Tag: Technology History

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
Aging Citizens Band Radio History Living

The Static We Left Behind

There was a time when the airwaves crackled with a distinct, unpolished kind of magic. It wasn’t the curated broadcast of a corporate radio station, but the raw, spontaneous voices of strangers sharing the same lonely stretch of highway or suburban night. When I previously wrote about the rise and decline of CB radio, I didn’t fully anticipate how deeply the piece would resonate. The influx of emails, comments, and shared memories pointed to a singular, striking truth: we don’t just miss the hardware of the 1970s; we miss the serendipity of the connection it offered.

In the decades since the fiberglass whip antenna faded from the American automotive silhouette, our society has become infinitely more “connected.” We carry glass slabs in our pockets capable of reaching anyone, anywhere, in an instant. Yet, paradoxically, we often find ourselves feeling more profoundly isolated. The modern digital landscape is largely an algorithmic echo chamber, meticulously designed to feed us reflections of what we already know and who we already are.

CB radio, by contrast, was a geographic lottery. You turned the dial, adjusted the squelch, and were instantly thrust into a transient community composed entirely of whoever happened to be within your physical radius. It was messy, chaotic, occasionally absurd, and deeply human. It was a localized town square operating on a 27 MHz frequency.

“We traded the spontaneous for the scheduled. We swapped the local for the global… We traded the crackle of static for the endless, frictionless scroll of the feed.”

Reflecting on the quiet that eventually fell over Channel 19, it becomes clear that the decline of CB radio was more than just a technological shift—it was a cultural one. We traded the spontaneous for the scheduled. We swapped the local for the global, and the intimately anonymous for the hyper-public. We traded the crackle of static for the endless, frictionless scroll of the feed.

But the fundamental human impulse that fueled the CB craze never actually disappeared. The desire to reach out into the dark void and hear a human voice echo back—the spirit of “Breaker 1-9, is anyone out there?”—remains hardwired into our psychology. We see fragmented echoes of it today in late-night Reddit threads, in niche Discord servers, and in the fleeting, unscripted interactions of multiplayer gaming. We are all still, in our own ways, searching for a shared frequency.

Perhaps the true legacy of the CB radio isn’t a cautionary tale of obsolescence, but a gentle reminder. It reminds us that in our highly polished, curated digital world, there is still immense, undeniable value in the unscripted encounter. We haven’t lost the need to connect; we are simply navigating a world with too much noise and too few open channels.

Categories
AI Work

Betting on Ourselves in the Age of AI

Every time tech takes a leap, we assume we’re finally obsolete. The current panic, which Greg Ip recently picked apart in the Wall Street Journal, is AI. We hear endless predictions of “economic pandemics”—server farms wiping out white-collar jobs overnight, leaving everyone broke and adrift.

It’s a terrifying story. It also completely ignores history.

Ip highlights the main flaw in the doomsday pitch: it misreads how markets work. We treat labor like a fixed pie. If a machine eats a slice, we assume that slice is gone forever.

“Technological advancements always cost some people their jobs—those whose skills can be easily substituted by tech. But their loss is more than offset through three other channels. The new technology enhances the skills of some survivors… it helps create new businesses and new jobs; and it makes some stuff cheaper…”

That cycle holds up. Take the 1980s spreadsheet panic, a perfect parallel. When Lotus 1-2-3 and Excel hit the market, bookkeepers freaked out. Then the number of accountants and financial analysts exploded. Software didn’t kill the need to understand money. It just did the math, letting people focus on strategy.

We’re seeing the exact same thing with software development. Coding isn’t dead. As AI makes writing basic code cheaper, demand for software just goes up. That requires more humans to architect systems and supervise the AI. The pie just gets bigger.

But my skepticism about the AI apocalypse goes beyond economics. It’s about why we pay people in the first place.

We don’t just buy services; we buy accountability. Ip notes that radiologists kept their jobs because patients want a real person explaining their scans. Google Translate has been around since 2006, yet the number of human translators has jumped 73%. When the stakes are high—a legal contract, a medical diagnosis—we want a human in the room. We want a real person on the hook.

The danger isn’t that AI will replace us. The danger is that we panic and forget our own adaptability. The transition will hurt, and specific jobs will disappear. We’ll need safety nets. But betting against human ingenuity has always been a losing wager.

Large language models are tools, not replacements. They handle the cognitive heavy lifting, much like tractors handled the physical heavy lifting. Tractors didn’t end farming; they just killed the plow.

Work will change. We’ll have to figure out which of our skills are actually “human.” But as long as we want the presence and accountability of other people, there will be jobs. We just have to evolve. And we do. It’s the human spirit. Or is this time “really different”?