Tag: Silicon Valley

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 Storytelling

The Closed Laptop

The conference rooms all look the same after a while. Same long table. Same chairs that cost more than they should. Same window with the same view of the same parking lot baking in the same California sun. You stop seeing them. You develop a kind of practiced receptivity, a professional openness that is also, if you are honest, a professional distance. You have heard the story before. You know where you are in the presentation without looking at the slide number.

Until the day someone sits down across from you and closes their laptop and says: can I just tell you our story?

Fred Wilson, the venture capitalist at Union Square Ventures, has spent forty years learning to tell the difference between founders who can build and founders who can make you believe. The skill he overweights now, he’ll tell you plainly, isn’t technical. It’s selling. Recruiting, fundraising, convincing customers, inspiring teams. “Actually being able to write code,” he said recently, “is probably not a big deal anymore.” What matters is whether you can cross the distance between your vision and someone else’s imagination and deposit something true and alive on the other side.

Most founders never figure this out. They build the deck instead. They pull the projector cable from the drawer — there is always a drawer, there is always a cable — and the room fills with blue light and bullet points and the comfortable geometry of a prepared presentation, and what never happens is the thing that needed to happen.

But there was this one morning.

He came in with his cofounder in the flat gray light that Silicon Valley gets in February, when the rain has stopped but the sky hasn’t decided what it wants to be. They were early. He set his bag down and sat directly across from me — not at the presenter’s angle, not with one eye already calculating the distance to the screen — directly across, the way you sit with someone you already know, or intend to. Neither of them reached for the cable in the drawer.

He looked at me with the particular steadiness of a person who has decided not to manage the moment.

Can I just tell you our story?

I want to be honest about what happened next, which is that I felt something shift before he said another word. Not a decision exactly. More like the precondition for a decision, the ground tilting slightly in a direction I hadn’t chosen. I was, in some way I couldn’t have defended rationally at the time, already with him. And I knew it, and I knew it was not an entirely reasonable response to a man who had been in the room for less than a minute, and I felt it anyway.

The laptop stayed closed for the next twenty minutes. No transitions. No bullet points. No hockey stick arcing toward a number reverse-engineered from a desired outcome. Just his voice and what he believed and the quality of attention you give a person when there is nothing else in the room to look at.

The deck came later. It was beautiful. By then it didn’t need to be anything except true.

Storytelling is not a skill in the way that financial modeling is a skill. It is older than that by such a margin that the comparison almost doesn’t make sense. What we are really talking about is the oldest technology human beings possess — a person in a room, a voice, an image made of nothing but words and the willingness to believe in them. It was doing its work around fires forty thousand years before the first conference room was built, and it has never once required a projector.

What the great storytellers understand, and what the best founders understand in the same unspoken way, is that a story is not a transfer of information. It is a transfer of inner states. When it works — when it really works — something that existed inside one person gets reconstructed inside another, and the listener emerges changed. Not persuaded. Not informed. Changed. These are different experiences, and only one of them makes a person willing to bet their career on something that doesn’t exist yet.

The deck puts glass between the teller and that possibility. The founder stands at the edge of the blue light pointing at things, and the room evaluates the things, and what never happens is the transfer. Everyone files out having formed opinions about the slides rather than beliefs about the person. Opinions and beliefs are not the same.

Wilson understands this even if he wouldn’t use these words. When he says the skill is selling, what he means underneath the selling is: can this person walk into a room and make other people inhabit their vision? Not convince them. Inhabit. The difference is the difference between reading about a place and being there. One of them changes how you act. The other one you forget on the drive home.

The projector cable is still in the drawer. Someone will pull it out next week, and the room will fill with blue light, and another founder will stand at the edge of it pointing at things, hoping that the right font and the right graph will do the work that only a human being, exposed and without props, can actually do.

It won’t. It never does.

The CEO who closed his laptop had been carrying a story he believed in, and he knew the story was the thing, not the packaging around it. He understood that the oldest container is also the most powerful one. His own voice. A room. Someone willing to listen.

I was ready to work with him before he said another word.

Categories
AI Business IBM Management

Making It Up As We Went Along

There was a building along Route 270 in Gaithersburg, Maryland where people kept secrets for a living. Not the cloak and dagger kind. The corporate kind, which in its own way requires just as much discipline. The IBM Washington Systems Center occupied a two-story modern building that looked, from the outside, like any other outpost of late twentieth century American business. Inside it was something else. It was where IBM sent its hardest problems, and where the largest IBM customers in the world — the ones whose names you would recognize immediately — sent their most urgent ones back.

I worked there as a manager. But before I was a manager there, I was a hire. And before I was a hire, I was like every other IBM professional on the outside of a particular line — a line I didn’t fully understand until I crossed it.

At IBM there was a protocol so embedded in the culture it had almost ceased to be a rule and become something closer to a religious observance. New products were not discussed until they were announced. Not hinted at. Not alluded to. Not whispered about with a favored customer over lunch. The announcement came in the form of something called a Blue Letter — a formal communication from senior leadership that functioned as the official moment a product entered the world. Before the Blue Letter, the product did not exist in any conversation you were permitted to have. After it, you could talk about nothing else.

Violation was not a career setback. It was a firing offense. Full stop.

That clarity had a kind of elegance to it. You didn’t have to calibrate how much you could say or navigate gray areas. The line was absolute. And because it was absolute, and because everyone knew the consequence of crossing it, the culture enforced itself. You didn’t need surveillance. You needed people to understand the stakes, and they did.

What I didn’t understand, from the outside, was what that line was doing to my imagination.

When you can’t see the roadmap — when the strategy and the unannounced products and the long arc of where the company is going are all behind a wall you have no access to — you don’t experience that as absence. You experience it as depth. The things you don’t know feel like they must be there for a reason. The gaps in the announced picture feel like the gaps in a great iceberg — what’s visible is impressive, but what’s below the surface must be more impressive still.

I had faith in IBM’s strategic intelligence the way you have faith in things you can’t fully see. And faith, uncontradicted by evidence, tends toward beauty. The hidden roadmap wasn’t just unknown — it was, in my imagination, a thing of coherence and intention and vision. It had to be. The alternative was too unsettling to consider.

Then I got hired into the Washington Systems Center and crossed the line.

There was no single moment of disillusionment. No specific product that shattered the dream, no strategy document that read like a disappointment. It was more like a gradual adjustment of the eyes — the way they adapt when you move from bright sunlight into a room lit quite differently than you expected. The room isn’t dark. It’s just not what you anticipated. And once your eyes adjust you can see perfectly well, but you can never quite recover the image you had of the room before you entered it.

The reality on the inside was messier than the dream on the outside. More improvised. More human. We were, in ways I hadn’t anticipated, almost making it up as we went along. Not carelessly — the people at WSC were extraordinary, the work was serious, the commitment was real. But the beautiful coherent roadmap I had constructed in my imagination from the outside bore only a partial resemblance to the actual thing. Strategy, it turned out, looked different up close. Less like architecture. More like weather.

I absorbed this alone. Nobody sat me down and named what I was experiencing. Nobody had the conversation with me that I would later learn to have with others. I found my way through it by degrees, the way you find your way through most things that don’t come with instructions.

What came out the other side wasn’t cynicism. It was something more useful — a clearer eye, a more grounded relationship to the institution I was part of. The faith hadn’t been wrong exactly. It had just been innocent. And innocence, once lost, can’t be recovered. But what replaces it, if you’re lucky, is something steadier.

Years later I was the manager. And I was hiring IBMers — good ones, experienced ones, people who had spent serious careers on the other side of the blue line. They knew the products cold. They knew the customers. They knew how to work. What they didn’t know, couldn’t know, was what waited for them on the inside of the wall they were about to cross.

I knew it. Because I had been them.

There is a particular expression that crosses a person’s face when the actual roadmap becomes visible for the first time. It isn’t dramatic. It doesn’t announce itself. It’s more like a subtle recalibration — a slight stillness, a momentary adjustment behind the eyes. The person in front of you is doing quiet interior work, reconciling what they imagined with what they’re now seeing. The gap between those two things is doing something to them, and they’re not sure yet what to do with it.

I learned to watch for that expression. And when I saw it I knew what was coming if I didn’t get ahead of it.

The danger wasn’t disappointment. Disappointment is temporary, and smart people move through it. The danger was what disappointment hardens into when it isn’t named and worked through — a corrosive cynicism that poisons not just the person carrying it but everyone around them. A talented IBMer who had invested a career in faith, discovered the faith was misplaced, and decided the whole enterprise was therefore hollow — that person could do real damage to a team. I had seen it happen, or the early stages of it, which was enough.

So I developed what I came to think of as the god is dead conversation.

The name came from Nietzsche, though the application was strictly practical. What Nietzsche meant — or one of the things he meant — was that when the organizing faith of a civilization collapses, the collapse doesn’t leave nothing. It leaves a vacancy that has to be filled with something else, something built rather than inherited. The god is dead conversation was about helping someone through that vacancy quickly, before they filled it with the wrong thing.

It wasn’t a long conversation. It didn’t need to be. What it needed to be was honest, and direct, and delivered before the cynicism had time to set.

I would tell them what I saw happening. I would tell them it was normal, expected, that everyone who crossed this particular line felt some version of it. I would tell them the dream they’d carried on the outside wasn’t foolish — it was a reasonable response to incomplete information, and the information had been incomplete by design, and the design had served real purposes. None of that made them naive. It made them human.

And then I would tell them what I’d learned on my own, without anyone to guide me through it. That the messiness on the inside wasn’t a failure of IBM’s intelligence or intention. It was just what strategy actually looks like when you’re close enough to see the seams. Every institution looks more coherent from the outside than it does from the inside. That’s not a scandal. That’s organizational life.

The conversations were tricky. There was real care required. You were asking someone to grieve something — the beautiful imagined roadmap, the faith in a hidden coherence — without tipping them into bitterness about what replaced it. You were trying to accelerate a process that, left alone, might drag on for months and quietly corrode their effectiveness. And you were doing it while also being their manager, which meant you needed them functional and engaged on the other side of the conversation, not just unburdened.

What I had going for me was credibility. I wasn’t delivering a message from outside the experience. I had made the same crossing. I knew the specific texture of what they were feeling because I had felt it myself — the diffuse quality of it, the absence of a single dramatic moment, the gradual adjustment of the eyes. When I told them I understood what was happening to them, I actually did. I think they could tell.

Trial and error had taught me the shape of it. What didn’t work I had found out the hard way, at some cost, early on. What I arrived at had been load tested by real people in real situations. It wasn’t a framework from a leadership seminar. It was something I owned completely, which meant I could adapt it in the moment rather than execute a script.

Most of them came through it well. Better than well, actually.

What I hadn’t fully anticipated — though in retrospect it makes complete sense — was what replaced the faith once it was gone. It wasn’t the steadier, clearer-eyed pragmatism I had found my way to alone. It was something more potent than that. Something that surprised me the first time I saw it and then became one of the things I quietly counted on.

They came out the other side feeling superior.

Not arrogant. Not dismissive of colleagues still on the outside. But quietly, privately elevated — because they were now keepers of the secrets they had once only believed in. The blue line that had shaped their entire professional identity, that had defined the boundary of what they could know and say and imagine, was now behind them. They were on the inside. They had access. They had been trusted with the actual roadmap, the real strategy, the unannounced products that the rest of the world was still constructing faith-based pictures of.

The believer had become the keeper. And keeping, it turned out, was a more powerful identity than believing. The believer is passive — sustained by what they imagine. The keeper is active, responsible, trusted. They carry something real rather than something projected.

It solved my practical problem neatly, though that wasn’t why it moved me. What moved me was watching people find their footing on the other side of a genuine loss and discover that the ground there was solid — different from what they’d imagined, but solid. They hadn’t just survived the crossing. They’d been changed by it in a way that made them more valuable, more grounded, more fully present to the actual work.

Which was, I suppose, what the god is dead conversation had been for all along.

I think about that blue line often these days.

We are living through a moment when artificial intelligence is advancing faster than most people can track, and the organizations building it — the labs, the research teams, the companies placing enormous bets on where this technology is going — have their own version of the wall. Not identical to IBM’s. The competitive and legal architecture is different. The culture is different. But the basic structure is the same: there is what has been announced, and there is everything else, and most people are working entirely from the announced side.

Which means most people are doing what I did before I crossed the line at WSC. They are filling the gaps with faith. And faith, uncontradicted by evidence, tends toward beauty.

The unrevealed AI roadmap looks, from the outside, like a thing of coherence and intention. The capabilities that haven’t been announced yet must be more impressive than the ones that have. The strategy must be more considered than what’s visible. The gaps in the public picture feel like depth rather than uncertainty — like the part of the iceberg below the surface, which must be vast because the part above is already remarkable.

I am not saying this faith is wrong. I held the same faith about IBM and it wasn’t wrong exactly — it was innocent. The people constructing faith-based pictures of where AI is going are doing a reasonable thing with incomplete information. The information is incomplete partly by design, for reasons that make competitive and strategic sense, just as IBM’s secrecy made sense. None of that makes the faith naive.

But I’ve been inside enough walls to know what the inside tends to look like. And I think it’s worth saying, clearly and without cynicism, that the reality is probably messier than the dream. More improvised. More uncertain. More human. The people building these systems are extraordinary — the work is serious, the commitment is real — but they are also, in ways that might surprise you, almost making it up as they go along. Not carelessly. But without the complete map that the outside imagines must exist somewhere, fully drawn, waiting to be revealed.

Strategy, up close, looks less like architecture and more like weather.

This isn’t a counsel of despair. It’s almost the opposite.

The IBMers who crossed the line and survived the god is dead conversation didn’t end up with less than they started with. They ended up with more — a clearer eye, a more grounded relationship to the institution, a more useful kind of engagement with the actual work. The faith they lost was the innocent kind. What replaced it was steadier and more durable.

I suspect something similar is available to anyone willing to look at the AI moment with clear eyes. Not the disappointed cynicism of someone who expected a beautiful coherent roadmap and found a human institution instead. Not the breathless faith of someone still on the outside of the wall, filling gaps with generous assumptions. Something in between — harder to sustain, more honest, ultimately more useful.

The technology is real. The progress is real. The stakes are real. None of that requires the roadmap to be a thing of beauty. It just requires it to be worked on seriously by people who understand what they don’t yet know — which, from everything I can observe, it is.

What I couldn’t give those IBMers, and what nobody can give you, is the experience of crossing the line yourself. The god is dead conversation only works because the crossing has already happened — because the person sitting across from you has already seen the actual roadmap and is already processing the gap between what they imagined and what they found. You can’t have the conversation in advance. The disillusionment has to be real before it can be worked through.

Most of us will never cross the line into the AI labs. We’ll stay on the outside of the wall, working from the announced picture, filling the gaps as best we can. That’s not a failure — it’s just the condition most of us are in, the same condition those IBMers were in for their entire careers before I hired them.

But knowing the wall exists, and knowing what walls do to imagination, seems like it ought to change something about how we hold our faith. Not abandon it. Just hold it a little more lightly. Stay curious about the seams. Remain open to the possibility that the most important thing about the unrevealed roadmap isn’t what’s in it — but what we’ve projected onto it.

The blue line is still there. Most of us are still on the outside of it.

And the hidden roadmap still looks, from here, like a thing of beauty.

Categories
AI Silicon Valley Technology

The View from the Edge

“Living on the edge” usually means you’re taking risks. One of the guests on the More or Less podcast used it the other way: as a diagnosis. A description of people who’ve lost their depth perception.

From where they sit, it looks like everyone is moving. The feeds are full of demos. The group chats debate which model won the week. Colleagues are building agents that book their dentist appointments and summarize their email while they sleep. David Sparks is selling a Robot Assistant Field Guide. The frontier feels like the present tense — not where things are heading, but where things already are.

When everyone around you has already crossed a threshold, you stop being able to see the threshold. You mistake the edge for the center.

The primary point — that the tech community wildly overestimates how much ordinary people want AI in their lives — lands harder when you hold it against that image. It’s not that the industry is wrong about the technology. It’s that it has miscalibrated the desire. Most people aren’t trying to optimize their Tuesday. They’re just trying to get through it. An always-on personal agent isn’t a solution to a problem they’re carrying.

Think about the woman in the Safeway parking lot, sitting in her car for three minutes before going in, scrolling back through her texts to find the thing her husband asked her to pick up. Egg product and cheddar cheese. She finds it, pockets her phone, and goes inside. The whole problem — the forgetting, the retrieval, the solution — lasted less time than it takes to read about it. She didn’t need an agent. She needed three minutes and a text thread she already had.

The edge distorts in a specific way: it makes appetite look like inevitability. From out there, adoption feels like a question of when, not whether. But whether is a real question. Most technology that could be woven into daily life never is — not because people couldn’t learn it, but because they didn’t want what it offered badly enough to bother.

The view from the edge is intoxicating. Everything looks like signal. But the middle is where most people live, and from there the signal looks a lot more like noise.

Which is why WWDC matters more than any model release this year. Apple doesn’t sell to people living on the edge. It sells to people who just want their phone to work. If Apple makes AI invisible enough — tucked into the camera, the keyboard, the thing that finds your photos — it stops being something you adopt and becomes something you already have. That’s a different motion entirely. Not convincing people they want AI. Delivering it before the question occurs to them.

Whether Apple can actually pull that off is a separate argument. But the watershed, if it comes, won’t look like a frontier crossing. It’ll look like a Tuesday that went slightly smoother than usual. Most people won’t even notice the edge they just walked past.

We will find out in a week or so.

Categories
AI Stanford

The Unit of Production Just Collapsed

The lecture was a Stanford CS session, AI-native companies, Garry Tan walking through what it now takes to build something. He’d rebuilt his old startup, Posterous, in five days on a modest Claude plan. A thing that once required a team and a runway. He said it matter-of-factly, the way you describe something that’s already obvious to you and hasn’t yet reached everyone else.

The argument Tan and his colleague Diana Hu were making wasn’t really about AI. It was about the economics of effort — specifically, what breaks when the cost of turning an idea into a working thing falls by an order of magnitude.

Their framing: AI-native organizations running as closed-loop systems, agents with access to the real artifacts of work, able to iterate without the error-accumulation that comes from handoffs and headcount. Revenue-per-employee ratios of a million dollars or more, with live examples already in the YC portfolio. Document processing, logistics, voice agents for specialized workflows.

What I kept hearing underneath all of it was a quieter claim: the mental model of what a startup requires is wrong.

Or rather, it’s right about the past and increasingly wrong about the present.

The assumptions embedded in “I can’t do this alone” or “we’d need to hire for that” or “we don’t have the bandwidth” — those are load-bearing assumptions, and the load is shifting.

I have some small version of this — not as a founder, but as someone who retired into curiosity. The blog, the reading, the daily effort to keep up with what’s moving: each one is a practice in staying oriented while the map keeps changing.

What I notice is that the constraint has shifted. It’s not information anymore. It’s not even tools. It’s the capacity to ask better questions of the abundance, to know what matters when everything is accelerating.

That’s the thing I find unsettling, yet also genuinely interesting: the skills that remain irreplaceable are the hardest ones to teach, and the hardest to evaluate in yourself. Knowing what matters. Recognizing when an output is almost right and almost wrong. Setting direction in ambiguous conditions and being willing to be wrong about it. These were always the valuable things. They were just obscured by all the coordination overhead that surrounded them.

The students in that Stanford course were asked to build something called a One-Person Frontier Lab — use the best available tools to extend your own reach over ten weeks. It’s framed as an academic exercise. It doesn’t feel like one.

But I’m not building. I’m mostly watching, and thinking about what this radical new fermentation does to everything downstream — to labor markets, to what a company even is, to how we’ll organize work and meaning when the old unit of production no longer applies. Those are slower questions. But they’re the ones that feel urgent to me.

The old excuses are getting lighter. Not that everything is possible — but that the weight of the usual constraints has changed.

What you choose to build, and whether you choose to build it at all, is more purely a decision than it used to be. That’s either clarifying or terrifying, depending on the day and my mood.

Categories
Etiquette Founders

How to Be in a Room

I happened to notice this in a post on X: Slow Ventures ran a 4-hour etiquette class for founders. This is actually a brilliant idea. A non-trivial amount of founders (and VCs) could use some help in this department.

So I asked AI: what would a course syllabus for something like this look like? Below is the response (which I quite like!):

This is a genuinely good idea — etiquette as a business skill is perennially under-taught, especially for technical founders who may have gone deep on product/engineering but shallow on navigating rooms. Here’s how I’d structure a day-long or modular course built around the Slow Ventures premise.

Founder Etiquette — A Field Guide

4-hour intensive · 5 modules · lecture + role-play format

Morning Block — First Impressions

Module 1 · 45 min — The room before the meeting

Arrivals, introductions, and presence under observation

  • Timing: the cost of early vs. late
  • Handshakes, eye contact, the graceful exit from a name you missed
  • Business card and phone etiquette
  • How to work a room without appearing to work a room
Module 2 · 45 min — Table stakes: dining fluency

The business meal as performance and ritual

  • Place settings, utensils, and the BMW rule
  • Who orders first, who orders what
  • Wine: fluency without performance
  • Dietary restrictions — yours and theirs
  • The check: the ritual, the timing, the dodge
  • When and how to talk business at the table

Mid-Morning Block — Communication

Module 3 · 45 min — Communication that doesn’t leak desperation

Written and verbal signals investors actually read

  • Email: subject lines, length, tone, and the follow-up cadence
  • Reading the room on Slack/text vs. formal channels
  • The thank-you note — when, how, why it still matters
  • Saying no, disagreeing, and delivering bad news
  • How to receive feedback without deflecting or over-agreeing
  • The exit from a conversation: graceful, not abrupt

Afternoon Block — High-Stakes Scenarios

Module 4 · 45 min — Investor relations: the long game

Board rooms, pitch meetings, and the unwritten VC code

  • Board meeting protocols — who speaks, in what order
  • Managing up to your board without managing them
  • The warm intro — how to ask, how to use, how to close the loop
  • Confidence vs. bravado: what the room hears
  • What to do when the meeting is going badly
  • Conference behavior: panels, hallways, and the after-party
Module 5 · 60 min — The edges: cultural fluency and hard situations

Where etiquette meets judgment

  • Cross-cultural norms: Japan, Middle East, Europe, India — the high-risk differentials
  • Power dynamics: how to behave when you have more (or less)
  • Navigating someone else’s social gaffe gracefully
  • Gift-giving: what to give, what never to give
  • The fine line between casual and sloppy in casual settings
  • Live role-play: difficult scenarios, coached debrief

A few things I’d stress if actually running this:

Module 3 is where the real behavioral work happens for founders specifically. The desperation signal leaks through email more than anywhere else — the too-fast reply, the over-explanation, the follow-up that arrives four hours after the first message. That’s the stuff that’s hard to see in yourself.

One thing Slow Ventures probably got right: four hours is about the right ceiling. Any longer and you start to feel like you’re at finishing school, which kills the energy.

After reviewing this, I thought it might be useful to add two role playing scenarios to the course. Here’s what the AI recommended:

First, add this role play in after Module 2:

Role-play 1 · 20 min — The wrong bread plate

Recovering from a gaffe without losing the room

A founder is taken to lunch by a partner at a firm they’ve been trying to get in front of for six months. It’s going well — good rapport, the partner seems genuinely interested. Then the founder accidentally takes the partner’s bread roll, realizes it mid-bite, and has to navigate the next ten minutes without letting the gaffe collapse the energy in the room. Secondary layer: the check arrives and it’s ambiguous who’s hosting.

This tests everything from Modules 1 and 2 simultaneously — table fluency, recovery under embarrassment, reading whether to acknowledge the mistake or absorb it silently, and the check ritual. Low-stakes enough to be funny, high-stakes enough to feel real.

Next, add this role play after Modules 3, 4, and 5:

Role-play 2 · 30 min — The meeting that’s going sideways

Holding the room when the numbers missed

A founder is in a board meeting presenting quarterly numbers that missed. Midway through, one board member interrupts with a pointed question the founder doesn’t have a clean answer to. A second board member piles on. The founder has to hold the room — not deflect, not over-apologize, not go defensive — while also reading which of the two is genuinely concerned versus performing concern for the room. Cultural wrinkle: the more aggressive questioner comes from a context where directness reads as respect, not attack.

This pulls from Modules 3, 4, and 5 simultaneously. It’s the hardest scenario in the course and should feel that way. The coached debrief afterward is where most of the learning actually happens — ending here means it lands with weight, which is the right note to close on.

More commentary from the AI:

The dining module is the most load-bearing. A remarkable amount of professional signaling happens at the table and most people don’t realize they’re broadcasting anything. The BMW rule alone (bread-meal-water, left to right, so you don’t eat someone else’s roll) is the kind of thing nobody tells you until you’ve already grabbed the wrong bread plate in front of a Series B lead. The live role-play after Module 2 brings this to life.

The live role-play in Module 5 is what separates a good version of this class from a mediocre one. You can lecture about graceful exits from bad meetings all day. Watching someone flail through one in real time, then getting coached on the spot, is what actually moves the needle.

Categories
AI AI: Large Language Models China

Cranes on the Horizon

In 2005, during my first trip to Shanghai and Beijing, the most striking feature of the skyline wasn’t the architecture—it was the cranes. More than I could possibly count, perched atop half-finished skyscrapers like a mechanical forest. Entire districts seemed to be mid-construction simultaneously, as if someone had pressed a button and the whole country decided to build everything at once. Dan Wang in his book “Breakneck” described China as the “engineering state” that approaches national problems with physical solutions. Back in 2005, coming from Silicon Valley, I thought I understood what growth looked like. I didn’t.

I’ve been thinking about that trip while reading Nathan Lambert’s recent piece, “Notes from Inside China’s AI Labs.” Lambert — who runs the Interconnects newsletter and does serious work tracking the open-weight LLM ecosystem — just returned from visiting essentially every major AI lab in China. Moonshot, Zhipu, Meituan, Xiaomi, Qwen, Ant Ling, 01.ai. He went in with genuine curiosity and came back with humility. That combination is rarer than it should be.

What he found was the cranes. Different domain, same energy.

Lambert’s central observation is about culture, not capability. The Chinese labs aren’t winning on any single technical breakthrough — they’re winning on execution discipline. He describes researchers, many of them active students, who bring no ego to the work. They absorb context fast, drop assumptions faster, and seem genuinely unbothered by the philosophical debates that seem to swirl constantly in the American AI community. When he tried to engage Chinese researchers on the long-term social risks of models or the ethics of AI behavior, those questions “hung in the air with a simple confusion. It’s a category error to them.” Their role is to build the best model. Full stop. To them, an LLM isn’t a philosophical entity to be interrogated; it’s a piece of infrastructure to be optimized.

That description landed for me. Not as a criticism of American research culture, but as a real observation about what the moment demands. Building good LLMs today is, as Lambert puts it, meticulous work across the entire stack — “all points of the model can give some improvements, and fitting them in together is a complex process.”

The work that matters most right now isn’t the 0-to-1 creative leap; it’s the thousand unglamorous decisions executed without complaint. Students who haven’t yet learned to lobby for their own ideas turn out to be well-suited for exactly this.

Lambert ends on a note that’s hard to shake. Looking up from his laptop on a high-speed train, he keeps seeing cranes on the horizon. He draws the same connection I did, though from the inside: the construction everywhere fits the broader culture and energy around building. “When I look up from my laptop and always see bunches of cranes on the horizon, it obviously fits in with the broader culture and energy around building in China.”

Twenty years after my first visit, the cranes are still there. They’ve just moved indoors — into server rooms and training runs and model releases that land every few months with quiet confidence. In 2005, what China was building was obvious: you could see the steel frames going up. What’s being built now is harder to see, which may be exactly why it keeps surprising us.

Check out Lambert’s essay – it’s remarkable. If the 20th century was defined by who could move the most earth, the 21st will be defined by who can move the most tokens. And right now, the cranes are moving faster than we think.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.

Categories
AI Programming Work

The Currency of Restlessness

There is a specific kind of vertigo that comes from watching a machine effortlessly perform your life’s work. For Aditya Agarwal, an early Facebook engineer and former CTO of Dropbox, that vertigo hit after a weekend of coding with an AI assistant. His realization was absolute: we will never write code by hand again.

When the specialized skills we have spent decades mastering become free and abundant, the foundation of our professional identity inevitably trembles. Agarwal captures the duality of this moment perfectly, describing it as a mixture of “wonder with a profound sadness.”

“There’s something deeply disorienting about watching the pillars of your professional identity, what you built and how you built it, get reproduced in a weekend by a tool that doesn’t need to eat or sleep.”

The conversation around AI tends to flatten this emotional reality into two distinct camps: the doomers who foresee total replacement, and the boosters who promise a frictionless utopia.

But lived experience is messier. We are capable of holding grief and wonder in the same hand.

We can mourn the craftsmen we were, even as we sprint toward the architects we are about to become.

Because here is the secret about the disorientation of progress: it passes.

Once the initial shock fades, what replaces it is a wild, unconstrained energy.

When the mechanical friction of creation vanishes—when a week’s worth of coding can be accomplished in an afternoon—the scope of our ambition expands. We are no longer limited by the keystrokes we can manage in a day, but by the edges of our imagination. We aren’t watching ourselves become obsolete; we are watching our lifelong constraints dissolve.

This shift is rewriting the social contract of knowledge work, starting with how we evaluate human potential. For decades, the corporate world has relied on a calcified heuristic for hiring: brand-name universities, FAANG experience, and years of tenure. We worshipped the resume.

Now, that playbook is breaking down. In evaluating engineers and founders navigating this transition, Agarwal notes that traditional pedigrees predict almost nothing about a person’s ability to thrive. The new dividing line isn’t generational, and it certainly isn’t educational. It is entirely dispositional.

“The trait that matters most isn’t intelligence, or credentials or years of experience. It’s someone’s relationship with change—not whether they’ve seen change before, but whether they run toward it.”

The new currency of the working world is restlessness.

Restlessness is the refusal to settle into the comfort of the way things used to be. It is the constitution of a builder who cannot stop tinkering, who treats every new AI tool as a puzzle to be solved before the day is out. In an economy where the “how” of knowledge work is increasingly automated, the premium shifts entirely to adaptability, curiosity, and vision.

This democratization of capability forces a deeply uncomfortable, deeply human reckoning. We have to let go of the identities we forged under old paradigms to become whatever comes next.

The technology didn’t create this human challenge—it merely made it impossible to ignore.

Categories
AI Farming History

The Harvest and the Algorithm: What 1990s Farms Teach Us About AI

There’s a strange kind of wisdom hiding in dusty old books about agriculture.

When you’re caught in the middle of a technological revolution—and with AI, there’s no question that we are—it’s tempting to keep your eyes fixed on the horizon. But sometimes the most clarifying thing you can do is look back.

Tracy Alloway at Bloomberg recently pointed to something genuinely instructive from the past: Richard Critchfield’s 1990 book, Trees, Why Do You Wait? America’s Changing Rural Culture, which traced the collapse of the family farm as industrial agriculture swept through the Midwest.

The broad strokes are familiar. As machinery got more expensive and efficiency became everything, scale won. The 80-acre husband-and-wife operation got swallowed by the 2,000-acre neighbor with access to capital. It wasn’t complicated. It was just gravity.

But here’s the part that should make your ears prick up.

The Seed That Was Supposed to Save Everyone

In the late 1980s, agricultural biotechnology arrived with a very specific promise. The idea was almost elegant: if you could bake the magic directly into the seed, you wouldn’t need all that expensive machinery, all those sprawling acres, all that fertilizer. The playing field would tilt back toward the small farmer.

Critchfield quoted an Office of Technology Assessment report from 1986 that captured the mood of the moment:

“The Office of Technology Assessment in 1986 forecast that biotechnology in crops would be more quickly adopted by richer farmers… Others argue that the more that gets built into the seed itself, the more it means higher yields at lower cost… If it reduced farm income, it could work to the smaller farmer’s advantage. As it is with all new technology, it is hard to foresee the consequences.”

You can feel the cautious optimism in that language. Hard to foresee the consequences. The understatement of a century.

What Actually Happened

The biotech did raise yields. Nobody disputes that. What it didn’t do was leave the gains in the hands of the people doing the actual farming.

Thanks to intellectual property law, patent protections, and a level of corporate consolidation that would have seemed cartoonish if you’d predicted it in advance, the value flowed straight upstream. We didn’t get “more in the seed, less paid for inputs.” We got more in the seed, and vastly more paid for proprietary inputs. The tech giants of agriculture captured the surplus. The farmers got the risk.

Now Listen to How We Talk About AI

We are told AI will democratize expertise. That a one-person startup will be able to code like a ten-person engineering team. That a small business will generate world-class marketing copy. That this is, finally, the great leveler.

Sound familiar?

Alloway’s analysis lands hard precisely because it forces the uncomfortable question: who will actually capture this value? The ownership structure of AI looks eerily similar to the agricultural biotech boom—proprietary models, walled-off training data, and a handful of enormous tech companies positioned to act as tollbooths between everyone else and their own productivity gains.

She’s right to note that “the ultimate distribution of benefits isn’t determined by technology alone. Policy also plays a role.” That sentence is doing a lot of quiet work.

If the agricultural analogy holds, productivity gains from AI won’t naturally flow to the individual worker or the small business owner. Without a robust open-source ecosystem or some deliberate policy intervention, those gains will be captured by whoever controls the compute and the models.

Where the Analogy Might Break Down

Here’s where I think there’s room for genuine optimism—not naive optimism, but structurally grounded optimism.

You cannot open-source arable land. Reverse-engineering a patented biological seed is genuinely hard, legally risky, and practically difficult. Code and model weights are different. They’re infinitely replicable. The marginal cost of distribution is essentially zero.

The battle between closed, proprietary AI and open-source models is still very much live. That’s not nothing. AI is fundamentally more commoditizable than a physical farm, and the history of software suggests that open ecosystems have a real shot when the community is motivated enough to build them.

Who Owns the Harvest?

Technology can reshape daily workflows in months. Power structures take decades to budge, if they budge at all. The mistake would be assuming the former automatically changes the latter.

The question worth sitting with isn’t what can AI do—that list gets longer every week. The question is who decides how the productivity it unlocks gets distributed. That’s not an algorithm problem. It’s a political and economic one.

If we want the AI revolution to be a rising tide rather than another tractor paving over the family farm, we have to look past the technology itself. We have to decide, deliberately, who owns the harvest.

Questions to Ponder

On history and pattern recognition: The agricultural biotech optimists weren’t stupid—they were looking at the technology and making reasonable inferences. What does that tell us about the limits of predicting who benefits from a new technology by studying the technology itself?

On open source as a counterweight: The open-source AI movement (Llama, Mistral, DeepSeek) is often framed as a technical story. Should we be thinking about it primarily as a political economy story—a structural check on proprietary capture?

On the role of policy: Antitrust law, data ownership rights, compute access regulation—which levers, if any, seem realistic? And who has the incentive to pull them?

On the worker vs. the firm: If AI raises individual productivity, does the gain show up in wages, prices, profits, or somewhere else? What would need to be true for workers to actually keep a meaningful share?

On commoditization speed: Software and model weights can be replicated freely—but does speed matter? If proprietary models establish deep lock-in before open alternatives mature, does the theoretical commoditizability even help?

Inspired by Tracy Alloway’s analysis at Bloomberg and Richard Critchfield’s Trees, Why Do You Wait? (1990)