Categories
AI AI: Large Language Models AI: Transformers Authors Podcasts Writing

The Billboard

The fog was still sitting on the hills when I put in my earbuds and headed out.

Sebastian Mallaby was talking about billboards.

Tim Ferriss had asked him the question he asks everyone: if you could put anything up there, for millions of people to see, what would it be? Mallaby has spent years inside the minds of the people who shaped modern finance — the hedge fund managers, the venture capitalists, the builders of things that changed how the world moves money. He has more material than most people accumulate in a lifetime. He could have said anything.

He said: Prepare your mind.

I kept walking. The houses were quiet in the particular way they get when school lets out for summer — no buses, no car doors, no kids at the corner. Somebody’s sprinklers were running.

The phrase comes originally from Louis Pasteur, who understood something that most people don’t: that chance is not democratic. It does not distribute itself evenly among those who wait. It finds the people who are ready. Chance favors the prepared mind. Pasteur said it, and then he proved it, and then the rest of us spent a century and a half learning it was true.

What struck me about Mallaby’s answer wasn’t the phrase itself. It was the way he said it had kept appearing in his research, surfacing in different decades and different worlds, like a message the material kept trying to send him.

He told the story of Arthur Patterson at Accel Capital. Before a new technology arrived, Accel would work through the implications — what company needs to be built, what founder fits the moment, what the right pitch looks like. So when an entrepreneur finally walked in, when the situation was live and competitive, they already knew ninety percent of what they were hearing. They could move fast because they had already moved slow.

That’s preparation as institutional practice. But Mallaby found the phrase again in a different register entirely, embedded in a single human moment that has always seemed to me like one of the hinge points of our era.

He was interviewing Ilya Sutskever, asking him why he had seen it so quickly.

In 2017, a paper called Attention Is All You Need appeared online. It described a new architecture for neural networks — the transformer — that would eventually rewrite the terms of what artificial intelligence could do. On the day the paper went up, Sutskever read it. And then he ran. He went down the corridor to find his collaborator Alex Radford and told him to stop what he was doing. Everything. Stop. We are going to build a language model on this architecture.

Not someday. Now.

Mallaby asked him how he had seen it so clearly, so fast. And Sutskever’s answer, in its essence, was the same two words: prepared mind.

He had been thinking about the problem of modeling sequential data since his PhD in Canada. For years he had been carrying a question the field hadn’t answered yet. And when the answer appeared — when the transformer showed up on a website one ordinary day — he didn’t have to reason his way toward it. He recognized it. The solution arrived and found a mind that had been waiting for it, that had already cleared space for it, that was already arranged around the shape of exactly this kind of answer.

This is what preparation actually is. Not the accumulation of facts. Not readiness in the generic sense, the vague self-improvement sense. It is the long, patient cultivation of a specific question, held close and kept alive until the answer has somewhere to land.

Mallaby chose that phrase for his billboard because it kept finding him — in the venture capital world, in the AI world, across decades and disciplines and very different kinds of genius. The prepared mind is not a personality trait. It is a practice. It is the work you do before the work arrives.

The sprinklers had clicked off by the time I turned back toward home. The fog was starting to lift off the hills. I was thinking about what I had been preparing for, whether I even knew.

Categories
AI: Diffusion Models Art and Artists

An Algorithm by Hand

He didn’t know what he was looking for the first time he walked into the Alhambra. You come in from the heat and the light hits the walls and for a moment you just stand there, your mind doing something it doesn’t have words for yet.

That was 1922. Escher was twenty-four years old, recently graduated. The Moorish artists who made these walls had been dead for six centuries. They had left no notes. No theory. Just the walls.

He came back in 1936. Some things you have to see twice.

What the artists in the Alhambra had discovered — without algebra, without proof, working in a tradition that forbade them from drawing a single living creature — was that there were exactly seventeen ways to tile an infinite plane with a repeating pattern. The Russian mathematician Fedorov would articulate this formally in 1891, centuries after the fact, the way mathematics sometimes chases art home and explains what it already knew.

Escher took the problem and made it harder. He asked: what if the edge between two tiles is also the outline of a fish? What if the sky is made of birds and the birds are made of sky? He would move a single line and the whole system would tremble. He did this for years. Revision after revision, in small notebooks, by hand.

There is a word for what he was doing. We just didn’t have it yet.

The word is algorithm.

An algorithm is a set of rules, followed in sequence, to solve a problem. We think of them as things that live in machines, in data centers drawing enough power to light a city. We think of them as fast. Escher’s algorithm was not fast.

He would begin with a grid. Hexagons, maybe, or the interlocking diamonds of a pattern he had traced from the Alhambra walls. Then he would ask the question that made everything hard: what lives here? Not what shape — what creature? What thing with a spine and a purpose and an outline that a human eye would recognize before the brain caught up?

The constraint was absolute. Every point on every edge had to satisfy two animals simultaneously. Change one line and you changed everything downstream, the way a single altered fact in a long investigation suddenly makes you reread everything you thought you knew.

He failed constantly. The notebooks are full of it. Half a lizard becoming nothing. A bird whose wing destroyed the fish below it. He would back up and try again, the way you back up on a road that has stopped being a road.

He was doing, neuron by neuron, what a diffusion model now does in milliseconds.

But here is the thing about milliseconds. They don’t leave notebooks.

Categories
Aging Atmosphere

The Atmosphere Business

There are some rooms you just want to be a part of. A restaurant critic wrote that recently in the Financial Times, and I’ve been turning it over ever since. Not because it’s surprising — anyone who loves restaurants already knows it’s true — but because it named something I’ve been experiencing without quite having the words for it.

I’ve been paying more attention to rooms lately. Not to what’s happening in them, but to the rooms themselves. The light temperature. The materials. The way a space settles around you when you walk in.

There’s a Greek restaurant in Palo Alto called Evvia that I’ve been going to for years. It has no modern feel whatsoever — no reclaimed industrial aesthetic, no Edison bulbs performing nostalgia, no carefully curated emptiness. Instead: a wall of jars and bottles filled with colored liquids, honey-blonde wood, light that feels like it was chosen by someone who understood that warmth is not a design choice but a form of hospitality. I couldn’t tell you what era it conjures. I just know that when I walk in, something in me slows down. And the food is superb. That matters too — not as the reason you came, but as the room’s final kept promise.

I don’t think I would have noticed any of that at 35.

At 35 you move through rooms. You’re pointed forward — toward the person across the table, the evening’s agenda, whatever brought you there. The room is backdrop. At 35 I was probably thinking about the wine list before I’d finished reading the menu, planning the next thing while the current thing was still happening.

Something shifts. I can’t name the moment it happened, because there wasn’t one. Just a gradual noticing — that I was paying attention differently. That the room had become as interesting as the reason I came.

The word that comes to mind is savor. Which has an interesting relationship with time. You’d think that with less of it ahead, you’d move faster, extract more, optimize. Instead the opposite happens. Each thing becomes more worth inhabiting fully. The scarcity makes you slower, not faster. More permeable.

The FT writer talks about wanting to be held by a room. That’s a passive construction — something done to you, not by you. I think that capacity to be held requires a surrender that younger people can’t quite manage. Too much forward momentum. Too much else to get to.

What comes with the savor, I’ve found, is peacefulness. Not contentment exactly — contentment can be a kind of settling, a lowering of expectations. This is different. Peacefulness has knowledge in it. You’ve seen enough to know what a good room is worth. You’ve been in enough bad ones — too loud, too bright, too eager to impress — to recognize when a room is simply, quietly doing its job.

Evvia does its job. The honey-blonde wood absorbs the evening. The jars catch the light. The atmosphere, as an old restaurateur once put it, is what they’re actually selling.

Later in life, I know how to buy it.

Categories
Authors Books

More John McPhee

Yesterday I shared a list of John McPhee paperbacks that are in our home library. Using Google Gemini I had it prepare a list of the books from a photo I had taken of the stack of books.

Today as I was on my morning walk listening to an Economist podcast about de Tocqueville in America, it dawned on me that I should come back to Gemini and ask it for a ranked list of McPhee books that I should consider adding to our home library. Here is the prompt I used:

Now give me a list of his books that I don’t own. Rank the list based on recommendations. Include the same info as before.

Here is that list – useful the next time I’m at a used bookstore!

Recommended John McPhee Books (Not in Your Stack)

This curated list ranks the best books by John McPhee that are missing from your current home library. It prioritizes his most historically significant, critically acclaimed, and widely loved masterpieces.

1. Annals of the Former World

Year Published: 1998

Summary: This monumental, 700-page epic is the culmination of twenty years of research and travel alongside geological experts. It traces the deep geological history of the North American continent along the Interstate 80 corridor. The book is actually a compilation of five smaller works (Basin and Range, In Suspect Terrain, Rising from the Plains, Assembling California, and Crossing the Craton), tracking plate tectonics, mountain building, glaciers, and deep time.

How Reviewed: Widely considered McPhee’s magnum opus, it won the 1999 Pulitzer Prize for General Nonfiction. Critics universally praised it for taking what is traditionally considered a “dry” science and turning it into a gripping, human, and philosophically profound narrative about the Earth’s violent history.

An Interesting Story: McPhee originally estimated the geology project would take him about one to two years. It ended up consuming two decades of his life. Because geologists make notoriously distracted drivers—constantly swerving across highway lanes to examine exposed rock formations on roadcuts—McPhee had to do most of the driving during their cross-country road trips just to keep them safe.

2. Coming into the Country

Year Published: 1977

Summary: An extraordinary three-part portrait of Alaska during a chaotic, pivotal era in the 1970s. The first part covers a dangerous, pristine wilderness canoe trip down the Salmon River in the Brooks Range. The second details the political gridlock of trying to establish a new state capital. The third and longest section is an intimate look at the rugged, eccentric, and fiercely independent settlers of the remote gold-rush town of Eagle near the Yukon border.

How Reviewed: A massive bestseller and critical triumph. It is universally regarded as one of the greatest books ever written about Alaska, capturing both the staggering scale of the wilderness and the complex, headstrong psychology of the people who flee to it.

An Interesting Story: While documenting the remote lives of wilderness settlers, McPhee met a man living in a cabin who had survived a brutal sub-zero winter with almost no food. At one point, the man’s entire remaining winter rations consisted of a single, frozen head of cabbage. McPhee’s meticulous fact-checkers at The New Yorker tracked down the source to verify the exact status and size of the cabbage before they would let him publish the story.

3. The Pine Barrens

Year Published: 1968

Summary: An exploration of a sprawling, million-acre wilderness of pitch pines, cedar swamps, and sandy aquifers hidden in the middle of heavily urbanized New Jersey. McPhee describes the unique ecology of the area and profiles the isolated, self-reliant residents—traditionally called “Pineys”—who lived off the land by gathering cranberries, digging bog iron, and hunting.

How Reviewed: A classic of regional literature and early environmental journalism. It was praised for exposing a secret, beautiful world right in the backyard of the busy East Coast, and it is widely credited with helping to spark the political movement that ultimately federally protected the region.

An Interesting Story: At the time McPhee wrote the book, there were major state plans to pave over the Pine Barrens to build a massive, multi-runway international jetport and a brand new city of 250,000 people. McPhee’s beautifully written, highly sympathetic portrait of the area’s quiet wilderness and historic communities turned public opinion so heavily against the developers that the entire project was permanently scrapped.

4. Levels of the Game

Year Published: 1969

Summary: A masterclass in narrative structure. The book is framed entirely around a single semi-final tennis match played at the 1968 US Open at Forest Hills between Arthur Ashe and Clark Graebner. As the play-by-play of the match unfolds stroke-by-stroke, McPhee weaves in the biographies of the two young men, demonstrating how their family backgrounds, races, and political worldviews directly dictate the way they play tennis.

How Reviewed: Frequently cited by sportswriters as one of the greatest sports books ever written. Critics were mesmerized by how McPhee transformed a simple, brief tennis match into a brilliant, microscopic psychological study of two contrasting Americas in the late 1960s.

An Interesting Story: The book’s structure is incredibly tight—there are no chapters or headers; it reads as one continuous, unbroken volley of text from the first serve to the final match point. Because the manuscript was so structurally dependent on the exact sequence of tennis play, McPhee had to build a massive physical storyboard using index cards on his dining room table to track the score of the match alongside his biographical flashbacks.

5. Encounters with the Archdruid

Year Published: 1971

Summary: A brilliant structural experiment in narrative journalism. The book profiles David Brower, the passionately uncompromising executive director of the Sierra Club (whom his adversaries mockingly called “the Archdruid”). McPhee takes Brower on three separate wilderness expeditions, pairing him on each trip with one of his primary ideological enemies: a mineral engineer in the North Cascades, a resort developer on a pristine Georgia barrier island, and a dam-building commissioner in the depths of the Grand Canyon.

How Reviewed: Highly praised for its absolute neutrality. Instead of taking a side, McPhee steps back and allows both sides of the environmental debate to articulate their values in real-time as they hike, raft, and camp together.

An Interesting Story: During the Grand Canyon rafting trip, David Brower and his fierce ideological opponent, Floyd Dominy (the commissioner responsible for constructing major western dams), were forced to share a tiny rubber raft through dangerous, churning whitewater rapids. Dominy, a tough-talking Westerner, was terrified of the water. Brower, despite hating Dominy’s dams, quietly guided the raft safely through the rapids, forging a brief, surreal moment of mutual respect between the two bitter enemies.

6. Draft No. 4: On the Writing Process

Year Published: 2017

Summary: A masterclass handbook on the art and craft of nonfiction writing. Pulling from his decades of writing for The New Yorker and his legendary writing seminars at Princeton University, McPhee reveals his highly structured, sometimes eccentric methodology for conducting interviews, organizing mountains of research, drawing structural diagrams, and editing drafts.

How Reviewed: Celebrated as an instant classic for writers, students, and journalists. Reviewers loved the book’s warm, humble, and practical advice, combined with hilarious behind-the-scenes anecdotes of the editing world.

An Interesting Story: McPhee reveals that during his early career, he suffered from such severe writer’s block that he would literally tie himself to his office chair with a bathrobe sash to force himself to stay at his desk and type. He also details his “search-and-replace” editing method: on his fourth draft, he reads his work with a dictionary, finding any word that feels slightly lazy or uninspired, circling it, and listing dozens of synonyms beneath it until he finds the perfect match.

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
Aircraft Bicycles Dayton Ohio History

The Ordinary

Part 1 of 3…

The man sitting atop a penny-farthing in the summer of 1879 is five feet off the ground. He weighs maybe one hundred and fifty pounds. The wheel beneath him is fifty-four inches across — taller than most of the children who stop to watch him pass. He got up there by running alongside the machine, hooking a foot on a small peg above the rear wheel, and vaulting himself upward in a single practiced motion. He will dismount the same way: a controlled fall forward, a hop, gravity made manageable by repetition. He has done this so many times that he no longer thinks about it. He thinks about the road ahead.

He is not a daredevil. He is a commuter.

The people on the sidewalk call his machine a penny-farthing, which is a joke dressed up as a name. A penny was the largest British coin; a farthing the smallest, worth one quarter of a penny. Seen from the street, the big front wheel and its tiny rear companion looked exactly like the two coins set side by side. Some wit had noticed, and the name stuck. The riders themselves refused it. They called their machine the ordinary — because to them, it was exactly that, the standard form, the rational machine, the obvious answer. They said ordinary with complete seriousness while everyone else was calling it loose change.

This tells you something about the people who rode it. And about the machine they thought they were riding.

The penny-farthing was not a circus prop. It was the highest expression of an engineering logic that had no other options. The pedals connected directly to the front axle. One rotation of the legs meant one rotation of the wheel. If you wanted to go faster, you needed a bigger wheel. It was that simple. It was that brutal. The geometry of human ambition ran directly through the circumference of that front wheel, and the front wheel kept getting bigger, and the riders kept climbing higher, until the whole enterprise teetered at the edge of what a human being could reasonably mount and survive.

The high-wheeler was not a mistake. It was the answer to a question no one yet knew how to ask differently.

The machines were built in Coventry, England, by craftsmen who bent and brazed steel frames by hand, fitted wire spokes under tension — a Starley innovation that made the wheel lighter than anyone expected — and pressed solid rubber tires onto rims by feel and experience. James Starley had essentially invented the industry in 1871, and Coventry became its Detroit: a concentration of metalworking skill that fed on itself, that knew things in its hands it couldn’t fully explain on paper.

Then, in the mid-1880s, someone put a chain on it.

The chain-and-sprocket drive seems obvious now, the way all elegant solutions seem obvious after the fact. Decouple the pedals from the wheel. Run a chain from a sprocket near the rider’s feet to a smaller sprocket at the rear axle. Suddenly the wheel didn’t have to be enormous — the gearing could do what only size had done before. The front wheel came down. The rear wheel came up to match it. The rider dropped five feet closer to the earth. The machine that emerged from this rearrangement was called, without any particular irony, the safety bicycle. It was safe. It was fast. It was something a woman in a skirt could ride, something a child could learn on, something that didn’t require a running vault to mount.

The ordinary had been a machine for athletes. The safety bicycle was a machine for everyone.

By the 1890s it had become something close to a religious phenomenon. Factories couldn’t keep up with demand. Doctors wrote approvingly of its effects on the nervous system, the cardiovascular system, the general disposition of the modern soul. Roads were improved because cyclists demanded it. The bicycle arrived before the automobile and prepared the world for it — softened the ground, culturally speaking, for the idea that ordinary people might move through space under their own mechanical power, faster than their feet could carry them, farther than their legs could take them. It was the first technology to feel like freedom to people who had never felt that way before.

In Dayton, Ohio, two brothers watched all of this happen and decided to get into the business.

Orville and Wilbur Wright were not, in the beginning, aviation pioneers. They were bicycle mechanics. They opened their shop in 1892, right at the peak of the craze, and what they learned there — the feel of a machine in motion, the gyroscopic principles of balance and control, the importance of getting the weight right, the importance of understanding what a human body can and cannot do at speed — was an education no university offered and no book could fully provide. They learned it with their hands. They learned it in the gap between the machine that existed and the machine that should exist.

The Wrights were not the only ones in Dayton thinking about bicycles. The Huffman Manufacturing Company had opened its doors the same year as the Wright Cycle Company — 1892, the peak of the craze, the same fever in the same city. Huffman would eventually become Huffy, and Huffy would eventually become the bicycle every American child found under the Christmas tree. Dayton was doing something in those years. It was a city that couldn’t stop thinking about how people move. The precision those Coventry craftsmen had developed — interchangeable parts, tight tolerances, the discipline of making things that had to work — migrated into every bicycle shop that followed, including a small one on West Third Street.

The chain drive had taught the world that the right mechanical insight could make an impossible thing ordinary. You didn’t have to accept the constraints you were handed. You could re-ask the question.

Orville and Wilbur had been paying attention.

When they went to Kitty Hawk in 1903, they brought with them a bicycle chain. It connected the engine to the propellers. The same principle — a sprocket, a chain, a transferred force — that had brought the penny-farthing rider down from his absurd perch now lifted two men off the ground for the first time in human history.

The man on the high-wheeler in 1879 did not know he was riding toward the Wright Brothers. He was just going to work. But the machine beneath him, the one everybody called loose change and he called ordinary, was already asking the question that would take twenty years to answer.

What happens when you finally get the wheel the right size?

The roller chain — the specific form that connected pedal to wheel and made the safety bicycle possible — was invented in Manchester in 1879 by a Swiss engineer named Hans Renold. He was refining a design that Leonardo da Vinci had sketched in a notebook around 1500. Leonardo could imagine it. He couldn’t make it. The world needed four hundred years of improving machine tools before anyone could hold the tolerances tight enough to build what Leonardo had already seen. The idea arrived centuries before the craft caught up. It is always this way.

Categories
Books Writing

John McPhee Home Library

Im an avid fan of John McPhee having first been introduced to him when he profiled Ted Taylor in the New Yorker in 1973. I’ve collected a stack of his books in our home library. One useful query I’ve used with Gemini is this:

This is a stack of the John McPhee books in our home library. Give me a list with: title, year published; summary; how reviewed; an interesting story about the book.

Here’s the result – good fun!

John McPhee: Library Stack Directory

A detailed guide to the nine books by legendary narrative nonfiction writer John McPhee shown in the library stack. The list is organized in order from the top of the stack to the bottom.

1. Oranges

Year Published: 1967

Summary: An immersive, multi-layered look into the history, botany, and global trade of citrus. McPhee tracks the orange’s journey from its ancient Asian origins through Europe and the Americas, culminating in a detailed portrait of the highly industrialized orange juice concentration plants of 20th-century Florida.

How Reviewed: Celebrated for showing how a single, seemingly mundane topic can be transformed into compelling, poetic literature. Critics praised it as a hallmark text of the “New Journalism” movement and creative nonfiction.

An Interesting Story: McPhee originally intended to write a quick, single article for The New Yorker. However, he became so fascinated by the complex subculture of Florida orange growers and the science of the fruit that he stayed for months, ultimately producing enough rich material to fill this entire book.

2. Pieces of the Frame

Year Published: 1975

Summary: A diverse collection of eleven narrative essays covering eclectic topics, including a family search for the Loch Ness Monster, the landscape and history of Atlantic City, the art of fly-fishing, and the deep geology of the Appalachian basin.

How Reviewed: Warmly praised for its boundless curiosity and McPhee’s signature knack for weaving disparate human interests with environmental science.

An Interesting Story: For the title essay, McPhee brought his wife and young daughters to camp right on the shores of Loch Ness. Instead of writing a cynical piece about a local myth, he embedded with the genuinely dedicated, scientifically minded monster-hunters who spent months using sonar to sweep the deep waters.

3. Irons in the Fire

Year Published: 1997

Summary: A compilation of seven essays focusing on obscure, highly specialized worlds. It features pieces on modern cattle-rustling brand inspectors in Nevada, the lifespan of military cargo planes, the manufacture of forensic charcoal, and the historical journeys of Plymouth Rock.

How Reviewed: Reviewers warmly received the collection, highlighting McPhee’s unique talent for mastering technical jargon and making niche professions completely accessible and thrilling to general readers.

An Interesting Story: In the essay “The Gravel Page,” McPhee introduces the world of forensic geology. He follows an expert who can solve complex criminal cases, like kidnappings or murders, simply by analyzing the microscopic dust, pollen, and soil types caught in a suspect’s shoe treads or car tires.

4. A Sense of Where You Are

Year Published: 1965

Summary: McPhee’s brilliant debut book profiles Bill Bradley during his time as an All-American basketball star at Princeton University—long before Bradley became an NBA champion with the New York Knicks and a U.S. Senator.

How Reviewed: Widely celebrated for its exceptional character development and meticulous analysis of athletic grace, launching McPhee’s legendary literary career.

An Interesting Story: The famous title comes from a moment when Bradley demonstrated his flawless spatial awareness to McPhee. While walking backward away from the basket without looking, Bradley flipped the ball over his shoulder and sunk it. When McPhee asked how he did it, Bradley simply remarked that he just had “a sense of where you are.”

5. The Curve of Binding Energy

Year Published: 1974

Summary: A chilling profile of Theodore Taylor, a visionary nuclear physicist who designed some of the world’s smallest atomic weapons. The book details how alarmingly easy it would be for a motivated individual to steal nuclear material and build a homemade bomb using unclassified, public information.

How Reviewed: Nominated for a National Book Award, this work shocked readers and terrified policymakers by laying bare massive vulnerabilities in domestic nuclear facility security.

An Interesting Story: The book created an immediate national security panic. Because McPhee’s descriptions of security flaws were so detailed and accurate, it acted as a massive wake-up call that forced the U.S. government to dramatically overhaul and tighten security protocols at nuclear stockpiles.

6. The Control of Nature

Year Published: 1989

Summary: An epic three-part exploration of human hubris versus the elements. It profiles three intense battles: the Army Corps of Engineers trying to stop the Mississippi River from changing course, Icelanders fighting a volcanic eruption, and Los Angeles residents combating massive mountain mudslides.

How Reviewed: Frequently heralded as one of McPhee’s ultimate masterpieces, praised for its cinematic tension, incredible environmental writing, and deep philosophical look at humanity’s defiance of nature.

An Interesting Story: During the 1973 eruption on the island of Heimaey, Icelanders refused to let lava swallow their vital fishing harbor. They rigged up miles of plastic piping and pumped millions of gallons of icy ocean water directly onto the glowing molten rock for months, successfully freezing the advance and creating a natural rock breakwater.

7. The Patch

Year Published: 2018

Summary: A late-career compilation split into two sections: immersive outdoor essays covering fishing, bears, and golf courses, followed by “An Album Quilt,” a series of shorter, mosaic-like reflections on historical figures, celebrities, and fellow writers.

How Reviewed: Reviewed as a poignant, comforting, and delightfully nostalgic anthology that showcases a master writer looking back over a lifetime of keen observation.

An Interesting Story: The title essay, “The Patch,” refers to a hidden, pristine pocket of water lilies in a lake in eastern Canada. McPhee’s family kept its exact coordinates a closely guarded secret for generations because it was an unrivaled fishing spot for chain pickerel.

8. Silk Parachute

Year Published: 2010

Summary: An elegant essay collection ranging from the geometric intricacies of lacrosse and the geology of chalk to a loving profile of longtime New Yorker editor William Shawn and deeply personal memories of youth.

How Reviewed: Celebrated for its unique emotional warmth, with critics appreciating the rare, deeply personal glimpses into McPhee’s own childhood and family relationships.

An Interesting Story: The title essay is a tribute to McPhee’s mother. When she was a young woman in the early 20th century, she impulsively went up in a biplane and did a parachute jump just for the thrill of it. In her nineties, she would playfully drop a toy silk parachute out of her window down to her adult son.

9. Uncommon Carriers

Year Published: 2006

Summary: A fascinating look into the invisible freight network that keeps the American continent running. McPhee journeys across the country alongside long-haul truckers, towboat captains moving massive barges on rivers, and operators of cross-country coal trains.

How Reviewed: Highly praised as an eye-opening, deeply engaging look at the modern logistics industry and the eccentric, highly dedicated people who master it.

An Interesting Story: To write the opening chapter, McPhee spent days living in the cramped cab of a chemical tanker truck driven by Don Ainsworth. Ainsworth was an elite driver who treated long-haul trucking like a fine art, teaching McPhee the high-stakes rules, specific lane etiquette, and hidden subculture of the open road.

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.