Tag: personal essay

Categories
Cars Design Honda

The Shape of Fear

There’s a red-and-silver Honda CRX that shows up in a parking lot near a park I walk regularly. Not always — it’s not a daily thing — but often enough that I’ve started to look for it. When it’s there, I stop. I stare longer than a car deserves. I’ve owned other Hondas. I never owned this model, and by the time I could have, I was a family man, and a two-door coupe with a back seat that barely qualifies as a suggestion wasn’t a thing you brought home. That’s still true. I still love it anyway.

For a long time I thought the pull was nostalgia — an artifact from a specific decade doing what artifacts from specific decades do, standing in for the whole era around them. But nostalgia doesn’t usually make you stop walking.

Something more specific was going on, and I only located it recently, looking at a rendering of Tesla’s Cybercab: the same silhouette. Not the same car, not the same era, not the same anything except the one thing that matters most in a side profile — the roofline. Low nose, a peak over the front seats, one continuous downward sweep to a short, cropped tail. No break at the B-pillar to speak of. Glass that continues the line of the roof instead of interrupting it.

Two cars, forty years apart, arriving at the identical answer to a formal problem. That’s the kind of coincidence that isn’t really a coincidence — it’s a shape that keeps getting rediscovered whenever the constraints line back up.

The Cybercab gets there because there’s no driver’s compartment to package around, no B-pillar structure fighting for space, nothing back there to make room for. The roofline can just fall away because there’s nothing left to interrupt it.

The CRX got there from the opposite direction — not by subtraction of function but by subtraction of everything else. Weight. Drag. Ornament.

What I didn’t expect, going looking, was how much fear was baked into that shape.

The CRX wasn’t dreamed up in some skunkworks with time to spare. It came out of what Honda’s own people described, at the time, as something close to an image crisis — the third-generation Civic was about to launch into a market with sharper competitors than the last one, and the man responsible for small-car development at Honda R&D was worried the company’s whole small-car identity was aging out from under it. The response was billed internally as a kind of renaissance, and the CRX was its opening statement — not a side project, but the leading edge of an “all-out attack.”

The person who actually shaped it, Hiroshi Kizawa, had already put his career on the line once, on the original Civic — a car he believed Honda’s future as a real manufacturer depended on. He came back and did it again, smaller and stranger this time: a two-seat coupe, under 900 kilograms, wrapped in plastic body panels molded in-house, chosen partly because they could someday be recycled — Honda thinking, in 1981, about what happened to the car after its life was over, which is its own small strangeness worth sitting with.

The reception at home was lukewarm in a way I find almost endearing now. One Japanese trade magazine at the time called it a dehydrated Camaro with some boy-racer posturing, allowing that it might not be beautiful but was at least likeable. That’s a strange epitaph for a car I’d call one of the most purely resolved shapes of its decade. But maybe that’s how it goes with real design — the people closest to it, watching it get made under pressure, can’t yet see what it will look like from forty years out, parked in a lot, still stopping people who weren’t even born when it launched.

Less than six months after the CRX reached showrooms, Honda started work on what would eventually become the NSX. The unglamorous little economy coupe, born from institutional anxiety and injection-molded plastic, turned out to be the warm-up act for the most serious sports car the company would ever build. Fear, it turns out, is not a bad place to start, if the people afraid of it are good enough to turn it into something worth being afraid for.

Which makes me think of Ferrari’s own version of this moment, playing out right now. Their first electric car, the Luce, is exactly the kind of institutional fear the CRX was born from — a company that has to prove it still belongs to the future, using a technology it didn’t choose. And where Honda answered that fear with a shape, a single unbroken line that turned scarcity into style, Ferrari answered it with a four-door liftback, roomy and glassy and, by most early accounts, nobody’s idea of a Ferrari silhouette. I wish they’d gone the other way. I wish somebody at Maranello had looked at what a wedge does when you strip a car down to its constraints — no engine bay to hide, no B-pillar to interrupt, nothing left but the line from nose to tail — and had the nerve to make the Luce look like it was afraid of something, the way the CRX clearly was.

I think about that shape differently now — not as a wedge from the eighties, and not as a preview of some robotaxi’s rendering either, but as a shape that seems to arrive whenever a design team is left with almost nothing to hide behind. No engine bay to speak of. No back seat to protect. No driver at all, in one case. What’s left, both times, is the same honest line — nose to tail, unbroken — and I wonder what it says that the shape survives every reason for making it, outlasting the fear and the plastic and the market anxieties that produced it, showing up again decades later for reasons nobody involved the first time could have guessed.

Categories
AI Apple Google

The Floor

I compared the frontier to a three-star chef making grilled cheese in “Context Rot” — the smartest models on earth spending most of their time on work beneath them, the way a chef trained at Le Bernardin might still melt cheese between two slices of bread on a Tuesday night and call it dinner. The comfort was the point: if the sharpest tool is saved for hard problems and something merely-very-good handles the rest, nobody’s losing anything. The floor was never the interesting part.

I’ve kept turning the joke over, and I think I had the wrong worry.

Watch what companies do with their AI spend, not what they say. Coinbase moved engineers off frontier models onto open weights and cut its AI spend nearly in half while usage kept climbing. Nvidia runs a closed model as orchestrator and routes the actual volume — the daily uncelebrated bulk of it — to open weights it controls. The frontier is becoming a dispatcher, deciding where the request goes and rarely doing the work itself. The instinct is to worry about whose open weights end up running that volume, and right now the most capable ones at scale are Chinese — GLM, Kimi — which makes it tempting to read this as a contest America is quietly losing: the floor of the AI economy built somewhere else, at a price export controls can’t touch. You cannot embargo a file already downloaded. You cannot price-match free.

But that framing has a hole. Google’s own Gemma family is open-weight and good enough to handle that daily volume without anyone reaching for GLM or Kimi. “Open weights are a Chinese story” only holds if you don’t count the open models the company running Android and half the internet’s search traffic has already shipped.

And once I saw that hole, a bigger one opened behind it. I’ve been trying Apple’s new Siri — arriving with iOS 27 this fall, genuinely surprisingly good in beta — and it made me realize open weights, of any nationality, were never going to cook most of the world’s dinners. Apple and Google are.

Consider what actually determines where the world’s routine inference runs. Not which model benchmarks best, not which weights are downloadable — what’s already installed. Apple ships to well over a billion active devices before routing a single query through Siri’s new architecture. Nobody has to be persuaded to try it, or hear about it on a podcast; it’s the thing that answers when you press the button you’ve pressed for a decade. Google owns the search bar and the Android default the same way. Between them, that’s most of the world’s phones — and phones are where most of the world’s questions get asked.

The open-weight framing assumes the floor is up for grabs, that whoever ships the best free model wins the daily grind by merit. But the floor was never a bazaar. It’s a set of defaults, owned by whoever already has the device in your hand, not whoever holds the most generous license. Apple didn’t need to win the model war to win this. Its heaviest reasoning tier is built with Google, running on Nvidia chips in Google’s cloud, under a deal reported at roughly a billion dollars a year — Apple doesn’t fully own the engine doing the thinking. It doesn’t need to. It owns the button.

That’s a quieter concentration than an export-controls fight, and a harder one to dislodge. An open model can be forked, distilled, undercut, or out-competed by the next release. A billion phones with an assistant built into the lock screen cannot be routed around. Whoever’s weights hum underneath barely matters, the way it barely matters to a diner which supplier delivered the flour. What matters is whose kitchen the meal came from, and whose name is on the door.

The grilled-cheese chef was never the risk. Two chefs are about to own nearly every kitchen on earth, and most of us will never notice — because a kitchen you’ve been eating out of for a decade doesn’t feel like something that was won. It just feels like home.

Owning the kitchen and getting paid for what’s cooked in it, though, turn out to be two different questions. That one’s for another post.

Categories
AI China Youth

The Arithmetic of Youth

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

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

The answer I got instead was the Cultural Revolution.

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

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

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

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

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

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

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

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

Categories
AI

Context Rot

Here is a small, possibly embarrassing confession: I have never, not once, gone looking for the best AI model.

I have a model. It lives in a browser tab — Safari, usually, on whichever device is nearest, occasionally Chrome if I happen to be at the desktop. It does what I need — drafts an email, untangles a sentence, tells me what a Norwegian emigration record from 1856 probably says — and then I close the tab and go on a walk.

Somewhere out there, presumably, a much smarter, much more expensive machine is doing something extraordinary with protein folding or hedge fund arbitrage or the outer edges of mathematics I will never visit. I have made my peace with never meeting it.

This did not used to feel like a confession. For a while there — a year, eighteen months — it felt like the central drama of the whole industry: which model was “best,” who had it, who had lost it, whether some lab’s quarterly earnings call would reveal that the frontier had quietly moved sixty miles down the road while everyone was looking the other way. Benchmarks were released like box scores. People argued about them the way people argue about batting averages, with the same weird intensity, the same conviction that a two-point difference in some abstract reasoning test settled something important about the future.

And then, at some point I can’t quite date — it crept up, the way these things do — I noticed I had stopped caring.

Not because the frontier stopped moving. It didn’t. It’s still moving, arguably faster than ever, in ways that occasionally show up in the news with all the drama of a soap opera (a delayed launch, a researcher poached, a stock down five percent in an afternoon, always something).

I stopped caring because none of it touched me. My model — whatever it was, this week — had long since crossed some invisible threshold past which more didn’t register as more. It was already better than I needed. It has been better than I needed for a while now. I suspect I am not unusual in this. I suspect most people, doing most things, most days, are operating comfortably inside a capability surplus so large they’ve stopped noticing it’s there, the way you stop noticing a room is warm.

If the top of the model isn’t for people like me — and it increasingly isn’t — then who, or what, is it actually for? I went looking for one piece of the answer and found, instead, a metaphor.

It’s called “context rot.” I have to admit, before I go further, that I’m not sure I’ve ever felt it myself — which, on reflection, is its own small piece of evidence. My sessions close in minutes, not hours. I ask, it answers, I leave. Whatever happens to a model over the fourth or fifth hour of sustained, dependent work is a country I simply don’t visit.

But other people do, increasingly — entire teams do, for entire projects — and what they’re finding out there is worth understanding, even secondhand. It describes something that happens to AI models when they’re asked to work for a long time on something complicated — not five minutes, but five hours; not one question, but a hundred small decisions stacked on top of each other, each one depending on the last.

You’d think the limiting factor would be room. Models have a “context window” — a stated capacity, like a gas tank, measured in tokens, and for a while the marketing numbers on these were the whole story: two million tokens! A library! And you’d think, as with a gas tank, that the thing runs fine until it’s empty and then it stops.

That is not, it turns out, what happens. What happens is closer to what happens to your desk.

You know the desk. Everyone has the desk. It starts the morning clean — an aspirational, almost insulting cleanliness — and by four in the afternoon it is a geological record of the day: three coffee cups, a stack of things you meant to file, a Post-it with a phone number you no longer need, the good pen buried under a printout of something you already dealt with an hour ago. The desk is not full. There is, technically, room. You could clear a space if you tried. But you don’t try, because functionally, cognitively, the desk has stopped being usable long before it ran out of surface area. You start looking for the stapler and forget what you were stapling. This — and I did not make this term up, I want to be clear, though I wish I had — is context rot. The window hasn’t run out. The signal has just drowned in its own debris.

Researchers watching this happen to long-running AI agents have found something almost cruelly elegant about how it fails: it doesn’t fail gradually, the way you’d expect a desk to get gradually messier. Errors compound. A task that takes twice as long doesn’t get twice as likely to go wrong — the failure rate roughly quadruples. Two mistakes early in a long chain of dependent steps don’t add up to a slightly worse outcome. They multiply into something close to total collapse, four hours in, for reasons that trace back to a single bad assumption made in the first twenty minutes and never revisited.

Here is where the frontier comes back in — not as the whole answer, but as a piece of one.

It is not that frontier models are smarter in the way a benchmark measures smart — better at a single hard math problem, a cleverer turn of reasoning. Plenty of models can do that now; the “good enough” tier has crept remarkably high.

It’s that frontier models are apparently, marginally, meaningfully better at not rotting. At keeping the desk usable at hour six. At knowing which of the forty things on the desk actually still matters and which is a coffee cup that should have been thrown out an hour ago. This is a genuinely different kind of intelligence than the one benchmarks were built to measure, and it is almost invisible from the outside — you don’t see it in a single exchange, you see it only in the difference between a project that holds together over three days and one that quietly, subtly, stops making sense somewhere around Tuesday afternoon and nobody notices until Thursday.

If that’s true — if the frontier’s real edge is durability rather than raw cleverness — you’d expect to see it show up in how the labs actually deploy their own models: saving the sharpest tools for the tasks that need to survive the longest.

I went looking for a real-world example and found one closer to home than I expected: Anthropic’s own Slack tool, the one where you tag the AI into a channel the way you’d tag a coworker, and it works alongside a whole team over days, learning the channel as it goes. It runs on a serious, capable, thoroughly frontier model — but not, it turns out, on the company’s very best one. That one is held back, reserved for a smaller and stranger set of problems nobody has solved before at all. I sat with that for a while. The tool built to survive a whole team’s whole week, in public, under the most sustained pressure any of their products face, wasn’t handed the sharpest blade in the drawer. It was handed the second-sharpest — which was apparently, entirely, enough. Which tells you something about where the two kinds of intelligence actually diverge: the merely-very-good model handles the desk staying clean for a week, in public, in front of a whole team, where one bad assumption made Monday and never revisited would be visible to everyone by Thursday. The truly new capability is being held in reserve for something else altogether.

I don’t have a tidy place to land this, and I’m suspicious of anyone who does. But here’s the closest I can get.

Imagine a three-Michelin-star chef — the kind of person who has spent thirty years learning to coax something transcendent out of a single scallop, who can tell you, by smell, that a stock has forty more minutes in it — standing at your stove on a Tuesday night making you a grilled cheese sandwich. It will, I promise you, be a very good grilled cheese sandwich. The bread will be evenly golden. The cheese will have reached some ideal, fully-considered state of melt. But almost none of what makes that chef extraordinary is actually being used to make it — none of the thirty years spent learning to hold forty things in mind at once without losing track of any of them, the exact skill, it occurs to me, that keeps a long, complicated project from quietly falling apart on day three. The technique is idling. The thirty years are in the room, present, available, and almost entirely beside the point, because a grilled cheese sandwich was never the place where thirty years shows up. It shows up somewhere else — in a dish you will never order, on a night you weren’t there.

What you got instead, on your ordinary Tuesday, was simply more than enough.

Categories
Aging Living San Francisco/California Street Photography

The Zone

Sail Away - San Francisco - 2014

I have been alive for nearly a third of the time this country has existed. It arrived the way facts do at a certain age, sideways, while I was thinking about something else, and it sat me down. Two hundred and fifty years, and my own decades take up a third of it — whether I meant to claim that much room or not.

I used to think the road was where I went to escape the smallness of a life. Now the road doesn’t call the way it once did. Some of that is willingness. More of it, if I’m honest, is a body that’s less steady, a bladder with a mind of its own. The body files its objections. I used to override them. I no longer do — not because I’ve grown wise, but because the overriding costs more than it used to and buys less.

But I want to tell you about what I got instead, most Fridays, for not quite a decade, because it isn’t nothing.

Doug came across on the ferry from Larkspur, and I’d meet him at the Ferry Building — watching the boat come in, watching him pick his way down the gangway with his camera bag, before either of us had said a word or made a single decision about where to walk. Then we’d head out along the Embarcadero, sometimes up into the financial district, and for the first ten minutes my mind would do what minds do. It would analyze. It would compose. There, the light coming off that glass tower, wait for the man in the overcoat to cross into it, no — too late, gone. Appraising and timing, the way I’d once weighed a stock, or a runway, or a route.

And then, without my choosing it, something released. There’s no threshold you feel yourself cross. But sometime after the tenth minute, the appraising stopped, and seeing took over. Not looking for. Not looking at. The street would stop being a set of problems to solve and become only itself: a longshoreman on a break outside a pier, a gull working the same patch of pavement three times, fog sliding under the Bay Bridge like it had somewhere to be. Doug, a few yards off, would go quiet the same way, and we’d shoot for an hour or two and then find each other again at the end of the block.

By then we’d have worked up an appetite for something other than pictures. Tadich Grill, if we could get in — the linen and the old wood and the waiters who’d been there longer than some of our careers. We’d order something plain and good, and that’s when the talking would start. Not small talk. The real kind. Work, kids, the state of things, whatever had lodged itself in each of us that week. The seeing on the street and the talking over lunch were not two different activities. They were the same hour, extended. One was attention paid to the world. The other was attention paid to each other.

I have flown airplanes and driven through weather I shouldn’t have, and I loved both for the demand they made on me — the total, narrowing attention that leaves no room for the self that worries. What I didn’t understand then was that a boat crossing from Larkspur, and a Friday, and an old friend across a table at Tadich, could ask the same thing of me, for free, without a single mile of my own driving.

Covid stopped it. Not gradually — the way most rituals fade, through scheduling and distance and the slow drift of people’s lives — but all at once, the way everything stopped that spring. The ferry didn’t run. The restaurants closed. We never quite picked it back up, not the way it was. I don’t think either of us decided to let it go. It just didn’t survive being interrupted.

A third of the country’s whole life, and it took me most of my own to learn what those Fridays were teaching me — and then to lose them before I’d finished learning it. I still see the ferry pulling in. I still see Doug on the gangway with his camera bag, in no hurry, already half in the zone before his feet touch the dock.

Categories
Aircraft Memories

The Wire and the Three Wires

I read this morning that the Navy retired its last C-2 Greyhound. It took me straight back to a deck fifty miles off San Diego, thirty-two years ago, and a young woman I never knew and watched anyway.

The deck comes up fast at sea. That’s the first thing nobody tells you about a carrier — that an airfield can ambush you, can rear up out of the ocean looking smaller than a parking lot, gray and pitching, while the C-2 you’re strapped into backward drops its gear and aims for four wires stretched across forty thousand tons of steel. You hit the third wire if you’re good. You hit anything if you’re lucky. Either way your body keeps moving roughly sixty miles an hour after the airplane has stopped, and the harness across your chest reminds you of that fact with some violence, and somewhere behind you a sailor a quarter your age in a yellow shirt is already waving the next plane in, because the ocean does not wait for you to catch your breath.

This was July of 1994, the USS Constellation rolling gently under a sky that hadn’t decided what color it wanted to be. There were four of us. We’d flown out of North Field that morning the way you’d catch a bus, except the bus had a tailhook, and we spent the day being shown around eighty acres of moving city — the flight deck, the hangar bay, the nuclear reactor spaces, the wardroom where men twenty years younger than us ate dinner with the particular speed of people who might be back at work in an hour.

One of the men with us had commanded that ship once, in 1966, when most of his year was spent on Yankee Station, running air strikes into a war the country back home had already begun arguing about. We were guests. We were, by the time the sun went down, members of something called the Tailhook Club, which is the kind of honor that means everything to you and nothing to anyone you’ll explain it to later.

That night we went up to the flight deck for the carrier qualifications, and somebody put us right next to the meatball — the lens of amber light a pilot chases down the glide path in the dark, the only thing standing between a good landing and a very bad one.

Four instructor teams worked the deck around us, grading each approach, calling out deviations nobody but a trained eye could see. Every airplane that came aboard came in close enough to feel — gear down, hook down, throttle slammed to full the instant the wheels touched, because if you miss the wire at night on a moving ship, you don’t get to think about it, you just fly.

One of the pilots qualifying that night was a Lieutenant named Kara Hultgreen, twenty-nine years old, finishing third in a class of seven — solid, unspectacular by the numbers, which is exactly the kind of detail that becomes unbearable in hindsight. She would go on to become the first woman to serve as a carrier-based fighter pilot in the United States Navy. Fifteen months later, on the twenty-fifth of October, 1995, attempting to land an F-14 aboard the USS Abraham Lincoln, she would die, the first female fighter pilot in American military history to be killed flying. We didn’t know her. We knew her the way you know anyone on a flight deck at night — as a set of running lights and a sound, judged the same as everyone else by the men standing next to us with clipboards.

The next morning we were up before the sun because the ship had a refueling to do, the carrier and an oiler closing on each other from miles out, two enormous vessels pointed straight at one another like they meant it, until at the last possible moment both turned in tandem and the oiler slid in alongside, parallel, close enough that the lines shot across between them looked almost casual. We watched it from the bridge with the executive officer. Afterward the captain — a man who’d led the Blue Angels before he’d ever commanded the Constellation, and who still had the jacket with the right patches to prove it — called up the two sailors who’d run the operation and thanked them in front of everyone, the way a good leader does when he wants the rest of the crew to notice who deserves it.

Then we stayed on the bridge to watch the air wing leave. Eight F-18s — six Navy, two Marine Corps. The Navy pilots flew it the way you’re supposed to: catapult stroke, climb out, gone. The Marines went last, and the moment their wheels cleared the deck they hauled the airplanes into a vertical climb, straight up, like the sky owed them something and they intended to collect. The executive officer laughed beside us. “There go your tax dollars for this year,” he said, and none of us argued.

Then it was our turn. Strapped into the C-2 again, facing backward, braced against a catapult stroke that takes you from zero to flying speed in about two seconds. And then you stop in mid-air or so it feels. Disneyland never built anything like it.

I think about the wire sometimes — the one you catch, the one that stops you. Hultgreen caught it that week, while I stood close enough to hear the engines roar past us in the dark, indistinguishable from the five other sets of lights that came down before her. You don’t know, standing there, which ones you’re watching for the last time. I didn’t, that night. The airplane that carried us home is gone now too, and somehow it’s the airplane’s retirement, not anything grander, that brought all of it back.

Just came across this YouTube video of the final arrested carrier landing of the C-2 Greyhound. End of an era – with good personal memories!

Categories
AI Thinking Tools

Outsourcing Thinking but not Understanding

There’s a line mentioned in a recent discussion by Andrej Karpathy that I keep turning over: You can outsource your thinking but you can’t outsource your understanding.

It sounds like a warning. Maybe it is. But the more I sit with it, the more it feels like something older — a distinction philosophers have been trying to draw for centuries, suddenly made urgent by the fact that we now have a tool that makes outsourcing thinking almost frictionless.

Here’s what I notice when I use AI well: I get the answer, and I feel satisfied. There’s a small dopamine tick. Task closed. But if someone asks me an hour later to explain the reasoning, I often can’t. The thinking happened — somewhere — but not in me. I was a conduit. A confident one, too, which is the dangerous part.

This is different from looking something up. When I Google a fact and paste it into a document, I know I’m borrowing. The seam is visible. But when I ask an AI to reason through a problem with me, the output arrives in first person, in fluent prose that matches my own register, and something in my brain says I worked this out. The seam disappears. That’s new. That’s the thing we don’t yet have good instincts for.

Karpathy’s deeper point is about construction. He’s a builder by temperament — his mantra, which he traces to Feynman, is that if you can’t build it, you don’t understand it. What you can’t yet construct, you merely think you understand. There are always micro-gaps in your knowledge, invisible until you try to arrange the pieces yourself and find they don’t quite fit. The AI doesn’t change that equation. It just makes it easier to mistake the map for the territory — and to feel strangely proud of a map you didn’t draw.

Hesse understood this, in a different century and a different idiom. In Siddhartha, the young seeker travels to meet the Buddha himself — the most perfectly articulated wisdom in the world, delivered by the man who actually found it. Siddhartha listens, acknowledges that the teaching is flawless, internally consistent, the most complete account of liberation ever assembled. And then walks away. Not from arrogance, but from recognition: even the Illustrious One cannot hand you his liberation. The path was his. He walked it. That walking is not transferable, no matter how perfect the description of the destination. Received knowledge, however exquisite, is not the same as earned knowledge. The gap between them is exactly the size of your own unlived experience.

That’s the same argument, made across two and a half millennia. Feynman says you have to build it. Hesse says you have to live it. Karpathy says the AI can do neither for you.

He’s also made a related observation about educational video — that a lot of content on YouTube gives the appearance of learning but is really just entertainment, convenient for everyone involved. Nobody has to do the hard part. AI-assisted thinking has the same shape, just more intimate. You’re not passively watching — you’re actively typing, prompting, engaging. It feels like cognition. But engagement isn’t understanding. Typing a question is not the same as wrestling with it.

I don’t think the answer is to use AI less. That’s not Karpathy’s argument either — he’s spent the last year building a school premised on AI tutors expanding what people can learn. The lesson is about custody. When I hand a problem to an AI, I need to stay in the loop as a learner, not just as a reviewer. There’s a real difference between asking give me an answer and asking help me build the reasoning. The first outsources thinking. The second — if you insist on it, if you refuse to be a passenger — can still leave the understanding in you, where it belongs.

But insisting is the work. And the work is now easier to skip than it has ever been.

Understanding isn’t a product you receive. It’s a residue — what settles in you after genuine struggle, after the confusion and the dead ends and the small hard-won moments of clarity. Siddhartha couldn’t get it from the Buddha. You can’t get it from the AI. Karpathy’s line is a custody argument: the thinking can travel, but the understanding has to stay home.

What unsettles me is that we’re building tools that make the borrowing invisible — that dress outsourced reasoning in the first person, that let us feel like we’ve understood something we’ve only processed. Siddhartha at least knew he was walking away from the teaching. He felt the gap. We might not even notice ours.

Categories
Living Serendipity Travel

The Conditions of the Unexpected

There is a flight I took in 2001 that I have never fully stopped thinking about. Not the flight itself — a forgettable three-hour hop in a middle seat — but the two-hour delay that preceded it. The gate agent’s apologetic crackling over the intercom. The way I surrendered to the terminal, found a bar stool, ordered something I didn’t need. The man next to me was reading a book I recognized. We talked for two hours. He told me about a job. I didn’t take it — but I spent three months considering it, which is its own kind of detour. I came out the other side different in ways I still can’t fully account for.

I have told this story before as a story about luck. I’m not sure that’s what it is.

Alexander Krauss spent years going through the records of science’s major discoveries — Nobel Prize winners, the landmark non-Nobel findings, more than 750 in all — looking for the mechanism behind what everyone had been calling serendipity. The telescope trained on an unexpected patch of sky. Fleming’s contaminated petri dish. The chance observation that shouldn’t have meant anything but did.

What he found upended the romance of the story. The discoveries that seemed most accidental, most shaped by the caprice of an unlucky sneeze or a mislabeled sample, turned out to follow a pattern. Nearly all of them happened shortly after a researcher gained access to a new tool. The accidental observation of cells under an improved microscope. X-rays discovered through a discharge tube nobody had pointed in that direction before. The first planet beyond our solar system, caught by a spectrograph that hadn’t existed a few years earlier. What looked like lightning striking the same improbable spot again and again was actually the same thing each time: a new instrument creating the conditions under which something unexpected could be seen.

Krauss calls this “engineering serendipity.” The phrase stops me every time I read it, because it sounds like a contradiction and turns out to be the most practical sentence in the philosophy of discovery. You can’t engineer the specific surprise. But you can engineer the conditions that make surprise likely. You can build the lens before you know what it will show you.

This distinction — between engineering an unexpected discovery and engineering the conditions for unexpected discovery — is one I’ve been carrying around like a stone in my pocket. Because I think it applies far outside the laboratory. I think it’s one of the central design problems of a life.

The book trend critics are calling “Digital Nostalgia” is, depending on how you read it, either the most sentimental or the most diagnostic thing happening in literary culture right now. The novels topping lists this spring are full of people losing their recordings, waking up in centuries without algorithms, mourning the weight of analog things. Ben Lerner’s new novel begins with a dropped phone in a hotel sink — the recording gone, the moment unrecoverable. Caro Claire Burke’s Yesteryear sends a social-media influencer back to an 1855 that is nothing like the one she curated for her followers: cold, filthy, unfiltered, and somehow more real.

What readers are reaching for in these books is not the past per se. It’s the texture of a life that wasn’t predicted in advance. The feeling of not knowing what came next because nothing had pre-sorted the possibilities. Nostalgia, in its root meaning, is pain at being far from home. What Digital Nostalgia seems to be mourning is something more specific: the disappearance of accident from everyday life.

I notice this in small ways. My phone knows where I’m going before I’ve decided to leave. The algorithm has predicted, with unsettling accuracy, what I will want to read next. The coffee shop I found by walking down an unfamiliar street now gets recommended to me, which is useful and also somehow diminishes the thing I found. The city I live in has become a more efficient version of itself. Less of it surprises me than used to.

This is not entirely bad. But something is lost in the smoothing. And the books people are buying tell you what.

The urbanist argument for cities has always included, at some level, an argument for density as a serendipity engine. You put people in proximity. You make them share transit and sidewalks and bars and parks. Intersections happen. Ideas cross. The great creative explosions of modern history — Florentine painting, Viennese psychoanalysis, the Bell Labs cafeteria — were products less of individual genius than of designed proximity. People who wouldn’t have met each other kept meeting each other.

What’s interesting about Krauss’s argument is that it generalizes this principle to the history of science in a way that makes it quantifiable. It’s not just that cities were generative because they were dense. It’s that they were generative because they were full of new tools — printing presses, coffeehouses, salons — that created new surfaces where minds could collide and refract in new ways. The tool doesn’t make the discovery. It makes the discovery possible, and likely, and reproducible by others.

Which brings me back to the airport bar.

The two-hour delay created an unstructured interval I hadn’t planned for. I didn’t know what to do with it, so I sat somewhere I wouldn’t normally have sat. The man next to me had a book that served as an opening. We were both temporarily outside our routines, which is another way of saying: we were both in a new instrument, looking at something we hadn’t known to look for.

What I’ve been slow to admit is that this kind of moment doesn’t just happen. It happens to people who are outside their routines. It happens in places where unlike people are forced into proximity. It happens when you sit down somewhere without your headphones, without a screen to retreat into, in the condition of being briefly unoptimized. The delay was the tool. The discovery followed.

So here is the tension I keep returning to: you can engineer the conditions for serendipity, but you cannot engineer serendipity itself, and the engineering has to be genuinely open-ended or it stops working. If you design a system that produces specific surprises, you haven’t built a serendipity engine. You’ve built a surprise dispenser, which is a different and lesser thing. Amazon’s “you might also like” feature is not serendipity. It is prediction wearing serendipity’s clothes.

The difference is whether the system preserves its capacity to show you something it didn’t know you needed to see. A new microscope could reveal anything. A recommendation algorithm reveals only a constrained neighborhood of the space of things you’ve already wanted. The former is a lens. The latter is a mirror.

I think this is what the Digital Nostalgia readers are grieving, without quite being able to name it: not the analog past itself, but the unoptimized interval. The moment between knowing what you wanted and finding it, when anything might happen. That space has been shrinking for twenty years, and the algorithm’s promise — to eliminate friction, to anticipate, to smooth — has turned out to be partly a promise to eliminate possibility.

The question I’m sitting with is whether it’s recoverable. Not globally — I’m not interested in the manifesto version of this argument, the call to smash the phones or return to the forest. But personally. Whether I can design my own life to include enough genuine aperture — enough unoptimized intervals, enough new tools, enough places where I am briefly outside my routine and available to be surprised — to keep the surprises coming.

I have some guesses about what this looks like. Reading outside my field. Saying yes to the conversation I don’t have time for. Choosing the longer route. Leaving earlier so the delay doesn’t feel like a crisis.

These are small things. They are also, if Krauss is right, approximately how all the important discoveries get made.

The flight eventually boarded. I didn’t take the job. But I thought about it for three months, which means I thought about my actual life for three months — what I wanted from it, what I was settling for, what I hadn’t been willing to name. The man at the bar didn’t change my path. He changed my angle of view, briefly, enough. I’ve been a little suspicious of smooth trips ever since.

Categories
History Living Telephones

The Coiled Tether

Do you remember the physical weight of a conversation? It lived in the coiled, plastic spring of a landline telephone cord. We would stretch it across the kitchen, pacing over linoleum floors, the coil twisting around our fingers as we talked into the evening.

That cord was a literal tether. It confined us to a specific radius, but in doing so, it anchored us to the present moment. When you were on the phone, you were nowhere else. You were anchored to the wall, and by extension, to the person on the other end of the line.

There was also the sheer tactile satisfaction of the device itself—the heavy, contoured plastic of the receiver that fit perfectly between shoulder and ear, and the definitive, emphatic slam of hanging up on someone, a punctuation mark that the gentle tap of a touchscreen will never quite replicate.

Then came the subtle, sharp click on the line. Call waiting.

“We traded deep, uninterrupted connection for the anxiety of possibility.”

It was our first taste of modern conversational fragmentation.

Before call waiting, a busy signal was a polite “do not disturb” sign hung on the door of an ongoing dialogue. It meant you were occupied, engaged, entirely spoken for.

The click changed everything. It introduced a sudden, silent geometry to our relationships. When that secondary tone sounded, you were forced into a split-second hierarchy: do I stay with the person I am talking to, or do I chase the mystery of the unknown caller? The phrase, “Can you hold for a second?” became a small, culturally accepted betrayal of the present moment.

We traded deep, uninterrupted connection for the anxiety of possibility.

Eventually, the mystery of the ringing phone was solved altogether by a small, rectangular box with a glowing LCD screen: Caller ID.

For decades, a ringing phone was an invitation to a blind date. You picked up the receiver with a mix of anticipation and vulnerability. It could be a best friend, a wrong number, a telemarketer, or the person you’d been hoping would call all week. You answered with a universal greeting—a neutral, expectant “Hello?”—because you had no idea who was stepping into your home through the wire.

Caller ID gave us the power of the gatekeeper. It allowed us to screen, to prepare, to decide if we had the emotional bandwidth for the name flashing in digital text. We gained control, but we lost serendipity. We lost the unfiltered, genuine surprise of hearing a familiar voice when we least expected it. We stopped opening the door blindly and started looking through the peephole.

Today, we are entirely untethered. There are no coiled cords tying us to the kitchen wall. We carry our communication in our pockets, capable of ignoring texts, sending calls to voicemail, and managing our availability with unprecedented precision. Yet, for all this freedom and control, it often feels as though we are more disconnected than ever.

The good old days weren’t necessarily better because the technology was superior; they were beautiful because the limitations of the technology forced us to be human. The cord forced us to stay put. The lack of caller ID forced us to be open. The absence of call waiting forced us to finish the conversation we started.

Sometimes, looking back, I miss the simple, undeniable commitment of answering a ringing phone, twisting the cord around my index finger, and just listening.

Categories
Authors Storytelling Writing

The Architecture of Resonance

There’s a particular kind of madness that strikes writers late at night, or in the stagnant hours of mid-afternoon, when you find yourself staring at a single sentence for twenty minutes.

You’re weighing a semicolon against an em dash. You’re wondering if “murmur” is too soft or if “whisper” is too cliché. All of this while knowing, with complete certainty, that no reader will ever stop to appreciate this specific choice. They’ll just read the sentence and move on.

So why do we do it?

In Draft No. 4, John McPhee — the legendary literary journalist who spent decades at The New Yorker — shares a principle he still writes on the blackboard at Princeton. It’s actually a quote from Cary Grant: “A Thousand Details Add Up to One Impression.” The implication, McPhee explains, is that almost no individual detail is essential, while the details as a whole are absolutely essential.

I find this idea endlessly useful. And a little reassuring.

Think about walking into a beautifully designed home. You don’t notice the precise angle of the crown molding or the specific undertones of the paint. You don’t walk in and say, “Ah yes, Alabaster White.” You just feel warmth, or elegance, or comfort. The impression is singular — but it’s entirely built from a thousand invisible decisions someone made before you arrived.

Writing works the same way. The rhythm of your sentences, the specificity of your verbs, the way a paragraph ends — these are the details. Individually, they’re expendable. Swap “murmur” for “whisper” and the piece survives. Delete the semicolon and the world keeps turning.

But collectively, they are the piece.

Start compromising — reach for the easy cliché, let a clunky transition slide, settle for vague where you could be specific — and the foundation slowly rots. The reader won’t be able to name the moment they lost interest. They’ll just close the tab. The impression shifts from resonant to flat, without anyone quite knowing why.

Writing, then, is an act of quiet faith. It asks you to labor over things no one will applaud. Nobody claps for an em dash. But the work isn’t really for applause — it’s out of respect for the whole.

We curate a thousand invisible things so the reader can feel one visible truth.

So the next time you’re agonizing over a single word at midnight, remember: you’re not just picking a word. You’re placing a tile in a mosaic. Cary Grant understood it. McPhee put it on a blackboard. You might as well make it count.