Tag: Disruption

Categories
AI

Hands He Can’t Feel

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

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

Bewildered, maybe.

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

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

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

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

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

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

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

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

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

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

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

He still believes that, mostly.

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

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

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

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

His father’s hands always knew what to do.

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

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

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

The ethic transferred even when the medium changed.

Even now, when the medium is changing again.

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

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

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

He types. The code appears.

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

Not because he doesn’t trust it.

Because that’s what you do when it matters.

Categories
AI

The Coach Who Wouldn’t Change

In 1975, a twenty-four-year-old Kodak engineer named Steve Sasson built the first digital camera. It was the size of a toaster, captured a black-and-white image at 0.01 megapixels, and took twenty-three seconds to record a single photograph to a cassette tape. Sasson showed it to his managers. Their response, as he later recalled, was essentially: that’s cute, but don’t tell anyone about it.

Kodak was not a stupid company. It was a dominant one. At its peak it held 90 percent of the American film market and 85 percent of camera sales. Film was not just a product line — it was the entire economic architecture of the company. Processing fees, paper, chemicals, the retail relationships built around the assumption that photographs needed to be developed. Digital threatened all of it simultaneously. So Kodak did what dominant companies do when confronted with a threat they can’t absorb into the existing model: they managed it. They ran studies. They filed patents. They made incremental moves. They protected the thing that was working rather than building the thing that would work next.

Kodak filed for bankruptcy in 2012. The digital camera had been sitting in their own archives for thirty-seven years.

Nokia’s version of the same story has a different texture. Where Kodak’s failure was about protecting a margin, Nokia’s was about identity. Through the 1990s and into the early 2000s, Nokia was mobile phones — not a major player, but the category itself. At its peak it held over 40 percent of the global handset market. The company had navigated a remarkable transformation earlier in its history, shedding paper mills and rubber boots to become a pure technology company. It knew how to change. It had done it before.

What it couldn’t do was change from a hardware company into a software one. When the iPhone arrived in 2007, Nokia’s internal assessments were, by most accounts, accurate. They understood the threat. They had touchscreen prototypes in development. What they couldn’t manage was the cultural distance between building phones that were superb physical objects — durable, reliable, made to exacting standards — and building phones that were primarily platforms for software that other people would write. The excellence that had made Nokia great was manufacturing excellence. The game was becoming something else, and manufacturing excellence was not only insufficient for the new game; it was actively in the way, because it oriented every decision toward the object rather than the experience.

Nokia’s market share collapsed from over 40 percent in 2007 to under 5 percent by 2013.

Andy Grove, who built Intel into the dominant force in semiconductors, called it plainly: only the paranoid survive. He meant it as a prescription. His successors treated it as a trophy.

Both stories have the clean shape of settled history. We know how they end. The verdict is in, the lesson is available, and it’s easy to read them now as cautionary tales about obvious mistakes made by people who should have known better.

This is the wrong way to read them.

Kodak and Nokia didn’t fail because they were blind. They failed because they were standing on a fulcrum — a moment when the old game and the new game were both plausibly real — and they chose the wrong side. At the time, that choice was not obviously wrong. Film was still enormously profitable. Nokia’s hardware was genuinely superior. The rational case for staying the course was real, and the people making it were not fools.

The reason the Kodak story is still told fifty years later is not that the mistake was obvious. It’s that it wasn’t — and they made it anyway.

Which brings us to now. Because there is a fulcrum in front of the enterprise software industry, and nobody knows yet which way it tips.

The companies in question — Salesforce, ServiceNow, and most of the SaaS category built over the last twenty years — were constructed on a simple and powerful premise: that businesses would pay recurring subscription fees for software that managed their customer relationships, their workflows, their data. The premise was correct. It produced some of the most durable businesses in the history of technology.

The threat AI poses to this model is not subtle. If an AI agent can handle a customer service interaction, manage a workflow, or synthesize a CRM record without a human touching licensed software to do it, then the per-seat subscription model — the economic engine underneath all of it — starts to look like film processing in 2003. Theoretically intact. Quietly at risk.

The responses of these companies have been instructive, and they’ve diverged.

Here is the honest position: we don’t know yet. The fulcrum is still in motion.

It’s possible that Salesforce’s Agentforce is the Kodak digital camera — the real thing, built by the right company, that gets buried under the weight of protecting what already works. It’s possible that the SaaS model is more durable than the threat suggests, that enterprises will pay for trusted platforms regardless of the underlying labor model, and that the companies racing hardest to cannibalize their own revenue streams are making a different kind of mistake. It’s possible that ServiceNow’s consistency is discipline, or that it’s the Nokia instinct to keep building the best version of the thing that used to win.

What the Kodak and Nokia stories actually teach — not the simplified version, but the harder one — is that the mistake is never visible in the moment it’s made. It only becomes visible later, when the fulcrum has tipped and the choice that was once defensible has become permanent.

The coach who wins five championships holds the philosophy and rotates the players. The coach who wins one holds the players and calls it philosophy.

The enterprise software companies standing at this moment have a version of the same decision. The ones who make it correctly will, in twenty years, be the ones we cite as examples of adaptation. The ones who don’t will be the ones we cite as examples of something else.

We just don’t know yet which is which. That’s not a comfortable place to stand. It is, however, exactly where we are.

Categories
AI Business Consulting

The Toll Bridge and the Terrain

For fifteen years of my life, I lived inside the fortress of information asymmetry. I was part of a payments consulting business, and our model was exactly what Andrew Feldman described on a recent Moonshots episode when he pointed a sharp finger at traditional professional services.

His observation was simple, cutting, and entirely true:

“Their role today is to stand between ordinary people and obscure knowledge. And the application of that obscure knowledge to everyday problems.”

When I heard him say that, it landed with a quiet thud of recognition.

For a decade and a half, my colleagues and I were the ones standing in that gap. The payments industry—with its labyrinth of interchange fees, compliance structures, clearing networks, and legacy tech stacks—is a monument to obscure knowledge. Clients didn’t come to us because we possessed some divine, unreplicable wisdom. They came to us because the map was locked in our heads, and navigating the terrain without us was a recipe for an expensive disaster.

We charged for our time, and we earned it. We untangled complexity and solved real, everyday business problems for people who just wanted to move money safely from point A to point B.

But looking back now, I can see the architectural flaw disguised as a premium service. The economic foundation of that entire era relied on friction. It relied on the fact that it took an immense amount of human energy to retrieve a piece of obscure data and map it onto a specific business dilemma. You weren’t just paying for strategic guidance; you were paying a premium on artificial scarcity.

We are living through a moment where the marginal cost of intelligence is rapidly trending toward zero. When the barrier of “obscure knowledge” evaporates, the traditional toll bridges begin to look absurd.

For anyone starting a consulting business today, the playbook would have to be entirely different. When an LLM can parse thousands of pages of network operating rules, interchange tables, and regulatory compliance frameworks in a handful of seconds, the gatekeeper’s standing ground liquefies.

If your value proposition is merely standing between a client and a hidden database, your business model isn’t just flawed—it’s obsolete.

Yet, this collapses into a fascinating paradox. You might assume that when you democratize expertise, you eliminate the need for the expert. But as Dan Shipper recently observed, the reality of AI is completely counterintuitive.

Shipper points out that AI effectively packages up “yesterday’s competence” and makes it cheap and ubiquitous.

Suddenly, anyone can generate a complex contract, a software pull request, or a payments flow strategy with the click of a button. But when cheap competence skyrockets, adoption explodes, resulting in an unprecedented glut of generic output—what the internet has collectively taken to calling “slop”. It’s the default, lazy answer that lacks soul, context, and nuance.

When everything begins to look and smell the same, a strange thing happens: the market’s demand for genuine difference sky-rockets.

The shift we are facing across all professional services—whether legal, financial, or consulting—isn’t about eliminating the expert. It is about changing the expert’s job from data-retriever to orchestrator and judge. The floor has been raised. Yesterday’s ceiling is today’s baseline.

What remains is the ability to read a room. To watch a client’s shoulders tighten when you present an option that’s technically correct but organizationally impossible. To notice the glance exchanged across the table before anyone speaks. No LLM parses that. The map is universal now; the guide still has to be in the room.

We don’t need fewer guides; we need fewer toll booths. The future of consulting doesn’t belong to those who hoard the map. It belongs to those who use a universally available map to help people actually walk the terrain.

Categories
AI Technology

The Bathwater Problem

Gary Kamiya was writing about the Tenderloin when he said it, but the line has been following me around: “The problem is that by saving the baby, you also save the bathwater.”

The pattern is remarkably consistent across every major information technology. Each one arrives promising to liberate the deserving — the faithful, the learned, the civic-minded — and each one immediately, inevitably, arms everyone else too. Gutenberg’s press was understood by its champions as a device for spreading the true Word; within decades it was the primary infrastructure for Protestant schism, Catholic counter-propaganda, astrological almanacs, and pornography. The reformers got their Bible. They also got their pamphlet wars.

The telegraph was greeted as a force for peace — shared information would make war irrational, commerce would bind nations. It also became the nervous system of commodity speculation, financial manipulation, and the first truly industrial-scale news hoaxes. The telephone: connection and the crank call, the crisis line and the threatening voice in the dark. Radio: FDR’s fireside chats and Father Coughlin. Television: Murrow taking down McCarthy, and also fifty years of manufactured consent. The internet: the largest library ever assembled and the largest sewer.

The pattern isn’t coincidental. It’s structural. Each technology expands what’s possible for human expression and coordination — and human expression and coordination contain both the noblest and the worst of us in roughly fixed proportion. The tool doesn’t change the ratio. It scales both sides of it.

What’s interesting historically is how each generation believes their technology will be different — that this time the architecture can be designed to select for the good. The internet era produced the most elaborate version of this belief: algorithmic curation would surface truth, network effects would reward quality, the wisdom of crowds would outcompete misinformation. Instead it turned out that engagement was the attractor, and outrage was the highest-engagement content. The bath got hotter.

The AI moment is the same belief system, restated with more technical sophistication. But the Kamiya line stands. You are saving a baby, and you are saving bathwater, and no one has yet designed a tub that can tell the difference.

The question isn’t whether the bathwater comes with the baby. It always does. The question is whether you turn on the tap.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.