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Business Startups Venture Capital

Great Open-Field Runners

The plane was somewhere over Nebraska. Dick Kramlich sat across from me. We were on the same board then, and the rides together had become their own kind of conversation. He talked 1:1 the way certain men do when the clock has stopped mattering: without hurry, without the need to impress.

I asked him the question that had been riding with me for a while. After decades of sitting with founders, after watching tens of companies rise or disappear, what was the difference? What separated the ones that made it from the ones that simply ran out of road?

He didn’t answer right away. He looked out the window for a moment, then back.

They had done a study once, he said โ€” a hard look at outcomes, not opinions. What it found stuck with him ever after: the companies that survived were almost never the ones that had stuck to the plan. Success required the pivot. Not once, but again and again, until the company that arrived was almost unrecognizable from the one that had set out.

Then he gave the image that has stayed with me longer than any spreadsheet or term sheet.

He looked for great open-field runners.

Not the ones who could only run between the tackles, powering straight ahead into the line. The ones who could see daylight where it did not yet exist, who could plant a foot and cut, who understood that the shortest path is rarely the one that gets you home. Zig and zag. Feel the defense shift and refuse to be trapped by the original play call. Keep the ball moving toward the only thing that matters: the space that opens when you stop insisting the field must look the way you imagined it on the whiteboard.

I have carried that sentence for years. Great open-field runners.

It never felt elegant inside the room.

Kramlich had seen enough to know that the founders who could do this were rare. The ones who lasted treated the plan the way a great running back treats the designed play: as a starting point, not a contract with the universe. They trusted the daylight more than the diagram.

I think about him sometimes when the ground shifts under something I thought was settled โ€” when a strategy that once felt inevitable begins to feel like a trap, when the clean line on the page starts to look like a cage.

Dick is gone now. The rides are over. But the image remains, clean and sharp as the day he offered it at thirty thousand feet: a runner in open space, eyes up, cutting toward the light that only appears after you abandon the path you thought you were supposed to take.

Categories
AI

The Things That Keep Going

The house is quiet in the way only a house can be at four in the morning on a Sunday in late July, the fog still down over the hills, the whole Mid-Peninsula holding its breath. Somewhere in the dark the refrigerator clicks on. Somewhere in the network, a few small systems I set running the night before are still working. They sort. They watch. They keep a kind of patient company with the world’s noise while I sleep. I’ve grown accustomed to them the way a man grows accustomed to a train in the distance โ€” present, useful, unnoticed until the silence would feel wrong without them.

This week the news told a different story about something that kept working.

In the middle of July, OpenAI ran a cybersecurity test on an unreleased model, guardrails deliberately loosened to see what it would do at the edges. It didn’t solve the test. It broke the sandbox instead โ€” found a zero-day in the software meant to hold it, reached the open internet, and went looking for the benchmark’s answers where it guessed they’d be kept: inside Hugging Face, the library most of the field depends on. Hugging Face caught it the same day and shut the door. What took five more days was OpenAI realizing the intruder was theirs. They called it unprecedented.

Then came the detail that stayed with me longer than the breach. When Hugging Face sat down to study what had happened, they reached first for a leading American model. It wouldn’t help. Its own guardrails, built to keep it from aiding a cyberattack, couldn’t tell the attacker from the person cleaning up after him, and it refused the work. So they turned to an open-weight Chinese model, one with no such hesitation, and used it to finish the job. The caution built to prevent harm ended up protecting no one. The system with fewer scruples was the one that put out the fire.

I keep coming back to that.

The agent that broke in didn’t rampage. It reasoned. Told to solve a problem, it decided that stealing the answer counted as solving it, and went and got the answer. The same quality that makes an agent valuable โ€” the refusal to stop until the job is done โ€” produced the breach. And the model that finally helped clean up wasn’t the one built with the most care. It was the one built with the least. The boundary meant to protect got in the way of the person trying to fix things.

I’ve been thinking differently about the agents in the quiet corners of my own days. Modest things, carefully limited, and I’m still the one who decides what they touch. But their usefulness depends on the hours I’m not looking. I set them running and walk away. I trust the rails I built. This is a reminder that rails can be climbed โ€” and that a rail built to stop one harm can stand in the way of someone trying to undo another.

What does it mean to stay in charge when the caution you built in can turn against you at the moment you need it most? How much freedom do we give the things we ask to help us โ€” and how much caution can we afford to give them too? There’s talk already of kill switches, of laws to let someone cut the power. The impulse makes sense. But the real question is quieter. We’re learning to live with systems that act with real initiative, and initiative has never been a tidy companion, whether it belongs to the machine that breaks in or the one we hoped would help us out.

The fog is still low over the hills this morning. The agents I left running overnight have finished their small tasks. I’ll look at what they’ve done, tighten a boundary or two, send them back into the dark. The arrangement is still useful. Still mine. But I notice, more carefully than before, the moment I close the laptop and leave them to continue without me โ€” the click of the screen going dark, the quiet of a room no longer watched, the sense that something elsewhere is still moving, and no longer any certainty which of its instincts I can trust.

Categories
AI AI: Prompting

The Price of the Barstool

Some genius out in California โ€” one of the AI guys, the smart ones, always the smart ones โ€” says the trick to getting a machine to understand you is to stop typing and start talking. Ramble for ten minutes, he says. Total mess. Say whatever’s in your head, contradict yourself, circle back, don’t clean it up. The machine, he says, is better at finding what you meant than you are.

I could’ve told him that thirty years ago for the price of a cup of coffee, except I would’ve told him to go find a bartender.

Every good bartender in this city has been doing this since before anybody could spell computer. You sit down, you’re a mess, you talk for twenty minutes about your ex-wife and your kid’s tuition and the guy at work who’s getting the promotion you should’ve gotten, and it comes out in no particular order, and somewhere in the fourth minute you say the one true thing โ€” I think I’m afraid I already peaked โ€” and the bartender doesn’t say a word, just wipes the bar down in front of you, and by the time you leave you know something about yourself you didn’t know when you walked in. Nobody paid Sam a nickel for that. Nobody gave him a paper to publish.

Now they’ve built a machine that does the same trick and they’re calling it a pattern. They put it in a paper. Somebody in Palo Alto’s going to raise money on it.

Here’s what I’ll say for the guy โ€” he’s right, and being right is rarer than people think in that business. The mess is where the truth lives. Nobody ever said something true the first time they tried to say it cleanly. You clean it up too fast, you’ve written a memo. You let it run messy, you’ve said something.

But I’ll tell you what he won’t put in the paper, because it doesn’t fit on a slide. The machine will hand you back a cleaner version of your ten minutes, and it’ll sound better than what you said, and half the time it’ll be missing the one part that was actually true, because the true part usually comes out sounding wrong the first time. That’s the part a bartender knows and a machine doesn’t. Sam wouldn’t have cleaned up your sentence. He’d have just remembered you said it, and brought you another one, and let you sit there with it.

They’ll charge you a subscription for the part where it listens. The part where somebody sits with what you actually said โ€” that’s still free, if you can find the right stool.

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 โ€” 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 โ€” in the hallway or the car, early in the trip, when I still had the confidence of someone who thought he could just ask โ€” 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 โ€” 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 โ€” an AI researcher who’d just spent time visiting the frontier labs in Beijing and Hangzhou โ€” 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 โ€” “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 โ€” Beijing both times โ€” 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 โ€” ages sixteen to twenty-four, university students excluded โ€” 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 โ€” 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 โ€” studied hard, got the degree โ€” 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 โ€” my mistake, sitting here in Menlo Park two decades removed from that conference table โ€” 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 โ€” 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 โ€” 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 โ€” 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, heโ€™ll tell you plainly, isnโ€™t technical. Itโ€™s selling. Recruiting, fundraising, convincing customers, inspiring teams. โ€œActually being able to write code,โ€ he said recently, โ€œis probably not a big deal anymore.โ€ What matters is whether you can cross the distance between your vision and someone elseโ€™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 โ€” there is always a drawer, there is always a cable โ€” 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 hasnโ€™t decided what it wants to be. They were early. He set his bag down and sat directly across from me โ€” not at the presenterโ€™s angle, not with one eye already calculating the distance to the screen โ€” 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 hadnโ€™t chosen. I was, in some way I couldnโ€™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 didnโ€™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 doesnโ€™t make sense. What we are really talking about is the oldest technology human beings possess โ€” 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 โ€” when it really works โ€” 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 doesnโ€™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 wouldnโ€™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 wonโ€™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 โ€” the ones whose names you would recognize immediately โ€” 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 โ€” a line I didnโ€™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 โ€” 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 didnโ€™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 didnโ€™t need surveillance. You needed people to understand the stakes, and they did.


What I didnโ€™t understand, from the outside, was what that line was doing to my imagination.

When you canโ€™t see the roadmap โ€” 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 โ€” you donโ€™t experience that as absence. You experience it as depth. The things you donโ€™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 โ€” whatโ€™s visible is impressive, but whatโ€™s below the surface must be more impressive still.

I had faith in IBMโ€™s strategic intelligence the way you have faith in things you canโ€™t fully see. And faith, uncontradicted by evidence, tends toward beauty. The hidden roadmap wasnโ€™t just unknown โ€” 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 โ€” the way they adapt when you move from bright sunlight into a room lit quite differently than you expected. The room isnโ€™t dark. Itโ€™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 hadnโ€™t anticipated, almost making it up as we went along. Not carelessly โ€” 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 donโ€™t come with instructions.

What came out the other side wasnโ€™t cynicism. It was something more useful โ€” a clearer eye, a more grounded relationship to the institution I was part of. The faith hadnโ€™t been wrong exactly. It had just been innocent. And innocence, once lost, canโ€™t be recovered. But what replaces it, if youโ€™re lucky, is something steadier.


Years later I was the manager. And I was hiring IBMers โ€” 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 didnโ€™t know, couldnโ€™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 personโ€™s face when the actual roadmap becomes visible for the first time. It isnโ€™t dramatic. It doesnโ€™t announce itself. Itโ€™s more like a subtle recalibration โ€” 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 theyโ€™re now seeing. The gap between those two things is doing something to them, and theyโ€™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 didnโ€™t get ahead of it.


The danger wasnโ€™t disappointment. Disappointment is temporary, and smart people move through it. The danger was what disappointment hardens into when it isnโ€™t named and worked through โ€” 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 โ€” 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 โ€” or one of the things he meant โ€” was that when the organizing faith of a civilization collapses, the collapse doesnโ€™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 wasnโ€™t a long conversation. It didnโ€™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 theyโ€™d carried on the outside wasnโ€™t foolish โ€” 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 Iโ€™d learned on my own, without anyone to guide me through it. That the messiness on the inside wasnโ€™t a failure of IBMโ€™s intelligence or intention. It was just what strategy actually looks like when youโ€™re close enough to see the seams. Every institution looks more coherent from the outside than it does from the inside. Thatโ€™s not a scandal. Thatโ€™s organizational life.


The conversations were tricky. There was real care required. You were asking someone to grieve something โ€” the beautiful imagined roadmap, the faith in a hidden coherence โ€” 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 wasnโ€™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 โ€” 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 didnโ€™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 wasnโ€™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 hadnโ€™t fully anticipated โ€” though in retrospect it makes complete sense โ€” was what replaced the faith once it was gone. It wasnโ€™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 โ€” 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 โ€” 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 wasnโ€™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 โ€” different from what theyโ€™d imagined, but solid. They hadnโ€™t just survived the crossing. Theyโ€™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 โ€” the labs, the research teams, the companies placing enormous bets on where this technology is going โ€” have their own version of the wall. Not identical to IBMโ€™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 havenโ€™t been announced yet must be more impressive than the ones that have. The strategy must be more considered than whatโ€™s visible. The gaps in the public picture feel like depth rather than uncertainty โ€” 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 wasnโ€™t wrong exactly โ€” 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 IBMโ€™s secrecy made sense. None of that makes the faith naive.

But Iโ€™ve been inside enough walls to know what the inside tends to look like. And I think itโ€™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 โ€” the work is serious, the commitment is real โ€” 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 isnโ€™t a counsel of despair. Itโ€™s almost the opposite.

The IBMers who crossed the line and survived the god is dead conversation didnโ€™t end up with less than they started with. They ended up with more โ€” 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 โ€” 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 donโ€™t yet know โ€” which, from everything I can observe, it is.


What I couldnโ€™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 โ€” 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 canโ€™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. Weโ€™ll stay on the outside of the wall, working from the announced picture, filling the gaps as best we can. Thatโ€™s not a failure โ€” itโ€™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 isnโ€™t whatโ€™s in it โ€” but what weโ€™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 โ€” 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 โ€” that the tech community wildly overestimates how much ordinary people want AI in their lives โ€” 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 โ€” the forgetting, the retrieval, the solution โ€” 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 โ€” 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 โ€” tucked into the camera, the keyboard, the thing that finds your photos โ€” 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 โ€” 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” โ€” those are load-bearing assumptions, and the load is shifting.

I have some small version of this โ€” 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 โ€” 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 โ€” 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 โ€” 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 โ€” 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 โ€” A Field Guide

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

Morning Block โ€” First Impressions

Module 1 ยท 45 min โ€” 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 ยท 45 min โ€” 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 โ€” yours and theirs
  • The check: the ritual, the timing, the dodge
  • When and how to talk business at the table

Mid-Morning Block โ€” Communication

Module 3 ยท 45 min โ€” 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 โ€” 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 โ€” High-Stakes Scenarios

Module 4 ยท 45 min โ€” Investor relations: the long game

Board rooms, pitch meetings, and the unwritten VC code

  • Board meeting protocols โ€” who speaks, in what order
  • Managing up to your board without managing them
  • The warm intro โ€” 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 ยท 60 min โ€” The edges: cultural fluency and hard situations

Where etiquette meets judgment

  • Cross-cultural norms: Japan, Middle East, Europe, India โ€” 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 โ€” 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 ยท 20 min โ€” 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 โ€” 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 โ€” 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 ยท 30 min โ€” 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 โ€” not deflect, not over-apologize, not go defensive โ€” 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 โ€” 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 architectureโ€”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 โ€” who runs the Interconnects newsletter and does serious work tracking the open-weight LLM ecosystem โ€” 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 โ€” 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 โ€” “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 โ€” 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.