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 Podcasts

A Remarkable Conversation…

Highly recommend this conversation between Harry Stebbings and Clay Bavor. Among many topics, I especially enjoyed the discussion about not investing in frontier models, the important values, the particular importance of craftsmanship, intensity, and family. And the special conversation about parenting and kids near the end. Just a delightful conversation to be able to enjoy!

Key Highlights:

• Founding Sierra: Bavor explains why he and Taylor chose to start Sierra, focusing on the transformative potential of language model-based agents (1:37 – 5:53).
• The AI Tech Stack: Sierra focuses on building enterprise-grade agent architectures and fine-tuning models on top of open-weights models rather than pre-training foundation models from scratch, prioritizing capital efficiency (5:53 – 7:15).
• Unbounded Demand for Intelligence: Bavor argues that there is massive, unmet demand for “frontier-level” intelligence in fields like coding, science, and legal work (7:15 – 11:41).
• Internal AI Operations: He details the use of Pinecone, an internal AI agent Sierra developed to navigate company data, streamline engineering, and assist in recruitment (18:36 – 22:00).
• Enterprise Strategy: Sierra employs a “forward-deployed” engineering model, embedding staff within client companies to ensure rapid, effective integration of AI, leading to quick deployment timelines (30:12 – 33:22).
• Board Governance: To keep pace with the speed of AI development, Sierra operates on a six-week board meeting cadence, utilizing comprehensive memos instead of traditional slide decks (39:07 – 41:13).
• Corporate Culture: Bavor emphasizes values like craftsmanship, intensity, and family. He also highlights the importance of working in-person to foster apprenticeship, mentorship, and a cohesive team culture (43:02 – 55:41).

Categories
AI AI: Large Language Models Apple

The Slipstream Strategy

Apple had a problem no amount of money could solve. An iPhone can’t draw the power or shed the heat of a data center, so ten different tasks can’t mean ten different models fighting for the same sliver of RAM. Apple’s answer was to freeze one small, efficient base model into the device and then swap tiny adapters in and out of it in milliseconds — a summarization adapter for your texts, a Siri adapter for on-screen actions, and a handoff to Private Cloud Compute for anything heavier. The phone behaves like it’s running many models. It’s running one model wearing many hats.

That architecture — a frozen base plus swappable adapters — is quietly becoming the default way serious AI companies build, and it’s worth understanding why, because it inverts the assumption most people still carry into this industry.

The assumption is that winning means owning a frontier model. Sierra co-founder Clay Bavor pushed back on that on a recent 20VC episode: pouring capital into your own pre-training, he argued, tends to leave you holding a highly perishable bag of floating-point numbers. Open-weight models improve fast enough that yesterday’s frontier is next quarter’s commodity. The companies playing this well aren’t racing to out-spend the labs. They’re slipstreaming behind them — taking the free, state-of-the-art engine and putting all their effort into what sits on top of it.

What sits on top is LoRA — low-rank adaptation. The old failure mode was catastrophic forgetting: fine-tune a model hard enough on your own data and it forgets how to reason generally. LoRA sidesteps this by leaving the base model untouched and training a small set of additional parameters alongside it — a thin layer of expertise bolted onto a frozen foundation. You get real domain depth without touching the thing that makes the model work at all.

The business logic that follows from this is the actual point, and it’s simpler than it looks:

You stop being hostage to any one model provider — if a better open-weight model ships next month, you port your adapter, not your whole product. You can serve hundreds of differently-customized clients off one base model on one piece of hardware, instead of running a separate giant model per customer. You can ship a fix in an afternoon, because an adapter is a few hundred megabytes, not a training run. And in regulated industries, your proprietary data can train an adapter that never leaves your own infrastructure.

None of this is really a story about model architecture. It’s a story about where the moat moved. For a while the moat was raw capability — whoever had the best model won. Apple and Sierra are betting the moat is now somewhere else entirely: in how tightly you can weave a commodity intelligence into a specific workflow, a specific dataset, a specific customer relationship. The engine is free. The adapter is the business.

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
Business History

The Architecture of Unseen Influence

We build our monuments over the wrong graves. It’s a bad habit of ours, this craving for the lone genius—the larger-than-life figure who supposedly commands the tides of progress by sheer force of will. But look beneath the surface of how things actually get built. The reality is messier. And a hell of a lot more interesting.

Take Thomas Edison. Secular saint of American ingenuity. The wizard who single-handedly lit up the dark. Except he didn’t. Edison wasn’t a solitary creator; he was a brilliant, ruthless aggregator of other people’s breakthroughs and a master of public relations. He invented the bulb, sure, but his real masterpiece was the myth of himself. In the process, he eclipsed the collective sweat of his own labs and the far more elegant alternating-current systems of his rivals. He’s our most overrated figure—not because he lacked talent, but because his shadow blinded us to how progress actually happens.

Morgan Housel nailed this structural blind spot by tracing the tangled ancestry of major turning points:

“Every current event – big or small – has parents, grandparents, great grandparents, siblings, and cousins. Ignoring that family tree can muddy your understanding of events, giving a false impression of why things happened… Viewing events in isolation, without an appreciation for their long roots, helps explain everything from why forecasting is hard to why politics is nasty.”

Look past the blinding light of the celebrity inventors and you find the long roots that actually remade our world. Take FCC Part 15. It’s an event almost no history textbook bothers to mention. In the early 1980s, a lone staff engineer named Dr. Michael Marcus looked at three chunks of the radio spectrum—stuff discarded as “garbage bands” reserved for industrial microwave ovens—and saw an opening. The entire telecom establishment thought he was chasing a recipe for chaotic interference.

Marcus didn’t blink. He spent years pushing through a dry, technical ruling in 1985 to open those garbage bands for unlicensed public use. A total footnote. Yet that single, unheralded bureaucratic open door laid the invisible foundation for Wi-Fi, Bluetooth, and the entire wireless ecosystem running your life today. Marcus didn’t get a ticker-tape parade; he got political friction and a quiet transfer to a back-office enforcement role. No hero on horseback. Just a guy in a cubicle who rewired the world.

We do the same thing with politics. We rank presidents by the volume of their rhetoric or the body count of their wars. Meanwhile, men like Chester A. Arthur get left in the dusty margins of trivia. Arthur was the ultimate product of the spoils system—a New York machine politician who climbed to power on institutional corruption. Then James A. Garfield was assassinated, and Arthur was thrust into the big chair. Something clicked. Instead of feeding the machine that birthed him, he turned inward, defied his old patrons, and signed the Pendleton Civil Service Act. He dismantled the very patronage system he’d mastered. It was a stunning act of quiet integrity that killed his political future but saved the republic’s administrative soul.

Or take Frances Perkins. Ask the average student who gave them the weekend, the forty-hour work week, unemployment insurance, and the abolition of child labor, and you’ll get a blank stare. Perkins was FDR’s Secretary of Labor. She wasn’t a regular on the campaign posters. She just stood in the back of the room, turning abstract economic suffering into concrete human safety nets.

I’ve been sitting with this for a few days, thinking about my own career—and the times I mistook the loudest person in the room for the smartest. I chased the visionary founders with the spellbinding pitches. I ignored the quiet engineers and the mundane infrastructure choices that actually determine whether an idea scales or snaps. It takes a few painful, expensive missteps to realize that the real compounding interest of progress is almost always generated in the dark.

History isn’t a solo act. It’s an intricate, mostly anonymous collaboration between accidental reformers, stubborn bureaucrats, and regulatory footnotes. If you want to understand where we’re going, stop staring at the stage lights.

Start looking at the wiring.

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
AI AI: Inference Semiconductors Uncategorized

5 Critical Management Lessons from the Founders at Etched

How two young founders are building what could become one of the most important companies in the AI era — and what their story teaches about leadership, execution, and building at the edge of the possible.

I recently listened to the latest Invest Like the Best podcast from Patrick O’Shaughnessey which was a remarkable conversation with Gavin and Rob, the founders of Etched, the company building specialized AI inference hardware that’s aiming to be radically better than existing solutions. Their story — starting as very young founders against massive skepticism, raising serious capital, and now shipping full rack-scale systems — is packed with hard-earned wisdom.

One of the comments Patrick makes at the beginning was how during his due diligence on the company he kept being told that semiconductor technology wasn’t a place for young people. You need seasoned, middle age experts to master this domain. Exactly not these founders.

Note: the following is based upon an AI’s analysis of the conversation transcript with me asking “What are the five most important management lessons from this conversation?” These lessons are relevant whether you’re leading a team, building a product, or simply trying to do meaningful work in our fast-moving world.

1. Velocity Compounds — Prioritize Speed Ruthlessly

In hardware, and increasingly in any deep-tech endeavor, speed isn’t just an advantage; it’s often the deciding factor.

Etched didn’t just design a chip — they built the full inference solution (chip, board, power delivery, interconnects, cold plates, and production processes) in parallel. They sent engineers to live in Bangalore for months to unblock vendors. They ran 24/7 shifts and did massive pre-work (including putting full chip designs on FPGA clusters) so that when the silicon finally arrived, they had working inference in racks in just 40 days.

Key takeaway: Look for every opportunity to parallelize. Accept higher short-term costs if they buy meaningful time. As they put it, “You win by shipping.” The best part is often no part — and the best vendor is no vendor, when vertical integration lets you move faster. Velocity, velocity, velocity.

2. Build Teams with Legends + High-Drive Talent

One of the most distinctive parts of their approach is how they recruit. They seek out “Legends” — people who have done the hardest versions of the problem before (like the engineer who built Nvidia’s HGX and DGX systems) — and pair them with exceptionally driven, somewhat naive high-performers who refuse to accept conventional limits.

They use “project-based recruiting,” mapping the hardest technical problems ever solved and persistently pursuing the actual people who did the real work. Their culture self-selects for people willing to move their families to San Jose to bet on two young founders taking on the world.

Key takeaway: For breakthrough work, average talent doesn’t suffice. The combination of deep experience and raw, first-principles energy creates magic. Invest heavily in finding and retaining these people — even if it takes 20 conversations. You can also learn a lot if the best in the world talent turns down the opportunity to work with you!

3. Assume It’s Possible, Then Solve the “Unsolvable” Problems

Repeatedly in their story, experts told them certain things were impossible. Their response? Assume it is possible and figure out how.

The most striking example was a clock domain crossing issue that required aligning signals to within 50 picoseconds — something many engineers said couldn’t be done. People quit. They solved it in about two weeks during a very dark period.

Key takeaway: When you hear “impossible,” treat it as the beginning of the investigation, not the end. Cultivate a “find a way” mindset across the team. The moments when things feel hopeless are often when the most important progress happens. I’m constantly struck by how often persistence results from simply realizing (or assuming) that something is actually possible.

4. Production Is the Real Product

Etched’s mantra is “Production is the product.” They obsess over not just technical performance but manufacturability, supply chain resilience, serviceability, and the ability to scale to gigawatts.

They made deliberate choices around process nodes and memory to avoid zero-sum competition. They built their own factory processes and test infrastructure early. Future designs are being simplified specifically for faster production cycles and higher reliability at massive scale.

Key takeaway: In any business that hopes to reach real scale, think end-to-end from the beginning. Technical excellence without production excellence is just a prototype. Optimize for output (tokens, units, whatever your metric is) at volume. There’s a lot of “zero to one” thinking here.

5. Bet Big and Stay Existentially Focused

Building in semiconductors requires enormous capital. Etched raised roughly $100 million early on when they were still very young and pre-tapeout — after most traditional investors had passed. They knew half-measures wouldn’t work.

This existential focus (this one product determines whether the company lives or dies) creates a different level of intensity that attracts talent, suppliers, and customers who believe.

Key takeaway: Match your ambition with appropriate resources and commitment. Clear existential stakes help filter for the right people and partners. In a world of distractions, singular focus on what truly matters is a superpower.

Final Thoughts

Gavin and Rob’s story is the combination of technical sophistication and deep human resilience. They faced a tough personal battle with cancer (in Rob’s case), widespread doubt, brutal technical challenges, and fundraising pressure — and kept moving forward with curiosity, determination, and humility.

In an age of AI and accelerating technology, the ability to build teams that can solve seemingly impossible problems at speed may be one of the most valuable capabilities a leader can develop. Their example reminds us that the future belongs not just to the smartest, but to those who can execute with urgency while maintaining clear principles. Velocity, velocity, velocity.

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.