think he’s right, and the reasoning behind it is worth spelling out. A model can be replicated, distilled, open-weighted, or commoditized. A mature harness has network effects, switching costs, proprietary context, distribution, workflow integration, and accumulated user behavior. That’s a much harder thing to dislodge.

We are watching intelligence become more abundant and more interchangeable at the same time that the systems built around that intelligence are becoming stickier. On the developer side, the strongest examples are already clear. Cursor turns the IDE into a multi-model agentic environment with deep codebase awareness. Claude Code runs long-horizon coding agents from the terminal, planning, editing, testing, and iterating. Grok Build, Claude Cowork and similar tools emphasize parallel agents and tighter control over local context. In each case the model is a component; the surrounding system does the real work of routing, memory, tool use, and evaluation.

The consumer version of the same idea is now taking clearer shape at Apple. The rebuilt Siri AI shown at WWDC 2026 is not trying to win the pure model race. It is built as a personal harness. A system orchestrator decides what stays on-device with Apple’s Foundation Models, what moves to Private Cloud Compute, and when heavier reasoning is required. Personal context—messages, email, photos, calendar, notes, on-screen awareness—is handled largely on-device through the Spotlight semantic index and App Toolbox. Apple is designing the system so that personal context can be used without giving Apple itself access to it. Conversation history lives in a dedicated Siri app for the user to revisit. And it all syncs across all your Apple devices.

Here is the part I think matters most, and it’s easy to miss if you only read the privacy story. Apple’s Foundation Models framework doesn’t just call Apple’s own models—it’s built to support cloud models from other providers, including Claude and Gemini, conforming to a common protocol. Which means the system orchestrator, not the user, decides which model handles which task. This request goes to the on-device model. That one goes to Private Cloud Compute. A harder one might go to Claude or Gemini. The user doesn’t need to choose, and increasingly doesn’t need to know.

That’s the inversion worth exploring further. The frontier model stops being the interface and becomes a component underneath someone else’s interface. The harness chooses the intelligence. And the company that owns the harness—the OS, the identity layer, the permissions, the apps, the sensors, the notifications, the semantic index tying all of it together—has a form of leverage that has very little to do with whose model is smartest this quarter.

That reframes the subscription question too. I don’t think the right question is whether Siri gets good enough to beat ChatGPT or Claude at reasoning. I think Siri doesn’t need to win that fight at all. It needs to win a different layer entirely—the ambient assistant layer, not the reasoning layer. They’re doing different tasks. ChatGPT or Claude might remain where you go when you think, when I need to reason about something. Siri becomes where I go when I need something done: find (or make) my reservation, text my friend, find that old photograph, update my shopping list, schedule that meeting, add this thought to my notes, figure out when we’re free next week, remind me about that thing we discussed three months ago. Apple’s advantage as an ambient assistant isn’t primarily that it has your personal data. It’s that it has OS-level authority over the world that my personal data lives in.

Of course this is still early. Execution will determine how much of the architectural promise becomes daily reality. Reliability, agentic follow-through, and the quality of the on-device models will matter as much as the privacy story or the multi-model routing. But the strategic bet itself is clear, and it aligns with the broader shift: durable value is migrating toward the systems built around the models, especially systems that sit atop private, permissioned, personal context that competitors cannot easily reach. My early personal experience with the new Siri in iOS 27 betas has impressed me so far. All of this also seems to apply to Google in the context of their Pixel family of devices.

This doesn’t mean frontier labs lose. Pricing power still exists at the high end for the hardest agentic and long-horizon work. Open-weight models will continue to pressure costs and expand access. Distillation remains a real risk. But the more the capability gap narrows, and the more a harness like Apple’s can route among interchangeable frontier models rather than depend on any single one, the stronger the case that value settles into whoever controls the context—not whoever trained the model.

The coming Anthropic and OpenAI S-1s will tell us whether the frontier labs can make their economics of intelligence work. The next generation of Siri, Gemini, ChatGPT, Claude, and whatever comes after them may tell us something even more important: who gets to own the primary relationship with the user.

The model may be the engine. But the harness is where the driver sits.

What a time to be alive!

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.