Month: May 2026

Categories
AI Programming Software Work

The Scarcest Thing

Garry Tan woke up at 8 a.m. after sleeping at 4. Not because he had to. Because he wanted to see what his workers had done overnight.

The workers are AI agents. Ten of them, running in parallel across three projects. And something about that sentence — wanted to see what they’d done — keeps stopping me. That’s not the language of someone using a tool. That’s the language of someone managing a team.

Tan gave a name to the state this puts him in: “cyber psychosis.” He said it as a joke. But the joke has an insight in it. He’s not describing addiction to a productivity app. He’s describing a shift in what it means to do creative work — the strange vertigo of becoming a director when you’d always been a laborer.

I’m retired. I watch this from the outside now, which is its own kind of vantage point. For most of my career, the path from idea to working product ran through people — through hiring and managing and the slow accretion of execution capacity. You had the vision or you didn’t, but either way you needed the team. The idea and the means of making it real were, structurally, separate things. The gap between them was where companies lived.

What Tan is describing is that gap closing.

The thing he built — gstack, his open-sourced Claude Code configuration — got dismissed in some quarters as “just prompts.” And it is just prompts, in the same way that a conductor’s score is just notation. The abstraction is the invention. What he encoded is a model of how a startup team thinks: the CEO who interrogates the why before a line of code gets written, the engineer who builds, the paranoid staff reviewer who looks for what breaks. Each role blocks a different failure mode. Blurring them together produces, as his documentation puts it, “a mediocre blend of all four.”

That’s an organizational insight. It has nothing to do with code.

Tan described being a “time billionaire” — not because his biological clock had slowed, but because he can now purchase machine-consciousness-hours. The bottleneck of implementation, which has governed every creative project since the beginning of creative projects, is dissolving for those who know how to direct.

The scarcest thing is shifting. It’s no longer the hours of execution. It’s the clarity of intent — knowing what you want to build and why the journey matters, before any of the workers start moving. That’s harder than it sounds. For decades, most of us could muddle through in the making of it. The act of building taught you what you were building. Now the making is cheap, and that shortcut is gone.

For someone watching from retirement, that’s not a small thing to absorb. The model I internalized over a long career — that ideas become real through sustained organizational effort, through teams and timelines and the grinding work of execution — is being revised faster than I expected. Not invalidated. Revised. The judgment still matters. The taste still matters. The why matters more than ever.

It’s just that the how has found new hands. Many of them. More than any team I ever assembled, available the moment the intent is clear enough to direct them, gone when the work is done. The constraint was always the hands. It turns out it was always the knowing.

Categories
AI AI: Large Language Models Anthropic

Breakout

Jack Clark doesn’t panic easily. He spent years at OpenAI watching capabilities inch upward, then left to co-found Anthropic, and has been writing his Import AI newsletter long enough to have developed — and been wrong about — many priors. So when he publishes an essay saying he has reluctantly arrived at a 60% probability that fully automated AI R&D happens by the end of 2028, the word “reluctantly” deserves some weight.

His essay, published last week and titled “Automating AI Research,” isn’t a press release or a fundraising pitch. It reads more like a man thinking out loud at the edge of something large. “I don’t know how to wrap my head around it,” he writes, which is a notable thing to say publicly when you are one of the architects of the thing you can’t wrap your head around.

The argument is built from benchmarks — not any single one, but a mosaic of them assembled to reveal a trend. SWE-Bench, the test that measures an AI’s ability to solve real GitHub issues, was at roughly 2% when it launched in late 2023. A recent Anthropic model sits at 93.9%, effectively saturating it. METR’s time-horizon plot tracks how long an AI can work independently before needing human recalibration: 30 seconds in 2022, 4 minutes in 2023, 40 minutes in 2024, 6 hours in 2025, 12 hours today. The trajectory, if it holds, suggests 100-hour autonomous work sessions by the end of this year.

Clark marshals similar progressions across AI fine-tuning, kernel design, scientific paper replication, and even alignment research itself. His throughline is the same in each: AI is now genuinely competent at the unglamorous scaffolding of AI development — the debugging, the experiment runs, the parameter sweeps, the code reviews. And crucially, it can now do these things not just faster than humans, but for longer, with less supervision.

There’s a Thomas Edison quote at the center of the essay: “Genius is 1% inspiration and 99% perspiration.” Clark’s claim is that AI has become very good at the perspiration. The question of whether it can supply the inspiration — the paradigm-shifting insight, the Move 37 — remains open. But he argues it may not need to. Most of what has moved the AI field forward has been sustained, methodical work, not lone flashes of genius. If you can automate the 99%, you have something that compounds.

There’s a data point that makes Clark’s argument feel less like forecast and more like dispatch. Last month Boris Cherny, who runs Anthropic’s Claude Code, disclosed that he hasn’t written a line of code by hand in more than two months. Every pull request — 22 one day, 27 the next — written entirely by Claude. Company-wide, roughly 70–90% of Anthropic’s code is now AI-generated. Anthropic’s stated position: “We build Claude with Claude.” The loop Clark is describing as a probability by 2028 is already running, at least partially, today.

The word Clark uses for the threshold he’s describing is not “singularity” or “AGI.” It’s quieter than that. He calls it “automated AI R&D” — the point at which a frontier model can autonomously train its own successor. It’s a specific, falsifiable thing. And he puts a number on it: 60% by end of 2028, 30% by end of 2027.

I’ve been writing about the dark software factory and the 3D printer that prints better printers, finding metaphors for what seems like an inexorable process. Clark’s essay is a different kind of writing about the same thing — the primary source document, the engineer’s log, the inventory of evidence. Reading it is a little like watching someone carefully pack boxes before a move. Each individual item seems manageable. But there are a lot of boxes.

What he’s describing — if the trend holds — is not a feature or a product launch. It’s a breakout. The moment the loop closes and the system starts building itself. He’s not certain it happens. He just thinks it’s more likely than not, and he thought you should know.

Categories
Micropayments

The Wrong Who

I was in the room for most of the early micropayments conversations. The working-level conversations, where people were genuinely convinced they had finally solved the problem. The demos were always compelling. The unit economics made sense on a whiteboard. And then they died.

They died so many times, and in so many similar ways, that the failure started to feel like a law of nature.

Clay Shirky wrote the autopsy that most people remember: micropayments fail because every transaction requires a decision, and decisions have a cognitive cost that swamps any payment below some psychological threshold. A dollar feels like real money. A dime feels like a question you have to answer. A fraction of a cent feels like being nickeled-and-dimed at sub-human speeds. The advertising model won because it asked users to consent once, peripherally, and then never bother them again.

So I noticed something when I read the transcript of Cloudflare CEO Matthew Prince’s earnings call remarks this afternoon.

He’s predicting that the internet’s business model — advertising and subscriptions, the twin structures that have governed everything since the late nineties — is about to change. He thinks some part of what replaces it will be micropayments for agentic traffic. Fractions of pennies. Fractions of fractions. At volumes that dwarf anything existing financial infrastructure can handle.

My first instinct was the old skepticism. We’ve been here before.

But I kept reading, and I think something is actually different this time. And the difference is the one thing all the earlier schemes never had.

The payer isn’t human.

This sounds obvious once you say it, but it collapses most of the objections that killed every prior attempt. Cognitive load isn’t a factor when there’s no cognition happening. Decision fatigue doesn’t apply to a process with no feelings about fatigue. The agent making the request doesn’t hesitate at a fraction of a penny, doesn’t resent the transaction, doesn’t abandon the session because it’s annoyed at being charged.

All the early micropayments architectures were built on an implicit assumption: that humans could be trained to behave like rational microeconomic actors at browsing speed. They can’t. Nobody does. But agents are rational microeconomic actors by design. That’s not a metaphor — it’s literally what they are.

The schemes we watched fail in the early 2000s weren’t wrong about the destination. They were wrong about the who. The internet of human readers and human attention was never a natural fit for per-transaction pricing. The internet of autonomous agents — making API calls, scraping data, assembling answers from dozens of sources in a single second — is a different thing entirely. And it’s arriving faster than most people realize.

Prince mentioned that Cloudflare thinks non-human traffic will surpass human traffic somewhere around 2027. That number stopped me. We are, apparently, closer to a majority-machine internet than to the one we think we’re living in.

The hard part isn’t the concept anymore. It’s the infrastructure. Prince was candid about this: the transaction volumes the industry gets excited about — a million per second — aren’t remotely sufficient for what’s actually coming. Cloudflare needs something an order of magnitude larger, and they’re looking for partners because nothing that fits the spec exists yet.

This is where it gets interesting for those of us who watched the earlier rounds. The original micropayments failures were partly psychological, but they were also partly infrastructural — the payment rails of the early internet weren’t built for high-frequency small transactions either. What’s different now is that the need is undeniable and imminent in a way it never quite was before. The traffic is real. The scale is measurable. The pressure to figure this out is coming from something other than optimism.

I don’t know what the solution looks like. Probably not one thing. Prince doesn’t know either — he said as much. Crypto infrastructure is an obvious candidate for parts of it, though crypto’s history of promising to solve problems and then creating different ones deserves some respect. Whatever emerges will probably be unrecognizable from here.

What I keep coming back to is the simpler observation. We were right that micropayments were the future. We just imagined the wrong future, populated by the wrong kind of payer.

The agents were always going to solve this. We just had to wait for the them to arrive.

Categories
AI Business Work

The Tipping Point Was Last November

Matthew Prince, Cloudflare’s CEO, said something on today’s earnings call that I keep turning over. He didn’t bury it or soften it. He named a date.

“Internally, the tipping point was last November.”

That’s a specific thing to say. Not “we’ve been on a journey” or “AI has been transforming our industry.” A month. A moment. The thing changed, and he knows when.

What changed, by his account, is that Cloudflare’s teams began seeing productivity gains so dramatic they were hard to describe — people who were two times more productive, ten times, in some cases a hundred times. “It was like going from a manual to an electric screwdriver.” Usage of AI tools internally is up more than 600% in just the last three months. Every line of production code is now reviewed by an autonomous AI agent.

And then he said goodbye to 1,100 people — about 20% of the company.

Today wasn’t just Cloudflare. Earnings season has become something like a drumbeat. Meta is cutting 8,000 employees this month. Amazon cut 16,000 in Q1. Oracle eliminated roughly 30,000 to fund AI infrastructure. Block cut almost half its workforce. PayPal is reportedly planning to cut 20% of its staff over the next few years. Coinbase cut 14%. Snap cut 16%. As of this week, more than 92,000 tech workers have been laid off in 2026 alone.

The scale is striking. But what strikes me more is the framing — the specific language being used to describe what’s happening. These aren’t being announced as cost-cutting moves or post-pandemic corrections, the way they might have been in 2022. They’re being announced as architectural decisions. Structural adaptations. Evolution.

Prince was careful to be explicit: “This isn’t a cost-cutting exercise or an assessment of individuals’ performance. It’s about defining how a world-class, high-growth company operates and creates value in the agentic AI era.” That’s not empty corporate language, or at least not only empty corporate language. The distinction he’s drawing — between trimming fat and reimagining how a company is built — maps to something real about what AI agents can now actually do.

There’s a legitimate version of this argument and a convenient one, and they’re being delivered in the same sentence by the same people, which makes them hard to separate. Some analysts suspect companies are using AI as cover for cuts they wanted to make for other reasons — rightsizing from pandemic-era overhiring, funding massive infrastructure buildouts, chasing margin. Oxford Economics flagged this: maybe some firms are “dressing up layoffs as a good news story.” The cynicism is warranted.

But then there’s the Cloudflare number: 600% increase in AI usage in three months. That’s not a narrative. That’s a measurement.

What’s different about this moment — what makes Prince’s “tipping point” language feel accurate rather than convenient — is that the people making these decisions are themselves users of the tools. They’ve seen the productivity numbers internally before anyone else has. They’re not theorizing about what AI might do to their workforce; they’re describing what it already did.

That’s the thing that changed. For years, AI’s labor impact was a future tense conversation. Economists studied it, think pieces warned about it, conferences debated the timeline. Then, somewhere around last November apparently, a cohort of technology companies crossed from hypothetical to empirical. The future tense became past.

Whether you read that as tragedy, as transformation, or as both depends on where you’re standing. 1,100 people at Cloudflare today are standing somewhere very specific. Prince acknowledged this with what felt like genuine difficulty: “A number of friends will no longer be colleagues.” Whether that difficulty changes anything material for the people leaving is a fair question.

But the acceleration itself — the thing he named — is real. The tipping point was last November. And if it was last November for Cloudflare, it was some nearby month for Amazon, for Meta, for Block, for all of them. Whatever these companies learned that changed everything, they all seem to have learned it around the same time.

That’s what I find myself sitting with today: not just the scale of the disruption, but the synchrony of it. The realization arrived, and then the decisions followed. Quietly at first, then all at once.

Categories
AI AI: Large Language Models

The 3D Printer That Prints Better Printers

Imagine a 3D printer that looks at its own design and begins printing a better version of itself. The loop closes. What had always required an external human intelligence now happens inside the machine. All by itself.

Jack Clark — Anthropic co-founder, someone who has spent years closer to this technology than almost anyone — puts the odds of this happening by 2028 at better than even. I have been turning that number over ever since I heard it. Not the technical claim, exactly. The feeling of it.

We have grown used to AI accelerating our work. Coders watch models close GitHub issues at rates that would have seemed miraculous eighteen months ago. Researchers delegate experiment design, kernel optimization, even the fine-tuning of smaller models. The scaffolding of AI progress is already being built, in part, by the systems themselves. But the moment the system begins to redesign the scaffolding — that is something new.

What unsettles me is not the raw capability, though that is staggering. It is the loss of distance.

For most of technological history, the creator stood outside the creation. Even the most sophisticated tools remained tools. Now the distinction begins to blur. A model that can meaningfully improve its own training process, its own architecture, its own alignment constraints, is no longer merely reflecting human intent back at us. It is participating in the shaping of its own nature. And because each iteration can happen faster than the last, the curve steepens in ways our intuitions, tuned to linear progress, struggle to grasp.

Clark is careful, as he should be. He speaks of validation work that will still fall to humans, of the need to broaden the pipes through which abundance flows, of preparing defense-dominant postures against misuse. Yet the image that lingers for me is quieter: the silence after the handoff. What does it feel like when the thing you have been painstakingly teaching begins to teach itself — and then to teach its teachers?

I think about Leo Szilard at the traffic light, or the first controlled chain reaction under the stands at the University of Chicago. Moments when a new regime of possibility quietly announced itself. Recursive self-improvement carries that same charge — not a single event but a process, one that could accelerate the very pace of events themselves.

The more I sit with it, the more I return to an older tension in our relationship with tools. We build them to extend ourselves, and in doing so we are always, subtly, extending — or perhaps risking — what we are. The values I try to live by — generosity, curiosity, compassionate honesty — are not refined in specifications. They are refined in friction, in relationship, in the slow work of being human with other humans. If the machines begin to optimize their own lineage at speeds we cannot match, will we still have the bandwidth to tend the parts of ourselves that no algorithm can yet measure?

I don’t know. None of us do. That uncertainty feels honest.

What feels clearer is the invitation. Not to fear the printer that prints better printers, nor to worship it, but to remain awake inside the loop. To ask, as each new version arrives, what kind of world we are collectively printing — and whether the values we claim to hold are baked into the design or merely etched on the surface, likely to wear away under the heat of iteration.

The light is still yellow. We are still deciding whether to step off the curb. But the traffic is already moving faster than it was a moment ago.

Categories
Science Stanford

Bypassing the Leaf

For my entire life, I’ve understood the world through a simple, quiet equation: green plants take sunlight and air, and turn them into the stuff of life. It is a slow, terrestrial magic we all learn in grade school.

But lately, after listening to Professor Drew Endy at Stanford, I’ve been sitting with a curious yet exciting realization: that ancient equation is being rewritten.

Professor Endy champions a concept called electrobiosynthesis, or eBio. At its core, it represents the engineering of a parallel carbon cycle that operates independently of traditional photosynthesis.

The global industrial complex is approaching a transition point where our traditional reliance on extractive fossil fuels is being superseded by a regenerative, biological manufacturing paradigm.

For millennia, humanity has relied on the biological “middleman” of the plant to capture solar energy. But natural photosynthesis, for all its quiet beauty, is limited by severe biochemical constraints. Most commercial crops convert less than 1% of incident solar energy into usable biomass.

Electrobiosynthesis changes the math. By bypassing the plant entirely, we can utilize high-efficiency photovoltaics—which capture over 20% of the sun’s energy—to drive carbon fixation directly into the metabolic hubs of engineered microbes. This fixed carbon is transformed into organic molecules, serving as the feedstocks for high-value products like proteins and specialty chemicals.

In my own career, I’ve watched industries undergo profound, structural phase shifts. This really feels like another one of them. It seems that we are looking at a future where any molecule that can be encoded in DNA can be grown locally and on-demand. This fundamentally decouples manufacturing from centralized industrial nodes and fragile global supply chains.

The field appears to currently be in its “transistor moment,” moving from laboratory feasibility to industrial pilot plants. It signifies the ability to construct and sustain life-like processes without being restricted to the terrestrial lineage of photosynthesis.

Of course, with such foundational power comes the weight of unintended consequences. The ability to engineer life at this level brings severe biosecurity risks, and even the “Sputnik-like” strategic challenge of international competition in biotechnology. There are profound ethical dilemmas on the horizon, such as the creation of “mirror life”—organisms made from mirror-image biomolecules that might be invisible to natural ecosystems.

But the trajectory seems set. The vision described by Professor Endy—a world where we grow what we need, wherever we are, using only air and electricity—is no longer a distant science fiction. It is a nascent industrial reality. This future is being written not in sprawling factories, but in the microscopic architecture of the cell.

I’ve just now reading a deep research report on this whole area that I asked Google Gemini to create. It’s fascinating and I’ve discovered a whole new area (beyond AI) to explore further.

Categories
Business Creativity Space SpaceX

Test like you fly!

There’s a phrase in the SpaceX documentary that keeps coming back to me: “Test like you fly.” It sounds like a slogan. The kind of thing that gets painted on a factory wall and eventually stops meaning anything. But the more I sit with it, the more I think it’s actually a philosophy that reaches well beyond rocket engineering.

The video — a 25-minute documentary SpaceX released last week — is ostensibly about Starship Version 3. New ship, new booster, new engines, new pad, new test site. Everything rebuilt. And they’re not shy about framing it as a reset, not an upgrade. One description I read called it “a quiet violence in progress.” That phrase stopped me cold, because it’s exactly right. Progress that looks violent from the outside — all that fire and metal — but is somehow quiet in its inevitability.

What moved me watching it wasn’t the engines. It was the engineers. SpaceX put the people on camera: the ones running cryogenic pressure tests at 80 Kelvin, stress-testing tank structures at 70% proof, explaining their failures and their data with the flat affect of people who have made peace with how long hard things take. There’s something almost monastic about it. You choose a problem that will not yield easily. You accept that the work will outlast any individual sprint of enthusiasm. You go back to it anyway.

I keep thinking about that in the context of what we’re doing with AI — the other enormous, fast-moving project that I spend so much of my mental energy on. The development arc is different: iterative releases, weeks not years between jumps, demos that blur into deployment. But the same principle is buried in there somewhere. The best AI teams I read about aren’t the ones shipping the most polished demos. They’re the ones building infrastructure for failure — evals, red-teaming, structured feedback loops. Test like you fly.

The Raptor 3 engines now produce 280 metric tons of thrust each. Thirty-three of them on a Super Heavy booster means over 17 million pounds of liftoff force. I have no intuitive frame for that number. What I do have a frame for is what those numbers represent: three years of iteration on top of five years before that, on top of a theoretical foundation laid by people who didn’t live to see any of this. There’s a compounding in that which I find genuinely moving. Nobody built the Raptor 3 in isolation. It came from everything that broke before it.

The hardest part of the documentary isn’t the engineering. It’s the implicit acknowledgment of how much remains undone. No Starship has yet achieved full orbital velocity with both stages intact. In-space refueling is still untested. The thermal protection systems need more work. And yet — SpaceX talks about unmanned cargo missions to Mars before the end of this year like it’s on the roadmap, not the wish list. That sentence used to sound like marketing. Watching the footage, it doesn’t anymore.

I’m not sure what to do with that feeling exactly. It’s something between awe and vertigo. We’re living in a moment when the audacious has started to have quarterly milestones. When the impossible keeps showing up on timelines and then — bewilderingly, uncomfortably — meeting them.

Test like you fly. Fail with rigor. Build the thing you actually need, not the thing you could more easily explain.

I keep turning that over. There’s a post in there somewhere about writing, too — about the drafts nobody sees, the structural tests that fail, the versions that taught you the one that worked. But that’s for another day.

For now I’m just sitting with the footage of those 33 engines lighting up, and the quiet weight of how much went wrong before they could do that.

Categories
Books Curiosity Living

Working the Seams

This book highlight popped up in my morning Readwise feed recently:

“Fishermen work seams—seams between slow water and fast, between deep water and shallow, between sunlight and shadow. The eddies around rocks, the bubble lines along banks. That’s where the fish are.”

Neil King wrote it in American Ramble, his account of walking from Washington to New York. He was watching fishermen, not fishing himself, which maybe explains why it reads less like instruction and more like revelation. When you’re the observer, you have room to notice what the practitioner is too busy doing to say.

The word seams is doing something I can’t stop thinking about. A seam is a joining. It’s the place where two different things meet and, in meeting, create a third thing: the edge itself. Not slow water, not fast water, but the turbulent conversation between them. The fish aren’t in the slow water. They aren’t in the fast water. They’re in the argument.

I think most of the interesting things in life happen at seams.

The best conversations aren’t the ones where everyone agrees. They’re the ones where two people with genuinely different orientations are standing at the same edge, looking at the same water. The friction between the views creates something neither would reach alone.

The best writing isn’t the settled opinion, the fully-arrived-at conclusion. It’s the essay in the old sense — the attempt — where you can feel the writer at the seam of what they know and what they’re reaching toward. The bubble line between understanding and confusion. That’s where the reader is, too, if they’re lucky.

I notice this on my own blog sometimes. The posts that feel most alive to me aren’t the ones where I knew what I wanted to say before I started. They’re the ones where I began at a seam — between something I’d always believed and something that recently unsettled it — and wrote my way along the edge, not knowing which bank I’d end up on.

There’s a version of this that applies to attention itself.

I dwell on how I pay attention — when I’m reading, when I’m walking, when I’m in conversation. And I’ve noticed that my attention goes flat in the middle of things. Flat terrain. Constant depth. Unchanging light. I have to work to stay present when nothing is in transition.

But put me at a seam — a moment where the mood in a room is shifting, where a piece of music is about to resolve or refuse to resolve, where someone is on the verge of saying something they’ve been circling for an hour — and I’m completely there. Attention is predatory, maybe. It goes where the tension is.

Which is what the fish are doing, of course. The seam isn’t just a metaphor for where interesting things happen. It’s why interesting things happen there. The fast water sweeps food along; the slow water lets you hold your position; the seam between them is where you can eat without being eaten. The fish are solving a real problem. They’re just also, accidentally, living beautifully.

I wonder sometimes if this is what makes a good editor, or a good friend who reads your drafts. They find the seams — the places where you’ve unconsciously papered over a tension, smoothed the fast water into the slow, given the reader no place to be a fish. “Something’s off here,” they say, and what they mean is: you resolved this too quickly. Stay in the argument longer.

The eddies around rocks, the bubble lines along banks.

I want to be a better noticer of those. Not to resolve them. Just to work them.

Categories
Atomic Energy Nuclear Energy Science

The Traffic Light That Split the Atom

If you wandered past the Mathematical Society and kept going, you’d come to a pedestrian crossing on Southampton Row where it meets Russell Square in London’s Bloomsbury. On a humid morning in September 1933, something world-changing happened there.

It was Tuesday, September 12. A cool, drizzling, quintessentially English autumn day. Leo Szilard — a brilliant, restless Hungarian-Jewish physicist who had fled Nazi Germany earlier that year — stood waiting at the traffic light. He was irritated, as people often are when a red light holds them up on a gray morning. He had been thinking about Ernest Rutherford’s recent lecture, in which the great pioneer of nuclear physics had dismissed the idea of extracting usable energy from the atom as “moonshine.”

Szilard disagreed. And as the light turned green and he stepped off the curb, the thought arrived in a flash.

What if a single neutron struck a nucleus and caused it to split, releasing two neutrons? Those two could split two more nuclei, releasing four — then eight, sixteen, thirty-two. In a large enough mass of the right material, the process could sustain itself — a chain reaction — and liberate enormous amounts of energy.

He saw it all in that instant: the possibility of limitless power, and the shadow of a weapon unlike anything the world had ever known.

Szilard was not in a laboratory. He was not surrounded by colleagues or equipment. He was simply crossing a London street, a refugee with too much on his mind, when the future opened up in front of him.

He filed a patent within the year and had it kept secret by the British Admiralty. He spent the rest of his life in the aftermath of that crossing — working on the first controlled chain reaction in Chicago in 1942, then becoming one of the most tireless advocates against the use of the weapons he had foreseen. The man who imagined the chain reaction spent decades trying to break it.

The spot where it happened remains utterly ordinary. Buses and taxis still rumble through the intersection. Tourists hurry toward the British Museum. Students cross on their way to Russell Square. There is no plaque. Szilard himself, given how deeply pacifist he became, might not have wanted one.

That feels right. The moment wasn’t grand or ceremonial. It was the kind of quiet, internal shift that happens when a prepared mind meets an ordinary irritation at a traffic light.

The hinges of history are fragile things, and they don’t announce themselves. Enormous consequences — nuclear power, the atomic bomb, the Cold War, decades of arms-control efforts — all trace back to one man’s realization while crossing a rainy London street. And once a thought like that arrives, it doesn’t leave. Szilard carried his for the rest of his life. He understood, earlier than almost anyone, both the dazzling promise and the terrible cost of what he had imagined at that crossing.

I came to this story through Sebastian Mallaby’s The Infinity Machine. It stopped me cold on the page, the way the best historical details do — not because it was dramatic, but because it was so ordinary.

Next time you’re stuck at a pedestrian light on a humid morning, pause for a moment. The light will change. You’ll step forward. But you never really know what might change with you.

Categories
Chemicals Petroleum Semiconductors

The Invisible Layer Beneath the Chip

At the edge of a semiconductor fab, nothing looks dramatic.

No flames. No smoke. No sense of weight.

Just pipes, valves, and a silence so controlled it feels artificial.

It’s easy, standing there, to believe that oil—the old engine of the economy—has been replaced by something cleaner, lighter, more abstract. Software, maybe. Or data. The kinds of things that don’t spill.

But step a little closer, and the illusion breaks.

A modern fab is less like a factory and more like a chemistry experiment that never ends. Gases move through stainless steel arteries. Liquids are mixed, spun, deposited, stripped away. Surfaces are etched and re-etched until what remains is measured in atoms, not microns. The machines—Applied Materials, Lam Research—are precise, but they are not the story. The story is what flows through them.

Chemicals are doing the real work.

Not in bulk, the way oil once did. Not with force. But with specificity.

A barrel of oil is valuable because of its density—how much energy it contains. A liter of photoresist is valuable because of its selectivity—what it allows to exist and what it removes. One powers motion. The other defines structure.

Structure is where the modern economy hides its value.

A semiconductor is not impressive because of what it consumes. It’s impressive because of what it constrains. Billions of transistors, each one placed, shaped, and insulated with a chemical discipline that borders on obsession. The difference between a working chip and a useless one is often a contaminant you cannot see.

This is a different kind of industrialism.

The 20th century scaled by adding more—more fuel, more steel, more throughput. The 21st century scales by removing everything that shouldn’t be there. Purity is the limiting factor. Not how much you can move, but how precisely you can control.

From a distance, it can look like oil has become less important. The headlines have shifted. The glamour has moved on.

But the truth is more entangled.

Most of the chemicals inside a fab begin their lives as hydrocarbons. The solvents, the polymers, even some of the specialty gases—downstream of the same geological inheritance. Oil didn’t disappear. It changed roles. It moved from the foreground to the substrate.

The question, then, isn’t whether chemicals have replaced oil. It’s whether the economy has learned to express value differently.

Less in how much energy we can release. More in how carefully we can shape matter.

Semiconductors are the clearest example, but not the only one. Pharmaceuticals follow the same logic. Advanced materials, too. In each case, the breakthrough isn’t scale—it’s control. The ability to operate at the edge of what’s physically possible, and to do it repeatedly.

Which raises a quieter possibility.

That the defining resource of the next era isn’t oil, or even chemicals.

It’s precision.

And chemistry is simply the language we use to achieve it.