Category: AI

Categories
AI Apple Bicycles History

The Best Lathe in the Shop

Part 3 of 3…

There is a version of this story where Apple is the Wright Brothers.

It is not an unreasonable version. Apple has done the safety bicycle move more times than almost any company in history — taken a technology the engineers built for engineers and brought it down to earth, made it a machine for everyone. The Mac. The iPod. The iPhone. Each one was a wheel coming down. Each one arrived after a period of apparent slowness, of critics saying Apple had lost its edge, of the industry having already moved on to the next thing. Each one was, in retrospect, obvious. Apple had been in the bicycle shop the whole time. You just couldn’t see what they were building.

So when Apple showed its hand at WWDC this week — a rebuilt Siri operating at the OS level, accessing your messages and mail and photos in real time, understanding context across apps, doing things the old Siri could only approximate — it is tempting to read it as Kitty Hawk. The long preparation made visible. The brothers finally leaving the shop.

It might be. It also might not be. That is the only honest thing to say.

What Apple showed was real. The new Siri, built on Apple’s own Foundation Models with help from Google’s Gemini, is not the Siri that became a punchline. It holds context. It moves across apps without being asked. It knows what you were doing five minutes ago and connects it to what you are doing now. It can surface a photo without opening Photos, build a navigation route from an image, draft a message in the tone of the conversation it is joining. These are not features. They are the beginning of an operating system that understands you, which is a different thing from an operating system that executes your commands.

The structure of the keynote said more than the words did. Apple led with fixes before features. iOS 27 is a Snow Leopard update — performance, reliability, the underlying machinery — and Siri AI was presented as one item on a long list rather than the main event. This is Apple’s tell. When they are doing something foundational they tend to understate it, the way a craftsman doesn’t announce the quality of his work but simply does it and lets you find it. The penny-farthing riders called their machine the ordinary. They didn’t think they needed to explain.

But here is the thing about the bicycle shop analogy that the optimistic version leaves out. The Wright Brothers knew what they were trying to build. They had been thinking about flight for years before Kitty Hawk. The bicycle shop gave them the craft knowledge, the physical intuition, the hands-on education in how machines move through space. What it did not give them was the destination. They brought the destination themselves.

The question Apple has not answered for me — the question this week’s keynote raised rather than resolved — is whether they know where they are going. Or whether this has only been a partial reveal and there’s much more behind the curtain?

The OS-level integration is the chain drive. Decoupling AI from the app, letting it run through the substrate the way a chain runs through a drivetrain, is exactly the kind of architectural insight that changes what a machine can do. It is not a feature you add. It is a rethinking of what the machine is for. Every previous AI assistant lived above the operating system, looking down at your data from a remove. Apple’s new architecture lives inside it, which is a different relationship entirely — the difference between a mechanic who reads about your car and one who has driven it for a year.

That is the Coventry precision. The tight tolerances. The discipline of making things that have to work at the level where failure is not an option.

What nobody knows, including Apple, is what you build with it.

There is also this: Tim Cook will not be driving this evolution. He announced that John Ternus takes over in September, which means this WWDC — this particular showing of the hand — is the last one Cook owns. Ternus is a hardware engineer, the man who built the Apple Silicon transition, the person most responsible for the Neural Engine that makes on-device inference possible. He is, in the bicycle shop metaphor, the craftsman who built the lathe. Whether he knows how to use it to make something that flies is the question the next several years will answer.

History is patient about these things. It lets the work speak.

In 1892, two brothers opened a shop on West Third Street in Dayton and started fixing bicycles. They were not trying to change the world. They were trying to make a living, to learn a machine, to understand in their hands what the books couldn’t teach them. The flying came later, and it came because of the shop, not despite it. The shop was the point. They just didn’t know it yet.

Apple has the best lathe in the bicycle shop. They have the chain drive architecture, the on-device precision, the installed base of two billion devices that will carry whatever they build into more hands than any other platform on earth. They have a new set of hands on the wheel starting in September, hands that know the metal intimately, that built the engine the whole thing runs on.

What they do not have yet — or if they have it, they are not showing it — is the image of what they are flying toward.

Maybe that’s the ordinary part. Maybe that’s always been the ordinary part. You don’t know what you’re building until you’ve built it, and by then the world has already changed, and everyone says it was obvious, and they are right, and they are also completely wrong about when the decision was made.

The shop is open. The lathe is running. Work is underway.

What happens when someone finally knows what to make?

Categories
AI Bicycles History

The Bicycle Shop

Part 2 of 3…

It is eleven-thirty on a Tuesday night and she is arguing with a language model about a spreadsheet.

Not arguing, exactly. That’s not the right word. She is coaxing. She is debugging. She is reading error messages that tell her almost nothing and rewriting prompts that almost work, and she has been doing this for two hours, and the spreadsheet still isn’t right, and she is going to try one more thing before she gives up and does it by hand. She is a data analyst at a mid-sized logistics company in Columbus, Ohio. She is not a researcher. She is not a founder. Nobody is writing about her. She is just a person trying to get a machine to do something useful, and the machine keeps almost doing it, and she keeps learning, in the gap between almost and done, something she couldn’t have learned any other way.

She doesn’t know what she’s learning. That’s the important part.

In 1892, two brothers opened a bicycle repair shop on West Third Street in Dayton, Ohio. The bicycle craze was at its peak — the safety bicycle, with its two equal wheels and chain drive, had just replaced the penny-farthing, that absurd high-wheeler everybody called loose change and the riders, with complete seriousness, called the ordinary. The brothers fixed flats and adjusted brakes and built custom frames and ordered parts from Coventry and kept the books and swept the floor. It was ordinary work. Nobody was writing about them either. What they were doing was accumulating, without knowing they were accumulating, a physical understanding of how machines move through space — the gyroscopic principles, the weight distribution, the thousand small calibrations that kept a rider from falling. They were learning in their hands what no university taught and no book fully contained.

Eleven years later they flew.

We tell the Wright Brothers story as a story about flight. It makes sense — flight is the thing, the miracle, the moment the world changed. But the actual story, the one that explains how Kitty Hawk was possible, is a story about a bicycle shop. It is a story about unglamorous preparatory work, about the education that hides inside the constraint, about what you learn in the gap between the machine that exists and the machine that should exist. Orville and Wilbur didn’t go to Kitty Hawk despite the bicycle shop. They went because of it. The shop was the point. They just didn’t know it yet.

We are in the bicycle shop right now.

The people building with AI today — the prompt engineers, the fine-tuners, the agent builders, the data analysts in Columbus arguing with spreadsheets at midnight — are doing work that looks, from the outside, like mere tinkering. Unglamorous. Iterative. Full of failure. The tools are awkward. The models hallucinate. The context windows run out at the wrong moment. Every solution opens three new problems. It feels like the penny-farthing: powerful enough to be useful, constrained enough to be maddening, requiring a kind of practiced vault just to get started.

But that awkwardness is the education.

Every time a prompt fails, the person writing it learns something about how the model thinks — about what it responds to, what it resists, where it gets confused, where it surprises you. Every agent that breaks in production teaches its builder something about the gap between what a model can do in a demo and what it can do under load, with real data, with users who don’t behave the way you expected. Every context window that runs out forces a decision about what actually matters, what is essential, what can be cut. These are not just technical lessons. They are epistemic ones. They are lessons about the nature of intelligence, about how meaning gets encoded and retrieved, about what it means for a machine to understand something versus to pattern-match on the surface of understanding.

The people learning these lessons right now don’t have a name for what they know. They just know it in their hands.

This is how it always works. James Starley’s craftsmen in Coventry bent and brazed bicycle frames by feel and experience, knowing things in their hands they couldn’t fully explain on paper. That embodied knowledge — the tight tolerances, the interchangeable parts, the discipline of making things that had to work — migrated into every bicycle shop that followed, crossed the Atlantic, and ended up in a shed in Ohio. The Wright Brothers didn’t invent precision manufacturing. They inherited it, absorbed it, and applied it to a problem nobody else had solved because nobody else had brought those particular hands to that particular problem.

The chain drive was the hinge. Before it, the bicycle’s design was locked — bigger wheel for more speed, higher and higher off the ground, until the machine teetered at the edge of what a human could survive. The chain drive broke the constraint. It decoupled the pedals from the wheel, let the gearing do what only size had done before, brought the rider back to earth. What had been a machine for athletes became a machine for everyone. What had been the ordinary became, almost overnight, something new.

We are waiting for the chain drive.

Not waiting passively — it is being built right now, in a hundred places at once, by people who mostly don’t know they’re building it. It might be the interface that finally makes AI genuinely accessible to people who can’t do the running vault. It might be the memory architecture that lets a model carry context the way a human carries context, not in a window but in something more like experience. It might be something nobody has named yet, something that will seem obvious afterward, the way all elegant solutions seem obvious after the fact.

What it will not be is the product of people who stayed away from the bicycle shop.

The analyst in Columbus closes her laptop at midnight. The spreadsheet is still not right. She has learned three things about how the model handles date formatting, two things about how it interprets ambiguous column headers, and one thing about her own assumptions that she didn’t know she was making. Tomorrow she will try again. She will get closer. At some point — not tomorrow, maybe not this year — she will get it right, and the thing she learned in the gap will be available to her for the next problem, and the one after that, and she will carry it forward without knowing she’s carrying it, the way craft always travels, in hands that have done the work.

She doesn’t know what she’s riding toward.

That’s the ordinary part. That’s always been the ordinary part.

Categories
AI

The Transit Authority

Today SpaceX went public. The valuation target was $1.77 trillion — already the largest IPO in history, surpassing Saudi Aramco — and the market wanted more.

I was curious about the S-1, so I read the TAM section. SpaceX claims a total addressable market of $28.5 trillion. Rockets and Starlink together account for about $2 trillion of that. The rest — $26.5 trillion — is artificial intelligence. Enterprise AI applications alone: $22.7 trillion.

IDC analyst Arnal Dayaratna said the quiet part out loud: “To be crystal clear, its positioning there right now is basically nonexistent.”

That is an honest sentence. It describes most TAM claims in most S-1 filings. The market did not care. The stock was up 25% anyway.

But the $22.7 trillion number is interesting regardless of whether SpaceX captures it. It asks a real question: how large is the enterprise AI opportunity, really? And what does capturing it actually require?

The answer has something to do with transportation.

We do not travel the same way for every trip.

Walk to the coffee shop. Take a scooter to the office. Ride share to the airport. Commute by train. Drive your own car on weekends. Fly when you need to get somewhere fast and far.

Each mode has a different cost structure, a different latency, a different quality profile. Nobody takes a plane to buy milk. Nobody walks to a meeting in another city. We allocate the mode to the trip, instinctively, without much thought. The routing decision is invisible.

AI inference is arriving at exactly this moment. Until recently, there was one mode: you called the big frontier model. GPT-5.5. Claude Fable. Gemini 3 Pro. You paid the tolls, you waited, and you got what you needed. It was like renting a plane for every trip. Expensive, but simple. There was nothing else on the road.

That is no longer true.

The walk tier is a model running on your phone or laptop — no network, no cost, no data leaving the device. Google’s Gemma 4 and Microsoft’s Phi-4 now handle classification, autocomplete, document summarization. You do not even notice you are using AI.

The bike tier is a small model running on your own hardware — a workstation, a private server. Fast, cheap, data stays on-prem. These models can handle tasks that required GPT-4-class APIs eighteen months ago.

The rideshare tier is cheap cloud inference. You are not driving, not owning, but you get there quickly and cheaply. What cost $22,500 a month in 2025 runs for $405 today. That is not a gradual erosion. That is a structural break.

The car tier is dedicated hosted compute — reserved capacity, predictable performance, always available.

Frontier models are the airplane. Dense reasoning, long-context synthesis, genuinely hard problems. You use them when you need to get somewhere fast and far. You do not use them to classify customer support tickets.

Here is the problem nobody had two years ago.

Picture the IT director at a mid-size insurance company. She deployed a frontier model API last year. Smart decision at the time — one vendor, one contract, everything works. Now she’s gotten the quarterly invoice and done the math. Roughly 80% of the queries hitting that API are things like: extract the date from this document, categorize this claim, summarize this email thread. Tasks a much cheaper model handles just as well. She has been flying everyone to a meeting across town.

She is not alone. Most organizations that built on frontier APIs in 2023 and 2024 are now discovering they over-provisioned for the average query and under-thought the distribution. The expensive mode works. That’s the trap. You don’t look for alternatives when the thing you’re doing works.

The routing layer is where this resolves. A routing layer is need that sits between the application and the model tier and asks, for each incoming query: what does this actually require? Simple queries go to the cheap tier. Hard queries escalate to frontier.

Route 90% of requests to the cheap tier, 10% to frontier. You cut costs by 86%. The quality loss on the 90% is negligible, because most production queries are not frontier-hard. Most trips, you walk.

Back to the $22.7 trillion.

The number is real in the sense that enterprise software currently costs a lot. The global market — CRM, ERP, HR systems, supply chain, all of it — runs roughly $700 billion annually. If AI agents eventually do much of the work those systems mediate, and if the value gets priced into the AI layer, you can arithmetic your way toward very large numbers.

But the routing story embeds an uncomfortable question: if inference costs are collapsing, and if smart organizations route most of their traffic to free or near-free edge compute, who actually captures the value?

The model providers need volume. But enterprise routing gives sophisticated buyers a systematic exit from frontier pricing for the bulk of their workload. You call the expensive plane only when you need to cross an ocean.

This is why the routing layer matters more than it looks. The company that becomes the transit authority — the entity that sits between all the modes and makes the dispatch decision — is structurally positioned to matter as much as any individual model provider. The transit authority does not own the planes or the trains. It knows where you are going and picks the right mode. That intelligence, at scale, is a moat.

SpaceX is not that company. IDC is right about that. But the $22.7 trillion figure, even as a promotional artifact of an S-1, is pointing at something real: the opportunity is large enough that the infrastructure for consuming AI efficiently may be as valuable as the AI itself.

The frontier model providers are the airlines. Necessary, impressive, expensive to operate, essential for the long haul. Emerging routing solutions are building the booking platforms — the systems that decide when you actually need a plane, and make sure you are not buying a first-class ticket to go ten blocks.

In transportation, the booking platforms eventually captured enormous value. Expedia, Booking.com, Google Flights. The airlines, which had all the brand and all the infrastructure, found themselves competing for placement in someone else’s interface.

That story may be ahead of us in AI. The models are the planes. Someone else may be Expedia.

Categories
AI Startups

A New Reason to Launch

“Before you launch, the speed you can build is now mainly limited by your imagination in what you tell AI. After you launch, the AI can watch your users and make improvements on its own.”
— Jared Friedman, Y Combinator

Jared Friedman watches hundreds of founders a year navigate the gap between idea and launched product. He notices patterns the rest of us miss. And what he’s describing above is not an incremental improvement in how software gets built. It is a change in the nature of the advantage.

This is a different kind of liberation than founders have known before.

The old liberation was launch early and the market corrects your wrong assumptions. Humbling, but useful. You were still the one doing the correcting, late at night, rewriting the onboarding flow based on what the data told you.

The new liberation he’s describing is something closer to multiplication. You launch, and now there are effectively more of you. The AI is watching session replays you’ll never have time to watch. It’s noticing the drop-off after step three that you’d have caught in month four. It’s holding the pattern of a thousand user paths simultaneously and asking what they mean. Your imagination seeded the thing. Reality is now feeding it.

That observation redraws the map cleanly. Pre-launch and post-launch used to differ in degree — you knew more after than before. Now they differ in kind. Pre-launch you are the sensing organ. Post-launch you’ve grown new ones.

The founders who feel this most viscerally, I suspect, are the ones building alone or in pairs — the people for whom every previous era of building had a hard ceiling imposed by human hours. They could only read so many support tickets. They could only run so many experiments. The ceiling is lifting and the feeling is of a room getting larger.

The core advice hasn’t changed. Paul Graham was saying “launch early” twenty years ago and it was true then. What’s changed is the reason underneath it — the mechanism that makes it true now is nothing like the one he had in mind.

The advice is twenty years old. There is a new reason and it is brand new. Most people haven’t noticed the swap yet. But they will.

That window does not stay open long.

Categories
AI

The Layers Don’t Hold

Stewart Brand drew the diagram in 1999, in The Clock of the Long Now, though he’d been developing the idea for years before that. Six concentric rings, each representing a layer of civilization, each moving at a different speed. Fashion at the outside, changing season to season. Commerce beneath it, slower. Infrastructure below that — roads, power grids, buildings. Then governance. Then culture. At the center, moving so slowly it seems not to move at all: nature.

The diagram is elegant, but Brand’s real insight is about the relationship between layers, not the layers themselves. He called the framework pace layers. The fast layers innovate. The slow layers stabilize. Fashion gets to be experimental and throwaway precisely because infrastructure doesn’t. Governance can afford to be deliberate because culture provides continuity underneath it. The whole system depends on this differential. Each layer absorbs shock from the one above it and passes only the most durable changes downward. It’s not inefficiency — it’s architecture.

Brand also had a name for what happens when the differential breaks down. He called it “layers crashing.” When a fast layer accelerates past the capacity of the layer beneath it to absorb and adapt, the system loses its self-correcting character. The fast layer doesn’t just move quickly anymore — it damages the slow layer’s ability to function. Infrastructure overwhelmed by commerce becomes fragile. Governance overwhelmed by technology becomes irrelevant. The stability that the slow layers provide isn’t guaranteed. It has to be continuously earned.

We are in a layers-crashing moment. The technology layer is moving faster than it has in any of our lifetimes, possibly faster than it ever has. And the layers below it — infrastructure, governance, culture — are discovering that the shock-absorption mechanisms they’ve refined over centuries weren’t designed for this.

Dario Amodei published a long policy essay recently. He opens with Treebeard — the ancient, slow-speaking tree from Lord of the Rings whom the Hobbits must somehow persuade to act quickly enough to matter. It’s the same intuition as Brand’s pace layers, arrived at from a different direction. The problem isn’t that governance is broken. The problem is that it was built for a different tempo, and the tempo has changed.

What’s new in Amodei’s essay — and it feels genuinely new — is the shift in register. For several years, Anthropic’s public posture on regulation has been: transparency first, binding rules later, once we understand the shape of the risks well enough to target them precisely. That posture made sense when the risks were theoretical. It makes less sense now. The pivot in the essay is Amodei’s own most advanced model, Claude Mythos Preview, which he describes as having “scrambled the global cybersecurity landscape.” He is using his own product as the evidence that the moment for incrementalism has passed.

The five policy areas he covers — regulation, macroeconomics, scientific innovation, civil liberties, geopolitics — each map onto a different pace-layer collision. The cybersecurity risk to financial infrastructure is commerce meeting governance too fast. The job displacement problem is commerce and culture in conflict, with governance lagging both. The civil liberties section is perhaps the most unsettling: the worry that AI hands governments tools of surveillance and coercion that the legal architecture of democracy — built for a slower world — simply cannot constrain.

The regulatory framework he proposes is modeled on the FAA: mandatory third-party testing of frontier models, government power to block deployment, four specific risk categories as scope limiters. It is more concrete than anything Anthropic has proposed publicly before. The FAA analogy is meant to reassure — we have regulated powerful technologies before, we know roughly how this works — and it largely does reassure. Though it’s worth holding alongside it a genuine open question: whether regulatory bodies can develop the expertise and independence to govern a technology this fast-moving before the technology moves again. The history of industry regulation suggests this is hard. It doesn’t suggest it’s impossible.

Brand’s diagram has one more feature worth noting. The arrows don’t only point downward, from fast layers shaping slow ones. They also point upward: the slow layers constrain what the fast layers can become. Culture shapes what commerce builds. Governance shapes what infrastructure gets funded. Nature sets limits that no other layer can override. The relationship is bidirectional, and the bidirectionality is the point. What Amodei is calling for — urgently — is for the slow layers to begin exerting upward pressure again, before the differential becomes so extreme that they lose the capacity to do so.

Whether they can move quickly enough is the question Brand’s diagram can’t answer. Treebeard wakes up, eventually. The forest burns faster than he walks.

Categories
AI

Hands He Can’t Feel

Note: a fictional story exploring how software development is changing in the world of Claude Code, Antigravity, etc.

The cursor blinks for maybe two seconds. Then the code appears, all of it, a function Pete Callahan had been turning over in his head for the better part of a morning, just there, complete and correct and formatted the way he would have formatted it himself. He reads it the way you read something you’re looking for an error in. There isn’t one. He leans back in his chair in a way that isn’t quite satisfaction and isn’t quite anything else he has a word for.

Bewildered, maybe.

Outside his window, Dayton is doing what Dayton does in February, which is endure. The city has always been good at that. The Wright Brothers built their first serious wind tunnel a few miles from here in a room above a bicycle shop, testing wing shapes that didn’t exist yet, failing in ways that taught them something. Pete grew up knowing that story the way you know the streets of the neighborhood you grew up in — not as history exactly, more as weather. Just a thing that was true about where you were from.

His father would have understood the wind tunnel. You build the thing to test the thing. You put in the hours. That’s how knowledge works.

Pete is no longer sure that’s how knowledge works.

His father, Ron Callahan, spent thirty-one years at Wright-Patterson keeping F-16s in the air. Not designing them, not flying them. Maintaining them. There is a difference and Ron has always understood it as a moral one. The pilot trusts you with his life in a way that is not metaphorical. You either know what you’re doing or you don’t. There is no almost.

He lives twenty minutes from Pete in a house that smells like coffee and WD-40, a combination Pete has never encountered anywhere else and that means, without his being able to say exactly why, that everything is okay. Ron is seventy-one now, still straight, still with the unhurried precision in his hands that Pete watched as a boy and tried to understand as a kind of language. On Sundays Pete drives over. They watch whatever game is on. Ron sets a mug in front of him without asking.

This particular Sunday Ron asks how work is going the way he always asks, with genuine interest and the slight remove of a man who has never quite been able to picture what his son actually does all day.

It’s great Dad. But it’s changing faster than ever before.

Ron nods. He has seen the F-4 give way to the F-16 give way to systems so sophisticated the maintenance manuals run to thousands of pages. He knows about change. You learn the new thing, he has always believed, or the new thing leaves you behind. Simple as that.

He hears his son’s sentence as a version of something he has said himself.

He’s not wrong, exactly. He’s just not quite right either.

Driving home Pete thinks about the kids he came up with, the ones from places like Dayton who found in code what the world didn’t always offer elsewhere — a domain where being right was demonstrable, where quality was real, where the machine didn’t care about your intentions. It had shaped him the way Dayton shaped him. Not as ideology. Just as weather.

He still believes that, mostly.

It’s just that the machine has changed its mind about what knowing means.

What Pete cannot explain, what he doesn’t have the language for yet, is that the change he is living through is not like learning a new aircraft. When the F-16 replaced the F-4, the mechanic’s relationship to the machine stayed intact. Hands on metal. Knowledge earned through repetition, through failure, through the slow accumulation of understanding what the thing wanted to do and what it didn’t. The new plane was more complex but the posture was the same. Man serving machine serving pilot. The chain held.

What is happening to Pete is something else. Something that doesn’t have a clean analogy in Ron’s world, or in the history of Dayton, or in the mythology of the American craftsman that Pete absorbed so completely he doesn’t even know he’s carrying it.

He is still building things. He is building better things, faster, than he ever has. But somewhere in the last eighteen months the relationship changed in a way he is still trying to locate. He used to be the one who knew. Now he is the one who directs something that knows, which sounds like a promotion and feels like something more complicated than that.

His father’s hands always knew what to do.

Pete is learning, at thirty-eight, to work with hands he can’t feel.

By ten o’clock the house has the particular quiet of a place that is usually fuller than this. Sarah’s coffee cup from this morning still on the counter. Her shoes by the door. The small evidence of a life that will resume at midnight when he hears her key in the lock, and until then it’s just Pete and the screen and whatever this is that he’s trying to figure out.

What he does, alone in the house on these nights, is push. He takes the thing further than the task requires. Asks harder questions. Builds something more complex than anyone asked for just to see where the edges are, just to understand what he’s actually working with. It is the same impulse that kept his father an extra hour on a Friday, checking something that had already been checked, because almost certain was not the same thing as certain and a pilot was going to trust this machine with his life.

The ethic transferred even when the medium changed.

Even now, when the medium is changing again.

He thinks about his father’s hands sometimes, late like this. The way they moved with that unhurried precision, never rushed, never uncertain, each motion the product of so much repetition it had passed through knowledge into something that lived below knowledge. Pete watched those hands as a boy the way you watch something you are trying to learn without knowing you are learning it.

He used to think he had built something like that himself. The ability to hold a system in his head, to feel where it wanted to go, to know. The hands that knew what to do.

What he is building now he cannot quite name yet. It is not that the knowledge is gone — if anything it matters more, sits heavier, earns its keep in ways it didn’t before. But the relationship is different in a way he is still trying to locate, still turning over on these quiet nights while Dayton endures outside the window and Sarah’s shoes wait by the door and the cursor blinks with the particular patience of something that does not need him to be ready.

He types. The code appears.

He reads it the way his father checked what had already been checked.

Not because he doesn’t trust it.

Because that’s what you do when it matters.

Categories
Aging AI Business Living

The Being Phase

There is a metric making the rounds in technology investing circles that is, on its face, about market share and revenue concentration. Alex Sacerdote of Whale Rock Capital calls it the New Rule of 40 for AI. The formula is simple: take the percentage of a company’s sales derived from AI, add its percentage market share in that AI category, and if the sum reaches 40, you have a winner. Celestica, a company most people have never heard of, scores extraordinarily well. It owns somewhere between half and sixty percent of the cloud Ethernet white-box switch market. NVIDIA doesn’t need a formula. It simply is what it is.

Sacerdote designed the metric to cut through a specific kind of noise — the companies claiming AI exposure they don’t actually have, the giants whose AI revenue hovers at one or two percent of their base while their press releases suggest otherwise. The framework is a detector. It finds the companies that have stopped becoming AI infrastructure and started simply being it.

I found myself less interested in the companies than in that distinction.

I spent years at Visa watching a network that had long since crossed that threshold. By the time I arrived, Visa wasn’t becoming the global payments infrastructure. It was the global payments infrastructure. The work was real — fraud detection, modeling, the daily labor of keeping something enormous running — but the existential question had been settled before I got there. The network existed. Merchants accepted it because cardholders carried it. Cardholders carried it because merchants accepted it. That loop had been closing for decades. We were custodians of a fait accompli.

There’s a particular feeling to working inside something that has already won. It’s not complacency exactly. The problems are genuine and the stakes are high. But the uncertainty has a different quality — it’s operational uncertainty, not existential uncertainty. You’re not asking whether the thing will survive. You’re asking how to run it well.

I didn’t have language for that distinction then. Sacerdote’s metric gives me some. The companies that score highest on his New Rule of 40 have resolved their existential question. They’re not fighting for position. They’re administering a position already held.

The question that has followed me out of that career, and out of several decades of watching technology cycles turn, is simpler and more personal than any investment framework.

When did I cross that line myself?

I have been writing at sjl.us since 2001. That’s not a boast — it’s a data point. Twenty-five years of thinking out loud, of ideas arriving rather than being argued, of the specific memory as structural anchor. The blog is not becoming anything. It is what it is: a record of a mind moving through time, accumulated into something that has its own weight and shape.

The book on payments systems exists. The career at Visa exists. The photographs exist. The train journeys exist. The years in Dayton exist, and the years on the Peninsula, and the particular way the light falls on the California coast at Pescadero in the late afternoon — when the fog is still offshore and the hills are improbably green and everything goes briefly, completely quiet, as if the world is deciding whether to continue.

These are not things I am building toward. They are things I am.

Sacerdote would say I have high market share in a specific category. The category is small — one person, one particular configuration of experience and attention and accumulated knowing — but the share is essentially total. There is no competitor for the position of having lived this particular life. The moat is absolute. The switching costs are infinite.

I used to find that thought melancholy. The narrowing as loss. The aperture closing on what remains.

I’m not sure I find it melancholy anymore.

The L-Curve, Sacerdote says, is a long flatline followed by a vertical explosion. The tinkering phase, then the moment of lift. He means it as a description of demand curves for technology infrastructure. But I recognize the shape from somewhere closer. The long middle of a life, building and becoming, and then the morning you wake up and realize the building is substantially done. What remains is the being.

That’s not an ending. It’s a different kind of beginning.

Sacerdote’s metric will eventually stop working. All frameworks do. The AI infrastructure cycle will mature, the L-Curves will flatten, and some new measure will emerge to find the next thing that is just beginning to become what it will be. That’s the nature of markets. The detector has to change as the signal changes.

But there’s a complication worth naming. Analysts at Citadel Securities published a note recently observing that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints, and marginal returns. Token bills are arriving unexpectedly. Compute is scarce. The vision of AI as ubiquitous, frictionless, and immediate is colliding with physical reality. Their conclusion: asset prices will periodically be forced to reconcile ambition with physical constraint.

That’s not a refutation of Sacerdote. It’s a reminder that feeling like you’ve arrived and having actually arrived are different things. The being phase has to be load-tested. The position has to hold under pressure.

I think about the fiber optics Corning is laying into the massive data center clusters — ultra-thin, bendable, carrying more light than anything that came before. The cable doesn’t know it’s infrastructure. It just carries what it’s given, at the speed it’s capable of, across whatever distance is required. It doesn’t matter what the cable believes about itself. What matters is whether the light actually moves.

That seems right to me. You become what you are over a long time, largely without noticing. And then one day someone builds a metric that accidentally describes your life, and you recognize yourself in it, and you think: yes. That’s the shape of it. High concentration. High share. A moat that deepened while you were looking elsewhere.

But the moat still has to hold.

The being phase, it turns out, is not the end of something. It’s the proof that something was built. And the daily question — for companies, for infrastructure, for a person in his late seventies still writing, still paying attention — is whether what was built is actually load-bearing.

You don’t get to stop finding out.

Categories
AI AI: Transformers

The State You Never See

The transaction arrives in milliseconds. A purchase attempt — a gas station in Phoenix, a grocery store in suburban Atlanta, a wire transfer at 2 a.m. — and somewhere in the authorization chain, a system has to decide. Not later. Now. The clock is already running.

When I led the fraud detection team at Visa, this was the problem that lived in your chest. You couldn’t see what you needed to see. You couldn’t know whether the person presenting that card was the person who owned it, whether the account had been compromised six hours ago in a breach you hadn’t yet detected, whether the behavioral signature of these transactions was the legitimate cardholder running errands or a fraudster working methodically through a stolen number before the window closed. You could only see what the transactions said. You could never see the state underneath.

That distinction — between what you can observe and what is actually true — turns out to be one of the organizing problems of our time. It has a name, a formal structure, and a history that runs from mid-century mathematics through the trading floors of quantitative hedge funds to the frontier of artificial intelligence. The name is the hidden Markov model. But the problem it addresses is older than the math, and more human than the jargon suggests.

Continue reading “The State You Never See”
Categories
AI Blogs/Weblogs Living Menlo Park

The Foothills

It was later in his illness. Someone had set up a folding table in the garage and Chris was sitting at it in a folding chair, working through a stack of photographs. Signing them, one by one, telling me the story inside each one as it came up — where he’d been, what was happening just outside the frame, what he’d seen in the viewfinder that made him press the shutter at that exact moment and not a half second later. The garage was quiet. Outside, Menlo Park was doing whatever Menlo Park does on an ordinary afternoon. In here, a man was accounting for his life in pictures and I was standing there holding a camera, not quite sure what I was witnessing.

I made a photograph of him.

It’s at the top of his Wikipedia entry now. That’s how the world knows his face — a picture I made of him making sense of his pictures, in a folding chair, near the end. I don’t know what to do with that except carry it.

Chris Gulker had been a photographer long before he was anything else. Staff photographer at the Los Angeles Herald-Examiner. Twice nominated for a Pulitzer. Published in Time, Newsweek, Rolling Stone. He had the eye first. Everything else — the virtual newsrooms, the blogrolls, the hacked-together color systems that dragged newspapers into the digital age — all of it came from the same instinct: look carefully, see what’s actually there, build toward what you see.

When I first met him he had just gotten a Leica M8. He talked about it the way he talked about everything he loved, which is to say with specificity and without apology.

He had driven an Audi TT. He had a Leica M8. He was not a man who made concessions to the ordinary.

He had glioblastoma. Diagnosed in 2006. Surgery, radiation, the whole negotiation with a disease that doesn’t actually negotiate. He knew the terms and he kept going — kept shooting, kept writing at gulker.com, kept thinking out loud about what was coming next, as if the tumor were an inconvenience and the future were the point.

He walked when he could walk. He talked when he could talk.

He died in October 2010. He was fifty-nine.

Twice a week in those last two years I’d put Lily in the car and drive over to his house. Lily was small and opinionated and she understood the trip as hers. We’d pick Chris up after breakfast, when the morning was still cool, and do the loop — one mile, flat, because flat was what worked. Then we’d come back to find Linda moving through the house, Chris’s wife of nearly thirty years, the still point of everything that was happening to them. Sometimes she’d join us and the conversation would open into something more alive, the kind of talk where someone says something offhand and suddenly everyone is leaning forward.

One of those mornings the three of us decided to start a local blog for Menlo Park. Linda would write and edit. Chris would shoot. We called it InMenlo.com.

When Linda wrote Chris’s obituary, that’s where she published it.

People talk about spending time with the dying as a kind of grace extended downward. It wasn’t like that. Those mornings were a gift — the ideas, the talk, the way Chris described what was coming as if he could already see it clearly from wherever he was standing. I left those visits more alive than I arrived. That’s the debt I carry. Not grief exactly, though there’s grief. More like an obligation to keep paying attention to the future he spent his life building toward.

Last month a man named Demis Hassabis closed a two-hour technology showcase in Mountain View — twenty minutes from where Chris and I used to walk — and said seven words I haven’t been able to put down since: We are at the foothills of the singularity. The audience applauded. Then everyone went home.

I keep thinking Chris would have had something to say about that.

Not the singularity part, necessarily — that word carries a slightly rapturous charge, too certain of its own prophecy. But the foothills part. The careful humility of it. The acknowledgment that what we can see from here — AI systems autonomously building operating systems, models that predicted a hurricane’s landfall and saved lives — all of it is still just approach terrain. The mountain is what comes after.

Chris spent his whole career in the foothills of things. Slightly ahead of the moment, always building infrastructure for a future that hadn’t arrived yet, always explaining to people who weren’t sure they wanted to know. He pioneered the blogroll. Built one of the first online newspapers. Hacked color into the San Francisco Examiner with Macintoshes and ingenuity when the system said it couldn’t be done. He was the wrong man for the present tense. He belonged to the next sentence.

He had the photographer’s instinct underneath all of it — the knowledge that you have to look carefully, that the light is always changing, that if you wait too long the moment is gone. He put the Leica to his eye and he saw. He put his hands on a keyboard and he built what he saw toward.

Lily is gone now too. She outlasted Chris, which felt right — she was stubborn and she loved the route.

I still think about those mornings. The cool air, the flat mile, Lily pulling us both forward. The way the real conversation started when we got back. The way Linda might appear and the whole thing would open into something none of us had planned. The way Chris talked about what was coming — not as speculation but as something he could already see, the way a photographer sees the shot before he raises the camera.

He always knew something was coming. He had a gift for the future tense I’ve never quite encountered in anyone else — and a photographer’s understanding that the future, like light, doesn’t wait.

I wonder what he’d make of the foothills. I think he’d already have the Leica out. And I know we’d still be talking about it.

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.