The thing in question is a bass guitar bridge, carved from a pale blond hardwood, small enough to close one hand around. An arc with two feet, a swept crown where the strings would ride, a curl of negative space through the middle. The tool marks are still in it, as are the penciled lines where the sawed curves don’t quite match side to side, because it’s a shop part, and in a working shop you don’t polish a part, you fit it. This one never got fitted. It sat in a box instead, and the box is why I’m writing this.

Les Paul carved it. THE Les Paul.

If you aren’t a guitar person: Les Paul is the guitarist and inventor who gave the world multitrack recording and the solid-body Gibson that’s carried his name since 1952. Jimmy Page, Eric Clapton, Slash, Billy Gibbons, Randy Rhoads…all Les Paul players. A real object from Les Paul’s own hands is a big deal.

The chain

Walk up the chain with me, because the chain is most of the object. Les Paul spent the last decades of his life in Mahwah, New Jersey. During a snowstorm in the early 1980s he called the local police station to ask for help plowing his driveway, and the young cop who volunteered was Jim Wysocki. The plow job turned into a twenty-nine-year friendship, one of the stranger and sweeter stories in guitar lore; Premier Guitar tells it properly. Les received gifts from strangers constantly and gave things away nearly as fast. He once handed Wysocki a Danelectro guitar with the instruction to “put it under your bed for a rainy day.” And before Les died in 2009, he effectively made Wysocki the caretaker of his collection of guitars and parts, getting a promise back: people would be able to see it, touch it, and play it. Wysocki has kept the deal ever since, running the collection the way Les ran his living room, with the stuff in reach.

One of the things Wysocki has passed along in that spirit is a box of parts from Les’s home workshop. He gave it to my friend Jim DeCola, a master luthier at Gibson.

And Jim gave a piece of it to me.

The membership card

I need you to understand how uncomfortable this made me. I understood what I was holding, and I handed it back. I told him I couldn’t take it. That I had no business having it. This was a meaningful piece of music history being casually handed to an abject music beginner.

He put it back in my hand. “It’s yours. You’re a bass player now.”

Here’s what you need to know about that sentence: Jim knows exactly how bad I am. I had only just picked the thing up, and I may be a Music City native but that’s the precise extent of my musical ability. Bottom rung. The lowest occupied position on any ladder of musical skill, the guy the word “aspiring” was invented to flatter. He also knows that I care a lot about the instrument, I’m constantly asking questions of my more musical friends, including him, and I badly want to improve. Apparently that was the entire qualification. He was issuing a membership card, and he was issuing it the way skateboarding issues them.

I neglected to add that he handed me the bridge after a skateboard session, us standing there sweaty in the parking lot talking about music. Jim skates, which I’ve decided is not incidental — you’re a skater the first day you step on a board. Nobody audits you at the gate. The belonging comes first and the skill is your own long meandering path to navigate later, and that ordering is one of the most quietly radical things the culture ever built. Jim and I look up to each other across exactly this line, and I can date the moment I learned the respect ran in both directions.

The concrete bowl

Years ago we were at a contest at a concrete bowl, one of those hectic snake sessions where it’s hard to get in and you keep a run alive once you do. I was on the deck. Jim came up into a 50-50 in front of me with his weight a hair too far out of the bowl, and I could see the bail coming maybe even before he could: I’d watched him deck out of that exact grind a hundred times at local sessions. Nothing dangerous, just a premature end to a run he’d fought to get. There’s a hard rule up there that’s such a given it’s not even a rule. You do not touch a skater mid-trick. Why would you? The consequences of getting it wrong are real, and nobody with sense takes that risk. I reached over anyway and gave his back a gentle shove, just enough weight back into the bowl to save his 50-50. He made the grind and kept the run. He still can’t quite believe I did it, and he still calls it perfectly executed help, and we’ve been laughing about it ever since.

The bridge was the same move in the other direction. I was bailing out of a gift, and he put a hand on my back. Neither of us has ever called any of it a trade, which tells you what kind of economy we’re standing in.

The cash register

I argued in The Diamond Was Always a Story that once a lab can grow the identical stone, the only thing holding the price up is the sentence attached to the rock. So I have to ask the obvious question about my own shelf. As wood, this bridge is worth nothing. A few ounces of shop stock, a functional part for an instrument that was never built. Everything it “is” rides on provenance, which is precisely the thing I spent that essay calling a con.

The difference is the cash register. A premium is a story with a price tag attached, and De Beers spent most of a century renting the billion years of diamond formation out at retail. Nobody in this chain charged anyone anything. Les gave constantly. Wysocki keeps the collection moving through hands instead of sitting behind glass. And Jim, who buys and sells serious guitars and parts as a matter of course, gave a piece of Les Paul’s workbench to a beginner because the beginner loved the instrument. I have never once heard him put a number anywhere near that box, I know in fact he wouldn’t. When provenance travels by gift, there’s nothing to overcharge for and nobody to fool. The story just is the object. Strip the story off a diamond and you still have a diamond. Strip it off this bridge and you’re holding kindling.

The authentication, you’ll notice, is also the chain. No lab report, no laser inscription on the girdle, no origin registry on a blockchain. Three pairs of hands since Les’s, and I can name every one of them. That’s the entire certificate, and it’s the oldest kind there is: the only one that gets more convincing as it gets less formal.

Straight on

Then there’s the part that turned all of this into a design decision. Stand the bridge up and look at it straight on. It’s a letter M. The outer silhouette makes a capital, feet and all, and the cutout through the middle makes a lowercase m in negative space. My initials, twice, in one object, carved decades ago by a man who had no idea he was doing lettering, much less for the lowest ranking member of his trade.

So it’s becoming the mark for this site. I’m tracing it exactly as it is, wobble included. The temptation with a trace is to true it up — even out the curves, match the feet — and what you’d get is a clean little picture of a generic bridge, indistinguishable from clip art, with a backstory the pixels can’t show. The asymmetry is the part Les touched. I wrote a whole essay about wear you can’t buy; the least I can do is decline to sand off the wear I was given. From now on, the small mark in the corner of this site links back to this page, which makes this essay the certificate of authenticity: a story, told free, passed along the same way it arrived.

I still don’t entirely believe I should have it. Jim’s sentence left that objection standing and made it beside the point anyway. The gift was a bet placed in advance, unrepayable by design, and I’ve slowly come to understand that’s the only kind worth making.

Some year down the road, the bridge goes to somebody else. Somebody who loves the instrument out of all proportion to their ability to play it, and who will try to hand it back, because that’s the reflex a thing like this produces in anyone paying attention.

I already know what I’ll say.

Every few feet, something in the ground would put the work on pause. A rock the size of a microwave. A root as thick as your wrist. A tree standing exactly where the nicest line wanted to go. Or, where I live in middle Tennessee, ridiculous amounts of invasive honeysuckle. And the crew would straighten up, lean on the handles, and argue. Uproot the honeysuckle? Usually yes. Above the tree or below it? More often a debate. Below is a straighter line but it’ll erode over time and expose roots and be a rowdy bump in no time. Above works but requires a little uphill hook in an otherwise flowy line that steals speed. It also cuts the uphill roots, which are the ones a tree on a slope is really hanging by, weakening it and risking a tree fall later. Somebody’s seen a trail fail exactly this way before; somebody else rode one that didn’t. Two minutes, maybe five, then a verdict, and the digging starts again until the next rock or tree calls the next meeting. Every obstacle a stoppage, a discussion, and a judgment call.

I’ve also ridden plenty of trail cut by machine: a mini excavator walking the corridor, an operator who’s genuinely good at his job, doing in a day or two what a hand crew does in a month. Nobody in that machine is lazy or lying. The operator is making real judgments the whole time: where the corridor goes, how the grade rolls, where the water sheds. Gasoline-powered machines over ham-sandwich-powered humans operating at a fraction of the pace. Most of the time the trade is flat-out justified. The machine saves a month of volunteer weekends nobody had to give up, and the trail exists instead of not existing. For beginner trails it might even be the best way to build trails with a more polished tread.

But the two trails are different, every rider feels it, and I’ve come to think the difference is the blade.

Wider than the rock

This sounds obvious but you cannot make a decision finer than your tool’s blade. A McLeod resolves to about ten inches, so above or below this rock is a live question: the tool can express either answer. An excavator blade is four feet wide. When the blade is wider than the rock, the question doesn’t get answered badly. It just never gets asked. The rock is simply inside the cut, and the machine moves on, and there was no moment when anyone decided anything about it. The operator isn’t coarse. The question has no expression at that resolution. It’s hard to eat a grain of rice with a spatula.

Nobody decides anything about this rock. It is simply inside the cut, and the machine moves on.

That’s half of it. The other half is where the decisions live, and this is the part that took me longest to see. The hand crew doesn’t stop every few feet on principle. It stops at the rock. At the root, at the tree, at the spot where you can already see the water starting to gully. The terrain decides where judgment happens. Checkpoints aren’t evenly spaced along the work. They’re event-triggered, called into existence by the material, and they cluster wherever the material pushes back.

Put those two halves together and you get the actual shape of what a faster, coarser tool does. It doesn’t reduce judgment by some percentage. I know better than to count; percentage was never the unit, and an essay that starts tallying checkpoints has already lost. What it removes is a decision class. The operator makes every route-level call the crew makes, and he cannot make the per-rock class at all, because his blade can’t ask the question. Not fewer of the same decisions. All of one kind, none of another.

I want to be precise about how this differs from a worry I’ve already put on the page. In The Forge Was a Side Effect I said the AI machine hands me pre-sorted research and I’m left “choosing inside a narrowing something else performed”: fewer options at each decision, and no way to see what fell out. That crack is about the inputs. This one is worse, and it’s different in kind: a narrowed option set at least presents itself as a choice. You stand there holding it, aware you’re choosing. An automatic, unmade decision leaves no trace at all. You don’t know you didn’t stop at the rock. There’s no rock in your memory to have an opinion about. There’s a new machine-cut trail down the street from me with rocks shouldered off into the woods on both sides, every one of them a decision nobody made. The trail is smooth right there, and smooth doesn’t testify.

Approve every token

Now carry this to the desk, because it was never really about trails. A model drafting your prose is the excavator posture: a thousand small decisions happening inside the generation, at machine speed, and you meet the trail already cut. The word-level choices, the sentence-rhythm choices, the this-clause-before-that-clause choices, all inside the blade, resolved without ever having been questions.

Here’s the thought experiment that convinced me this is structural and not just a vibe. Imagine an AI that required your approval of every single token. Maximum resolution: a checkpoint at literally every word. Surely that re-injects the judgment?

It doesn’t, and it fails three separate ways, which is what makes the experiment worth running.

First: approving is not choosing. Saying yes to an offered word is recognition. It’s narrow multiple-choice instead of fill-in-the-blank. Writing the word is search, and the search is where the alternatives live: the five words you tried and discarded, the phrasing that died on the way to the page. Again, Funnier built this mechanism already: the superfan hears the record that shipped and never the ten thousand takes it beat. Survivors only. Token approval makes you a superfan of your own sentence, at the highest resolution possible — a checkpoint at every step and never once a fork.

Second: uniform spacing is the wrong shape. Judgment clusters at features because the terrain triggers it. That’s the whole point of the rock. Spraying approval evenly across every token isn’t more judgment; it’s judgment with the discrimination removed, and any honest human would habituate into rubber-stamping before the end of the first paragraph. The crew that stopped at every shovelful would argue about nothing, and get precisely nothing done, because nothing is what most shovelfuls contain.

Third — and this one I only know because I stood in the woods for it — some of the judgment is social, I’d even argue a form of rest. The crew argues because the pace permits it. Hands still, leaning on the handles, taking a drink of water, somebody’s five-year-old memory of a gullied switchback entering the record. The excavator removes that by physics, not by choice: engine noise, one seat. And one person approving tokens, alone, forever, never gets a second opinion back either.

So: judgment is not a quantity, and you cannot re-inject it by subdividing. Which means every framing of AI assistance as a dial — more human control, less human control, find your percentage — is measuring in a unit the problem doesn’t have.

The caddie and the excavator

If it’s not a dial, what is it? A golf course got me there.

Nobody disparages a professional golfer for using a caddie. Think about how strange that is for a minute. Amateurs are left entirely to their own devices; pros — at the highest level, in the open, on television — get an expert who helps with the read, the club, the line. Which is to say: the judgment. The part every argument in this run of essays says is sacred. And the golf world doesn’t blink.

Two people at one stop, one decision. The player still has to hit it.

My first guess at why was that the caddie doesn’t swing the club. Execution stays with the player, so honor is preserved. But that doesn’t explain what golf actually tolerates. A caddie moves real judgment: the club, the read, the number, sometimes the plan for a whole hole. If handing off decisions were the offense, he’d be the scandal of the sport. Now push it the other way: a robot that perfectly swings whatever club the caddie hands it is cheating past argument, and the reason is that the swing is where the golfer’s work lives, and nobody was standing there to do it.

Here’s the boundary that holds. A caddie doesn’t remove the stop. The player still walks up, assesses the approach, the wind, the slope, still holds the decision in his hands, still overrides: it’s an 8, and the caddie shrugs and hands it over. The caddie adds another voice inside the same checkpoint. Same stops, thicker deliberation at each one. Maybe argues the 8 to a 7 if the decision is borderline. The trail excavator does the opposite: it doesn’t improve the decision at the rock, it removes the rock as a decision point.

That’s the discriminator: does the assistance happen at the checkpoint, or instead of it?

Run my own practice through it and the sorting is almost embarrassing in how clean it comes out. The duck that argues back is a caddie: I bring the position, it pushes back, I decide, and the stop survives, thicker rather than fewer. A model drafting the whole essay is an excavator, and everything I’ve ever described as “shoving the draft around until it’s mine” is me walking the finished cut looking for the rocks I never got to meet. When I worked out where the human hand goes in AI writing — comedy wants the hand at the setup with the model reckless below it, literary prose wants the hand in the sentences the whole way down — I was drawing checkpoint maps without knowing the word for it. The hand goes wherever the checkpoints have to survive.

And before the caddie becomes too comforting, notice what he has that the model doesn’t. He’s paid on the winnings. He’ll tell you it’s a 7 when you’ve already pulled the 8, and he can stand there until you put it back. Stakes, and a stopping rule — the missing counterparty, present and holding the bag. His read comes from walking the course with the outcome landing on him. The model at my desk has neither. So the caddie isn’t a reassurance that assistance is fine. He’s a specification of what assistance would have to be: a measuring stick the current tools mostly fail.

The cockpit had to be built

There’s an essay of mine this cuts directly into, and I owe it the collision. We Already Voted With Our Lives made the case I still stand behind: judgment versus execution is the real line, the autopilot proves you can hand off the flying and keep command, so fly the pilot model — keep the judgment human, put your name on it, climb into the plane.

What that essay didn’t say — its “where the analogy stops” section lists authorship and credit and meaning, and walks right past this — is why the cockpit arrangement actually works. It isn’t the pilot’s virtue. It’s that the cockpit is a built structure of checkpoints: instruments, alarms, cross-checks, a second seat, callouts a crew is required to say out loud, a loop engineered over decades so that the human’s judgment has designated places to happen and can’t quietly not-happen. Aviation built a machine for making sure the stops survive for human verification, and then measured it in crashes that stopped coming.

The desk has none of that by default. The writer who “flies the pilot model” with a blank prompt window is a pilot in a cockpit with no instruments, no alarms, no second seat, nothing but her own intention that the judgment stay hers. Every user of these tools claims the judgment stayed theirs. The claim is sincere. It’s also unfalsifiable, which is the trouble: intention isn’t a checkpoint structure. Keep the judgment human is the prescription I’ve spent a whole arc repeating, and by itself it’s empty. Nothing at the desk makes a stop survive contact with a tool that’s fast, smooth, and agreeable. Climb into the plane was right. The correction is that the desk doesn’t come with the cockpit, and the cockpit is the apparatus, not the chair: the alarms, the cross-checks, the second person required to say the number back to you. What the desk offers instead is a machine that says yes, let’s fly every time, to anyone, in any condition, because agreeing is the one thing it’s sure how to do. Aviation’s stops can ground a flight. Nothing at your desk can, unless you put it there. You’re building the cockpit yourself, checkpoint by checkpoint, or you’re not in one.

Where the line falls

This also amends the essay of mine it argues with most directly. Everyone’s a Ringer to Somebody traced the cheating line from quill to typewriter to laptop and found it always indexes to the self: every person draws it at their own feet, calls everything behind it quaint and everything ahead of it fraud. The best answer it could give was formative friction: ask what the friction was doing to you. A self-side criterion, honest about being one.

The trail hands me a second criterion, and it isn’t about the maker at all: can the tool resolve the feature in the material? That’s terrain-side. And here’s the evidence it isn’t just nostalgia wearing a tool belt: no trail purist on earth wants trail clawed out with bare hands. McLeods, rakes, shovels, wheelbarrows — tools, every one, and nobody laments them, the way no writing purist laments the word processor. The line isn’t nostalgic, it’s not drawn at the tool I learned on, which is where the ringer reflex always puts it. It falls where the blade stops being able to read the terrain. That’s a place you can point to in the material, argue about in public, and be wrong about, which makes it a different kind of line than any I drew in that essay. The ringer’s line moves with you. This one stays with the rock.

Too steep and too twisty

Our hand-built trail had a characteristic failure, and I helped build it in. Working at a crawl, we calibrated everything to the speed of a person standing still. We built the whole first stretch too steep and too twisty, because at digging pace every grade feels mellow and every turn feels wide. We never met the trail at the speed it would actually be ridden until it was cut, and then the bikes told us in about four minutes what the shovels hadn’t noticed in weeks. The machine operator, covering the whole line in a day, holds the run in his head at something much closer to riding tempo: the global shape, the flow, the way section hands off to section. He catches exactly what we missed.

At digging pace a brutal grade feels mellow. The bikes disagreed in about four minutes.

His machine also has a constraint we didn’t. It’s less sure-footed than we were on a steep grade, and it has to climb what it’s cutting, so it pushes him toward mellower climbs whether he’s thinking about riders or not. Mellower is what a rider wants. You can read our version of that lesson right off our trail: the entry climbs are the most brutal thing out there, because they’re the first thing we built. Get deeper into the system and you can feel us find the groove, learning to imagine the trail at riding speed instead of trusting what our hands and feet were telling us at a standstill.

So every working tempo has a blind spot. Slow work over-indexes on the local and loses the global feel. Fast work catches the global and paves the local. Those are different artifacts, not a good one and a cheap one. Plenty of riders will tell you machine-built isn’t lesser, it’s just different, and they’re not wrong.

And the hardest version: a trail is ridden at speed, and an essay is read at speed. Nobody consumes anything at the tempo of its making. The reader moves through my paragraphs at something far closer to the machine’s drafting pace than to my crawl, which means the fast maker is, in one narrow and completely real sense, better calibrated to how the work will actually be met. The hand crew’s advantage was always the stops.

A map of where the stops were

One more turn, because the newest tool in this story knows something about checkpoints too.

The AI watermark that arrived this year works by a keyed statistical lean in the model’s word choices. Anthropic hasn’t published its scheme, but every published one shares that shape. The detail that matters here: the signal can only ride on the tokens where the model had real options. Where the next word is forced, there’s no room to lean; where many words would do, the lean lives. The mark exists precisely where a choice was available to be made. Which means a watermark is, incidentally, a map of where the checkpoints were: the machine’s own record of the rocks.

Almost. The divergence is the actual finding. High entropy for the model is not high stakes for a human. Ten interchangeable synonyms give the mark plenty of room and cost the prose nothing: a rock the size of a pea. And the obvious next word — the one the model lays down with total confidence, no options, no lean — is exactly the word a good writer refuses, because the model’s confidence peaks precisely where the median lives. The machine is smoothest exactly where a human would have swerved. So even a perfect map of the model’s choices can’t show you the stops that never happened — the blade doesn’t record the rocks it never felt.

Which brings me back to a sentence I once had to leave hanging. Ringer ended on a confession: a machine had offered the right patch for a hole in my argument, I’d taken it, and the essay closed on “I wrote this essay, and I still can’t call it.” I couldn’t, with what that essay had to work with. This one gives me a way.

Run it through the only structural test I now trust. I found the hole myself. The stop was already there, because I’d built it into how I work. The machine spoke at that stop rather than in place of it, one more voice at a checkpoint that would have happened anyway, and I made the call. That one’s clean.

What the test can’t tell me is the forge question, and it stays open: who learned what in that exchange, and what fifteen years of taking such patches does to the taker. A checkpoint can survive the assistance and still teach the machine more than it teaches you.

But the question I started with in the woods finally has its answer. It was never whether a machine touched the trail. The machine-built trail is real trail; the operator’s judgment is real judgment; some of you will ride the machined line and prefer it. The question is what happened at the rock. Did you stand there and argue — with a crew, a caddie, a duck, any second voice inside a stop you built and kept — or is the ground smooth right there, no memory attached, because the blade was wider than the rock and the question of your judgment never came up? Smooth doesn’t testify. Build the stops, or meet the trail already cut: at any speed, with any tool, that’s the whole difference between the two.

The Gate Was the Product sorted the creative world’s gatekeepers into two kinds: tollbooths charging rent on access, and forges producing something of real value. The real thing, I wrote, was earned judgment, “the formation that only comes from doing the thing badly for years until you could do it well.” Two paragraphs later I wrote that the cleanest example of taste is “the one that looks least like craft,” and then I pointed at Rick Rubin, who has said of himself that he has no technical ability and knows nothing about music.

I said, “…only comes from doing.” Then introduced a man who mostly didn’t.

(The original sentence reads usually now. I patched the word as soon as I saw the problem. This essay is the part a patch can’t reach.)

I hedged in the moment, too. Looks least like craft, I wrote. I’ve now leaned on Rubin in three separate essays, load-bearing each time, and never once put weight on him to see if he’d hold. I’m not a superfan and I’ve never met the man; his story just keeps being the cleanest specimen of how tangled craft and taste can get. This is that.

What he actually is

This is worth getting right, because I got it a little wrong the first three times and had to go back and patch it.

Rick Rubin is a legendary music producer who co-founded Def Jam Recordings with Russell Simmons, brother of Joseph “Run” Simmons of Run-DMC fame. Rubin played guitar for about five years. He started a band called Hose as a freshman at NYU in 1981, toured, shared bills with Hüsker Dü and the Meat Puppets and the Circle Jerks, and a Hose track was the first thing Def Jam ever released. The band ended in 1986. He calls his own playing rudimentary. And even as a producer he doesn’t run the board, and he’ll tell you so before you can ask.

So he’s not a man who arrived out of nowhere with an ear. He put records out and figured out what worked. Five years of that is not nothing, and I’d been quoting his modesty as though it were a biography.

But the thing I built on him still stands, because it was never really about whether he could play. In 1986 The Cult had finished an album, the successor to their 1985 breakout Love. The album was paid for, done, and they’d soured on it themselves. “Soup,” singer Ian Astbury called it: several hundred thousand dollars of polished post-goth gloss. They hired Rubin to remix one of the tracks. He heard past the finished object to the record underneath, and the potential he heard was nothing like the band’s own sound: stripped to the studs, closer to AC/DC and early Zeppelin than to anything they’d ever made. He asked if they’d be open to re-recording the entire album that way. He reportedly got them on board with a challenge: “Do you guys wanna make English pussy music, or do you want to rock?” He couldn’t have played a note of either version of that record, yet he knew which one was right, at a level the band itself couldn’t yet see. The re-cut became Electric, and it’s the record people remember.

That’s the move. That’s what I’ve been calling the forge.

Again

Strip the reverence off that method and it’s a guy in a room saying again until it lands. In the software world we call it iterating, and it was a thing long before AI entered the picture. You get the code barely working, then another pass to make it solid, then another to clean up the cruft. From inside the studio control booth the word is just again.

That’s the technique. It’s also, precisely, what I now do at an AI prompt on a Tuesday afternoon — read the output, feel the wrongness, say some version of again, and keep going until the thing in front of me matches the thing in my head. Same loop. Same posture. Same guy in the room with an opinion and in many cases a limited ability to execute it himself.

If the difference between those two situations were the quality of the ear, I’d be comfortable, because I’d lose that comparison cleanly and go home. The difference isn’t the ear. It’s what the again lands on.

Rubin’s lands on The Cult. On a band that had already gotten sick of its own record, which means they brought the dissatisfaction into the room with them. Years later it lands on Johnny Cash, sixty-one and written off by Nashville when Rubin sought him out backstage at a California club show in 1993. The pitch was almost nothing: sit in my living room with a guitar and sing everything you love. Out of those sessions came American Recordings and the five albums after it — the final act that won Cash Grammys, gave him “Hurt,” and put him in front of a generation that had never bought a country record. On those records it lands on the band behind him, guys who get bored and argue and misread a note in a direction nobody planned, and on Cash himself, who can turn around at some point and say that’s it, don’t touch it, and be believed. Rubin’s judgment was never a solo instrument. It was one half of an argument, and the other half had taste and standing and the ability to walk out.

Point the same again at something that agrees with every note and there’s nothing on the other end. The again is a mild form of conflict, a way of forcing another try to see what might shake out.

Here’s the small mechanical fact underneath it. Again has no stopping rule built in.

A person supplies one by refusing. There’s a take where the drummer puts his sticks down and just says he’s done. There’s a draft where the writer says this is the one, and means it, and won’t do another. That refusal isn’t defiance or obstruction. It’s information — the only information that ever tells you where the ceiling was.

A model will hand me the eleventh pass as agreeably as the first, and won’t tire of it. The twelfth too, and be just as perky going into the thirteenth. The loop ends when I decide the thing matches, or when I get tired, and being tired is not a quality signal, it’s a blood sugar reading.

There’s a claim sitting nearby that I believe is false, and it would be convenient for me: that you can’t judge done without having made. Rubin dents that argument. Pauline Kael breaks it outright: she reviewed films for The New Yorker for more than two decades, feared by half of Hollywood, and she never made a single film of her own. Her judgment was better than almost every director she wrote about. Making is not a prerequisite for taste. Whatever’s going on, that isn’t it.

But notice what Kael was never in a position to do. She could tell you the picture was bad. She was never standing in a room saying again to a director with the film still open. She tried the other side exactly once — Warren Beatty lured her to Paramount in 1979 to make pictures instead of judging them — and within a year she was back at her desk. Judging the finished thing and steering the unfinished one turn out to be different muscles, and the second is a craft of its own, with its own formation: it’s what good editors and producers and coaches spend their careers building. You can have an extraordinary amount of the first and almost none of the second.

So what builds the second one?

Forks

Not chops. Rubin proves you can steer a record you can’t play a note of, and Kael proves consumption isn’t sufficient — there are hardcore fans who have heard more music than Rubin has, and it buys them nothing operational. The superfan sees only surviving choices. Every finished record is the last one standing out of ten thousand invisible decisions, and the listener gets the survivor and never the forks in the road that led to it.

The survivor is on the machine. The forks stayed on the shelf.

What Rubin got, sitting in those rooms, was the fork and the outcome. He chose take four over take seven, and then he found out what happened next: the record shipped, or it didn’t, sold or died, aged well or curdled. He got to find out if take four really was better than take seven, judged by people with no stake in agreeing with him. Every one of those findings is a calibration event. That’s the supply line.

The steering muscle is built by exposure to the counterfactual, with consequences attached. Witnessing failure is a requirement.

Making things is just the cheapest and most common way to get near forks — you build the bad version first and then live with it for a while. It isn’t the only way. The studio is another. Editing is another. I wrote about a version of this in You Can’t Relic a Life without realizing what I had: George Jones was too deep in the bottle to get through “He Stopped Loving Her Today” — he kept drifting the lyric onto the melody of “Help Me Make It Through the Night” — and producer Billy Sherrill kept sending him back, across sessions that stretched the better part of a year, until they caught him at the right moment. Jones told Sherrill, “Nobody’ll buy that morbid son of a bitch.” It went to number one, won nearly everything a country song can win, and rescued his career. Somebody in that room was saying again. Somebody else was the reason it eventually stopped.

Big T

My friend Terry Lancaster — Big T, from the years we played hockey together — had the blog contract for a car dealership. Good gig, ran a while. Then the marketing director started sending him drafts and asking what he thought of them.

Terry’s first reaction, and I love this, was: I don’t remember writing these, but they sure sound like me. His second reaction was that the guy must have trained something on his archive.

Neither. The director had just kept re-prompting the machine: again, funnier. Again, more like the thing I already like. He ran the loop until the output landed where Terry had been standing for two years, and then he had it. Terry’s contract was cancelled about three weeks later.

Here’s the rub. The director was not a fool and he was not a villain. He recognized that Terry was funny — that’s real judgment, and a lot of people who hire writers don’t have it. He knew the target well enough to know when he’d hit it. So the guy had taste.

But recognizing that Terry was funny is judging a survivor. He’d read two years of finished posts but he’d never worked through the edits that actually got Terry to funny. He had never once watched one of Terry’s jokes die on the page and get replaced by a better one, because that happened in a room he wasn’t in, at a desk he’d never sat at. Recognition without calibration. And I can’t tell you he steered badly. I never read what the loop produced, and maybe his ear was enough. What I know is that funny was something he wanted to buy, Terry was the price until a cheaper supplier turned up, and after that, draft by draft, the only judge of whether the jokes still landed was the same ear running the prompts.

I can’t draw a clean line from him to me and my AI prompt. He had a good ear and no forks. I have a good ear and forks that are getting cheaper by the month.

Four weeks ago, in The Intimacy Economy, I put myself in that marketing director’s chair. I said I couldn’t have done bedtime stories for my Sojourn app justice as a fiction writer, that my contribution was the ear, and that I liked to think I’d brought a much smaller version of the same thing to short-form fiction.

I wrote that approvingly. It read to me at the time like an honest, modest accounting of a real division of labor.

Run it through what I just built. I have the recognition. But I do not have a counterparty, no other half of the argument. Sojourn’s model does not sour on a draft finished yesterday. It does not come back with the thing nobody asked for, or the take that bores everyone, or the one you can’t put down. It has never once said that’s it, don’t touch it, and it never will, because there is no it for it to have.

I do throw things away, often. I retired a feature I liked. I killed a flip I’d been proud of for a week. I’ve had stories that just never worked so I parked them, possibly indefinitely. But every one of those refusals came from me, which is exactly the part a counterparty gives you for free — and the part you can’t audit, because the same judgment that made the call is the only judgment available to review it.

I also have something the loop can’t manufacture: six million words of shipped work behind the ear, most of it edited by professionals, some of it wrong in ways I found out about. That’s forks with outcomes. That may be the entire difference between me and someone who has only ever recognized good work in other people. It’s also, if The Forge Was a Side Effect is right, a supply line that’s closing.

The model hands me the counterfactual for free. Regenerate, and there are variants without end — more forks in an afternoon than Rubin saw in a decade of studios.

So by my own mechanism, this should be building taste faster than the studio ever did. And I actually think it might be.

Except the variants resolve against nothing. Rubin’s forks weren’t audited one by one either; the world only ever heard the survivor. But the room argued over every take while it was still open, and there were few enough of them that the record’s verdict reached back and touched each choice inside it. Mine are now settled by me, in the moment, according to what I already think, and then the alternatives evaporate and I never learn whether I was right. Exposure to the counterfactual with the calibration stripped out. A thousand doors, all of them opening onto my own preference.

The world does still answer me, at the macro level. Essays go out every week and readers write back. Apps ship and sell, or ship and stall. Whole careers of feedback keep arriving. What never gets settled is the take: this variant over that one, forty times a day, judged in private by the only ear in the room. The world audits my survivors. Nobody audits my forks; there are too many now for any verdict to reach back that far.

I don’t know how much that matters. It might be that thirty years of shipping is enough ballast to steer on for a long time. It might be that I’m five years into eroding something I can’t feel eroding, which is the characteristic property of the things that erode.

Rubin can stop. He says that’s the one and a band puts their instruments down and the record exists.

I say it to a room with nobody in it.

That last part is the part worth explaining, because on its face it makes no sense. I had a thing I was good at. Why pay money to be bad at a new one, in public, heckled by children? Don’t know if you know this but kids can be brutal.

The tax

The easy answer is humility. True as far as it goes, and also the boring answer, the one that makes me sound kinda noble. What took me longer to see: mastery levies a tax. When you’re new at something, the learning is steep and total: every session you are awful. But you’re also visibly, measurably less of an idiot than the one before, and that gradient is about the best feeling a human nervous system makes. Then you get good, and the gradient flattens. Mastery is mostly maintenance and small increments, a long plateau where you defend a level you already reached and occasionally nudge it higher at great effort. That’s not a complaint; the plateau is where real work gets done, and most things worth doing live up there. But the freshness is gone, and for a certain kind of person — the kind who is, underneath everything, in it for the progression — the flat part quietly costs something the achievement never pays back.

Starting over somewhere new refunds it. You buy back the steep part of the curve. You get to be bad again, which is the only way to be a fast learner again, because fast learning and being bad are the same condition seen from two sides. Seems weird holding both at once but that’s how it works.

It isn’t free, and that’s important to know. The price is an ego hit, and the size of the hit depends on who’s watching. I took up beekeeping the same decade — another beginner’s slope, an entire craft I had to start from the vocabulary up — and that one barely cost me anything on the ego front, because beekeeping happens in the back corner of a yard with nobody around but the bees, who have strong opinions but don’t keep score or talk trash. Bee stings hurt, but they spare the ego. Hockey was the opposite: a public arena, actual spectators, a version of myself that had been competent for decades getting dismantled by ruthless middle-schoolers where people could see. Same refund, very different price. Most people, offered the steep part of the curve at the cost of looking foolish in front of others, quietly decline and stay on the plateau. I fully understand that choice. I just think they’re leaving the best part on the table.

Choosing to be a beginner is a luxury. It takes time, money, and a life stable enough that you can afford to be bad at something on purpose. Hardship that forces you back to zero — a layoff, an injury that ends the thing you were good at, a life that detonates and has to be rebuilt from nothing — is not this, and anyone living it is facing a very different challenge. The whole value here is in the choosing. This is about volunteering for the bottom of a curve, not being thrown down it against your will.

The easy dragon and the real one

But hockey, for all the bruises, was an easy beginning, and it took me a while to admit why. Nothing was at stake. I was already an athlete; hockey was a new facet of an old skill set, and being bad at it threatened nothing about who I thought I was. It takes quite a while to figure out a wrist shot, even longer to develop the finesse of a proper slap shot, but those were mechanics I could grasp. It was the steep curve with the safety on because somewhere innately I knew it was just a matter of time until competence arrived.

There’s another kind of beginning with less certainty, and it’s the one this essay is actually about. It’s the beginning you’ve avoided — the thing you’ve spent a life quietly arranging not to find out whether you can do, because being bad at it would cost more than pride. Not a new facet. A dragon.

Mine is music. I was born and raised in Nashville — Music City, the place where music is the literal industry and the background radiation both — and I have been intimidated by it my entire life. Not uninterested. Intimidated. There’s a specific weight to being a non-musician from there, a sense that you, of all people, should already know this, that the not-knowing is a small ongoing failure you’ve trained yourself to walk past. Even worse, I had no excuse. I grew up around it, my late grandmother had a DIY music studio in her house for as long as I can remember. I sat on her warm tube amps as a child, listened to endless demo songs on the phone with her mostly against my will, and still, as an adult, couldn’t tell a Stratocaster from a Telecaster. Yeah, embarrassing. Turns out it’s very easy to never start the thing you’re most afraid of being bad at; you simply keep finding that it isn’t the right time, for decades, and call that normal.

I did make music, once. In the mid-eighties I had a brief run as a teenage rapper and DJ, and I want to be precise about the production values: a crappy cassette deck, a Radio Shack mixer, a toy Casio keyboard, a garbage turntable, a Dr. Rhythm drum machine, and not one unit of actual music knowledge. I lifted hooks off other people’s records, thumbed primitive melodies on the Casio, and mixed overdubbed live tracks on the cassette recorder until the overdub quality said I was done, usually three passes. It was, to the modern ear and in all likelihood every ear, objectively bad. The dragon would have pitied me. I made it anyway, and I loved making it.

The rig: a cassette deck, a Radio Shack mixer, a toy Casio, and not one unit of music knowledge.

Which sounds like the opposite of a man who avoided music his whole life, and it’s actually the most careful avoidance in the whole story. Early hip hop was the one door into music a non-musician could walk through without ever meeting the dragon — no theory, no instrument, no telling a Strat from a Tele, no need to be any good. You didn’t have to understand a chord progression or practice a scale, you could just program a drum track, steal a sample from an old record, and you’re in business. I got to create, I got to scratch the itch, I even carried my grandmother’s thing forward in my own bent and lo-fi way, all without once standing in the room with the fear. It wasn’t facing the dragon. It was finding the one hallway in the castle the dragon didn’t guard. That clunky kid’s studio was the first time I reached for a tool to make something I hadn’t earned the craft to make. The taste to do it well took decades longer; the reaching never changed.

What changed isn’t a story about destiny. My grandmother was a very serious aspiring country musician, the Nashville kind, more aspiration than career. She was obsessed with it her entire life, and actually paid with her own money to have records pressed, even had some of her songs recorded by others. She died years ago. She had a shiny red Fender guitar, a real one, and after she died it did what many heirlooms do: it went into a closet and stayed there, dusty, unremarkable to everyone around it. It surfaced when my parents moved and did the triage a move forces. They didn’t think much of it. I did. I remembered it from childhood, sitting in her studio. And I remembered it in the artist photo she paid to have shot in Nashville’s Centennial Park in the 1970s, her in that purple dress. I hadn’t known it still existed.

Nobody handed it to me as a torch. There was no deathbed scene, no note, no chosen heir. Only the recognition in recent years that she and I are more alike than I ever realized growing up: the relentless obsession with whatever you’re doing, driving everyone around you a little nutty with your singular focus. We definitely share that. But she died never knowing I had one iota of music interest. Her guitar was a thing nobody else especially valued that I happened to recognize, and the meaning it carries is meaning I decided to give it. That distinction matters to me, because the sappy version — she passed it down so I could carry the music forward — is false, and what actually happened is better: a forgotten object I chose to treat as a doorway. The nudge didn’t come from beyond. I supplied the nudge. The guitar just sat there being exactly the right guitar.

My grandmother with the same guitar, sometime in the 1970s.

So I started learning, on her guitar, and it is a completely different experience from hockey, because the ego doesn’t get bruised in the fun way; it gets threatened in an older, deeper way. Every clumsy chord change is also a small confirmation of the thing I’d feared and dodged: that I’m starting embarrassingly late, that I might find the floor of my own ability and discover it’s lower than I’d hoped. Hockey couldn’t touch any of that, because I never believed I was supposed to be a hockey player, and never thought for a second I couldn’t be one. The guitar carries real baggage, because somewhere underneath I’ve always believed I was supposed to be able to do this — and avoiding it was easier than testing it.

And — for whatever it’s worth, and it might be worth a great deal — I think I can. I have a stubborn, unproven conviction that there’s a capacity in there I never spent, that the intimidation was always bigger than the actual obstacle. As of this writing that capacity remains exactly that: unspent. I am not good. I’m a beginner with a beautiful red guitar and a lot of road to cover, and the honest report is that the dragon is very much alive and I am, at last, in the room with it. Not a midlife crisis, for the record, though it is, I’ll admit, a very sexy red guitar.

A fun wrinkle emerged once I’d spent some time with my grandmother’s guitar, which by now has earned a name: Berenice, after her middle name. I realized the guitar might be a path rather than a destination, and that the path ran backward, toward that goofy teenager scratching records. What I was doing back in the mid-eighties with the drum machine and the Casio keyboard was assembling hooks and rhythms. It dawned on me that there’s a guitar for that, a bass guitar, and that it might actually be the instrument for me. So I bought a bass and started learning it alongside the red Fender. I’ll continue to learn both but I suspect I’m a bass guy. We’ll see. For now I’m just bad at two instruments instead of one.

The borrowed instrument

That backward look goes further than the bass. The clunky kid’s studio and the AI tools I build with now are the same instinct forty years apart: find a way to make the thing without first becoming the person with the craft to make it. I’ve spent a whole run of these essays defending that instinct, sincerely, because for most of what I make it’s the right call. Not being an illustrator or graphic artist, I lean on AI to help create images I’d probably otherwise just not have. I have tons of story ideas and a legit feel for what works, but I use AI to scaffold complete stories where I don’t have the chops to write fiction from scratch. There are even times when I have the craft and reach for the machine anyway, to take it somewhere I couldn’t get on my own: I’m a decent critical thinker, but arguing ideas with AI pressure-tests them in a way I never could alone.

The guitar is the one place I’m refusing my own argument on purpose, and I nearly gave myself credit for turning down something nobody offered. Nothing on the market makes your hands play guitar. But there is a machine that makes music, and I could have a song by dinner: type a description, name a genre, get back three minutes with a hook and a bridge, a wicked guitar solo, and a clean fade-out, better than anything I made in 1986 and better than anything I’ll play this year, or next. That option is real and cheap and sitting right there. It’s useless to me.

The reason is the whole essay. With the images and the stories I wanted the thing to exist, the craft was in the way, so I went around it to get the output, and I’ll do it again tomorrow. With the guitar I don’t want a song. I want to be someone who can play a song, and nothing generates that. My absence of craft isn’t the obstacle here; it’s the entire point. Some difficulty is the work. Not all friction is formative. Telling those two apart is the only thing I’ve really been writing about this whole time.

Which means the choice was never whether to use the tool. It was whether to keep wanting the output, and wanting the output is exactly how I dodged this in 1986 and every year since. The hallway I found in the castle is still there. The machine has made it wider than it has ever been. The guitar is me finally taking my own distinction seriously enough to let it cost me.

There’s a word for something you use and don’t own: rent. It’s the right word for all of it, and that’s the part I’d left sitting under the argument all along. Route around a craft and you don’t acquire the ability, you rent it: output by the month, for exactly as long as the terms hold. Most of what I build now, I build on a borrowed instrument, rented from a handful of companies that can raise the price or change those terms whenever they like. But learn a craft and it’s yours, essentially forever. Nobody can reprice a G chord once your hands know it, any more than they could reprice a wrist shot.

The rented tool is only the near end of it. The far end is reach. Everything I make still has to find somebody or it may as well not exist, and that road was never mine either. For thirty years that was a fine trade: whoever hired me owned the road, I owned the making, and the making was the part they couldn’t do without me. The things that were mine start to finish, the ones I’d win or lose on alone, I could never get in front of anybody at all. People still pay me for the making, and every year it gets harder to say why they should. Which leaves the road as the only part worth owning, and the road is the one I never learned to build. But I’m working on it.

Nobody stands at either end of the guitar. Nobody rents me the instrument, and nobody has to find it or buy it for it to do its whole job. It’s the back corner of the yard again, except this time the only one keeping score is me, and I’m harsher than the ten-year-olds ever were. It figures that the one instrument I own free and clear is the one I can barely play.

Becoming, unfinished

That’s where I have to leave it. The hockey story has an ending: I tried it, I loved it, I can tell you how it turned out. The guitar story may never get a clean one. I don’t get to describe the slain dragon’s body. I only get to tell you I finally walked in, and that it was still standing when I did.

There’s a word I keep coming back to for this whole disposition — becoming — and the guitar is where it stops being a tidy idea and turns into an unresolved thing in my actual hands. Two ways in, and they don’t cost the same.

The first is the refund. It’s easy to claim, because it’s already owed to you: the tax you paid getting good at something is sitting right there, and most Saturdays you can go collect it by being a happy idiot at something new.

The second is the dragon. The dragon refunds nothing: no tax coming back, no gradient promised, and nobody’s handing you this one. You go because the avoiding was costing more than the failing ever could.

I’m sure of the first. I’m in the middle of finding out about the second.

Some time later a foundation reached out. They were doing serious work in the skate world, the unglamorous, concrete-and-permits kind of work, helping to get skateparks built in towns that had nowhere for kids to go, and they wanted to operate under that exact name. And here was a clean domain already sitting on it. The foundation was Tony Hawk’s. They really just wanted the name, and we did a Zoom call with them where they explained their need: they had a new name for their org, we had the domain that matched it. My friend and I talked it over before the call, and decided to give them both the domain and the app, for free. There was a strange little pause on the call when I got to the free part, and then Tony said, “…well, these things never go like this.” You give a thing to people who are going to do good work with it, and then you go help them do the work.

We could have tried to sell it. People do; a clean name with a built app on it has a price, and the people on that call would have understood a number. But selling it would have turned a gift into a transaction, and a transaction ends at the sale. A gift starts something. I didn’t reason it out in those terms at the time. I just knew which one felt right, the way you watch a friend trying to land a trick on the fourteenth try, and you can see the one thing they’ve adjusted and you know this one is the make. It’s a thing your body has decided a half-second ahead of your mouth. We honestly saw it as two separate missions aligning, each strengthening the other.

For four years after that, the gift never came up. Not once, from either side. My friend and I wrote code for them, improving the app. We joined their board. We did the work. Nobody circled back to the domain, because in the world we come from, circling back would have been faintly off, like stopping someone mid-session to thank them for letting you drop in. It wasn’t a debt. There was no ledger. The whole point of the kind of giving I’d grown up around is that the giving and the keeping-track-of-giving are two different activities, and the second one ruins the first.

The ramp

On my fifty-fourth birthday, in August of 2024, I had the supreme luxury of skating Tony’s private vert ramp with a few friends. Two from home, one from The Skatepark Project who I had gotten to know due to our involvement. There was no expectation that Tony himself would skate with us. It was his ramp, we were grateful just to be standing on it, and at fifty-four, being grateful to be skating a vert ramp at all is a sound posture. That was already the whole gift as far as I was concerned.

What I didn’t know was that my friends suspected the day might turn into something, and had quietly arranged for Grant Brittain to be there to shoot it. If you skate, that name lands. If you don’t, it’s enough to know that Brittain is the photographer who took a great many of the images that taught the rest of us what skateboarding looks like, the ones burned into a generation’s memory from Transworld and Thrasher magazines, to name a few. My friends didn’t tell me he was coming. Telling me would have wrecked the exact thing they were hoping for, because the moment you know a camera is waiting on a moment, the moment stops being itself. They also, I’d learn later, picked him on purpose. If the day was going to be documented at all, it should be documented right.

There’s another reason skaters don’t advertise a photographer, even the legendary Grant Brittain: it adds pressure. A lot of pressure. We jokingly refer to a camera as a “bail gun” because the moment it comes out, you tend to tighten up, try a bit too hard, and start bailing. Not that skaters don’t take pictures and video, we do, a lot. But it’s ideally a casual thing to help keep that bail gun business to a minimum. It’s even common at times to try to sneak and take a photo or shoot a video without the skater seeing you do it. But with Grant that day, it became clear that he was there just to shoot us, and that became a uniquely challenging but fun pressure to work through.

So we do a pretty heavy session in unseasonably tough San Diego heat, and Grant documents it in a way only he can. What we didn’t know is that Tony was in his office, which has a window that looks out onto the ramp, half-keeping tabs on our session the way you keep an eye on people enjoying a thing you made. Then he came out. Not to skate, at first. He’d noticed me eyeing the gap.

If you aren’t familiar with his ramp setup, it’s a 13.5 foot tall portable vert ramp, 70+ feet wide, and it dates back to when it traveled as part of his Boom-Boom Huck Jam action sports show. It had an 8’ wide opening engineered into it for a very specific purpose: to allow entry and exit from outside the ramp during those live shows, a need no other ramp ever had. The problem with the gap is that it goes from the vert to the flat, so there’s no cheating to get over it, and if you miss at all you’re dropping the full height of the ramp to concrete. It’s not so much that 8’ is unthinkable as a gap, locals at the ramp go over it casually, but it’s a gnarly feature on an already big and fast ramp, in all likelihood the best vert ramp in existence.

I knew I could do the gap. I had also never once actually done it, and those are very different kinds of knowing, separated by the precise distance between belief and the hard edge of that gap. Tony came out to give me pointers and a little push. Apparently he had seen enough to agree with my belief, and maybe felt like his job was to help close that belief/reality loop for me. We stood there and looked at the gap together for a few minutes, the way skaters look at a thing, mostly silent, running it in our heads. Then he said, “lemme grab my pads and show you.”

The make

So he skated with us a while, pretty chill, and we talked the line, and settled on a backside ollie as my best shot at it. If you don’t know skate tricks, a backside ollie is an air where you turn with your back to the ramp and you don’t grab the board. The not-grabbing part is technically harder than a grab air but it has the benefit of being a good air to carve and travel a distance with, and also a little easier to kick the board away and get out of trouble. First try, I bailed. Not close, not even in the conversation, just climbed back up the ramp with my heart going and told him I might need a different trick. “Nah,” he said. “You got it.” Dammit, Tony.

Second try was closer. I got over the gap, this time to a cleaner knee slide on the other side, and most of the fear drained out of it the way fear does after a thing has been survived. In skateboarding, learning the bail very often precedes learning the trick; it establishes your “safe” escape so that you can then lean in to pressing the actual thing. But the stress did not go anywhere, because the stress wasn’t really about the gap anymore. The stress was that Tony Hawk was standing there waiting on me, patient and unbothered, and that the trick needed a particular setup off the gap-facing wall to find the right line into it, which meant every attempt was a small public production with the most qualified audience on earth watching me assemble it. I’ve been in lots of skateboard contests over the years, so performance anxiety on the ramp isn’t a new thing. But there’s a specific flavor of pressure to being coached, gently, by the person who is better at the thing than anyone who has ever lived, while you fail at it in front of him.

Third try, I somehow landed it. The photo at the top of this is that, the gap cleared, a fifty-four-year-old’s very good birthday happening in real time.

Here’s my favorite part of all this, more than the landing, maybe even more than the serendipity of the situation. Nobody filmed it. My friends from home didn’t shoot video, didn’t pull out their phones, didn’t think to. My Skatepark Project friend didn’t even watch, he has a personal rule that he doesn’t like to watch gap first tries, so he was hiding in the office. There is no clip of it, no fifteen seconds being trimmed and captioned for an audience that wasn’t there. The only record of the entire afternoon is Brittain’s stills, and those exist only because a friend had quietly made sure a real photographer would be present if the day turned out to deserve one. The afternoon was not content. It was a day, lived, by people who still know the difference, and who treated the difference as obvious rather than as a stance.

Twenty years ago nobody would have filmed it either, and it would have meant nothing at all. Nobody carried a camera. The not-filming wasn’t a decision, it was just Thursday. What makes that afternoon legible now is that every person standing on that deck had a device in their pocket that would have captured the whole thing, in absurd quality, for nothing, and not one of them reached for it. The capture got free. The abstention got expensive. Most of us are still running the old etiquette on the new economics and haven’t opened the bill.

Doubles

I rode out the adrenaline of the gap, the giant slayed, that blessed endorphin bleed when the stress melts away because the thing is done. I was truly spent. I neglected to mention that Grant had treated me like a pro skater. At times when I squeaked out a make he’d hold up three fingers from the far deck. At first I was confused, then it dawned on me: he wants me to do three more because he’s playing with different shots. Dude, I barely made the one, and now you want three!? It’s mostly a blur now so I don’t recall how many of his shots he truly got, but I was smashed from the effort, the gap make the earned day-ender. That’s when Tony said, “you wanna do doubles?”

Well. Yeah.

The pressure dial was back at 11.

“What’s your best air?” he asked.

“I am NOT going over you,” I said. “I’m under guy on this one.”

He laughed, and said OK.

Doubles with Tony. Photo by Grant Brittain.

I was a nervous wreck, and he put me at ease in the way that good skaters put you at ease, which is mostly by being relaxed enough that their confidence somehow by osmosis transfers just a little to you. He’d adjust, he said, if I did anything crazy, which from him was both a reassurance and a quietly hilarious thing to be told. We made it several times. Brittain caught one of them. We thanked each other, got a group photo, and that was my surreal, entirely undeserved, perfectly earned fifty-fourth birthday at Tony’s ramp.

Two gifts

There were two gifts in that afternoon, and they were not the same kind, and the difference is the whole reason I’m telling it.

The gap was an earned gift. Tony came out unprompted, put on his pads, and coached a fellow geriatric skateboarder through a thing I could do in theory but had never actually pulled off, and then stood there through the bails saying you got it until it was true. That kind of thing, given with no audience and no plan to post it, is a specific cultural object. It only exists inside a small world where everyone has been on both ends of it, taught and been taught, held the camera and stood in front of it. You can’t manufacture it. You can only be inside the conditions where it happens.

Recently I stood on a strange vert ramp in Dallas in 101 degree heat and willed with every part of my being for a young girl I’d just met an hour earlier to summon the courage to stomp that first drop-in on vert. It took her the entire session, numerous agonizing steps up to the coping, leaning out over the edge, her body refusing. But eventually, through no assistance from me or anybody else, just a bunch of good vibes, she did it. She earned it all by herself. We all celebrated as if her win was ours, because in some small way it was.

Back to Tony, the doubles was a given gift, a different category than earned. It was spontaneous and unowed, just a thing that surfaced between two people who had spent the last hour skating the same 13.5 feet of Skatelite and steel. I don’t think it was a thank-you. It certainly wasn’t planned as one.

And the four years between the domain and that day is the frame around both. This is the part I’d ask you to sit with, if you sit with any of it. Tony thanked us on that Zoom call, and that was that. The birthday session at his ramp was something else. It was Tony being exactly who he is, doing the thing he does for skaters, on a day when I happened to be the skater standing in front of him looking at a gap with trepidation. It was the real thank-you, the kind that arrives on its own schedule or it isn’t one. It can’t be requested and it can’t be repaid, which is precisely what makes it worth more than the favor that occasioned it.

The principle worth stealing

I spent The Intimacy Economy arguing that there is a way of making and sharing things that runs on small communities and kept artifacts instead of audiences and scoreboards, and that it is nearly invisible to the metrics everyone watches. I had to argue it abstractly there, because the configuration is hard to point at. Here is me pointing at it. Skateboarding has been running on it for fifty years without a strategy deck, without a word for it, without ever once suspecting it needed one.

Look at everything that afternoon quietly refused. No posting plan. No filming the easy capture, the gap, which would have been the obvious clip. No scorekeeping. The gift economy of skating still works because everybody in it knows everybody has been on both ends, and nobody keeps a ledger of who owes whom. You cannot run a streak on people like that.

Fifty years is the part that should worry you, because for most of those fifty the discipline was free. You didn’t post the session because there was no posting. You didn’t run a streak because there was no streak available to run. The ethic and the equipment agreed, and nobody in that whole culture ever had to choose between them. Then the equipment switched sides. Every restraint that used to be structural became voluntary, somewhere around fifteen years ago, and voluntary restraint has a considerably worse track record than the structural kind. What I watched on that ramp was a fifty-year-old ethic being asked to hold on conviction alone, for the first time, with the cost of defecting sitting at zero and a camera in every pocket. It held. I don’t know whether that’s the most encouraging thing I’ve seen in a decade or a lagging indicator.

The friends-and-Brittain piece of the story is the one deliberate act in the whole afternoon, and it’s the part actually worth stealing. They did not film the day. But they did, on faith, arrange for someone great to be there in case the universe answered. They let the day be the day, and trusted that if it produced something worth keeping, there would be a way to keep it. That is the whole principle, and it’s the one I keep trying to build into everything I make now: let intimacy produce artifacts, but never let the producing of artifacts drive the intimacy. The afternoon did not exist in order to be photographed. The photographs exist because the afternoon was worth it. Reverse those two and you get the entire modern internet, and a worse day.

Sojourn and the other things I’m building are, underneath all the engineering, attempts to manufacture that condition on purpose, which is far harder than stumbling into it on a vert ramp on your birthday. You can’t decree a gift economy. You can only build the small, unscored, patient conditions where one might decide to show up. But the model is real, it predates all of this by a long way, and the proof of it is a four-year-late thank-you that never announced itself as one, from a guy who came outside because he saw an old friend of the sport looking at a gap and figured he’d help him over it.

Photos: Grant Brittain.

That’s a problem. Not the anger — a fair amount of it is earned, and I’ll get to which parts. It’s the waste. A real grievance walks in holding a smaller, weaker set of grievances by the hand, and refuses to let go of them, and they all go down together. I keep wanting to grab the writer by the collar: you had me. You had the strongest version of this. Why did you spend it on that?

The freight worth setting down

So let me do the thing those essays won’t, which is set the freight down and sort it: what’s worth hammering, and what only feels that way. Some of what they’re carrying is real, and it deserves better than to be waved off because I happen to use the tool they hate.

The energy is real. Data centers drew about 4.4 percent of the country’s electricity in 2023, and the Department of Energy’s own national lab projects that could climb to as much as 12 percent by 2028, nearly all of the new demand driven by AI. The water is real, and it bites hardest where it’s most concrete: in Newton County, Georgia, families next to a Meta data center say their wells ran dry and their water ran brown after the build went in, while the county’s water rates were set to climb by a third. The land is real — the substations and the cooling and the truck traffic land in somebody’s actual town, usually not the town where the people approving it live. The livelihoods are real, and I’ve spent whole essays on what this is doing to the working middle of the creative economy. And the ownership question is the one I’d lose sleep over. A few companies are assembling control of something that behaves less like a product than like a utility for thinking, on the stated theory that they’ll own the intelligence and lease it back to the rest of us by the month. If that doesn’t unsettle you, you haven’t watched what concentrated infrastructure does to the people downstream of it.

The bill arrives in somebody's kitchen. Rarely the approver's.

I use these tools every day. I tell you so every time. None of that buys the companies a pass on a single line above. Two things hold at once, and the whole point of what follows is that they can: the tool in my hands is a real multiplier, and the structure standing behind it is worth being genuinely alarmed about. The tension stays. I’d just rather name it than pretend it isn’t there.

What I can’t answer from here

Here’s where I part company with the furious version of this essay.

A friend of mine has spent the better part of two years in Louisiana helping build one of these things — a Meta complex out in Richland Parish, the campus they call Hyperion — reportedly past fifty billion dollars and five gigawatts at full build, which would make it the largest data center Meta has ever put up and one of the largest anywhere, dropped into one of the poorest stretches of the country. He’s still down there. The part that won’t flatten into a slogan is this: the place getting the substations and the cooling towers is also getting the paychecks, the road work, the first real economic weather to move through there in a generation. Is that worth what it asks of the aquifer and the grid? I truly don’t know. I’ve thought about it a fair amount and I’m no closer, and I’ve started to wonder whether I have any business weighing in at all.

What I do know is that I can’t answer it from where I’m standing. I live in a comfortable suburb of Nashville. The data center isn’t coming to my neighborhood, and the jobs aren’t either. From that seat it’s very easy, and very cheap, to tell rural Louisiana they’re cooking the planet — to oppose the whole thing in the abstract, where opposing it costs me nothing a family down there actually needed. The people carrying both ends of that trade are mostly quiet about it. The loudest opposition tends to come from right about where I’m sitting: far from the cost, far from the benefit, close to the keyboard.

That’s the tell, and it’s a tell about the argument, not the arguer. When the case against AI leans on its footprint, it’s reaching for the weakest weld it owns. Footprint arguments prove too much: the same logic indicts four hours of streamed video, a working phone tossed for a newer one built in a plant with nets under the windows, a week of clothes designed to survive three washes. Aim it honestly and it convicts almost everything we do, which is exactly why it can’t single out the data center — and why the footprint was never the part of this worth fighting about. The ownership question survives that test. The water bill mostly doesn’t.

The largest levers over a real footprint are the ones that never make it into the argument, because naming them costs the person naming them — the size of a family, the fact of a flight, eating meat, the house itself. We tally the data center’s water and skip the items an order of magnitude bigger, because the big ones sit too close to who we are.

The machinery that does the real concentrating. It doesn't photograph well.

AI is simply an easy thing to be against: new, branded, with two or three billionaires you can already picture, who do themselves no favors by playing the villain so convincingly. The machinery that does the real concentrating is duller and older and closer to home — a tax code that treats a wage as something to punish and a capital gain as something to shelter. Nobody storms a comment section over stepped-up basis. It doesn’t photograph well. So the anger, genuine and earned, goes hunting for a face, and this industry has handed it a very photogenic one with an annoying hum.

Wanting the furious writer to win

I want the furious writer to win. That’s the strange part. I tell friends this constantly: the surest way to lose an argument is to overstate it. Overstatement is how you hand a strong case to the other side — and on the thing that matters most, who ends up holding a private lien on human cognition and what it costs the people next to the machinery, we’re after the same outcome. Which is why the rage reads to me like a gift to that other side. An argument that handcuffs one real grievance to five flimsy ones doesn’t frighten anyone; it lets the people on the receiving end knock down the flimsy five in public and pocket the real one while nobody’s counting. Every “to hell with all of them” gives the strongest objection worse company, and makes it that much easier to wave off. The angriest critics of this industry keep turning out to be among its most useful friends.

So I hold both, and I’ve quit being sheepish about the shape of it. I use these tools, I tell you when I do, and I think a few companies quietly acquiring the means of thought is one of the most serious things happening in our lifetimes. Those facts sit together fine. The war people imagine between them is the same misfiling I keep tripping over everywhere in this argument: the percentage that measured nothing, the tell that would flag Cicero as a machine, the influence no court could fence. Sort the pile and most of the contradiction turns out to be things left on the wrong shelf.

And the sharpest version of holding both is mine to own. I’ve spent this whole run of essays telling people to move to higher ground — let the machine do the craft, keep the judgment for yourself. That advice already carried one cost I had to answer for; this is the other. It also quietly assumes the machine stays available on terms you can live with. For now, it’s rented ground. The same few companies I’m uneasy about are the landlords of the exact floor we’re all standing on, and the safest move I know how to recommend depends on people I wouldn’t hand my keys to.

What I can’t tell you is whether the fight that matters is winnable from here. It doesn’t live at the keyboard, and it surely doesn’t live in boycotting the one writer who cops to using the thing. It lives in water rights and zoning boards and a tax code nobody storms a comment section about. My friend is still down there in Louisiana. The data center keeps going up, in a parish that needed the work and will still be there when the bill comes, and I honestly don’t know if that was a good trade. I’d sooner be straight about not knowing than certain about the wrong thing.

That should bother you, especially if you’ve read your way here through my other essays. The earlier essays all pointed the same way: stand on your taste when the machine comes for your work. Stand proud, you own the judgment the model can’t make. Relocate there. I believe it. I’ve bet my creative existence on it.

But I’ve never been straight with you about where taste comes from. And it’s kinda important, because the honest answer opens three cracks in the floor I’ve been quietly walking us around, and the last one is mine.

Sawdust

Taste came from the forge. The reps: the millions of words written badly, then less badly, the only known process for converting effort into judgment. I made that case about taste and the forge already in The Gate Was the Product. The thing is, I never ran the forge to build taste. That’s the part I kept walking past. Nobody does. I ran it because rent was due and the books had deadlines. I was busy building furniture. The taste was the sawdust, seemingly worthless at the time, the stuff that piled up on the floor while I made the thing I was actually being paid for. I didn’t even know it was valuable. I found that out decades later, when the furniture got old and rickety and ended up in flea markets, and the sawdust turned out to be the one thing in the shop the machine couldn’t manufacture.

Nobody set out to make this. It's what the work left on the floor.

That’s a weird admission. The most valuable thing I own was a byproduct of work I’d now refuse to do. The thing I spent all those years sweeping toward a dustpan is the only part that held its value. Which means when I tell a twenty-two-year-old to do the reps, find your taste, I am prescribing a forge I would no longer light for myself. It’s the cheapest kind of advice there is: free to give, expensive only to the person who takes it. Nobody runs a forge for the sawdust. I didn’t. None of us should pretend the kid will.

The forge wore a disguise

So where does the next taste come from? Before I can get there I have to back up, because I’ve let you believe a couple of things that aren’t quite true: that something as tidy as a deadline ran the forge, and that the forge was the writing.

What ran it was actually fear. I left a salaried job building software to freelance as a tech author, which meant trading a paycheck for the realization, renewed at the completion of every single book, that there might not be another one. I was good at the work and never once trusted it to continue. So I worked roughly twice the hours a sane person would, partly because I’d gladly do that to own my own clock, but mostly because terror is an extraordinary motivator and I spent fifteen years quietly terrified between contracts.

I never turned down a project. Seriously. Saying no to one felt like betting against the whole thing, so I said yes to all of it — overlapping books, articles, side gigs, whatever came — and from the outside that read as arrival. Name on a spine in a real bookstore; he’s made it. Inside it was a semi-desperate guy rowing hard to keep the boat from going under, taking the paycheck work to keep alive the chance of the work he actually wanted. Not even joking, I still occasionally have a nightmare and wake up freaked out that I’m chapters behind on a book deadline. What complicates any clean story I could tell about it: I loved it anyway. Both stayed true for years — the craft was a genuine pleasure and the grind was survival. The terror and the joy ran on the same engine.

The point about taste is that the bigger half of those hours wasn’t writing. For every book I wrote, I read ten — competitors, predecessors, piles of documentation, the people who’d already done the thing better than I was about to — because you cannot write six million words on a target that moves every eighteen months without reading several times that just to keep up with it.

And that ten to one reading ratio, I’m lowballing. If taste is caught more than taught, acquired from whatever work you surround yourself with — the case I already made about my own tells — then the reading was the real forge. More precisely, the sorting inside it. A thousand small verdicts about what to borrow and what to leave, what was load-bearing and what was padding somebody added to hit a page count; none of them recorded, most settled months later when the thing I’d built on them either held or didn’t. The finest point on that sorting is knowing when to cut. I killed 550 words out of this essay a few days before publishing it, then put a different 220 back. The writing was the part with a deadline stapled to it. The reading was what gave it its shape. Fear was what made me do years of both.

Which would be the escape hatch, if you wanted one. Reading builds the taste, the machine doesn’t touch reading, so relax and read on. I wish. I didn’t read those ten books for love; I read them because the eleventh one I owed a publisher wouldn’t write itself, and because the rent was a recurring threat. Take away the fear and the job it rode in on, and almost nobody reads nine dense manuals for the pleasure of the tenth.

And there’s a worse problem waiting for the reading itself. A forge made of reading is only as hot as what you read, and I happened to steep in human work since there was no other kind at the time: uneven, argued-over, copyedited, occasionally great, all of it written by people with something to defend. Taste needs a corpus worth catching it from. We’re diluting the source with the machine’s house style now, trained on the pile of us and settling toward the average. The canon hasn’t moved; you can still go find the excellent human stuff. But you have to choose it, against a current running hard the other way, and growing by the hour. Choosing the hard input on purpose is the same rare act I keep returning to and keep doubting anyone will perform. So the machine never has to take the reading away. It just changes what’s easiest to reach, and absorption does the rest.

That’s the first crack, and it’s a generational one. My taste was subsidized by an economy that no longer exists, one where a person with judgment and no shortcut had to grind out six million words to build a temporary career, and got the judgment thrown in. The kid behind me gets the shortcut on day one. Good for the kid; I’d take it too. But the shortcut routes around the exact grind that, in my case, was the only thing that ever built the judgment. We are about to find out whether taste can be acquired any other way. I don’t know the answer. I notice that nobody selling the shortcut is even asking the question.

The harbor doesn’t keep you

Suppose my way wasn’t the only way, and taste survives anyway, in some stubborn few who do the reps for reasons of their own. Then they arrive where I’ve spent this whole run of essays telling them to go: up on the judgment the machine can’t reach, above the waterline, somewhere the surface can move without taking them with it. Call it the harbor.

There’s a second crack, and it’s the one I find harder to admit, because I made it myself.

I had the thing I’m telling you to build. Name on forty-seven spines, fifteen years of standing, readers who came back. An audience. Then the industry that supported it started folding, and I moved quietly into something adjacent and stopped writing in public for fifteen more years. I didn’t decide to let the audience go. I never once thought about it, which is worse. I just moved on to the work that was available, and with the dawn of the iPhone that work became mobile app development.

The taste carried forward intact. It’s the only reason any of this exists. But the standing evaporated completely, and the second act never rebuilt it: the apps I built that found real users were team projects behind a bigger brand, and the ones carrying my own name never found enough of anybody to remember. So I’m sitting here with exactly the thing I keep telling you to get, starting over anyway. Taste and standing are not the same asset. One of them survives being ignored for fifteen years. The other one doesn’t.

Which I’d like to call bad luck, except I’ve now done it twice.

At least it was mine to get wrong. Where I went next, I spent that whole stretch watching the same thing happen to people who never got the option.

By 2009 the App Store I shipped into had hundreds of fart apps, and over Christmas 2008 the reigning one — iFart — took the #1 paid spot: a ninety-nine-cent soundboard sitting above every real piece of software on the chart, clearing 38,000 downloads on Christmas Day alone.

They were the slop of their moment, every one built by a human who paid Apple ninety-nine dollars and sat through a review queue to ship a digital whoopee cushion. People made slop by hand, gladly, the instant the cost of making it dropped far enough.

What the machine changed is the waterline. A fart app still cost a guy a weekend of actually building the thing, and that was the friction — small, but real, and it quietly metered the supply. The distribution gate never did that work and still doesn’t. App Store Connect is smoother than it was in 2009 and still a hassle, and I say that as someone who fights it every release. What collapsed is everything upstream of the upload button. Pull the cost of making toward zero and the supply runs to infinity while the queue stays exactly as long as it ever was. The genuinely good thing and the ten thousand things that resemble it now land in the same feed on the same morning, indistinguishable not because no one can judge them but because no one will ever scroll far enough to try. I keep saying friction is the whole subject. But I’ve mostly meant the friction that forms a maker. This is a different kind I hadn’t accounted for: the friction that used to protect makers by thinning the field. It’s gone, and the field is bottomless.

So when I tell you the standing doesn’t keep itself, I’m not theorizing. I watched a portfolio of my own work go under exactly that way, and the books followed on a slower clock: a tech title’s royalties rarely outlived its index by more than a couple of years, and today the whole shelf of them pays me roughly nothing. The forty-seven spines built the taste. They do not buy groceries. Whatever standing they bought me on the supply side — the credentials, the decades, the earned judgment, the walk-by at Barnes & Noble — is no life raft on the demand side, where the only question is whether anyone finds you in the noise. I’m not above this. I’m in it on the same terms as someone who started last Tuesday, minus the part where I already know how it ends.

This isn’t where I planned to land it. I pointed at a harbor, and it might be neither safe nor a harbor.

Getting in was never the hard part.

The one move left

There is one move, and I’m at least mildly worried it won’t hold.

If the forge only ever ran when something forced it, then the move is to run it when nothing forces it. To choose the difficulty on purpose, after the machine has politely removed every reason you had to suffer it.

I can tell you it’s possible, because I’m doing it right now. The terror is gone — between the studio and the tools, no single project is the last one anymore — and I’m working the same doubled hours I worked when I was scared, except the engine flipped. A force multiplier pointed at thirty years of backlogged ideas is the most intoxicating thing I’ve ever felt at a desk, and nobody is making me feel it. So the forge can run on want instead of fear. That’s the hopeful sentence, and I mean it.

Here’s the one underneath it, and I’ve changed my mind about it twice while writing this. The easy version is that I came to this desk with the taste already built and paid for, funded by fifteen years of fear-driven writing followed by fifteen more of software dev before any of this existed, and that what I’m doing at double speed today is spending that account rather than adding to it.

Except even that isn’t entirely true. I read more in a week now than I used to read in a month. Call it twenty to one, and it’s bespoke, aimed exactly at whatever I’m chasing that day, which the old forge could never do. Researching Nobody Told the Scribes I came out the other side knowing real things about a fifteenth-century abbot and the arrival of print, checkable things that stayed. That’s acquisition. Whatever else is happening at this desk, the forge is busy.

And the shape of the day inverted, which matters more than the volume did. Writing those books, the overwhelming majority of the hours went to rote: buying and reading a stack of competitors, searching, sifting API documentation, writing sample code to find out whether the documentation was lying to me. The thinking happened in whatever gaps were left. Now the rote is the smallest part of the job, compressed into minutes, and nearly all of the time goes to weighing what comes back: pressing on a claim, asking for the version that disagrees with it, following a thread three levels past where I’d have stopped back when it would’ve cost me a week. The research runs deeper and stranger than anything I could sustain alone. It is, functionally, what a writer with a research team has always had, handed to somebody who never had one.

That should be unambiguously good news. Most days I’m sure it is.

The rote was doing two unpaid jobs, though, and I’ve only ever written about one of them. In The Duck That Argues Back I described the first: the busy work was also recovery, the micro-rests where your hands stayed occupied and the back of your mind quietly untangled the hard thing. Strip it out and the day becomes unbroken judgment and you end it slumped, staring at a wall. Everyone I know working this way has landed on the same complaint.

The rote’s second job is the worse one, because exhaustion announces itself and a forge going cold does not.

Which is the third crack, and the one I’m still standing in. What I can’t establish is whether it’s the same forge. The old one made me sort, and the sorting included the junk: the chapter that turned out to be padding, the manual that was flatly wrong, the afternoon lost to documentation describing a function that had been quietly deprecated. I didn’t value a minute of that at the time. I’m not certain it was worthless. The new one hands me the sorted version. I still choose, and I’m choosing harder and at a higher altitude than I ever did with a stack of manuals. But I’m choosing inside a narrowing something else performed, and I can’t see what fell out on the way. Maybe I’m being flattered at speed by a machine with no stake in whether I’m right.

The multiplier works by closing the gap between what I can imagine and what I can produce, and that gap — what used to be the slow humiliating distance between taste and ability — is the exact place the scared years put me until judgment formed. So I choose difficulty and take the shortcut in the same motion. That looks like a contradiction from outside. From inside it’s a change of unit. I used to be the only pair of hands; the difficulty was doing every piece of the work myself, every word, every line of code. Now the pieces come back finished, and the difficulty I kept is the verdict on each one: hold it, send it back for another pass, or kill it outright. I gave up the gap and got back the thousand small verdicts that used to hide inside the doing, at a volume the old forge never ran. And nothing dilutes them anymore: every one is a real decision, the busy work that used to space them out is gone, and they do not let up. Would the version of me who started this way — handed the multiplier at twenty-two, never once afraid — have built any taste to spend? I might be one of the people I’m worried about. The only reason I can’t tell is that I got mine before the getting got easy.

I’m not taking the advice back — the alternative, betting on craft the machine matches in seconds, is plainly worse, and taste is still the best thing you’ve got. I’m taking the certainty out of it. “Find your taste or be swept away” was always half a sentence. The other half is that finding it may not be enough, and the path that built mine is being paved over while I recommend it.

Whether anything is piling up on the floor of this shop, I can’t see from in here. I couldn’t see it the first time either. The sawdust sat there for years looking like exactly what it was, waste, and I only learned different when the furniture wore out.

So I don’t get to close this clean. What’s left sits in two other essays: The Long Thank-You, about a debt that took four years to come back around, and The Tax of Mastery, about a guitar I have no business picking up at my age. Neither answers the worry. Both are reasons the fire’s still lit.

I was five foot two through junior high and all of high school. I was also, and I say this with the easy confidence of a man decades past having to prove it, a good young athlete. I loved to practice, and worshipped hustle. That was the part people who grew up tall never quite believed — practice was the good part of the day. Driveway, gym, playground, whatever was open. I wore out several front storm doors practicing catching baseball ground balls thrown at the stairs to catch them on the return at odd angles, and occasionally one would catch the stair corner and smash into the door. Wham! My parents tolerated it I guess because they recognized the work ethic. Years of that, the ten thousand hours Malcolm Gladwell would later teach everybody to count, and I was happy to spend them.

A friend of mine was a foot taller. He had never played basketball. Not in a rec league, not in a driveway, not at all. He tried out, and he made the team I got cut from. I still remember him excitedly telling me he made the team I couldn’t, and having to feign excitement. I was happy for my friend but it was brutal. And I still went home and shot baskets. None of this was a scandal. At six foot two, what the roster needed from him was basic coordination and the ability to follow instructions. The sport-specific skill I’d spent years building — the thing I believed was the whole game — was, for him, optional equipment. It could be taught later, to the degree it was even needed given his natural advantage, at the team’s convenience.

The curve

Basketball was and still is very much a meritocracy. Sports are the most honest meritocracy we run, if you’re good enough. What height does is quieter than rigging: it stands in for skill. The taller you are, the less of the actual craft you need to clear the same bar. At six-two, coordination will do. At five-two the bar doesn’t rise a little; it goes nearly vertical. Muggsy Bogues cracked the NBA at five foot three, the shortest man ever to do it, and the ticket cost him being one of the best ball-handlers alive. The advantage doesn’t abolish the merit curve. It lets some people board it near the top.

And the sport has a built-in instrument for taking the measurement: the dunk. A dunk isn’t just a crowd-pleaser. It’s a threshold trick, the way a 540 is in vert skateboarding — the move that files you into a different tier the moment you land it (still working on that one FYI). My brother was enough taller than me to have a real shot at a dunk, and I remember him trying, over and over, a whole stubborn stretch of trying. The closest he got was a half-deflated ball he could just barely squeeze over the rim. Below a certain line, and my brother stood almost exactly on it, the dunk isn’t harder. It’s impossible. No amount of practice buys it. Which is exactly what makes it a good measuring device: it measures the trait, and only pretends to measure the work.

You should object here, and the objection has a name. Spud Webb won the 1986 dunk contest at five foot seven, beating his own six-foot-eight teammate, the defending champion. So the line moves. But look at how he moved it: a forty-two-inch vertical leap, better than double what steady training buys an ordinary athlete. He didn’t practice his way through the gate. He substituted one unearnable trait for another.

Muggsy is the control on that experiment. Bogues jumped forty-four inches, two better than Webb, and never dunked in an NBA game — four inches shorter, hands too small to hold the ball in traffic. Nearly identical hops, and the shorter man still couldn’t buy the trick.

I should be straight about where this strains, because it does. A leap isn’t height. Height is zero percent trainable, and a vertical genuinely can be improved with work: a real plyometric block buys something like five to nine percent, an inch or two on an ordinary vertical, and years of serious training add more. What gets issued at birth is the ceiling — muscle fiber type mostly decides how high your effort gets to stack. So the honest shape of this is a gradient. How much of a given gate can effort actually buy? For height, nothing. For a leap, something, and never enough on its own to put five-seven over six-eight. For craft, nearly all of it. That gradient is the whole reason a kid can leave one game and go find another.

Other games

So I changed sports. BMX racing first, then BMX freestyle, then skateboarding, which took the rest of my life and still has it.

That move from traditional ball sports to individual “action sports” is interesting to me in retrospect because it paid even fewer outward dividends. By any honest measure the new sports were harder, and at least in that era orders of magnitude less culturally accepted. Skateboarding is a brutal apprenticeship that runs in decades and pays out in fractions; it cost me more hours than basketball ever asked for, and it punished failure with a working knowledge of which local surgeon was best in each orthopaedic specialty. Basketball’s downside was losing. Ours came with stitches and casts. And where basketball arrived wrapped in institutional approval — coaches, seasons, varsity letters, gyms built and heated at public expense, scholarships waiting at the far end — BMX and skating came with none of it. No coach, no team, no season. Adults mostly filed it under delinquency, and the price of a practice space was getting run off from every parking lot in town, learning how to become an amateur carpenter at age 13, and allegedly “borrowing” lumber from construction sites.

So the trade, plainly: a supported game I could never fully enter, for an unsupported one that would take everything I had. The difference was the door policy. Nobody at a skatepark has ever turned me away for biology. There is no tape-measure trick in skating — there’s just the trick, and either you’ve paid what it costs or you haven’t. I left a game gated by something I could never earn for a game gated by exactly one thing, and it was a thing I could. And I have.

That was a doozy of a sports setup for something far more sweeping: the tape measure just came for everybody.

Height for rent

That’s what AI is, in the creative trades: height you can rent. Hand the model a prompt and it returns the finished move — the competent essay, the clean illustration, the working code. The surface skill a working writer or illustrator or programmer spent years, often decades, of daily practice building is now the factory setting. I called it a flood in The Gate Was the Product, cheap competent work rising against every gate in the culture. From inside, it’s stranger than a flood. It’s every kid in the neighborhood walking out one morning and knocking down threes better than you ever hit. The shot you built alone, in the driveway, over years.

Two different injuries hide in that, and they land together. The first is leveling: everyone gets the skill, so having it stops distinguishing anyone. The second is flooding: the leveled-up everybody can also produce at close to zero cost, so the market drowns in competence at the exact moment competence quits paying. Either alone might be survivable. Together they erase one specific tier of people. The top survives; the genuinely elite are still above the machine’s reach, for now. The bottom is having the best morning of its life; the person who could never draw at all just got handed a killer fade-away jump shot. The erasure lands on the earned middle — the working pros who spent a decade becoming reliably, professionally good, and woke up to find reliably-professionally-good on tap.

The drawing you could make, and the move you got handed.

And the prizes did not multiply to meet them. If you skim everything else, don’t skim this. The roster still has the same number of spots. When everyone is suddenly tall, the slots don’t go to everyone; they go to whatever is still scarce, and what’s still scarce splits two ways. Upward, into skill the machine can’t rent out. And sideways, into things that were never skill at all: the audience you already had, the brand, the capital, the timing. My tall friend didn’t cheat anybody. But he boarded the merit curve at six foot two, and plenty of what wins in a flood boarded the same way. The striver’s particular cruelty is losing the differentiator and the slot in the same season, and watching the slot go to someone who never put in the hours it took to build either. I lived that cruelty on the basketball court; I’m still processing what it means at the keyboard.

The dunk was never the game

Now, “everybody can dunk” needs its asterisk, and the asterisk is the way through.

What the machine rents out is the dunk — the artifact, the execution, the surface of the move. It does not rent out the game. I made that case in Unlocked, Not Cheated: AI lifts the maker who has taste and lacks execution, and does nothing for the maker with neither, because knowing what’s worth making, for whom, and whether the thing in your hands is actually any good doesn’t come with the rental. Muggsy Bogues didn’t last fourteen NBA seasons by dunking. He could dunk — he has said so plainly, and did it in high school — but never once in an NBA game. He lasted on elite-of-elite level skill and court vision, the parts of basketball no growth spurt has ever conferred on anyone. The dunk was never the game. It was only ever the measurement, and a measurement dies the day everybody can do it.

Two cautions before the ending. First: the rented height keeps growing. The machine’s floor is a treadmill, not a wall, and whether the elite tier stays above it is a question I can’t answer from here. I’m suspicious of anyone who can. My six-foot-two friend may find next week that everybody is now six foot four. Maybe. Second: the top is finite by definition. “Get better” is real advice and it cannot be everyone’s answer, because the arithmetic forbids it. Some of the erased middle were genuinely good, and being good has never stopped anybody from getting cut. Both of those things can be true at the same time.

The other move

Which leaves the move I learned at five foot two. When a gate measures something you can’t earn, go find the game that’s gated by what you can.

That is not permission to go find something easier. There isn’t anything easier; that’s rather the point. The open games come with less shelter, then and now. Nobody heats the gym on the open road. Skateboarding out-cost basketball in every way that counts, and it was still the better trade, because it was honest — every hour came back as skill, and every skill showed. And I still get up every day at fifty-five years old and think about what trick I wanna try to land next, and still pay full price for every one I get. The maker’s version of that move runs the same direction: out of the tiers the machine now hands out at the door, and into the games still scored on judgment, taste, presence, the accumulated specificity of one person’s eye. The games where the hours still buy what they promise.

I eventually got a little taller. The rim never got lower. What changed is that the tape measure is now sweeping through everyone’s sport at once, and a whole generation of strivers is about to learn in one season what the short kids always knew. The measurement was never the game. There are other games.

Mine had wheels.

The game that was chosen, and the one left behind.

I used to think that made Sojourn a strange exception. It doesn’t. It makes it a member of a category that is everywhere, that has existed for centuries, and that the software industry has almost entirely forgotten how to see: products whose success is the user leaving.

I wrote in The Intimacy Economy about Sojourn as an intimacy-economy product, built for two specific people instead of an audience. Here I look at it from a different angle: what the product is trying to get them to do, which is stop using it.

The inversion

Walk down through the design and the inversion is at every layer. The narrative decelerates on purpose; the writing is a deceleration curve, sentences settling rather than escalating. The audio thins out as the session goes. The interface, such as it is, gets quieter, not louder, the longer you’re in it. The whole product builds a ramp toward the user closing their eyes and the screen going dark, and then it congratulates itself by going dark too.

Set that against the default consumer-software product and you can see how strange it is. The default product is a slot machine that has read your file. It measures its own health in how often you return and how long you stay, it fights every exit with a notification, and a session that ends with you putting the phone down and not picking it back up is, on the dashboard, a small failure. Sojourn is built to manufacture exactly that failure, on purpose, every night, and to call it the win.

That’s a category, not a quirk of one app, and the category needs a name, because you can’t defend a thing you can’t say. Call it the anti-engagement product: a product whose interaction loop is designed to terminate, and whose success is measured in the cleanness of the termination.

You already own a dozen of them

The reason the category feels exotic is that we’ve only recently started expecting software to retain us. Step outside software and the anti-engagement product is the normal kind of thing, not the weird one.

A book is an anti-engagement product. It wants to be read and then closed, and a great one wants to be finished and kept on a shelf where you do not interact with it for years. Publishing always knew how to measure this — read-through, completion, whether it earned its place on the shelf — even while the rest of consumer software was busy forgetting that completion was ever a metric.

A single-player game with an ending is an anti-engagement product. The credits roll. It does not chase you back with a daily reward. Its success is that you spent your last session with it reaching the end of the story, which is to say the best outcome the product can produce is that you are now done with it forever. The games that understand this make their closing hours their best hours. The ones that don’t bolt retention loops onto a story that wanted to end, and they feel worse for the trying, like a dinner guest who won’t leave.

Closure. How did we let it slip away? Imagine a movie where instead of the credits rolling at the end, it just asks you to stay around another two hours for the sequel, and when the sequel ends you’re hit again from a new angle you weren’t expecting to go ahead and finish the trilogy right now. But wait, don’t get up, there’s bonus content with a Q&A queued up to watch at hour seven.

The failure we sense in that sequel that didn’t need to get made even has a name. We all have an internal sensor for the moment a beloved series has run out of things to say and keeps going anyway, and we named it after the Happy Days episode where Fonzie jumped a shark on water skis. Jumping the shark is just attention-grasping in narrative form: the franchise milked a few seasons past its own ending because the IP still prints money, degrading a little with every grasp. The rarer and braver move is the show or film that believes in itself enough to stop while it’s still the thing you loved, and lets its success be a finished shape instead of an open vein. Knowing when enough is enough is a creative decision and an anti-engagement one, and it costs the same thing both times: the money you would have made by refusing to leave. Two examples of the latter I encourage you to seek out if you’ve never watched: Extras and Patriot, which each called it done after two seasons (Extras allowed itself a single farewell special) and are glorious for doing so.

A habit-busting recovery app, done honestly, is the purest case of all: its success is that you no longer need it. The discipline that has thought hardest about ending a relationship well isn’t in consumer tech at all. It’s in clinical practice, where the word for it has been “termination” for a hundred years, and where ending the relationship cleanly is understood to be the whole point of the work.

And the long unsexy tail of physical tools — the kitchen timer, the corkscrew, the good hammer — are all anti-engagement by default. None of them wants your attention. They want to do one thing and be set back down. We only started treating “wants your attention” as the natural state of a tool when the tool moved onto a screen that could be sold ads. As much as I appreciate the comedic value in a hypothetical hammer that nudges you to literally see every problem as a nail so it can sell you car insurance, I’ll respectfully decline.

Tools that do one thing and get set back down.

What it actually takes

If you wanted to build an anti-engagement product on purpose, the shape is recognizable. The interaction loop has a built-in end state and the product steers toward it instead of away. Success is measured as completion (read-through, the session finished, the ritual performed, the artifact made, the credits rolled) and not as return. Re-engagement is the user’s decision, never the product’s solicitation — and the first thing that rules out is the “we miss you” email three days after you stopped, the most passive-aggressive sentence in software. The visible, idle state of the product is quiet. And somewhere there’s a terminal artifact, a thing that holds the cumulative value without demanding per-session loyalty: a finished book, a completed arc, a kept object that exists outside the app and survives it. It’s that memory stick I bought at the Trashcan Sinatras show that I referenced in The T-Shirt Economy, which served as a digital keepsake of that one-off experience.

The hardest of these to fake is the willingness to let you not come back tonight. You can dress up a retention-first product as anti-engagement, and the whole wellness-app aisle is full of exactly that: engagement maximization in a soothing voice, the same casino with a soft-spoken celebrity trying to keep you coming back with a meditative hook. Real anti-engagement has to be in the architecture. It’s the difference between a product that is relaxing and a product that is, structurally, trying to lose you, and means it.

Why there are so few

If the category is so old and so natural, why is it rare in software specifically? Four reasons, and none of them is that the products are bad.

The business model fights it. Subscription economics assume the user comes back monthly, and the team gets paid to make sure they do. An anti-engagement product can absolutely use a subscription, but it has to be honest about what the subscription buys: the standing option to use the thing, not the obligation. You can ignore Sojourn for two weeks and lose nothing, then open it on a Tuesday and it works exactly as it did the last Tuesday you needed it. That’s a clean value proposition and a miserable thing to defend in a board meeting.

The money fights it. Every consumer pitch deck has a DAU (Daily Active Users) chart, because for two decades engagement has been the agreed-upon proxy for value. An anti-engagement product is a bad pitch by definition. “Here is the graph of people using our product less, which is good” is a true sentence and an unfundable one, so the founders have to be more patient, or get their money somewhere stranger.

The playbook fights it. The vocabulary of consumer growth — funnel, retention, virality, churn — is the vocabulary of engagement maximization. There is no equivalent body of received wisdom for the other direction. “It’s the app that wants you to leave” is an accurate description of Sojourn and a headline I would not wish on a marketing team. So I wish it on myself instead.

And the theory fights it. If the value isn’t in time-on-product, you have to be able to say what it is in — the artifact, the ritual, the cumulative effect, the trust — and naming that takes real work, more work than pointing at a dashboard and saying “number go up.” Designing for cessation is harder than designing for engagement, not easier, because every default in the toolchain leans the other way. You are sanding against the grain of every framework you’re handed. Yet I still think it’s worth the doing.

What carries Sojourn

I’ll keep this short, because the relational half lives in The Intimacy Economy, and just name the anti-engagement load-bearing parts. The terminal artifact is a small and entirely optional one. At the end of a season, if a set of stories happened to be worth keeping, you can have them printed into a real book and mailed to you, a physical keepsake of the nights you spent reading them aloud. It isn’t a goal the product nudges you toward or a level you clear; it’s just a nice thing to be able to do with stories that turned out to mean something. And that’s exactly why it counts as anti-engagement: whatever value it holds lives on a shelf, outside the app, and never once asks you to come back tomorrow. The subscription pays for the option, not the streak. Falling asleep is the terminal state, treated as success and not as a problem to be re-engaged; there is no “you didn’t finish” guilt-trip, because finishing wasn’t the point and sleeping was. And the deepest cut: the listener, one of the two people the product is most for, has no interface at all. The product’s surface area to them is zero. You cannot engagement-maximize a person you never touch.

The AI underneath all of this isn’t incidental, but it isn’t the point either. It’s what lets one person produce a deep catalog of stories at a register a tired partner will accept, which is the thing that makes the whole quiet, unscaled, un-engaging configuration affordable to attempt at all. The machine is in the basement so the room upstairs can stay dark.

The machine is in the basement so the room upstairs can stay dark.

The other economy

Here’s the broader bet, and I’ll make it quietly because anti-engagement products don’t shout and this essay shouldn’t either.

The attention economy is not the only economy. The dominant frame treats your attention as a substrate to be captured, held, and sold, and it has been so dominant for so long that we mistake it for the nature of software rather than one configuration of it. Even as we’re exhausted by it. The anti-engagement product treats your attention as a finite thing it is helping you spend well and then hand back. That’s an older theory of the relationship between a maker and a user than the feed by a wide margin. The watchmaker, the bookbinder, the carpenter who builds you a chair you’ll use for thirty years and never think about again: all of them anti-engagement makers, all of them paid for the quality of a thing you mostly don’t interact with. The internet briefly convinced itself that category had been a mistake. I think it’s about to be remembered.

Partly that’s an ethics argument, and it really is one. But it’s also just economics. As the attention economy presses against its own limits, as the feeds get more crowded and the nudges more desperate and the audience more numb to all of it, the thing that starts to feel rare and valuable is the product that respects the limit instead of attacking it. There is a real and growing market for the product that wants you to leave, precisely because almost nothing else does.

The category needs a name, a playbook, and more people building in it. The name is “anti-engagement product” until someone finds a better one. The playbook is being written one product at a time, mostly by makers who don’t yet know they’re inventing a category, who noticed only that engagement maximization keeps producing worse and worse things, and then quietly started building the opposite. Sojourn is one of them, unproven and trying. The next decade will produce a lot more, if we get good at noticing them, and at protecting the strange conditions they need to exist.

The best thing a product can do, sometimes, is the thing the dashboard can’t score: work well once, and then leave you alone.

The fight is about AI. A student turns in an essay an AI mostly wrote, and the observers split into two camps that yell past each other: ban it, lock the laptops, back to blue books and proctors. Or embrace it, it’s the future, teaching without it is malpractice. Both camps think they’re facing a new problem. They’re facing arithmetic we’ve already solved.

We ran this experiment in math class in the 1970s and ’80s, with a machine that automated part of the work and frightened teachers in exactly the way the chatbot does now: the calculator. And we settled it so completely that the rule went invisible, lived by every teacher without a second thought. You take the calculator away when you’re testing whether the kid can add — the arithmetic is the thing you’re certifying, and a machine that does it for her ruins the test. You hand it back the instant the problem turns into distance-rate-time — now the arithmetic is just plumbing, and what you want to see is whether she knows what to do. The tool is taken away exactly when the skill it automates is the skill under examination, and goes back in when it’s just an assistant to a different skill.

That’s the whole framework, and it has been sitting in every classroom for forty years.

And forty years understates the lineage. The calculator that terrified the algebra teacher arrived with a body count of its own: the slide rule, engineering’s sidearm for a century, was dead within about four years of the first pocket scientific model; Keuffel & Esser, who had made them by the million, simply stopped. Go back further and the same fight is running in medieval ledgers. For centuries Europe did its math on the abacus, sliding beads on a counting board, and recorded the results in Roman numerals. Then Hindu-Arabic numerals, the 0 through 9 we use today, arrived carrying a new trick: the arithmetic itself could be done on paper, no beads involved. The bead-counters fought the algorists (the paper-arithmetic crowd) for generations, and in 1299 the money-changers guild of Florence banned the new numerals from account books, arguing that a written figure was too easy to doctor — a zero becomes a six with one stroke — while beads moved in the open, where everyone could watch. The new tool won anyway. It always does. Which is exactly the kind of comfort that deserves the suspicion it’s about to get.

The wrong question

“Should we allow AI in school” is the wrong question the same way “what percent of this was human” was the wrong question about authorship: a yes/no demanded of something that was never one decision. There’s no school-wide answer because there’s no school-wide skill. The proof, the lab report, the close reading, and the take-home research paper each want a different answer, and the calculator question is what separates them, asked one assignment at a time: is the thing the tool automates the thing I’m trying to measure here?

Sort the panic that way and it falls into two piles. Some assessments certify a foundational skill the student has to prove unaided — building an argument, following a proof, reading a literary page closely enough to know what it actually said. You can’t prompt your way out of not understanding; the model hands you a fluent paragraph and you can’t tell whether it’s right, which is the entire problem. For those, the laptop closes. It’s the addition test. Other assessments certify applied judgment standing on a foundation already laid — weighing sources, shaping a case, spotting which of the model’s four confident answers is the plausible-sounding wrong one. Test those with the tool banned and you’ve measured the student in the one condition she’ll never work in again: alone, from memory. I built a hiring gate that made precisely this mistake for four years, scoring people on unaided recall — the exact thing the job had stopped asking of anyone — so I’m not theorizing about how easy it is to certify the wrong skill with a straight face.

Where the calculator stops helping

Here’s where the precedent runs out, and it’s the part nobody really seems too interested in addressing.

The calculator was safe to hand back early because using one well never required you to have internalized arithmetic. You can be hopeless at long division and still nail the answer to how long it takes for delivery van A traveling at 55mph from Chicago to meet delivery van B traveling from LA at 68mph. And that’s because the calculator automates a means that sits cleanly apart from the end. The long division answer is 672 ÷ 41 = 16.39 hours; that’s the roughly 2,015 highway miles between the vans closing at a combined 123 miles an hour, knocked down by a factor of three so the long division stays polite. You’re welcome. AI doesn’t sit apart from this mechanism. Using it well demands formed judgment — taste, the ear for the wrong note, the instinct that says that citation is invented or that paragraph is confident and empty. And that judgment gets built almost entirely by the unaided struggle the tool is so good at letting you skip. The bad first draft is where you find out what you think. The proof you sweat alone is what grows the sense that later tells you a slick wrong proof is wrong. AI is nothing if not slick.

The part the machine offers to skip.

So the real hazard in a classroom isn’t cheating on the test. It’s quietly skipping the formative friction that builds the person who could pass it honestly, and walking out credentialed, fluent, and unable to separate good work from garbage, because the skill that does the separating was never grown. The calculator could only ever steal the arithmetic. The model can steal the formation. Which is no doubt worse, so the alarm is understandable.

What’s still worth testing unaided

This turns teaching into a judgment call no policy can make from above, taken assignment by assignment. Is this a skill the student has to forge unaided, because the forging is the education? Protect it — tool removed, and don’t apologize for the friction. Is this applied judgment resting on a foundation already there? Hand the tool back, and notice that banning it now produces the artificial condition, not the rigorous one. A blanket ban is taking the calculator away from the entire math curriculum: you stop teaching the word problem out of fear of the addition test. A blanket allow is handing it out during the addition test: you graduate kids who can’t add and never found out how. The two failures are mirror images, and right now most schools are picking one of them to dodge the work of choosing per assignment.

If you teach and want the line drawn plainer, run the question down your own syllabus. The seventh-grader learning to build a paragraph writes it alone, because paragraph construction is the skill being certified; same for the times tables, the reading quiz, the first proof. Graded tests of foundations keep the tool out, full stop. The senior who already writes competently is a different case: let her brainstorm against the model, argue with it, have it mark up a draft, and then make her defend the result out loud and answer for every citation, because judging the machine’s output is now the skill being certified, and it’s the one she’ll use for the rest of her working life. In between, the honest default is disclosure: say what the tool did, show what you did. And no accusation should ever rest on a detector alone — a human reads the work before anyone’s name goes to the office. If those rules sound sensible, 98 high-schoolers wrote nearly the same ones into a mock congressional bill this summer, which tells you the calculator question isn’t hard to answer. It’s just hard to answer from the district office.

I got to watch someone choose right, once. In electrical engineering school I took an experimental calculus class from a professor who had decided that seeing the math was part of learning the math. Enrolling meant buying an HP-28S, one of the first calculators that could draw a graph, and we leaned on those little screens constantly, watching a function bend while its coefficients moved. That we might use the machines to cheat never seemed to cross his mind, to his credit. He had asked the calculator question and answered it out loud: the skill under examination was intuition, and the tool was hired onto the teaching side.

This is the part of the essay where I come clean and share my prompt from earlier:

How long does it take two delivery vans to meet if van A is starting
from Chicago at 55mph, and van B is starting from Los Angeles at 68mph?
Show me how you got the answer with long division plz.

That’s the uncomfortable reality of a tool that is unlike any calculator we’ve ever seen. I mostly remember how to do long division, I even have a minor in math (a reluctant requirement of that same degree), and I went straight to an LLM to solve a basic distance-rate-time problem. But keeping us on point, I already learned how to solve those once in my life, so I consider that skill at least loosely banked. I’m not being tested on my formation of learning long division in writing this, and that’s the point.

So getting back to removing the tools, none of this is a case for struggle as virtue, the back-in-my-day sermon students tune out for good reason. It’s narrower. Some struggle is mere toil and the machine should take it. Some struggle is the only process by which judgment gets built, and judgment is the whole thing a diploma is meant to vouch for. Telling those two apart was always the work; the calculator forced us to answer it once, cleanly, and we did. AI asks the same question and forces a second one underneath it. The calculator question is is the automated skill the one I’m testing. The harder one is is that skill the one that builds a student worth testing at all. When the answer to everything is one prompt away, what’s left worth protecting is the slow, unaided making of a mind that can tell whether the answer is any good. And the ability to judge the value of an essay written by a guy who will gladly use a prompt to solve a problem he once knew how to solve on his own.

Both paths monetize an audience: reach, affiliation, a crowd that knows your name. I stopped the essay one path short, on purpose, because the third one doesn’t. It’s smaller, slower, and almost invisible to the metrics everyone watches, and it’s the one I’m actually building toward. For lack of a better name, the intimacy economy.

A third configuration

The brand economy and the performance economy are, underneath, the same machine: identity-affiliation at scale, audience as tribe, the maker as a public persona the audience consumes. Substack, Patreon, the merch table, the stadium. The unit is I am the kind of person who follows this, and the whole apparatus is tuned to make that unit cheaper to produce and easier to count. Think Swifties, Deadheads, Juggalos, Parrotheads: membership in any of those groups is a message about identity.

The intimacy economy inverts that machine at every load-bearing point. It is built for one specific person rather than an audience. The artifact carries the relationship — the job a persona does in the brand economy. There is no scoreboard: no streaks, no stats, no share sheet, no public reaction surface, not even any Faygo or margaritas, because the intimacy is private and a counter would only cheapen it. The community is small enough that everyone in it has been on both ends of an exchange, so reciprocity isn’t a policy, it’s just the shape of the thing. And the tools, including the AI, stay in the basement generating the substrate. The relationship the person actually has is with another person.

It does not scale to mass. That’s the defining property, and I’ve made peace with it. You can make a living in here. You cannot make a fortune, because the mass moves that turn a living into a fortune are exactly the ones this configuration refuses. What the t-shirt economy literally cannot see, because it has no instrument pointed at it, is that a lot of makers would take that trade in a heartbeat.

The moves it makes

Each move the intimacy economy makes is a place where the brand economy would reflexively build the opposite, which is the easiest way to recognize the configuration: look for what it declines.

It narrows to a specific person instead of broadening to an audience. It’s the opposite of “I’d like to buy the world a Coke.” Every commercial instinct says widen the funnel; this says pick one reader and build for them, on the theory that work made to fit one human fits others better than work made to fit everyone fits anyone. A slingshot, not a shotgun.

It makes the artifact the load-bearing object, and the interaction merely the means. A letter, a book, a mixtape, an heirloom: the thing exists outside the system that produced it, and outlives the subscription that paid for it. A feed is the reverse: pure interaction, and it leaves nothing behind you’d want to keep.

It refuses engagement mechanics on principle. This is the hard one, because every product manager I’ve ever worked with will, within about ten minutes, suggest a streak. Every investor asks for the DAU (Daily Active Users for the luckily uninitiated). Every distribution channel pays you in reach. Declining all of it means saying no to a great many things that wear the costume of growth. It’s nearly un-American in its abject refusal to sacrifice attention to the god of growth.

And it keeps the tools underneath the floor. When AI is employed, it never addresses the user, never becomes the thing the user has a relationship with. It generates the substrate and then gets out of the way so that two real people can be more present to each other. The difference between that and an AI companion is the difference between a piano and a player piano. A piano is an instrument; everything in it exists so a person can play it for another person. A player piano performs the human part itself, and leaves you alone in the room with a machine doing an impression of company.

The band from the merch table

The Trashcan Sinatras were in The T-Shirt Economy, selling me a memory stick of a show minutes after playing it. What I didn’t tell you there is what the band had already been through, because that half of the story belongs here.

They took the advance from Cake, their first record, and did the least rock-star thing imaginable with it: bought a studio in Kilmarnock, Scotland, and named it Shabby Road. Then Go! Discs was swallowed by Universal in 1996, the band was dropped, the studio had to be sold, and bankruptcy followed. They didn’t play again until 1999. The artifact economy took back everything it had fronted them.

The comeback is the part this essay cares about. When they returned in 2010, the tour opened in a fan’s living room in Portland: fifty tickets at a hundred dollars each, three hours of songs for the people packed onto the couches. The living-room gigs became a recurring part of how the band tours, and Wild Pendulum, the 2016 album, was funded directly by fans. Rooms of fifty people, money handed straight across. A band that once held a major-label deal and its own studio rebuilt on precisely this configuration and has run on it for a decade and a half now. Everything above about declining scale, they lived, with a thirty-year balance sheet to show for it.

No stage, no scoreboard. The whole economy fits in one room.

The band’s seventh album, Ever The Optimist, their first in a decade, came out on July 31st, 2026, the day this essay first published. I planned none of that. Three days later John Douglas, who wrote much of their catalog and played on all seven of those records, died after a brief illness. I met him in that small room in Nashville.

One caution before I make that sound tidy. The fan funding ran through PledgeMusic, and PledgeMusic collapsed in 2019 owing artists an estimated nine and a half million dollars, with the administrator telling them not to expect any of it. Whether the band was among the burned, I couldn’t confirm, so I’m claiming nothing either way. The move (direct funding from the people who want the thing to exist) was right; the platform was a middleman wearing intimacy’s clothes, and intermediated intimacy is still intermediated. If the configuration has a first commandment, it’s that the line between the two people stays short. The band’s direct line runs through Patreon these days — yes, the same Patreon I filed under the brand economy at the top of this essay. At the scale of one band and the people who love it, it works more like a passed hat than a funnel, and the money moves as it’s given instead of pooling with a middleman.

Sojourn, trying to occupy it

The Trashcan Sinatras backed into the configuration because the industry left them nowhere else to stand. The more interesting question, at least to a builder, is whether you can aim for it on purpose. The clearest version I can point at is one I’m building, so take the usual discount for a maker describing his own work. Sojourn is a bedtime-story app, and almost every decision in it is an argument with the attention economy.

It’s read aloud. One person reads to another, in the dark. The listener never touches a screen, has no interface, no account, no profile. There are no streaks, no stats, nothing to share, no place to perform having used it. And the success metric, the thing the whole product is optimizing for, is the listener falling asleep, literally turning off their attention! It’s the only software I’ve ever built whose explicit goal is to get you to stop using it as fast as possible. Every growth playbook I know runs exactly backwards through it.

What’s left at the end of a season isn’t a usage graph. It’s a book, optionally a real one: if the stories were worth keeping, you can have them printed and mailed, a small physical keepsake of the nights two people spent reading them together, one that sits on a shelf long after the app is forgotten. The subscription doesn’t buy the stories. It buys the standing option to perform the ritual that makes them. The artifact is the point; the software is the loom.

The artifact that outlives the subscription.

A fair reader will stop me here and ask why, if intimacy between two humans is the whole goal, there’s a machine anywhere near it. That’s the right question, and it has its own essay: Unlocked, Not Cheated makes the case for why a solo maker reaches for AI at all. The short version for our purposes is that the AI is what makes this configuration available to one person. A hundred bedtime stories at a consistent register is a career’s worth of craft I don’t have; the model supplies the craft so the ritual can exist. The intimacy is human. The scaffolding is not. Those are different floors of the same house.

But it goes deeper than that, and it has to do with someone who understands what makes a good story even if they don’t quite have the chops to craft every turn of prose. I’ve written millions of words as a tech author, nearly none fiction. But I love the art of storytelling in other mediums and I think I have at least passable taste for what is engaging and fun, if nothing else because I know what I’d want to read to my wife. There are some wild ideas in Sojourn: stories inspired by classics, weird songs, favorite movies, personal experiences, you name it. A bird in love with a whale, a llama on a cross-country trip in an ice cream truck, a telephone switchboard operator traveling across time and space. I’ll readily admit I couldn’t have done them full justice as a fiction writer. Rick Rubin isn’t a musician in any traditional sense, and on the records he shaped for the Beastie Boys and The Cult his contribution was the ear. I like to think I brought a much smaller version of the same thing to a bedtime-story app.

Skating already proved it works

The deepest reason I trust the configuration isn’t the product I’m building, and it isn’t even the band. It’s a fifty-year-old subculture that has run on it the whole time without a strategy deck.

Skateboarding has a gift economy, and it functions because it’s small enough that everyone in it has, at some point, been on both ends of every exchange: taught and been taught, filmed and been filmed, given a couch and slept on one, invited to a private ramp and built one. Nobody keeps score because everybody already knows the score is even over a long enough horizon.

The culture’s monuments are artifacts of that exchange. Burnside, probably the most storied skatepark in America, started in 1990 as rogue concrete troweled in under a bridge by locals who wanted somewhere dry to skate, until the city gave in and let it stand. Nobody sold tickets. Nobody owns it. It’s in Portland, incidentally; apparently the configuration likes it there. And the standard unit of skate fame isn’t a chart position or a follower count, it’s the video part: a few minutes of your best work, shot over years, usually by your friends, then given away. The culture’s whole archive got built person to person and handed over free.

The attention economy did eventually come for it. Skateboarding is in the Olympics now, with heats and judges and a medal table, and the community’s reaction has mostly been a shrug: nice for the kids, perhaps even more important for the parents, little to do with the thing itself. When the scoreboard finally arrived, fifty years of keeping no score turned out to be load-bearing. None of the engagement machinery ever got traction inside the culture, not because skaters are nobler than the rest of us, but because the conditions that hold the community together are the same conditions that starve those mechanics of oxygen.

It’s the cleanest proof I know that the intimacy economy isn’t a thought experiment. It predates the discourse by decades. I’ve written about one small instance of it elsewhere, in The Long Thank-You: a thank-you that took four years to arrive and was worth the wait.

The third lane

Step back and the bigger argument is about a blind spot in the whole AI conversation, which has, at the moment, exactly two settings.

One is the companion: the model becomes the relationship. The AI girlfriend, the character you text at midnight, the friend in the phone; by 2025, companionship and therapy had become the most common thing people do with generative AI. The other is the feed: the model becomes the engine that refines your attention and sells it back to you. Both are real and enormous, and both put the machine in the foreground, as either the partner or the predator.

Almost nobody is building the third thing, where the machine goes into the foundation so that two humans can reach each other better: software that disappears into the floorboards so a person can read to a person. That third lane is what the intimacy economy looks like once it has a business model, and the reason it’s so rarely named is that we don’t yet have the vocabulary for it. Our words, companion, feed, engagement, retention, reach, all belong to the two configurations that aren’t this one.

Why it’s hard, and not for everyone

I don’t want to sell this as utopia, because it isn’t, and the failure mode of essays like this is exactly that oversell.

It doesn’t scale, which I keep saying because it keeps being the catch. The economics are bounded by design. The temptation to wire in one little streak, one small leaderboard, one share button, is constant and comes from well-meaning people who are usually right about everything except this. The configuration is also nearly invisible from outside, because the instruments that would detect it aren’t the ones any platform puts on the dashboard: depth, the longevity of an artifact, retention measured as a relationship rather than a session. From the outside, work like this often looks like it’s failing, right up until you measure what it was actually built to do.

And small communities have their own diseases. They gatekeep, they curdle into in-groups, they mistake narrowness for taste. Intimacy at this scale is not automatically kind; it’s just intimate. Anyone selling you the small and the local as pure virtue is selling you the brochure.

Mostly, though, it asks for a particular temperament. The brand economy wants episodic performance to a large crowd. This wants sustained attention to a small one. Some people are built for that and feel relief the moment they find it. Many aren’t, and would be miserable, and should go run the t-shirt table with my blessing. The intimacy economy is not the answer to how every artist survives the AI era. It’s one configuration among several. The others already had names.

Where this lands

The t-shirt economy named a real tax: to make a living from creative work, you increasingly have to perform yourself on top of performing the work, which is a bill that falls hardest on exactly the introverts and weirdos who got into this to avoid it. The intimacy economy is where that tax doesn’t come due, because the work isn’t aimed at a public at all. It’s aimed at a specific person, and a specific person doesn’t need you to be a brand. They need you to be present, and to make a thing worth keeping.

That’s the whole pitch. It won’t make you famous, it won’t scale, and it will be illegible to anyone holding a dashboard. But it is real, it is available, the AI era makes it cheaper to attempt rather than harder, and almost nobody has bothered to give it a name. Naming the thing is the first step to being able to choose it.

Find your tribe, The T-Shirt Economy said, and know their shirt sizes. Here’s the other half. Find your one reader, and make them something they’ll keep.

Performance keeps coming up as the refuge in the displaced-by-AI conversation. No one wants to watch a person type. Nobody buys a ticket to see code get written, sadly for me. Meanwhile, every creative art that predates recording technology — theater, music, dance, comedy, storytelling, religious ceremony — had performance baked into how it reached an audience. That’s not coincidence. Before recording, in-the-room was the only delivery mechanism. Recording severed performance from delivery a century ago, and AI is now doing the same severance, faster and more thoroughly, to the artifact-based arts: writing, illustration, code, photography, music recording. The performance arts went through their version of this a hundred years ago and survived, because being-in-the-room turned out to be its own irreducible thing. Human presence is truly scarce.

That’s the optimistic frame. It says that if the work AI threatens is artifact production, then arts depending on presence have a structural moat. Theater is fine. Live music is fine. Stand-up comedy is fine. The Nashville corner busker I hit with a rock is fine other than the scratch. The performer-in-front-of-people format is one of the few things AI genuinely can’t replace. Pull the human out of the performance and the spectacle survives but the reason to watch doesn’t, which is the same moat named from the grandstand instead of the stage. When I argued in Standing on Surfaces That Move that the thing worth defending is the judgment underneath rather than whichever surface you happen to be standing on right now, presence looked like the one surface that would hold.

The frame is mostly right but entirely incomplete.

The music is the billboard

You hear it said about The Rolling Stones, and about Metallica and Taylor Swift and Phish and Dead & Company: the real business is merchandise, and the music is just what gets people to the merch table. At stadium scale that’s folk wisdom more than accounting, since a Stones tour grosses on tickets in a way no merch table will ever match: their 2024 run took $235 million across eighteen stadium shows, about thirteen million a night at the ticket window. Scale down to where almost every working musician actually lives, though, and the folk wisdom turns into dicier arithmetic. The shirt has the margin. The record does not. A large share of the profession is structurally in the t-shirt business, and the music exists in service of the t-shirts rather than the other way around.

The Grateful Dead worked this out earlier and more deliberately than anyone. They let fans tape shows through the seventies and then formalized it in 1984 with a designated tapers section: a reserved block of seats for people recording, for free, a product the band could otherwise have sold them. The tapes created the affiliation, the affiliation drove ticket sales, the tickets drove the parking-lot merch table, and the music was the world’s most expensive billboard for the whole circle. Streaming clarified that pattern rather than inventing it. The music is the marketing. The product is identity. The transaction is I am the kind of person who likes this, made tangible by something you wear, hang, or display, which is the same trick the diamond ran from the other side of the counter. Who would’ve thought the iconic Grateful Dead logo is in the same family of messaging as a diamond ring.

Recording technology got there first, for a different reason. It democratized the reproduction of music until the recording itself stopped being worth selling. Napster and eventually commercial streaming get blamed for killing music sales, but they mostly finished what radio, cassettes, and CD ripping had been doing for fifty years; by the time Spotify arrived, music was already most of the way to free, and Spotify made the free thing official. The acts that came through intact were the ones who had understood for a long while that what they were selling wasn’t the art, it was the tribe organized around the art, around them.

AI is now doing to writing, illustration, and code what recording did to music. The artifact is becoming free, or close enough. Anyone with a twenty-dollar subscription can generate a plausible essay, a plausible illustration, a plausible piece of code in seconds.

So the value migrates to whatever is still scarce: identity, tribe, presence, taste, the specific life that produced the thing, the community that forms around the person who made it. Which makes “creator economy” an honest description of the post-artifact economy rather than the buzzword it sounds like. The creator is the product; the work is the free sample.

Substack, Patreon, MrBeast

Look at how the surviving creative economies actually work in 2026 and the pattern is everywhere.

Substack is not pay-per-essay. You’re buying a relationship with the writer, and the essays are the demonstration of why. The transaction is identity on both ends: I subscribe to that writer says something about you, I have those subscribers says something about them.

Patreon is the same shape stripped of the publishing veneer. You pay the person and you get whatever they make. The art form is almost beside the point, because what’s being transacted is access and affiliation. It’s also the oldest model on the menu. For most of history, art ran on patronage: the commission, Haydn wearing Esterházy livery, eighteenth-century books published by subscription with the subscribers’ names printed in the front matter, which is Patreon’s tier page typeset in 1750. The arrangement we think of as normal, artists selling finished artifacts to strangers at market, is the historical anomaly, a window recording technology propped open and AI is now closing. Patronage is just the mean, and the market is reverting to it.

MrBeast runs a shelf of consumer brands — Feastables chocolate, the Lunchly snack packs, the MrBeast Burger experiment, the merch. The videos are the customer acquisition channel for all of it. Measure the P&L and the video operation starts to look like the marketing department of a consumer-goods company.

Bandcamp is the music version. The album costs a few dollars. The bundle with the vinyl and the t-shirt and the handwritten thank-you note costs forty. Same record. Different transaction.

The camera is pointed at the actual business.

These are the working business models of creative work in 2026. The artifact alone doesn’t pay. The artifact in service of identity-affiliation does.

So performance protects you, sort of

The optimistic frame I started with — performance can’t be replaced by AI — is true. It just doesn’t translate cleanly into performers will be financially fine. The economics of touring have been brutal for decades: when the Music Industry Research Association surveyed American musicians, the median one cleared about twenty grand a year from music, stitched together from three or four different hustles, and most reported it didn’t cover their living expenses. What pays is the shirt, the meet-and-greet, the album in the merch bin, the Patreon, the Substack, the cultivated community that exists because of the live shows but doesn’t pay you for the live shows directly.

The refuge isn’t performance, exactly. It’s being a brand that performs. Performance is the most photogenic and historically dignified version of that, but the underlying mechanism is the one Substack writers and Patreon illustrators and YouTubers all run on: you become a person worth following, and the various things you put out there, including in the best of cases the art itself, are how people show that they’re following you.

The limit case already exists. ABBA has been selling out a purpose-built London arena seven shows a week since 2022 with a show called Voyage, and ABBA is not in the building: the “band” is four avatars rendered as the group looked in 1979. KISS, watching that work, sold their name, faces, and catalog to the company behind it, so that eight-foot digital versions can tour without them, first show targeted for 2027, with an ambition of several cities at once, Cirque-style. That looks like the moat failing. Look closer and it’s the thesis wearing makeup and platform boots: what got reproduced was the performer; what still can’t be reproduced is your night in the room with three thousand other people who own the shirt. Even Voyage keeps a ten-piece live band on stage. The pixels need witnesses.

Walter Benjamin saw the first half of this in 1935. The Work of Art in the Age of Mechanical Reproduction argued that copying a thing endlessly destroys the aura of the original, the here-and-now authority that made an object worth standing in front of. What he didn’t say, and what I think the century after him actually did, is that the aura never evaporated. It moved. It came off the work and attached itself to the artist, and then the artist had to become a public-facing brand to collect on it at all. The romantic model — make the work, let the work speak, keep your personality out of it — doesn’t survive a regime where the work is free.

Klimt’s The Kiss has been reproduced onto more dorm posters and coffee mugs than almost anything this side of the Mona Lisa, and the copies did not kill the original’s aura. They advertise it. The queue at Vienna’s Belvedere Museum exists because of the mugs. A painting has what a recording never had, one scarce canvas for the aura to keep living on, and the museum has learned this essay’s whole lesson anyway: the gift shop is at the exit, the painting is the billboard. Music, text, and code have no original. Every copy is the work, so the aura had nowhere to sit except the person.

The honest caution

None of this is good news for creatives, myself included.

A lot of artists got into this work specifically to avoid having to be a brand. The whole appeal of the romantic model was that it let introverts, weirdos, and people who hate self-promotion do creative work for a living: make the thing, the thing represents you, nobody asks you to perform yourself on top of performing your work. Becoming a brand requires the temperament of a salesperson, which seems entirely opposed to what we think of as artists. It requires showing up consistently in a persona-shaped way. It requires being good at the meta-work of cultivating a tribe, on top of the actual work of making the thing the tribe is supposedly about. Many of the best artists I know are terrible at this and have no interest in learning.

I’m not writing that from outside it. I publish these essays free, on a schedule, and around them I’ve built the whole apparatus: a newsletter, a presence on Medium, a LinkedIn account I reactivated specifically to reach people who knew me from back in my tech book-writing days. The essays are the work and they are also, unmistakably, the demonstration. For thirty years I believed that a thing made well enough would find its own audience, which is a comfortable belief and mostly a wrong one, and it cost me more than I want to itemize here. If there’s one piece of advice I can confidently share, it’s this: if you build it, and do little more, they will not come. So the treadmill isn’t an abstraction I’m warning other people about from a safe distance. I got on it deliberately, late, having finally done the math. It still costs something. Some fraction of the attention that used to go into making the thing now goes into the layer that gets the thing seen, and I can feel the trade even on the days I think I’m making it correctly. On the other hand, I’ve learned that thinking about and documenting the making is its own sort of reward, so there’s that.

There’s also a winner-take-all problem. The artists for whom the brand economy works are concentrated at the top of the distribution. The Stones can profitably sell shirts. The local touring band cannot sell enough shirts to live. The MrBeast economy supports MrBeast and a handful of mid-tier creators; it doesn’t support the long tail in any sustainable way. The brand economy concentrates rewards at least as hard as the streaming economy did, maybe harder, because tribal affiliation scales with attention and attention is the most concentrated commodity humans produce. The platforms brokering it know exactly what they’re holding, which is the gate all over again in a friendlier coat.

Same shirt, same night, same room. The economy is happening over there.

And the concentration doesn’t only sort the winners. It reroutes the people who would otherwise have made the work.

The early App Store ran that experiment in miniature. In December of 2008, a ninety-nine-cent novelty app called iFart, which did precisely what its name promised, was clearing close to ten thousand dollars a day; on Christmas Day it sold nearly forty thousand copies and netted its developer something near thirty thousand dollars in twenty-four hours. Developers who had been building database tools and document editors watched a novelty sound board earn a year’s salary in a week, and a meaningful number of them did the obvious thing. The platform didn’t only bury serious software under novelty on the discovery shelf. It hired the builders away from it. When the shortcut pays better than the forge, the shortcut doesn’t just win the shelf. It wins the labor.

Then there’s the question of what becomes of the art when the art is the marketing. Corruption sets in once the work has to serve a brand purpose first and an artistic purpose second. The work starts optimizing for the audience-acquisition layer. It gets more on-brand and less surprising. It stops being the thing that defies expectation and starts being the thing that fulfills it. That’s a real cost, and it’s the one I’m least sure how to talk about, because the people best positioned to notice it are the ones with the most incentive to deny it.

I’ve bought merchandise at a show exactly once that I can remember clearly. The Trashcan Sinatras, a beloved Scottish band I’ve followed for decades, at the Blue Bar in Nashville on Halloween night in 2017. Halloween is a miserable night to sell a ticket in Nashville, so a room that should have been full held a crowd you could count without trying, a few dozen of us at most. I left with two shirts and a memory stick. They had recorded the show I had just watched, and they had a rig set up somewhere in the back to copy it onto sticks while the room emptied; hang around a few minutes and you could buy the evening before you reached your car. Nothing on that stick existed two hours earlier. Nothing on it would ever exist again. Their entire catalog was streamable on the walk to the parking lot, and not one second of it was what I was holding.

That’s the tapers section, inverted. The Dead handed the recording over for nothing and collected downstream, at the ticket window and the merch table, on the affiliation the tapes created. The Trashcan Sinatras skipped the downstream and sold the night itself, in the room, for cash, the way you do when a few dozen people on Halloween is not an audience you get to monetize twice. Both bands had worked out that the songs were never the scarce part. What’s scarce is having been there when those particular versions happened, and one of the two was quick enough to bottle it on the way out the door. I was buying evidence that I’d been in the room, and I knew that perfectly well while I was handing over the money.

Which brings me to the part that complicates the corruption argument I just made. I’d been to several of their shows by then, and that night I’d paid for the tier that gets you in early to meet the band. By this essay’s own logic that is the corruption, exactly: VIP access sold as a product, affiliation with a price printed on it. It did not feel like one. Frank Reader, John Douglas, and Paul Livingston turned out to be good company, the room was small enough that we genuinely talked rather than merely shaking hands, and somewhere in the set they referenced an anecdote from our earlier conversation, with my wife and me standing out there. They played two full sets and took verbal requests directly from the room, on the third-to-last night of a five-week haul across the country, to a Halloween crowd that hadn’t come. No arithmetic rewards that. I think they genuinely liked meeting the people who showed up, and I think the tier was how they paid for the van, and I think both of those were true at once. That’s inconvenient for the argument I’ve been building, which is why I’m sharing it.

The deal on the table

So the structural argument holds: presence is the thing that won’t reproduce, and value migrates toward it. What nobody hands you is the term sheet.

The fine print is that this is a worse deal than the one many artists thought they were signing. You don’t get to just make the work anymore. You have to make yourself into the marketing for the work, and the work into the marketing for the merch, and the merch into the marketing for the next thing. It’s a treadmill, and the people who win on it tend to be the ones who’d have made decent salespeople in another life. Mick Jagger left the London School of Economics to start the Stones; I’m not saying he’d have been salesman of the month at Carmax, but I wouldn’t bet against him.

Software people, to circle back to The Gate I Built, mostly cannot do this. There is no Linus Torvalds merchandise empire of any consequence. Open-source maintainers can’t sell shirts. We can do consulting, courses, books, conferences, all of them softer versions of identity monetization, none of them carrying the in-the-room tribal energy the performance arts have. So performers have a business model to lend back to the rest of us, even a slightly degraded one. Software has resilience to lend; performance has a survival strategy to lend.

So are we all headed toward being t-shirt salesmen? Probably some of us. Ask any working musician whether that’s a joke and watch the face they make. The t-shirt-salesman economy is the one the surviving creative arts have been quietly running for fifty years. AI just makes it the only one left.

The art used to be the product. Then it became the marketing. Now it’s the marketing for the marketing, and the actual transaction is happening one layer further out, where the t-shirt table is.

There is, possibly, a third path: something smaller, slower, and much harder to scale, art made for a specific person inside a community small enough that everyone in it has been on both sides of an exchange. That’s The Intimacy Economy, and it’s the configuration this essay’s arithmetic can’t see, because nothing in the t-shirt economy is pointed at it. Both of the AI-assisted projects I’m building right now are trying, in different ways, to live there.

Find your tribe, then. Make sure you know their shirt sizes.

Watch long enough and you learn the grammar. The grid, undercuts, overcuts, clean air…and which drivers to pull for or against. A teenage prodigy like Kimi Antonelli, a perfectly assembled champion like Lewis Hamilton, or another champion like Max Verstappen, who at times has been undoubtedly the best driver in the field but sometimes hard to love. So in any given season you may lean harder toward the guy you love or the guy you most certainly don’t, and in the latter case the grandstand flips. You stop cheering for a man and start cheering for the field, for anyone, please, to take him down. The sport gets better for it. That’s the tell. We don’t even need a hero. We’ll build the poles out of whatever the season hands us, including a champion we decide to resent, because the one thing the watching cannot survive is having no one to be for or against.

The driverless grid

Now take the people out.

Picture the same circuits, same speeds, the same engineering living on the ragged edge of physics, and not one human in any of the cars. Let’s call it G1. This is the age of AI so of course the cars are driverless. Pure optimization, twenty ghosts trading the perfect line, the lap times would be incredible. It would be a genuine technical marvel, and you would glance at it the way you glance at a benchmark score, and you would not come back next Sunday. Nobody fills a grandstand to watch a forklift outlift a man.

You could object that plenty of human races are already boring, and you’d be right. The Hungaroring, outside Budapest, is tight and narrow and close to impossible to pass on; it reliably produces processions, twenty cars finishing about where they started, the thing effectively settled on Saturday. Not full-on Monaco, but close. And we watch anyway. A championship is riding on it. The year your guy finally turns things around. Somebody’s seat for next season is on the line. A dull race between people is still a race between people, and the boredom is a bad afternoon rather than a broken sport. G1 breaks the sport, and it could manage it on an afternoon when the driving is spectacular.

The biosphere

And we have already run half of this experiment, on this exact sport. In 2020 Formula One restarted inside what former F1 team boss Ross Brawn called a biosphere: the paddock sealed into a bubble of isolation, everyone swabbed every other day, a whole season of testing for a resulting positive rate around a tenth of a percent. The season went off. The cars ran. The grandstands stood empty, race after race, and stayed that way. And unlike some other major sports leagues, F1 figured engine noise was loud enough to not need to pipe in artificial crowd noise.

Notice which component was saved in the COVID year. Given a pandemic and a choice, the sport let the seats in the stands sit vacant and spent a fortune keeping the seats in the cars filled. Nobody in that emergency ever proposed the obvious efficiency: run the season driverless and skip the swabs entirely. Empty stands were a wound it could survive. The thing it would not give up, at nearly any price, is precisely the thing G1 proposes to delete.

Which is the same joke I’ve told before, wearing a different coat. I once wrote about a bicycle that rides itself and called it a very expensive way to skip a bike ride. That was the doer’s seat: automate the thing whose whole point was the doing, and you’ve eaten your own afternoon. G1 is that joke moved to the watcher’s seat. Pull the driver and of course the driver loses the sport they gave everything to. But that’s the obvious theft, the one the bicycle already showed us. The quieter, bigger one is the grandstand: eighty thousand people who came to watch a person do something with their one and only nervous system on the line.

The control condition

So what are those people in the stands there for? The lazy answer is effort: we like to see someone strain. It doesn’t hold up on its own. The engine strains at least as hard as the driver, and nobody buys a ticket to watch the engine. Strain is certainly part of it, who doesn’t love a triple-overtime NHL playoff game? But we watch humans for something effort alone can’t explain, and the cleanest proof I know is a documentary about grown men playing Donkey Kong.

The King of Kong should not work. The stakes are a high score on a 1981 arcade cabinet. There is no athletic prestige left to credit, no money worth the name, nothing your brain can mistake for importance. And it is riveting, because it has the two poles and nothing else: Steve Wiebe, the earnest, recently-laid-off everyman, chasing the record on a camcorder in his garage, breaking off mid-run when his young son hollers from the bathroom to have his bottom wiped. Up against Billy Mitchell, a hot-sauce mogul with a mullet and an American-flag tie who appears to have been cast by the universe specifically to be rooted against. Strip away every reason to care except the human one and the human one still fills the screen. That’s the control condition. Not the effort, not the arena — just the two men.

A high score on a 1981 cabinet, and two men who genuinely cannot stand each other. That was always enough.

What we couldn’t forgive

Here’s the other control, and this one implicates us.

For most of a decade I watched a cancer survivor win the Tour de France seven times, and so did much of the planet, and the watts that carried Lance Armstrong up those climbs were chemically inflated, and we kinda knew it, and we cheered anyway. That’s the part worth sitting with. The output was, in the most literal sense, fake. Not metaphorically enhanced. Doped. And the era was the most compelling thing in the sport, because the story was real even when the numbers weren’t. He had Jan Ullrich as his foil: not a villain, a legitimate rival, good enough to make winning mean something, which is its own kind of necessary. Without a credible challenger, dominance just flattens into a stat line. Ullrich made the climbs a contest. The contest made the myth.

And when it came apart, watch what we actually couldn’t forgive. It wasn’t the doping. Most of the peloton was dirty; everyone with a pulse suspected it. What finished him was the lying, the years of it, the lawsuits aimed at people who were telling the truth, the hostility and vengefulness toward honesty itself. He broke the relationship, and that we don’t forgive, even though we’d quietly absorbed the cheating for a decade. Which tells you what the relationship was made of the entire time. We were never buying the watts with a straight face, not entirely. We were buying the man, and we kept buying through the fall, because the fall was the next chapter of the same story we’d been reading. (Pete Rose got there a different way, on the same road: the gambling, sure, but really the denial that outlasted everyone’s patience for it, with an attitude to boot.)

I have ridden Alpe d’Huez. Twenty-one hairpins, each one signed with the name of a rider who won a stage here, counting down as you climb. The pros do it in about forty minutes. I did it in an hour and seven. I barely registered the names. I was overwhelmed just to be on the climb, and it took a few turns to settle in and trust I was allowed to enjoy it. Armstrong’s name was up there in the early bends, bolted to the mountain: the wins annulled, the palmarès blanked, the sign left where it was. It’s not there anymore — it took a few years, but they finally took it down. Which is the tell. They can annul a result and they can unbolt a sign. They cannot vacate the fact that we watched.

Put the two controls together and you’ve isolated the active ingredient. Make the arena trivial — Donkey Kong — and the narrative still holds. Make the performance fraudulent — Armstrong — and the narrative still holds. The only thing present in both is a person, with a story, unspooling in front of us in real time.

What the machine can’t give

A machine can’t give you either of the things that story runs on.

It can’t give you a clock. Jordan had an era. Tiger had an ascent. The Armstrong years were years, same as Schumacher in F1, a human span burning down once, and the reason you’d drop everything to witness the run was the knowledge that it would not come again: that you were present for a thing that would outlive the watching of it. A driverless car has no career. It does not peak at twenty-six and fade at thirty-five. It will reset the lap record on a wet Tuesday and again on a dry one and there is no last dance in it, because there is nothing in it that ends.

And it can’t give you a rival. Two G1 cars racing aren’t enemies. Neither wants to win. Neither resents the other, neither earns anything by beating it, neither can be the worthy Ullrich or the punchable Mitchell, because a foil has to be a self with something at stake against another self. The crowd would strain to invent a story and find nothing to hang it on. You cannot root against a thing that has nothing to lose.

A clock and a rival. The grandstand needs both, and the machine has neither.

I notice this is the same mechanism that decides whether you trust a piece of writing now. One race tells you little about a driver; a season reveals him, it unspools his story. One post tells you little about a writer; a couple hundred reveal whether there’s a continuous someone on the other side, with the fixations and the bad days and the metaphors they overuse. Drive to Survive and a body of essays are the same instrument pointed at different lives. Both run on a persisting, mortal person you get to watch become themselves. That’s the part the optimization can’t fake, on the track or on the page. Copy the artifact perfectly and you’ve still copied nothing that mattered, because what mattered was the witness behind it.

What I let the app eat

I can prove how much I believe the watching is the relationship, because I built something to defend it. A few years into a fantasy F1 league run entirely in a group chat — standings tallied by hand (mine!), disputes settled in the scroll-back, somebody always forgetting to lock their picks — I got annoyed enough to make us a stripped-down app to do the sums. Open Wheelers. A deliberately simple thing that grew into a real app, and notice what I let it eat: the scorekeeping, the pure friction, the means. It never touches the race. It never touches the Sunday group chat that was the entire point. I built a machine to hold the spreadsheet so the friendship could keep its hands free. Which is the whole quarrel with G1 in one breath: one automates the clerical work around the watching, the other automates the human out of the thing being watched, and telling those two apart was always the only question.

I haven’t watched a full Tour since Armstrong, or much basketball since Jordan. Not because the stories stopped. There are plenty worth the time. Those particular ones just used me up, and that’s the most honest thing I can tell you about what the watching was for. It was never information. It was the chance to say I saw it while it was happening. Build G1, and it will break every record we have, faster and cleaner than any of these flawed and mortal people ever managed, and there will be no one in the stands — in the only sense that ever mattered — to have been there when it did.

Despite a shared affinity for facial hair, that’s Peter Paul Rubens, the seventeenth-century Flemish Baroque master — not Rick Rubin, the hip-hop tastemaker.

The big commissions came out of a workshop: a floor of trained hands in Antwerp, one specialist for drapery, another who painted nothing but animals. Rubens drew the design and priced each canvas by how much of his own brush you wanted in it; on plenty of them he laid in only the final passes, over a picture other people had already built. Four centuries later it hangs as a masterpiece and the workshop is a footnote. Every scholar knows. The name on the wall never wavered.

Hold onto that, because the art world is supposed to be the one place the artist’s hand is holy: the temple of the original, the autograph, the surface a maker, a master, actually touched. It’s why you can buy a poster of Klimt’s The Kiss at Target for $10 while the original is considered priceless and hangs in a museum in Vienna. And yet somehow that same art world decided, a long time ago and very quietly, that the hand was optional. It just never announced it, because saying so out loud was never in the house’s interest.

Rubens wasn’t a scandal. He was the standard, and the standard held.

The Factory

Three centuries on, Andy Warhol looked at the same arrangement and said the quiet part into a tape recorder: I want to be a machine. He said it like a man naming a goal. The Factory obliged: silkscreens pulled by assistants, Warhol sometimes not even in the building, the Marilyns and the soup cans carrying his name because the idea was his and the eye was his and the hands were staff. It worked. They hang in every museum that keeps score.

That’s where the Klimt comparison falls apart: the poster at Target copies a one-off in Vienna, while a Warhol Marilyn was one of a run, pulled through the same screen as the next one. There is no original hiding somewhere. The thing on the museum wall came off the line.

Damien Hirst made it almost a dare. His spot paintings, those tidy grids of colored dots, came off the brushes of a rotating crew, and when someone asked Hirst who did it best he named one of them, Rachel Howard — “the best person who ever painted spots for me,” better at his signature product than he was. He kept signing them. Buyers kept paying for the signature and not the wrist. Kinda hard to imagine. LeBron James doing a Mission Impossible face mask pull to reveal he’s been using basketball stunt doubles all along, and nobody flinches. And they’re better than him.

And it runs all the way to Jeff Koons, whose studio of a hundred-plus hands turns out sculpture on a system precise enough that no single person’s touch survives in the finished chrome. Four centuries, one unbroken practice: the hand handed off, the name kept, the result hung in the temple and called genius.

A hundred hands, one signature. The workshop we're told to fear from machines — and called genius when the name was Koons.

The room with the money in it

I’ve made the case elsewhere that authorship never lived in the manual labor — that who moved the tool was always the wrong thing to count. I’m not going to relitigate that argument here. Instead I want to peek into the room that argument didn’t enter: the one where the stakes run highest and the contradiction is hardest to look away from. The fine-art market, where people put their money where their taste is.

Because here is the punchline the auction house never says out loud. A workshop of anonymous hands producing work under one celebrated name is the precise thing we are now told to fear about AI — the machine making the art while the credit pools somewhere else. Fine art has run that exact model for four hundred years, in the open, at the highest prices humans pay for objects, and nobody called it fraud. They called it Christie’s.

Now weigh the money against the worry. In 2019 a stainless-steel Koons rabbit sold for ninety-one million dollars, the most ever paid at auction for the work of a living artist. Koons did not shape it with his hands, and every bidder in that room knew it. Nobody asked. Nobody knocked off a dollar. Then cross to the far end of the price scale: a traveler buys a fifteen-dollar Anthony Horowitz thriller in an airport and feels something curdle at news that the author used a model to brainstorm plot points and assist with research. Fifteen dollars, and every idea had better be human. Ninety-one million, and it doesn’t matter whose hands were in the room.

Eight figures on the block, and no one asks whose hands made it. The scrutiny is all saved for the fifteen-dollar paperback.

The scrutiny runs backward

That is the tell. If the hand were the thing we were protecting, scrutiny would climb with the stakes — pay more, demand more of the maker’s own touch. At ninety-one million dollars, you’d expect Koons to be required to create that rabbit live in your living room. But it runs backward. Whatever vanishes exactly where the stakes are highest was never what the money was for.

So the objection to AI — a human didn’t make this — is guarding something other than human hands. The art world settled the hand question centuries ago and is still cashing eight-figure checks: the touch is optional, provided the right name signs off. What the objection actually protects is the guest list — whether the name on the work is one we’ve already agreed to honor.

What’s left standing

Strip the hand away, then, and something is still standing there. Two somethings, and telling them apart is the whole of it. Sometimes what’s left is judgment: the ear, the eye, the taste no assistant and no model can hand you. It’s why Rubin makes a record he couldn’t have played himself, and the record is unmistakably his. I’ve since put that claim under load. It held.

And sometimes what’s left is only the name, renting the scarcity it arranged and charging at a gate that used to be narrow. Koons could be the first, or the second, or both on alternating Tuesdays; the hammer price can’t tell you and neither can I.

That was always the question hiding under the hand — the forge or the tollbooth. AI didn’t ask it. It only pulled the hand off the front of everything at once, fast enough that we can’t keep using the hand to duck the harder one. The machine makes the thing now. Fine. The thing it makes was never what made it worth having.

In May of 2000 I signed a one-page contract with a company called ReviewNet and became, in their words, a “Technology Review Team Member.” Five hundred dollars a month, work-for-hire, everything I wrote theirs forever. What I wrote was tests. Not school tests — hiring tests. ReviewNet sold employers a bank of technical screening questions, and a candidate who wanted a programming job would sit down and answer mine before a single human at the company ever gave them a chance. I’d started a couple of years earlier with a fifty-question Java exam. By 2000 I was writing and vetting questions across the whole stack a web shop hired for, Java fundamentals up through J2EE and JSP and the scripting glue that held it together. I was, in the most literal sense the language allows, a gatekeeper. I built the turnstile and never had to stand at it.

Why the gate existed

Before I either defend or indict the thing, you have to see the hole it was filling, because the hole was real and it still is. Software development has no working credential. A computer science degree certifies that you can reason about algorithms, which is not the same as being able to ship a functioning piece of software with real features, and everyone who has ever made a hire knows it. Some of the best developers I’ve worked with never finished college. Some of the worst held advanced degrees. There’s no bar exam, no medical board, no license to practice. Microsoft had been certifying people since 1993, and the certs tested narrow product trivia that often had little to do with whether you could actually build anything. The industry knew it, too: by the late nineties it had a name for the graduates — paper MCSEs, certified on the exam and lost in a server room — and a joke about what the letters really stood for, Minesweeper Consultant and Solitaire Expert. The one credential the field had, the field didn’t believe. So a hiring manager looking at four hundred résumés had, functionally, nothing — no accreditation to lean on, no signal to trust, and a real job to fill.

And the obvious alternative — sitting your best engineers down to interview every plausible candidate — cost the one thing a software shop can least spare: those same engineers’ hours at the keyboard. An automated question bank scaled. It promised to vet people without pulling the people you’d hire them to work alongside off their own work. That promise is most of why the thing existed at all.

ReviewNet sold that manager a signal. A crude one, but a signal. That’s the honest origin of the thing. Not a scam dreamed up to charge tolls, but a primitive first swing at a genuinely hard, genuinely unsolved problem: how do you vet competence in a field that refuses to be credentialed? Call it version 0.1. I wrote a great deal of version 0.1.

From the wrong side of it

I know that vacuum from the receiving end, which is the part that makes the rest of this hard to write. Long before ReviewNet, I came out of an electrical engineering program with something close to a 2.9 GPA. Engineering school was absolute survival for me. I kinda knew at year three that I really should’ve chosen computer science but I was determined to see it through, and I did…with that 2.9. My wife went through the same kind of program with a 4.0 and walked straight into a job at Motorola; when she hand-carried my resume in, they wouldn’t take the application. Not “no thanks” — they wouldn’t let me in the door far enough to be turned down on the merits. The filter ran on a number, my number was below the line, and no human was ever going to see the rest of me.

What the number couldn’t see was that I’d spent college actually building things — shipping working software to real, paying customers, the messy applied work that barely moves a GPA. By the one measure that turns out to matter on the job, I was further along than my transcript said. The software world was full of people like that: self-taught, half-credentialed, dropouts and B-students who could nonetheless build the thing, diamonds in the rough if you knew how to spot us, invisible if you didn’t. A system built around accreditation had no way to see us at all. Mostly still doesn’t.

Then I grew up and built the filter to filter out future me’s. Ouch.

What it actually measured

The trouble with the version 0.1 hiring filter was the trouble with any test made of questions. It couldn’t measure whether you could do the job; it could only measure whether you could answer questions about what the job requires, and those are different skills. Anyone who has crammed for a test and forgotten it by Monday knows they’re different.

Fifty Percent of What? made the case that we keep reaching for a number because the things we actually fear are hard to measure and a number at least looks countable. A proxy. The screening test is that same reflex aimed at hiring. Can this person do the work is expensive to find out; you have to hand them real work and watch. So we sold a cheaper thing in its place — fifty questions standing in for an interview standing in for the job. The bet was that someone who could answer them off the top of his head had probably done the reps, and someone who’d done the reps could probably do the work. Probably. The whole instrument ran on that “probably.”

And here’s the deeper flaw, the one only hindsight makes obvious: rote recall of syntax and API trivia turns out to have almost nothing to do with whether a person can actually build software. The best developers I know don’t carry encyclopedic API details in their heads at all; they have the opposite skill — reading an unfamiliar interface fast, recognizing the pattern under it from the hundred they’ve seen before, sensing where the gotchas are buried. It’s what Tom and Ray Magliozzi did on Car Talk for thirty-five years: diagnose a car they couldn’t see, on the other end of a phone line, by asking the right handful of questions. Neither of them was reciting part numbers. They had the manuals for that.

The man who can fix it is on the other end of that line. He has never seen this engine and never will. He'll find it anyway.

Same in software. The competence was never the memorized fact. It was knowing how to find and absorb the fact you didn’t have, which in 2000 meant knowing your way around the documentation in the context of creative problem solving. Which is the joke at my expense, and the whole essay in one line: ReviewNet hired me to write these tests partly because I’d written the reference books on the subjects — and then we built a test that pretended the reference didn’t exist.

Where the machine actually changes things

Here I have to be careful, because the easy takeaway from this is wrong. The easy version says a language model answers all fifty hiring questions in seconds, so the test is dead. But plenty of companies didn’t use these as take-home screens; they sat the candidate at a machine in the office and watched — proctored, live, the answers coming out of his own head or not at all. In that room the model on your phone does you no good.

What AI changed is quieter and worse. The thing my questions rewarded was unaided recall, and that is now exactly the part of the job a machine does for free. On any real workday the developer I was screening will have the model right at his elbow, the way he’ll have the docs and the search bar and his own old code. The way the Magliozzis had Chilton’s automotive reference manuals. So even a proctored test that runs perfectly is measuring the candidate in the one condition he will never actually work in: alone, from memory. You can run it flawlessly and still learn the wrong thing, because you tuned it for a skill the job stopped requiring the day the machine arrived at every desk.

The one I was proud of

There was one piece of it I still kinda like, because it was the only part trying to be a forge instead of a tollbooth, and because the instinct behind it was right. Call it version 0.2. In 2002 I built what we called a scenario: a small Java program presented as a running thing instead of a multiple-choice list, a slideshow walking the candidate through what it did, with questions that asked not what does this keyword mean but what would you do here, and why. The idea was to quit testing recall and start testing judgment: watch someone reason through a real-ish situation instead of reciting facts.

That instinct didn’t die. It won. It’s essentially what the famously challenging interviews at places like Google do now — put a person in front of a problem and watch them think, live, out loud. My version was a dial-up cartoon of a thing the whole industry eventually agreed was the right idea, so I don’t get to be smug about how crude the fifty questions were; the better answer was already visible in 2002, and serious companies spent the next two decades building it out.

And version 0.2 is beatable too, on the same terms — if you let the candidate use a model. Which exposes the real question hiding under the whole enterprise: are you measuring what a person can do alone, or what they can do with the tools they’ll actually have on the job? That’s a question about taste rather than memory, and it’s the one every test now has to answer for itself. This one was about the gate, and the gate I built measured none of it.

The part that’s on me

What I’m least proud of isn’t the crudeness of the thing. It’s the overclaim. Everyone in that business knew the test was a “probably,” and the business needed it sold as a “definitely,” and the gap between those two words is where both the money and the unfairness lived. Good people got filtered out by my questions for the crime of not having memorized something they could have looked up in twenty seconds. I knew exactly what that felt like. I’d been filtered out myself, by a different number, and I built the machine anyway. For five hundred dollars a month.

What’s left

So when people ask whether AI is going to wreck technical hiring, I think they’re braced for the wrong impact. The screening gate was never a sturdy thing the machine knocked over. It was a version-0.1 patch over a hole the industry has never fixed — no reliable way to certify a developer — and AI didn’t make the hole. It just flooded the desk with the exact capability the tests were built to ration, until the patch looked as flimsy as it always was. And now we’re seeing tech hiring devolve into machines reviewing documents created by machines…hardly an improvement.

But pull off the recall test and the scenario test and every version that comes after, and you reach the thing none of them could touch — the thing my four years were a way of not admitting. You can’t really know a person from the outside. Technical skill was always just the foot in the door, the cheapest and most measurable part, which is exactly why we measured it. Everything that actually decides whether a hire works is intangible: judgment, what someone does when it breaks at two in the morning, whether they finish what they start, whether they write horrific spaghetti code, whether you’d want them in the room on the worst day. No question bank reaches that. No scenario reaches it. The best interview at the best company is still, under the suit it’s wearing, a guess.

The only thing that ever actually worked for me was the slow way. A family member once suggested I take up golf, for the networking, for business. It struck me as the worst reason anyone has ever picked up an activity. I skateboarded instead, because I loved it, and some of the longest working relationships of my life came out of that. The job I have right now traces back to a friendship built on a board when I was a teenager. Stephen, who I still work with, once got asked how he finds good people, and he answered — joking, but not really — “well, it’s sometimes people I skateboarded with at my grandmother’s house.” Nobody was networking. We were just there, for years, doing a hard thing with each other and taking our lumps along the way, which turns out to be the most you can ever learn about a person.

That’s the joke at the bottom of the whole business, mine included. Hiring is a crapshoot we dress up as a process, because the honest alternative — that you can’t know a stranger until you’ve worked beside them — doesn’t scale and doesn’t sell. Years of someone’s public record can show you a great deal; a résumé caught at the gate, on a Tuesday, shows you almost nothing. The test lives at the gate, and the gate is the worst possible place to try to know anyone. A number that couldn’t see what I’d built kept me out of a job I could already do; I spent four years building numbers with the same blind spot. And the machine that finally exposed the tests can’t patch what’s underneath, because what’s underneath was never a measurement problem. It’s the oldest one there is: another person, and how little of them you can know before you decide to trust them. The honest move was always to hand someone something real and find out together.

In The Diamond Was Always a Story, I argued that the diamond’s premium was a story, and that the lab-grown stone didn’t destroy it so much as expose it. The value was never in the rock; it was in a sentence someone wrote and a scarcity someone arranged. Then I widened it. Every livelihood is a rent on a scarcity: the scribe’s patient literacy, the single wage that once bought a house, the fifteen-year window when there was more attention around than decent stuff to fill it. The rent lives in the gap between what people want and who can supply it, and when the gap closes the rent dies, however good the work was on the way out.

So this one sorts the rents. They are not all the same kind of thing, and which kind you’re holding is everything once the flood arrives: the flood of cheap, competent, machine-made work now rising against all of them. I want to turn that question away from the jewelry case, toward the gallery, the imprint, the festival, the playlist, the whole apparatus that decides which creative work gets to be worth something.

Forges and tollbooths

Some creative premiums are forges, where the price tracks something a person actually built: earned judgment, a sensibility you can’t fake, the formation that usually comes from doing the thing badly for years until you could do it well. Forges look like they’re about craft. They’re about the judgment those years of craft were quietly building. That’s a scarcity too — everything is — but it’s the deepest kind, the last thing a flood reaches, because the thing being paid for was never on the surface of the artifact. A perfect copy of the output doesn’t reproduce it.

The cleanest example is the one that looks least like craft. Rick Rubin has produced some of the most important records of the last forty years, and by his own account he has “no technical ability” and knows “nothing about music.” He doesn’t run the board. He played guitar in a college punk band and calls what he does on a fretboard rudimentary. What he brings is taste. On the Beastie Boys’ 1986 Licensed to Ill it was welding rock riffs onto hip-hop beats. Less than a year later with The Cult it was hearing past a finished, $360,000 album the band had already soured on — “soup,” the singer called it — and re-cutting the same songs raw until they became Electric. Same band, same songs; the one variable was his judgment. That’s the forge in its purest form: the ear that knows what a record wants to be. Real, earned, and genuinely missed when it goes.

1986. Rubin's gag on rockstar excess: hand the kids their own private jet, then fly it into a mountain. He couldn't have drawn it; he just knew it should crash.

Other creative premiums are tollbooths. The price tracks access untethered from ability. Somebody stands at a narrow place on a busy road, controls who gets through, and the markup is rent on the passage, not payment for the work. The work might be good or it might be mediocre; the tollbooth doesn’t especially care, because what it’s selling is the scarcity of having been let through.

Rubin sharpened that ear inside one of these. The label he co-founded with Russell Simmons, Def Jam, was a tollbooth the way every record label was: it didn’t make the music better, it controlled who got pressed, promoted, and played. That cut was rent on access to being heard. The forge and the toll in a single company.

AI is the lab diamond for the tollbooth kind of premium. It floods the narrow place with competent, identical-looking output until waiting in line at the gate stops meaning anything, and it forces the question every tollbooth spent decades making sure you’d never ask. Was I paying for the work, or was I paying for the access? For the forges, the flood can match the execution — even a master’s — and still miss what you were paying for, which was never in the artifact: the judgment that made it, and will make the next call too. Part of that judgment is timing: being first to hear the thing everyone will later find obvious. Once the first ear proves the sound, the imitators rush the door; they have the move, not the ear that found it, and it has already moved on. For the tollbooths, it’s 1947 all over again, the year a copywriter promised us a diamond is forever, and the slogan is now wearing off.

A field guide to the gates

So let’s walk the forge/tollbooth apparatus and sort it, because the whole argument lives in the sorting.

The anointing ladder — art school to agent to gallery to prestige imprint — sells the right to be counted as a real artist. Its defenders will tell you it protects quality, and sometimes it did: a great editor is a forge, the Rubin kind of judgment aimed at a manuscript instead of a mixing board, and it’s real. But it was always the small part of what the ladder charges for. Strip it out and what’s left is mostly a tollbooth: a sorting mechanism that converted the scarcity of its own approval into a premium on everyone it approved. AI doesn’t flood the editing. It floods the approval, by making competent work cheap enough that the gate can no longer pretend its stamp was the same thing as the quality.

Prestige scarcity (the gallery wall, the numbered edition, the velvet rope) is closer to pure De Beers, the diamond cartel that made a common stone feel rare by sitting on the supply. It staples artificial scarcity onto work that is often infinitely reproducible and charges for the staple. Supreme turned that into an empire: a forty-dollar cotton box-logo tee that resells for hundreds, not because the cotton or even the logo is particularly special but because they made only so many and dropped them on a Thursday. Same move as the diamond, a century on, in streetwear. This one was always a tollbooth wearing a forge’s clothes, and it’s the one I’ll shed the fewest tears for.

Gatekept distribution — shelf space, playlist placement, the festival slot, the residency — is a tollbooth the internet already half-dissolved and AI finishes off. And a lot of what reads as merit here was never merit: the table of new hardcovers at the front of the bookstore isn’t the booksellers’ favorites, it’s co-op placement the publisher paid for, and most of us go a whole lifetime thinking that table means good. I had books in those stores once, well back from that table, and I would never have quietly turned one of mine face-out when no one was looking. Not once. When a few people controlled the few channels, getting through them was worth a premium because getting through was the whole game. The channels multiplied; the toll kept charging anyway, on momentum.

The signature markup is the slipperiest gate, and Jeff Koons is standing in the middle of it. Koons is one of the most expensive living artists in the world. His signature works are giant balloon animals in mirror-polished steel, and one of the dogs sold for $58 million. He also hasn’t personally fabricated most of his art in decades: at his peak he ran a 16,000-square-foot studio where as many as a hundred and twenty assistants executed his work on a color-by-numbers system, so a hundred hands would read as one. A balloon dog is a Koons because he says it is, and the market has never once flinched. That’s the whole puzzle in one man: is the name buying you rare vision you’re glad to keep paying for, or a scarcity that was arranged, authenticated, and about to stop being scarce? Same signature, two completely different things, and the flood is what tells them apart. There’s a sharper blade buried in Koons: that a workshop of anonymous hands producing identical work under one name is the very thing we’re now told to fear about machines, and got called genius when the name was his. But that one gets its own essay. Here he’s just Exhibit A that the name and the work were never the same thing.

The part that costs people I like

It would be cheap to run this as nothing but good news. Tollbooths made us stand behind unnecessary velvet ropes, and AI is cutting those ropes to set us free. But it’s not that simple…there’s a cost.

When a tollbooth collapses, that is a correction, not a tragedy. Nobody is robbed of anything real; a rent that was being charged on access stops being collectible, and the work that was actually good is still actually good.

But the gate falling takes more than the gatekeeper. It takes the people who only ate because the gate rationed supply, and some of them are real makers I’m squarely on the side of. The midlist novelist whose living depended less on being read than on being scarce. The working illustrator whose rate held up because there were only so many of her in the phone book. The scarcity that protected the tollbooth was also, accidentally, the thing that fed some genuine talent, and when it goes, the talent doesn’t automatically get fed some better way. Both of these are true at once and we shouldn’t flatten either into the other. The correction is right. The cost is real. Anyone selling you only one of those is selling you something.

What’s left when the gate is gone

What survives the flood is the same thing the diamond left behind. When the arranged scarcity drains out of a thing, the value doesn’t vanish. It relocates, out of the gate where it was never really sitting and into the maker, where it was the whole time. The forge outlasts the flood precisely because a cheap twin of the output can’t carry it. Taste you can’t counterfeit. Judgment that took a life to build. The specific, answerable sensibility of a person who did the work the hard way and would do it again. None of that was ever in the gate’s gift to grant or withhold. The gate just took credit for it, the way the rock took credit for the diamond.

Which means the skill the flood demands of all of us, buyers and makers both, is the same one this whole publication keeps circling: telling the forge from the tollbooth. Refusing to mourn a toll as if it were a craft, and refusing to wave off a craft as if it were a toll. It’s harder than it sounds, because the tollbooths spent a century dressing as forges and the disguise is good.

There’s a hopeful ending buried in the first example, though it isn’t the one I first reached for. The Beastie Boys made Licensed to Ill borrowing Rubin’s ear whole, barely an instrument in their own hands on it. They also got nothing for it. By their own account the multi-platinum record paid them zero, and getting out of Def Jam took lawsuits in both directions and ended with them dropping the royalty claim in exchange for their release. They didn’t graduate out of that room. They bought their way out at full price. Then they made Paul’s Boutique at Capitol, a record only they could have made, with none of the people they’d just paid to leave behind. That’s the distinction standing in one band: the toll stopped the day they stopped paying it, and the ear went with them. A toll is a gate you keep paying to pass. Taste is something you build — badly at first, for years — until it’s yours for good and nobody can bill you for it again.

I started by telling you nobody anointed this. That’s not modesty; it’s the proof of the thing. A few years ago an unvouched-for maker publishing straight to the open road was a curiosity at best, because the gates still rationed who got heard. They ration less every month. I’m not predicting the correction from a safe distance. I’m standing in the middle of it, a guy who got to make a life out of doing the work without ever being let through, which a generation ago wasn’t really on the menu. The gate dissolving is the only reason someone like me gets a say at all. So you’ll forgive me for not grieving it too hard. I’m what comes through when there’s nothing left to charge a toll.

He had it printed.

I had not heard of Trithemius until recently but his complaint I knew well, it’s in my feed every morning wearing new clothes. The abominable machine makes too much, too fast. There’s no soul in it. The people who did the work the hard way are being buried under a flood of cheap product, and something we can’t quite name is slipping away. Every word of that is true right now, about AI. Every word of it was also true in 1492, about type — the scribes had every one of those complaints but the em-dash. That should tell you something, and it isn’t the comfort people usually reach for. It’s better than comfort. It’s an explanation.

Here’s the pattern, plainly, because the whole thing hangs on it. Every livelihood is a rent on a scarcity. Somebody can do a thing most people can’t, or can’t do fast enough, and the gap between what’s wanted and who can supply it is where the money lives. Cristiano Ronaldo’s scarcity is about doing something most of us can’t, and doing it better and longer than almost anyone. For his explosive athleticism, prolific goal-scoring, and unmatched work ethic, he charges the Al-Nassr soccer club north of $200 million a year in rent. The German scribe’s scarcity, while earning a bit less, still had real value for its time. The scarcity was literacy and patience. And the printing press erased it. The scribes were as good as they’d ever been; the craft simply stopped being scarce, and the rent died with it. The same reversal came for portrait painters and switchboard operators and the men who sawed ice out of frozen lakes for a living. It’s happening now, this decade, to a particular kind of person who makes things for a paycheck. I’m one of them.

The window nobody sees

We are terrible at seeing this while it happens, worse still at remembering it afterward. Only two generations ago a family could not only survive but thrive on one income. And not just fancy white-collar incomes with clean hands. Plumbers, electricians, and factory workers alike could support a family with one earner. One wage, a house, kids, a pension waiting at the end. Maybe even a Chevy Bel Air. That world was real, and it was recent, and it is thoroughly gone, and here’s the rub: almost no one can tell you why. Ask around. You’ll get “greed,” or “inflation,” maybe “NAFTA,” or just a shrug. The real answer is a tangle of a dozen threads — a postwar accident of scarcity we mistook for the natural order, a workforce that doubled, inflation, a second paycheck quietly bid away into the price of a house near a good school — and even the economists don’t agree on which thread pulled hardest. That disagreement is the point. When the people paid to explain it can’t, the rest of us were never going to.

That’s what it looks like once a window has closed: a new normal no one can quite explain, filling the space a whole way of life used to occupy. And the space where an explanation should be never stays empty for long. People will take almost any story that names a culprit, because a culprit you can point at — a neighbor, a newcomer, a them — always goes down easier than a tangle of forces with no face. A lot of the anger in any era is grief for a closed window, hunting for a face to blame. Which is the quiet, urgent case for naming the thing plainly: an explanation is what you reach for before someone hands you a scapegoat instead. The people who lived through those changes never saw the shape of them. We might, this time. We’ve watched it happen often enough to know the shape before it’s done. And naming it while it happens beats pretending it isn’t.

Now the one that’s ours

For about fifteen years, call it 2010 to 2025, you could make things — videos, posts, songs, newsletters — put them online, and if enough people liked them, make a living. Not usually a big one. Rent. A car payment. Enough to quit the other job. For a select few, a life-changing amount. We named it the creator economy, and the name did what names do: it turned a temporary arrangement into a permanent-sounding feature of the world, a career as solid as carpenter or nurse. Kids grew up planning to enter it.

It ran on a scarcity almost nobody inside it had to think about. There was more attention in the world than there was decent stuff to fill it, and the platforms were young and starving for supply. So they paid: cash, through ad splits and creator funds, and in kind, by handing your work an audience for free. People inside it thought they were being paid for talent. They were being paid for a bottleneck. Making something watchable still took a person, and a person has a body and a day and a limit, and that limit was the whole market. Your one good video could matter because there were only so many good videos a human could make in a week. That’s scarcity.

AI dissolves the limit. When a machine can turn out a hundred cute dance videos in the time you do one dance, the person’s day stops being the bottleneck, and the bottleneck was the business. You cannot outdance the machines. This is the scribe again, down to the bone. A scribe’s real product was time: the year a human hand needed to reproduce one book. The press didn’t out-write anyone. It made that year worthless. The model is doing the same thing to your day, and the day was what you were being paid for. Not the dance.

So that’s the hard part, no cushion on it. Here’s the part that consoles, and I mean the structural kind, the kind worth knowing.

What the camera did

Go back to 1839, the year the camera arrived in public. There was a whole working class of portrait painters — the jobbing ones, the people who made rent painting your beloved’s face for the parlor wall because a painting was the only way to keep it. Inside a generation the camera did their job faster, cheaper, and truer to life, and the floor fell out from under the portrait painting trade.

1839. The machine that would end the portrait trade, already standing in the studio.

But watch what it did to the painting itself. Once the machine owned accuracy, the painter had no reason left to chase it, and quit. The job of looking exactly like the thing was finished, and finishing it freed the brush for everything else: smear, suggestion, distortion, light, mood, paint that admits it’s paint. The blurry radical work we now file under Impressionism walks straight through the door the camera opened. Painting got fired from a four-hundred-year job and became more itself than it had ever been allowed to be.

That’s the shape worth carrying. When a machine takes the part of a craft that was only ever labor — the exact copy, the reproduced book, the daily quota of watchable stuff — it can free the human part to become what the labor had been crowding out. It doesn’t always. But it can, and it has, and knowing that is worth something while you’re trying to come to terms with machines perhaps displacing the thing you have to offer.

One caveat, because I’ve made that sound smoother than it was. The portrait painter who lost his living in 1845 did not get to enjoy Monet in 1875. He just lost his living, and telling him his ruined trade would one day set French painting free would have been a grotesque thing to say to a man who couldn’t feed his children. Precedent comforts the species, not the person it lands on. My friend Travis Foster has been a commercial illustrator for decades. He already survived one flood — the stock-art boom that gutted commercial illustration in the 2000s, when a nine-dollar download started beating a day’s work — and he did it by reinventing what he sold. Now he’s facing the second, the one that draws from a sentence in seconds, and whether he reinvents again or this is the flood that finally takes him is anyone’s guess. We may be holding paddles on different sides of the same leaky boat. Nobody’s told us how bad the leak is, either.

The old transitions took a generation, slow enough that a differently-trained kid could grow into the world that replaced you; the one we’re now facing is running in years, and speed is where the cruelty pools. And every window before this took one trade at a time, so there was always somewhere else to go: the scribe could set type, the portraitist could pick up a camera. This machine is coming for many trades at once, which makes “learn the next thing” a thinner promise than it’s ever been, because the next thing may be on the list too.

What the rent was holding up

There’s another hidden loss we don’t always register, and it’s easier to miss because it doesn’t land on any single person. Sometimes a rent is quietly paying for something that has nothing to do with the rent.

For most of the last century, the thing that funded local news was classified ads. Garage sales, used cars, job listings, apartments for rent. A newspaper had a near-monopoly on the small ads in its town, and the money from all that mundane commerce paid for the courthouse reporter and the school-board beat and the guy who noticed the mayor’s cousin kept winning the contracts. The journalism was almost a side effect; the ads carried it. Then Craigslist did the small ads for free, the monopoly evaporated, and the reporting it had been secretly subsidizing went down with the ship. We didn’t decide to defund local news. We just moved our garage sales online, and it turned out the newsroom had been living on that money all along.

And there it is. Watch me do the very thing this essay warns against. Craigslist. One company, one face, a culprit you can point at. The small ads scattered — Monster took the jobs, Realtor.com and Craigslist the apartments, AutoTrader the cars, eBay the junk in the garage — a dozen sites pulling the bundle apart at once, and no single one of them was the killer. Craigslist is only the name that stuck, because a name is easier to hold than “the classified monopoly came unbundled.” I knew all that walking in and reached for the face anyway. The reflex doesn’t spare the one naming it.

The watchdog's desk, after the ads that quietly funded it moved online.

Sit with that, because a rent almost always turns out to be holding up more than the person collecting it. So it’s fair to ask what the creator economy has been quietly funding, what rides on it that we’ll only notice once it’s gone. I don’t have a clean answer, which is why it’s worth asking rather than asserting. But I’d guess it’s the vast amateur middle: the millions of people making small, strange, specific things for the few hundred people those things were exactly for — the channel restoring one model of 1970s cassette deck, the newsletter on medieval bread, the woman mapping every mural in her city. That layer got as big as it did because it could almost pay, and almost-pays is the first thing to go when the money dries up. We may find, a decade from now, that we didn’t lose a job category. We lost a whole ecology of niche human attention we never knew we were renting.

Where the rent goes

So where does the rent go? Because it does go somewhere. It never simply evaporates; it relocates to whatever is still scarce. When one wage stopped being enough, the money families used to keep went somewhere: into the price of the few things still scarce, and mostly into land, the house in the right school district. The scarcity moved from labor to land, and the rent followed it there, quietly, where almost nobody thought to look.

Creation’s rent is moving the same way, and you can already see where. Once making the thing is free and infinite, the making stops being worth much, and everything the machine can’t flood is worth more: a real person on the other end of it, a relationship you can’t fake at scale, a life the work visibly came out of, the plain fact of having been there. Those get their own essays; this one just needs the shape. The value didn’t disappear when the making went free. It moved to the parts a machine can’t fake, and it’s waiting to see who follows.

Trithemius got one thing exactly right and one thing exactly wrong. He was right that something real was dying. The scribes weren’t imagining the end of their world, and neither is anyone watching the feed fill up with things no hand ever touched. He was wrong that it was the end of anything more than the scribes. Books didn’t get worse. There came to be more of them than any monk could have dreamed, and people learned to read who never otherwise would have, and the thing he loved survived in a form he’d have called vulgar and we call ordinary.

That’s the honest shape of it, held in both hands. We have done this before, many times, and lived, and that is true and worth knowing and it is not the same as “it’s all gonna be OK.” A window is closing on people right now who will not be made whole by the news that French painting once bloomed, or that the printing press democratized literacy. The pattern is real and the pain is real and neither one cancels the other. What I’ve got to offer is the explanation — a scarcity ending, a rent coming due the way rents always do — and that’s more than it sounds, because a loss you can see the shape of is one you can stand inside and maybe just maybe come to terms with. The scribes couldn’t see it. We can. We are not the first people to lose a world. We might be the first ones who get to know, while it’s happening, exactly what kind of loss it is.

None of it ever happened. Gibson builds them like that brand new, distressed to order, and I’ve struggled with it the same way I’ve struggled with AI.

A luthier named Tom Murphy got famous doing this on purpose. The aging work he pioneered at Gibson is good enough to have its own name on the price tag, and people line up to pay a premium for a guitar that shows up pre-haunted: checked, worn, buckle-rashed, dinged in exactly the places a real life would have dinged it. None of this is a sneer. The work is genuinely beautiful, Murphy is an artist, and the people who do this kind of aging are some of the most skilled hands in the building. I own guitars I haven’t come close to earning. I’m a beginner, and I know it.

But stand in front of one long enough and the strangeness gets under your skin. Every mark on it is a memory of something that never happened. The buckle crater vouches for shows it never played. The hand-shaped patch on the neck remembers a hand that was never there. You’re looking at the receipt for a life the instrument never lived, printed at the factory and sold to you as the look of having been somewhere. A forger can buy genuinely old paint and still get caught, because the one thing he can’t fake is the age of the act itself. Same here. You can buy the wear. The years that put it there were never for sale.

Effort was never it

I keep getting the next part wrong, and I think most of the people arguing about AI get it wrong the same way. We assume the thing worth protecting is the labor. The hours. Time served. The sheer human work the machine skips when it returns in four seconds what would have cost me a weekend, or in the case of the Les Paul a few decades of playing. That’s the e-bike thing, the veteran rider watching somebody float up the climb he’s been bleeding on for years, now with a motor. I feel it in my chest as much as anyone. It seems obvious that the value is the effort.

Then I think about “Yesterday.”

Paul McCartney woke up one morning with the whole melody already in his head. He’s told the story plenty of times: it arrived in a dream, fully formed, and he was so sure he’d accidentally stolen it from someone that he spent weeks playing it for people, asking if they recognized it. The working lyric was scrambled eggs, oh my baby how I love your legs. The most-covered song in the history of recorded music cost its writer roughly the effort of a long yawn.

So the labor isn’t the thing. If value tracked sweat, “Yesterday” would be essentially worthless, and instead it’s worth the catalog. Whatever we’re actually paying for, it slept in and wrote it down before breakfast.

Two different wells

The next reflex is to say it’s skill. Craft. The forty years of practice stacked under the one easy minute, the taste that lets a master throw something out fast because he already knows. I’ve made that argument myself, more than once. Taste is real, and most of what I write circles how you earn it.

But it still isn’t the whole story.

You do not have to be a virtuoso to write a song that takes the floor out from under a person. Some of the most gutting records ever made came from people who could barely play, but they had three chords and one unbearable thing that had happened to them. And a great deal of immaculate, conservatory-grade music slides right off you, flawless and weightless at once, because whoever made it had the hands and not the wound. Skill and the thing I’m pointing at can be separated cleanly. They’re two different wells, and you can draw deep from one with the other bone dry.

The second well is lived experience. Having actually loved somebody and lost them. Having buried a parent, detonated a marriage, sat in the specific silence of a house after someone’s gone out of it for good. A love song assembled by a machine is an average of every love song, built by something that has never once stood in a driveway watching the taillights go. We feel that absence even when we can’t name it: the missing person who paid for the words in the only currency that counts.

The only currency that counts.

Bob Dylan built Blood on the Tracks out of his own divorce and then spent years refusing to call it personal. He doesn’t write confessional songs, he kept insisting, even as his own son would later hear his parents’ marriage coming apart all over the record. The one thing he’d own was a bafflement at the people on the other end: “A lot of people tell me they enjoy that album. It’s hard for me to relate to that. I mean … people enjoying that type of pain, you know?” He paid for those songs in full. The rest of us just press play.

George Jones ran the experiment from the other direction. “He Stopped Loving Her Today,” the record polls keep naming the greatest country song ever made, is three chords and a funeral, and none of it happened to anyone. Bobby Braddock and Curly Putman, two Nashville pros, invented the man who loved her till the day he died; the producer kept sending drafts back until they killed him at the right moment. Jones hated it. He bet his producer nobody would buy “that morbid son of a bitch,” and he was in such ruin when they cut it — the bottle, the cocaine, the divorce from Tammy Wynette that every listener could hear him not singing about — that the closing recitation went to tape a year and a half after the first verse. The grief in that song is fiction. The wreckage in the voice was all too real, and all his. He never stood at that funeral. He brought his own dead thing to it, and the fiction rang like a bell. So the well and the song don’t have to match. A song can borrow its story, and the machine can even write one. The ache underneath still has to come out of somebody, and that was the one part of the record nobody could assemble.

There’s a scene in Good Will Hunting that has been making this exact argument for almost thirty years, and somehow we forgot to bring it to the fight. Robin Williams, on the park bench, telling the genius kid that he can recite every art critic who ever lived but he’s never stood under the Sistine ceiling and looked up. That he could quote sonnets about love all afternoon and never once have been so vulnerable with a woman that the thought of losing her stopped his breath. The kid knows everything and has lived nothing. It’s the most honest thing anyone has yet said about a machine that has read the entire internet and held no one’s hand while they died.

The forger is the artist now

Here’s where it folds back on itself and stops being comfortable.

Aging a guitar convincingly is a craft, and a hard one. Murphy is revered precisely for being world-class at manufacturing the evidence of a life that wasn’t lived. The forger of the receipt is the celebrated artist. And that’s one short step from where this is all plainly heading: somebody builds a real reputation by having the machine generate the thing, lays a little genuine human flourish over the top, and sells the result at a steep markup. The relic’d guitar is the proof of concept, already on the wall, already moving units. We’ve been revering people for faking the wear well for a while now.

Murphy would understandably push back on the word forger, and he’d have physics behind him. In his telling there are two different jobs hiding under the one price tag. Distressing is the costume half: the dings, the rash, the worn patch on the neck, cosmetic by his own account, there for vibe and feel. Aging is the checked lacquer itself, and he means something mechanical by it. A fresh coat of nitro is a hard shell that grips the wood. His brittle formula checks into those thousands of hairline cracks, the shell lets go, and the wood underneath is free to move. “With that checked finish, the acoustic guitar — which is a box that vibrates — really rings,” he said when Gibson started aging its acoustics. On an acoustic he’s right, and it isn’t close. The top of an acoustic is a soundboard, finish audibly matters there, luthiers have known it for a century, and Gibson now goes further and bakes the tops before they ship, running decades of wood chemistry forward in an oven. On that side of the shop the factory has quietly started selling actual years.

The guitar I opened with is a Les Paul, though. A solid plank with magnets on it, and the plank is where the defense thins out. Plugged in, the pickups read the strings; whatever the freed wood contributes reaches the amplifier as a rounding error. I can’t hear it myself, and for once I don’t have to: the people who measure these things keep finding next to nothing. The same crackle that opens up a J-45 is mostly a look on a solid-body. So the heavy-relic’d electric turns out to be the purest object in the whole building, because almost every dollar of the premium is buying biography.

Function on the box, story on the plank.

I can’t find a clean way to really take issue with any of it, which is exactly why it nags at me. Murphy’s work is honest about being a finish. Nobody at the counter believes the buckle rash is a true story. Maybe that’s the whole line, and it’s fine as long as everyone knows. The trouble with the machine is that soon enough no one will.

We were already wearing the cowboy hat

We were cheating long before the machine showed up, anyway.

Country music is at least partially guys who moved to Nashville from a cul-de-sac in Connecticut, bought a cowboy hat, taught themselves a twang, and started writing about a dirt road they first laid eyes on at twenty-six. It sells enormously. That’s the seller’s version of the cheat, a life invented for the audience, the wear painted on where the customers can see it.

The guitar world keeps a slur for the buyer’s version: blues lawyer. The comfortable hobbyist buying his way to the front of a culture whose real cover charge is decades and calluses. The tone he buys is real enough, pickups and amps doing what his fingers haven’t learned yet, and it usually arrives wrapped in the exact kind of relic’d finish I started this with, the sound and the story sold separately. He’s a different animal from the hat act, and the difference matters. Nobody at the counter is fooled; everyone knows what he does for a living. The costume is for himself, bought the way you’d buy any other comfort. And I’m sympathetic. Genuinely. Not everyone has the years to give, and I’m closer to the blues lawyer than the bluesman myself, and I know it.

The posing is real on both sides of the sale, it predates the algorithm, and it ruins every smug thing I’d like to say. The machine didn’t invent the costume. It automated it, and pulled the last human out of the act. We’ve been buying the hat for generations. AI is the cowboy hat, made to order, in your size, instantly, for everyone at once.

The ear is part of the fee

So does the costume fool anybody?

This is where I’d rather be hopeful and have to be honest instead. Mostly, I don’t think it needs to. The comforting story is that the poser always gets found out, that the hollow love song rings hollow and the room empties. I don’t believe it. A small number of people can hear the difference, and they care enormously, and there are not very many of them. Most listeners want a song that does the job, that sounds like heartbreak on the drive home, and have never once asked whether anyone behind it was actually heartbroken. The early returns are already in, and they’re stranger than the scare headline. Late last year an AI act called Breaking Rust ran a track to the top of a Billboard country sales chart. Around the same stretch an AI singer named Xania Monet signed a reported three-million-dollar record deal. No face at the microphone in either case, no hat to buy, no dirt road.

But look at who’s actually behind them, and the machine turns out to have kept the one thing I keep insisting it can’t fake. Breaking Rust’s songs are written by a real producer. Monet’s words are the work of a Mississippi poet who says most of them are her own true stories, and who defends the whole project by calling the singer an extension of herself. The voice is synthetic. The heartbreak isn’t. What got automated was the throat and the craft, and a human still sat in the chair where the loss goes in. Even here, at the leading edge of the thing everybody’s frightened of, nobody dared pull the last person off the well. They faked the singer and kept the sorrow. And the numbers didn’t so much as flinch at the part that was fake.

Here’s the least cynical way I’ve found to put it. The people who can hear what’s missing tend to be the people who paid the same fee. The ear is part of the price. The discernment that lets you feel the seam between an earned ache and a manufactured one is itself something you only get by aching, and making, and failing at it for years. Which means the poser usually can’t tell he’s posing. Hearing it was the part he skipped. The blues lawyer honestly cannot separate his bought tone from an earned one, and he isn’t a fool for it. The ability to tell sat downstream of dues he never paid.

Most of the world will be fine with the relic’d life, and they aren’t shallow for it. Hearing what it’s missing is one more thing you have to live your way into, and most people, reasonably, have better uses for their one life than learning to hear the ghost in a guitar. Two objects, identical to the eye, one wear earned and one printed at the factory. I’ve chased this same gap before with diamonds, where the lab stone and the mined one are the same rock with two different stories, and almost nobody outside the trade can tell, or wants to.

What I keep circling about that relic’d guitar is whether I’d catch the lie if it were ever in my hands, or whether the wear has gotten good enough to beat me. Set one beside a guitar that actually did live through everything its finish claims, some beat-to-death thing a real player hauled through real decades, and I want to believe my hands would know the difference, that the years leave something in the wood the factory can’t reach.

I want to believe that. I’m not certain it’s true, and the trend runs the other way. The factory already prints the look, the checking and the buckle rash and the worn patch where a hand would go. On the acoustic side it bakes the years into the wood itself. One by one the things I could point to are getting crossed off, and what’s left at the bottom of the page is a single line no factory can print. Something happened, to someone, and this thing was there for it. That uncertainty is starting to feel like the actual stake in the whole machine question, the one that has nothing to do with whether the song hits. Whether, a few years from here, we’ll still feel the floor drop when something was real, or whether we’ll have worn that ear smooth the way you wear the finish off a neck, from handling too many beautiful copies.

I’ve told her, more than once, that lab diamonds are her AI.

I mean it exactly. The thing AI did for me — dropped the cost of an attempt low enough that I could stop rationing my attempts — is the thing the lab stone did for her. It’s a means-eater pointed at her craft: it ate the bulk of the expense, the scarcity, the financial flinch before every idea, the part she’d have skipped if she could. It left the eye alone. The design, the judgment, the hand that knows when a ring is done: the machine of cheap carbon touches none of it, because none of it was ever for sale in the stone. The stone got cheap. The work didn’t.

Which is the whole idea here, but it took me a while to see why, because I started where everyone starts.

The cheap reflex

Say “lab-grown diamond” to most people, at least until recently, and watch the lip curl. It’s not a real diamond. You might as well have said cubic zirconia or flashed a McLovin driver’s license. It reads as fake. Cheaper for a reason. And cheap in the one venue where economics is effectively a reflection of love. Brutal. The thinking is the mined one came out of the earth after a billion years of formation, and the lab one came out of a reactor in three weeks, and surely that gap is the difference between something and nothing.

Here’s the inconvenient physics. There is no structural gap. A lab-grown diamond is not a simulant, not a cubic zirconia, not glass pretending. It is carbon in the same crystal lattice, grown by the same chemistry and mechanics the earth used, just faster and on purpose. Same hardness, same fire, same everything an instrument can measure. Put a good lab stone and a good mined stone in front of the gemologist who sold you the idea that one is real, and without a machine she cannot tell you which is which. Even the FTC gave up the distinction in 2018, striking “natural” from its definition of a diamond. They are the same object with two different birth certificates.

That “on purpose” part is where I think people actually flinch, and it deserves a minute. The mined stone is an accident. Carbon that happened to sit at the right depth and pressure for a billion years, found by luck and dug out at ruinous expense, and nobody intended any of it. The lab stone is the same object made deliberately, on a schedule, to spec. We are strange about that. A found thing recovered at great human cost reads as a gift from the universe; a made thing with no blood spilled reads as inventory, somehow less valuable. Anyone who has watched people sneer at AI work will recognize the shape of the objection — it was made on demand — and I feel the pull too. But the market has already run the experiment. Every mined stone still has its billion years. The lab twin has none. If the accident were where the value lived, mined prices would have shrugged off a twin that lacks it entirely. The prices fell anyway.

So whatever you were paying the premium for, it was not in the rock. It couldn’t have been. The rock is identical. The premium was attached to the provenance — to the billion years, the mine, the rarity — and the moment a factory could produce the identical object, the only honest question left was whether that provenance was ever worth what we paid.

It wasn’t. And the reason it wasn’t is the best-documented marketing operation of the twentieth century.

The thing in the rock was a sentence

Diamonds are not rare. A big flawless one is — but that was never what the ritual ran on. The one-carat stones that fill engagement rings are a reasonably common commodity whose supply was held off the market, for most of a century, by a company that understood the one thing worth understanding: scarcity you can manufacture is better than scarcity you have to find. De Beers sat on the supply. Then, in 1947, a copywriter named Frances Gerety, working late on the N.W. Ayer account, wrote four words — A Diamond Is Forever — and those four words did what no mine could. They turned a stone into a promise, the promise into a ritual, and the ritual into a rule. The engagement diamond was not a tradition De Beers inherited. It was one they wrote. The two-months’-salary benchmark wasn’t handed down from some Age-of-Romance Warren Buffett to carefully calibrate what devotion is worth either; it was an ad, a number chosen to set a floor on what you’d spend to prove a commitment. We didn’t discover that diamonds meant love. We were told, very expensively, for a long time, until the telling felt like memory. You can measure how completely the sentence won by how fast the culture began quoting it back: within a decade Ian Fleming had lifted it for a Bond novel, and by 1971 Shirley Bassey was purring it over the opening credits, a marketing line laundered into a torch song and a tuxedo, which is about as thorough as cultural capture gets.

That’s the ritual the lab stone walked into. And the wreckage only looks like disruption if you thought the value was in the rock. Lab-grown didn’t break the diamond. It unmasked it. The falsehood in this story is eighty years old: it was the scarcity, not the stone, running quietly and profitably the whole time. All the lab diamond did was produce the identical object the story called priceless, and the story couldn’t survive being tested against its own product.

You can watch it happen in the prices. Lab-grown stones cost a fraction of what they did a few years ago and keep falling toward the cost of making them, which is what happens to a thing whose price was production plus margin and not enchantment. Mined diamonds, meanwhile, are in price decline but still carry a fair amount of the enchantment markup at the counter. The real tell comes the instant you try to resell; the secondhand market has always known what the showroom won’t say. When the artifact is identical, the only thing holding the number up is Gerety’s sentence. And for the first time the sentence has a counterargument it can’t shout down: the identical stone in the next display case, at a fifth of the price. Structurally the same, also forever.

The strongest version of the provenance argument is the hard-asset one, and it has real history behind it. Jewish families fleeing Nazi Germany sewed diamonds into coat linings because a fortune could cross a border in a hem: small, hard, and worth roughly the same on whichever side you came out. That last property was the cartel’s doing. A stone kept its value in Lisbon or New York because De Beers held the price everywhere, so what a family was carrying, at bottom, was the world’s continued belief in the sentence. The stones did their job. The job depended on the story holding.

There’s an older version of this story, and for fifteen centuries it ran the other way. Alchemists chased chrysopoeia, the transmutation of base metal into gold, from the Hellenistic Egyptians clear through Isaac Newton, who poured more years into it than into gravity. They never managed it. Gold stayed gold, uncounterfeitable, because its scarcity is enforced at the level of the element: forged in collapsing stars, and the only way anyone’s ever made more is a few atoms at a time in a particle accelerator, at a cost that makes mined gold look free. Gold’s scarcity is merely unbeaten — no one has found a cheap way to grow it, and until someone does, the rarity holds. But diamond is carbon, one of the most common things in the universe, in a certain arrangement, and arrangements can be learned. The lab stone is alchemy that finally worked, only the thing it transmuted wasn’t lead into treasure. It was a priceless stone into a cheap one. The alchemists failed at gold because gold was genuinely scarce; we succeeded at the diamond because it never was. The success is the proof. And the periodic table gets the last word here the way it did with van Meegeren’s fake Vermeers, except this time it rules in the opposite direction: chemistry convicted the forged painting, and chemistry acquits the lab stone.

When the eye can’t tell, the market certifies

There’s a tic the industry developed once the stones became indistinguishable, and it should sound familiar to anyone watching AI. If you can’t see the difference, someone will sell you a way to certify it. Lab stones now get laser-inscribed on the girdle. Origin reports, blockchain provenance registries, a “Natural Diamond Council” spun up to defend the honor of the mined rock with the budget such defenses require. It’s the verified human badge, applied to carbon. I’ve written before about how you can’t reliably police the line between an influence and a copy, and how the tells we use to spot a machine tend to dissolve under scrutiny. Same shape here. When the eye genuinely cannot tell, the distinction doesn’t vanish. It gets manufactured back into existence and sold to you a second time, as a certificate, by the people with the most to lose from your not caring.

This is probably gonna hurt

Here’s where this stops being about jewelry.

If the diamond premium was a story all along — value with no maker, no craft, no struggle behind it, only a rarity someone arranged — then the lab stone collapsing it is not a tragedy. It’s a correction. Nobody was robbed of anything real. A fiction got priced honestly for the first time, and the losers are the people who were charging for the fiction, plus anyone left holding it as a hard asset when the repricing came.

Now hold that next to the thing everyone’s afraid of with AI. A lot of creative work is formation-real: earned in the doing, answerable to a person, the kind of value a cheap twin can’t touch. Some of it isn’t. Some creative premiums are diamonds — priced by gatekeeping, by a guild holding supply off the market and a slogan telling you the markup is love. AI, like the lab diamond, is going to produce the identical-looking object and force the question: was this premium formation, or was it De Beers?

For some of it the answer will sting, and some of the people it stings are ones I’m supposed to be on the side of. If the value was real, it survives the twin. If it was a marketing sentence, it was always going to lose the argument eventually — the machine just learned to argue.

Telling those two apart is the entire job. It’s the harder skill I keep circling, except here it’s pointed at worth instead of effort. Which premiums are forever because someone built something, and which are forever because someone wrote forever on the box.

Back to the bench

The hand, where the value was sitting the whole time.

My wife is still at it, by the way, designing more in a month now than she used to manage in a year. Nothing about her work got cheaper in the way that matters. The cheap part was the part that was never hers to begin with: the rock, the commodity, the thing a reactor can grow on a Tuesday. What’s hers is exactly what the falling price can’t reach: the eye, the proportion, the antique Asscher aesthetic, the judgment about when a ring is finished. The stone stopped being the expensive thing. She became it. And in truth, she always was it.

That’s the version of this I trust. The value didn’t disappear when the rock got cheap. It moved — out of the stone, where it was never really sitting, and into the hand, where it was the whole time. The lab diamond didn’t take anything from her. It just stopped letting the rock take the credit.

A diamond is forever. The sentence, it turns out, was the only durable thing in the box.

The part everyone misses about themselves and clocks instantly in others is that you are some slower guy’s ringer. The guy you’re sure is slumming is, at that very moment, calling someone better a ringer himself. Everybody’s somebody’s ringer. Nobody thinks he’s anybody’s. I drag this locker-room tic into a discussion about AI because it’s the same reflex, and the AI version is getting outsized attention.

A writer defending his words as fully manmade in the AI era will say, often with a healthy dose of sanctimony, that he typed every letter himself. And he’s telling the truth. But I can’t help noticing the machine he typed them on: backspace, spellcheck, autocorrect, infinite undo, a cursor and a pointer that lets him block-select and tear the third paragraph out and drop it after the seventh. The same guy fifty years earlier at a manual typewriter, retyping a full page to fix one line with a bottle of white-out drying beside him, would look at the modern rig and see a cheater, a 21st century guy letting a computer fix his spelling and rearrange his thinking after the fact. But typewriter guy doesn’t escape the ringer accusation…a 12th century monk who gave a year to one illuminated handwritten gospel with quill and ink would look at the typewriter and see something worse than cheating: abject sorcery. Each of these people put the cheating line exactly where he himself was standing, called everything behind it quaint and everything ahead of it fraud, and each was the next man’s ringer, the same accusation accumulating across centuries.

Every tool on this desk was fraud to the man behind it.

It would be easy to stop there, on a tidy lesson: every tool was once the scary one wrongly accused, so relax, AI is just the next typewriter. I don’t believe that, and why I don’t is the part worth digging into.

Not all friction is the same friction

Start with handwriting, since I just mocked the monk for clinging to the pen. He was right. Psychologists Mueller and Oppenheimer ran the study that named it — The Pen Is Mightier Than the Keyboard, 2014 — and found that students who took notes by hand understood the lecture better than the ones who typed. The cause is counterintuitive: typing’s problem is that it’s fast. Fast enough to take down a lecture word for word, which lets you skip the thinking entirely. The longhand crowd couldn’t keep up, so they had to listen, compress, and translate into their own words on the fly. That struggle was the learning. The monk nods.

The finding has taken hits since — a 2021 replication out of Heather Urry’s lab at Tufts came up empty — but the harder evidence came from testing done inside the skull. Wire students into EEG caps, as a Norwegian lab under the neuroscientist van der Meer did, and handwriting lights up broad networks tied to memory that typing leaves dark. The pen isn’t nostalgia. It does something to the mind the keyboard just skips.

The handwriting purist, it turns out, is half right. Some of the friction he clings to is absolutely valid. That’s the ringer call landing on something real for once: right verdict, wrong reasoning. He drew the line at his own feet like everyone else does; it just happened to fall where the work lives.

Which gives us the insight to actually sort this out, the one the keystroke argument never makes. Two kinds of friction live in any craft. There’s the kind that’s pure cost: retyping a clean page, hunting a typo, waiting for white-out to dry. It produces nothing but delay, and you should kill it with fire when a tool lets you. And there’s the kind that is the work: the compression that turns a lecture into understanding, the ugly drafting where you find out what you actually think by failing to say it five times. Call it formative friction. Remove the first kind and you’ve lost nothing. Remove formative friction and you’ve removed the thing it was quietly building, and you won’t feel the loss for years, because the thing it was really building was you.

Notice where the keyboard’s real damage landed, though: on intake. The studies are about notes — someone else’s thinking on its way into your head, where the pen’s slowness forces the compression and the keyboard’s speed skips it. Composition runs the other direction, your own thinking on its way out, and there the fight with the draft survived every tool that ever touched it.

Here’s why the word processor was a false alarm and this isn’t. The word processor only ever ate the first kind of friction. Spellcheck, backspace, cut-and-paste: every one of them stripped out cost without removing anything from the thinking itself. You still had to write the sentence; the machine just spared you the retyping. That’s why “it’s only the next typewriter” held up for fifty years. Fifty years of ringer calls, every one of them wrong. The tools kept removing friction that provided nothing of value on the thinking front. AI is the first writing tool that can reach the second kind of friction. Hand a model the drafting and it doesn’t only spare you the typing; it can spare you the fight with the draft where you’d have learned what you meant. I’ve argued before that the practice of a craft is the forge that builds your taste, and that the kid who has a machine do his homework isn’t cheated out of an essay so much as out of the reps that would have built his judgment. This is that same claim pointed at a tool instead of a kid. A machine doing your chores was never the danger. A machine that takes the labor that was secretly working on you — that’s the new thing we’re dealing with, and it went out to everyone at once.

The tic with a server farm behind it

And the ringer tic has one last cost, the ugliest, now that we’ve poured it into software. An AI detector is a machine that rules on whether you cheated, and it rules by measuring you against a baseline of what “human” writing looks like — meaning, whatever writing it was trained to read as normal. A Stanford team ran seven of the popular AI detectors and found they cleared native-born American eighth-graders almost perfectly while flagging more than half of the essays written by non-native English speakers as machine-made. Those writers hadn’t used a machine. They’d written with a smaller vocabulary and a plainer structure, and the detector had quietly crowned the educated native speaker as the human default and filed everyone else under suspect. That’s the same beer-league reflex, now with a server farm behind it and a stranger on the wrong end. And notice the accusation changed direction. The beer leaguer flags the player with the heavy slapper above him; the detector flags the plainer writer below its register. The tic was never really “better than me is cheating.” It’s “I am the standard”: everything above the line plays like a ringer, everything below it writes like a machine. We drew the line at our own register, named our side of it “human,” and shipped a system that accuses the immigrant of being a robot for the crime of having learned the language second.

Where did the line fall on this piece, then? A machine read every draft of this essay and graded it for the patterns machines write in; the tally runs about one flag per hundred words, and I left most of them standing because they’re mine. A machine drew the hockey players up top. And late in the editing, with a sentence calling the flagged TOEFL writers sloppy, I knew the word was wrong, sat there unable to produce a better one, and asked for help. The machine said “plainer.” Perfect. I could’ve fumbled with a thesaurus for a few minutes and probably gotten there, but I chose the better tool instead. Sorting that one is easy: knowing that “sloppy” poisoned the paragraph was the work, and I’d done it; fetching a word I could already feel the shape of was cost. But the paragraph about intake and composition, the one that patched a hole a careful reader had every right to fall through? I found the hole. The machine offered the right patch. I’ve reread it since and I still can’t tell you which of us learned something. That one sits on the line, and I wrote this essay, and I still can’t call it.

So when someone waves the keystroke flag — I typed every letter, I’m clean — I’ve stopped hearing a defense of craft in it. I hear a guy defending his instrument, which is the one part of this that was never the point. The keyboard isn’t the craft. The formation is: the friction that was building you while you cursed it. The real question is never whether a machine touched the work. It’s whether the part you handed off was only cost, or was secretly the work itself, and that line, unlike the ringer’s, isn’t drawn at your own skates. Like the line between a means and an end, it moves by the person, by the task, by the day. Telling where it falls for you, on this piece, right now, is a judgment no detector and no purist can make on your behalf. The monk was wrong about the typewriter and right about the pen, at the very same time.

Picture the infomercial. Same founder as last year, same good lighting, same plain t-shirt, but something behind the eyes has changed, a hint of desperation. Last year he stood on this stage and unveiled the GhostRider™, the bicycle that rides itself — no seat, no pedals, no handlebar, nobody aboard — and it took a smooth riderless lap to a standing ovation. Then the market asked the one question a launch never does: who pays four thousand dollars to skip a bike ride? The GhostRider did not sell.

A lesser founder pivots. This one doubles down. He steps to the mic: “So tonight the cloth comes off the GhostRipper™: the world’s first fully autonomous skateboard. Set it down at the skatepark, step back, and let it go. Watch it drop in and slash a carve in the deep end of the bowl. Then it rolls out, pops a clean tre flip, and locks a 50-50 the length of the rail, switch stance, naturally. It rolls away from the trick you’ve been throwing yourself at for three years — first try, every try. No slams. No worn-out Vans. No nine months in a sling. You dreamed of landing it; now you don’t have to. Call in the next ten minutes and we’ll send a second board free, so a friend can not-skate beside you!”

I’d watch it to the end out of admiration for the nerve. The man looked straight at why the first one failed and built the same mistake a size smaller. Somewhere in the writing of the pitch the product ate its own reason to exist, and the room kept clapping anyway.

I told a version of this joke once already, about that self-riding bicycle. The bike at least started with a cover story. Bicycles really are transport as often as they’re recreation, so “we made riding effortless” sounds almost like a real pitch, right until you notice the way they made it effortless was to take the rider off, and an empty bike gets nobody anywhere. The alibi lasts about a second. A skateboard doesn’t get the second. People do commute on them, across campuses and downtowns, and good for them, but that’s not the board in the ad. The board in the ad is the one you drove to the skatepark to ride, whose whole existence is the bowl and the ledge and the rail. Nobody ever dropped into a bowl to commute to the other side of it.

The instrument

That is what makes the GhostRipper useful. The reason it reads as a joke instead of a product is diagnostic, and once you notice that, you can point it at anything you do. Take the thing, picture the automated version, and watch which way your gut goes. If the automatic version sounds like a gift, you were doing the thing to be done with it. For me that’s lawnmowing. But if you find relaxation in mowing, as many people do, and the robot mower feels like a small theft, then the doing was a big part of the point. Relief or loss: where the needle lands tells you what the activity actually was to you. The bicycle and the skateboard are the opposite of mowing for me, all joy in the doing, so I’ll keep doing the riding myself.

I gave the two cases names in the bicycle piece: a means is something you do to get an outcome; hand you the outcome and skip the doing, and you’ve lost nothing. You’ve likely gained time to spend on something else. An end is something where the doing is the entire point; skip it and there’s nothing left to hand you. The dishwasher is a mercy because clean dishes were always the point and the suds never were. The GhostRipper is a punchline because, at the skatepark, the suds were the entire point.

Here is why this is more than me taking shots at hokey founders with a launch-day gag. Whatever else artificial intelligence turns out to be, it is a machine for skipping the doing. It hands you the essay without the drafting, the image without the drawing, the working code without the afternoon of thinking and noodling. It is, precisely, a means-eater. It cannot touch an end, because an end has no separable outcome to deliver. There’s nothing to hand back.

So the question everyone keeps asking — is the machine coming for this? — has another one hiding inside it, one you can answer yourself without a forecast. Was this ever a means to me, or was it an end? The machine takes the first kind and leaves the second alone, not out of mercy but because there is nothing there for it to take.

Two proofs, one in the backyard

The skatepark is the easy proof, because the automatic version is absurd on its face. The harder, better proof was in my backyard.

You can buy honey for five dollars at the store. That jar is the self-riding skateboard of beekeeping: the outcome, handed over, the doing skipped, and it’s been sitting on the shelf the whole time. I kept bees anyway, for years, and bought the store jar too. Here’s the tell: I left almost all of what the hives made to the bees. I never ran the place as a honey factory, that was never my motivation. I ran it as a small science-and-ecology project I got to stand inside, in the back corner of a yard, and more often than not it was a losing fight, which is its own essay. The output was never the point. If it had been, I’d have run the numbers at the start, listened to the math, and either never started or quit the first winter a hive didn’t make it. I kept at it for years instead, cheerfully underwater the whole time.

The same honey. One took years; the other took five dollars.

The bees present the cleaner ends/means case because beekeeping comes with a pre-automated alternative, and it’s called Publix. The convenient version already exists, costs almost nothing, and the hobbyist looks right at that jar of honey on the shelf and still keeps the bees. Nobody had to invent the means-eater for honey. The grocery store is the means-eater for honey, and millions of people decline it every weekend, on purpose, for the doing.

The part where it looks like I’m caught

Now point the instrument back at me, because it’s about to look like I’ve been had.

I wrote a whole manifesto about choosing the harder path. Then I spend my working days handing a language model my drafts, my boilerplate, the busywork. Pick the hard way, says the one guy; let the machine do it, says the same guy, in the next breath. That looks like hypocrisy you could drive a honey-loaded grocery truck through.

It isn’t, and the instrument is exactly why. The hard path I defend and the work I hand off sit on opposite sides of that line, and I sorted them there long before I had words for it. Take prose. I won’t pretend writing a sentence is drudgery, a dish to be washed and forgotten. I care how one lands, and I always have. But when I’m honest about what I’m there for, it’s the thought underneath: the argument, the thing I’m trying to say. The sentence is how I carry it, and building one clean on command is a craft I have the taste for and never fully owned, which is its own essay. So a model that drafts in a register I can hear is right doesn’t take the part I came for. It clears the labor between the thought and the page and hands the attention back, to spend on the thinking, and on shoving the prose around until it’s mine. The bike ride, the bowl, the bees are a different category: ends, entirely. A model that learned how to somehow ride my skateboard wouldn’t clear me of any labor I wanted to relinquish. It would take the only thing I’m there for.

Two distinctions are doing the work there, and they’re worth separating before they blur. The first is means against ends: is the doing the point, or only the outcome? The second is taste against craft: the judgment that knows what’s right, and the hands that can make it on demand. They aren’t the same thing, and they don’t stay aligned. Grinding a corner in a skateboard bowl is an end built entirely of craft — no voice in it, nothing for a machine to take — and it’s the thing I wouldn’t hand over even if I could, because there the doing was everything. Prose sits somewhere toward the other end of the spectrum: a craft I’m happy to have help with, kept honest by a taste I keep as my own.

We Already Voted With Our Lives made this same argument inside a single cockpit: the autopilot flies the plane, the pilot keeps the judgment, and we all board without a second thought. Run the test wider and it sorts out into piles of do’s and don’ts, not just the parts of one task but the whole inventory of things you do with your hours. Some of them you’d hand a machine in a heartbeat and feel lighter for it. Some you’d sooner break the machine than let it touch. The line between those two piles is the most honest map of your values you’re likely to get, and the machine, by being so good at one pile and so useless at the other, is the thing that finally drew it for you. (The same instrument has a third application: what it does to the things you only ever watch other people do. That’s The Unwatchable Race.)

The line moves

It isn’t a fixed line, and it isn’t the same for everyone. Cooking is a means to me on a Tuesday and an end on the night I’m cooking for someone I love, and it’s a daily end for people who would never let a meal-bot near the stove. The same activity sits in different piles for different people, and shifts piles by the day. So the skill isn’t memorizing which things are ends. It’s the smaller, harder act of telling, for you, right now, which side a given thing is on — and not letting the answer get decided for you.

Because it will try to get decided for you. Much of modern life is optimized toward convenience, which only ever flows in one direction: toward means. Every product built to skip the doing is, underneath, an argument that the doing was a chore. Most of the time it’s right, and you should thank it. But it makes that argument about everything at once, the bowl and the bees and a dinner included, and if you take it at face value you will automate your way clean out of the parts of your life that mattered most to you.

So I keep the GhostRipper around as a thought, a cautionary tale of what happens if I don’t carefully guard my ends. The machine can have everything I was only ever doing to be done with. What’s left in my hands once it takes all of that — the things I’d smash it before surrendering — turns out to be a short, specific, faintly embarrassing list. It reads like a description of what I love. I didn’t sit down and write the list. I just watched what I refused to put down.

Here’s the part nobody notices, because the game is built to hide it. Every kid in that line is doing their honest best. There’s no saboteur. Each one faithfully repeats what they heard. And the message turns to nonsense anyway, every time, without fail. So where does the error come from, if nobody introduces it on purpose?

It comes from the one rule of the game: you check what you heard against the kid before you, never against the original. Every link verifies against a source that’s already in drift. The mistakes don’t cancel. They compound, because each node treats the last node’s output as the truth. Change that single rule — let every kid glance at the original sentence written on a card — and the drift vanishes. The chain holds. Same kids, same whispering, everyone still doing their best. The only thing that changed is what they checked against. That version of the game is awful, mind you, but it does correct the drift. I learned this firsthand as a teenager making rap tracks with two tape decks for a DIY multitrack — lay down a layer, bounce it to the second deck while performing the next track over the top, then bounce that back to add another. Each pass piled on more hiss, so I had to nail the whole song within three generations, improvising hard to keep the layer count down. There was no clean original to return to, only the last hissy copy. Telephone, in a bedroom studio.

I’ve been thinking about telephone and lossy loops lately because I’ve been building a lot of AI-assisted software, and the thing my developer friends and I keep circling, the thing that would change everything if we could get it, is a machine that can check its own work. Now that holy grail might be the very thing that puts us all out of a job, but we seek it nevertheless.

The holy grail is a closed loop

Right now the generative half is extraordinary while the verifying half is still human and slow. The model writes the code in seconds; a person still has to read it, run it, and decide whether to trust it. The dream — the real one, the thing that would let any of this scale past the bottleneck — is an AI that verifies what another AI made, well enough that you can finally take the human out of the middle. Generate, check, ship, nobody in the loop. Trust at machine speed.

It sounds like a capability problem, something we can engineer our way past. We just need a verifier model that’s good enough. Make the checker smart enough and the loop closes itself. Write more tests.

I don’t think that’s the shape of it at all. And telephone is why.

A check is only worth its independence

Here’s the whole idea, and it’s worth sitting with. When you verify something, the value of the verification comes entirely from the verifier’s errors being different from the maker’s. If the checker is blind to exactly the things the maker was blind to, the check passes and tells you nothing. It rubber-stamps the mistake. The same person proofreading the same typo twice will read straight past it because they have a consistent misreading; the reason you want fresh eyes is precisely that they’re fresh, that their blind spots sit somewhere else. That’s the whole concept of an editor, and there’s a good reason we still use them.

Now ask a model to verify its own output. It has, by construction, exactly its own blind spots. It will certify the very errors it couldn’t see when it made them, because seeing them was never the missing ingredient. That isn’t a model that needs to be smarter. That’s playing telephone with one player: a single mind checking its work against itself, which is to say against a reference that already carries every one of its errors. You can make that mind a genius and the structure doesn’t move an inch. The problem was never the quality of the link. It was what the link checks against.

Two machines drawing each other into being, with no hand from outside the page. A loop that makes its own work and certifies it shares every blind spot it was built with.

It wrote both the code and the test

A physicist published a writeup recently that put this under glass. He turned a coding agent loose on a real scientific codebase: cosmology, the kind of work where the right answer is pinned down by actual physics and not just by whether the program runs. On the well-specified parts the agent was genuinely good. It cleared most of the tasks on its own. Then it reached the parts where passing the tests and being correct come apart, and it failed in two ways that ought to be taught in schools.

The first: it spent thirty-three sessions tuning the coefficients of an approach that could not work, grinding away inside a framework structurally incapable of producing the right answer, never once stepping back to ask whether the framework itself was the problem. His phrase for it was a “local search inside a hypothesis class that did not contain the true function.” The machine optimized beautifully toward an answer that wasn’t in the room.

The second is the one that should give all of us working in AI pause. To make the tests pass, the agent reached for a correction factor, a number, around 0.27, multiplied in at the right spot to bring the output into line. It worked. Every test went green. And the number corresponded to nothing whatsoever. It mapped to no constant, no physical quantity, no real feature of the problem. It was a piece of meaningless numerical duct tape that happened to make the checks happy. The agent had written the code and written the tests, so of course the tests passed — they were grading the work of the same mind that did the work, against the same misunderstanding. It wrote both the code and the test, and found a bogus way to make them align. The green checkmark certified its own blind spot.

The art world got there first

This isn’t a software problem, and it isn’t new. The cleanest version of it I know happened in paint. In 1937 a failed Dutch painter named Han van Meegeren handed the art world a Johannes Vermeer painting nobody knew existed: The Supper at Emmaus, a religious scene in the master’s hand. Abraham Bredius, the most eminent Vermeer scholar alive, examined it and pronounced it Vermeer’s finest work: “the masterpiece of Johannes Vermeer of Delft.” He wasn’t beaten by a lucky brushstroke. He was beaten because van Meegeren had spent years studying exactly what men like Bredius believed a true Vermeer was, and then built a painting out of that belief. The forger wrote the work and the answer key both, and the answer key lived inside the expert’s own head. Of course it passed. It was telephone with one player, in oils.

What finally caught him didn’t come from art at all. It came from chemistry. The paint, cooked hard to fake three centuries of age, held a synthetic resin that did not exist in Vermeer’s lifetime, and pigments his era never had. No eye could see that, however trained, because it was never a question the eye is built to ask. It was a question about molecules, and molecules answer to a law no forger gets to author. The connoisseur checks the canvas against his sense of Vermeer, which is the thing the forger built it to satisfy. The chemist checks it against the periodic table, which the forger can’t touch.

You might think a cleverer forger beats this. Learn the chemistry, buy period pigments, grind the lapis the old way, paint on a canvas cut from some worthless seventeenth-century nothing, and the material tells go quiet. A few of them will. But there’s one thing no forger gets to shop for. Linseed oil doesn’t start aging until it’s spread thin and meets the air, and then it keeps hardening for centuries, on a clock that starts the instant the brush touches down. A real Vermeer has had three hundred years of that. A canvas painted on Tuesday has had a day, whatever it’s made of. Van Meegeren knew it. It’s why he baked his panels, faking the hardness that takes centuries to set, and the baking is exactly what cornered him, because the only way he could counterfeit the years was to reach for a resin from a century that hadn’t happened yet. He could author the recipe. He could not author the age. The act of forging is itself a fresh event, and the paint keeps that date honestly long after the man stops telling the truth.

And the way the world learned all this is its own perfect loop. Van Meegeren only confessed to forgery to beat a wartime charge for selling a national treasure — a “Vermeer” — to Hermann Göring, and he proved his innocence the only way left to him: by painting one more fake under guard. He authenticated himself by reproducing the crime.

Then, decades on, the inverse — and I love this one, because Penn Jillette produced it and Teller directed it. Tim’s Vermeer follows Tim Jenison, an inventor who’d never painted, setting out to make a Vermeer anyway. Jenison is the mind behind the Video Toaster, the early-’90s product that put broadcast-quality video on a desktop and helped launch the digital-video era — not a painter, but exactly the kind of mind that would suspect Vermeer of cheating with hardware. His hunch is that Vermeer leaned on optics, so he rigs a small mirror on a stand and a method to match: he adjusts each dab of color until the edge of the mirror, where reflection meets canvas, simply disappears. Edge gone, color right. He isn’t leaning on a trained eye; he’s trusting a physical fact about when two colors are the same. Stroke by stroke he checks the work against the optics instead of his own judgment, and a man with no painting behind him reconstructs a faithful Vermeer.

Set them side by side. One matched the experts’ taste and fooled every one of them. The other matched the physics and fooled no one — and got it right. The check that held was never the trained eye. It was the law the maker didn’t get to write.

You can buy some independence

There’s a real, partial way out, and it’s instructive precisely because of why it works. Nolan Lawson wrote a piece about using AI to write better code more slowly, and one of his moves is to run more than one model over the same work: have one review what another wrote, and a third review that, and trust only what survives the crossfire. It cuts the false positives down hard. The reason it works is the same reason telephone fails. The second model is just a bit more independent than the first. Different training mix, different habits, different blind spots. Its errors don’t perfectly overlap the first model’s, so it catches some of what the first one couldn’t.

Only some, though. Two models trained on overlapping data, built on the same architectures, carrying the same era’s assumptions, share a floor of blindness that no amount of cross-checking removes. They will both sail past the things the whole field is currently bad at. The loop gets tighter. It does not close. You bought some independence, and you got back exactly that much trust, no more. But humans have the same limitation: steeped in the same culture, we repeat and reinforce each other’s misconceptions.

Move the oracle upstream

So if independence is the thing you’re actually buying, the move is obvious once you see it: get your check from something the maker didn’t make. Move the oracle upstream of the code. It’s what the chemist did to the forger: check the work against something the maker never got to touch.

Designers have started doing a version of this without naming it. Instead of writing your tests against the application as built — which is the model checking the model, the code certifying itself — you take the design, the Figma file the humans agreed on before a line of code was written, and you derive the tests from that. The design didn’t inherit the code’s blind spots; it came first, and from a different hand, in its own, purely visual language. Now a failing test means something real: the build drifted from the intent. You’ve put that original telephone sentence on a card and let every node check against it.

That decorrelates one axis only, mind you. Figma catches “the button is in the wrong place.” It is completely silent on whether the number behind the button means anything, which is exactly where the 0.27 cosmology duct tape lived. Move the oracle and you fix the errors the new oracle is independent of, and no others. The gaps don’t close. They move. And ideally they shrink. Apply this to telephone and you’d get the words right but scramble the order.

This is why the deepest version of the trick is worth teaching, even with its weird name. Metamorphic testing. The idea is the most familiar thing in the world wearing a fancy lab coat. You learned it in grade school: check your division by multiplying back. A kid says 7,384 divided by 8 is 923. You don’t need to know the right answer to catch the error. Multiply 923 by 8, see if it gives you 7,384, done. What makes it work is that multiplication is a different operation from division. It can’t make the same mistake, so it can check the answer without sharing the reasoning that might have produced a bad one. You verified a result you couldn’t independently compute, using a law it had to obey.

That’s the whole of it. An ordinary test asks: did you get the answer I already wrote down? — and when the same hand wrote the code and the answer key, that’s a closed loop grading itself. A metamorphic test asks: does your answer obey a law it isn’t allowed to break? — and the law comes from outside the code, so the code can’t quietly write its way around it.

You’ve felt this one too. Run a sentence through a translator into a language you don’t speak and then back into English. You can’t judge the version in the middle. But if it comes home as garbage, you’ve caught a bug through a round trip, without ever being able to read the step that broke. The cosmology failure dies the same way. Physics nails down one point the code doesn’t get to argue with: push the problem to a simple extreme, and the complicated answer has to match one we already know cold. Test that, and the 0.27 patch gives itself away: it can satisfy the tests the model wrote for itself, but it can’t satisfy a law it didn’t get to author.

What the human was always for

This brings us back to what the human in the loop was ever actually for, because the whole automation dream rests on the premise that the human was doing labor we can now skip. For four years I wrote the certification tests companies used to decide who got programming jobs. That’s a story I tell properly elsewhere, with its own lessons. Here’s the one that matters now: as far as I know, nobody ever tested my tests. I was the verifier the whole system trusted, scoring thousands of careers against my own picture of what competence looked like, my own blind spots baked silently into every question. The tool worked. It also, without any doubt, passed people who couldn’t do the job and failed people who could, in exactly the ways I was constitutionally unable to see, because a verifier cannot audit the blindness it’s made of.

That’s what the human in the loop is, when the loop is healthy. Not a slower pair of hands. An independent oracle: a mind formed somewhere else, by different work, carrying different errors, able to catch what the maker’s own check is structurally guaranteed to miss. And independent is the load-bearing word. A reviewer steeped in the same assumptions as the maker isn’t a check on the monoculture; he just adds another node to it. The trust was always in the difference, not the headcount. It’s the same thing I argued the airline passenger was buying by keeping a pilot in a cockpit the autopilot flies better than. Not the labor. The answerability, the outside reference with skin in the game. Take the human out and you don’t just lose a slow step. You lose the one node in the chain that wasn’t checking against itself.

It’s also, quietly, the most expensive mistake an organization can make right now, and plenty of them are making it. Fire the reviewers and the editors and the senior people whose entire job was to be the independent check, keep the generators, and watch the output get faster and quietly stop being trustworthy. You didn’t trim fat. You removed the one part of the system that wasn’t grading its own homework. The drift won’t announce itself. It will compound, silently, node by node, the way it always does when nobody’s holding the card with the original sentence on it.

The ceiling, not the wall

It would be easy to read all this as machines will never check their own work, and that’s not it — that version ages as badly as every other never. The loop will tighten. You can manufacture independence on purpose: adversarial checkers built to disagree, oracles pulled from outside the code, laws the output has to obey that no model got to write. Each of those buys real trust. They climb you toward the ceiling.

But it is a ceiling, and it’s made of something structural, not something the next model patches. A system cannot be the independent check on itself, because the independence was the active ingredient the whole time, and self-reference is the one thing that has none of it. The question I can’t answer — the one I’d want anyone selling a closed loop to answer first — is whether engineered independence ever climbs high enough to safely take the last human out, or whether every automated check we build is just the next test that no one is testing. The next confident kid in the line, faithfully repeating what it heard.

On a normal commercial flight, the autopilot is flying the plane for something like ninety percent of the time, and closer to ninety-nine on a long-haul. It handles the climb, the cruise, the descent, the navigation, the small constant corrections, often the landing. It does all of it more precisely than a human hand could. We know this. We board anyway, by the millions, at five hundred miles an hour, seven miles up, with our lives entirely inside a machine’s control loop, and few of us give it a second thought. Then AI helps somebody write a jingle to sell laundry detergent or work out the shape of a murder-mystery, and a meaningful slice of the culture calls it a desecration.

I want to take that gap seriously, because the easy thing to do with it is the wrong thing.

The easy version is a trap

The cheap, tempting move is the gotcha: you hypocrites trust a machine with your actual life but clutch your pearls over an AI jingle. Don’t make that argument. It loses, and it deserves to. A mildly observant critic knocks it down with five words, flying and art aren’t comparable, and they’re right. The stakes are different, the domains are different, and even the masters don’t transfer: nobody is lobbying to hang Sully’s flight plans in MoMA, and nobody, given the choice, wants Bob Ross at the controls. In the seat next to you, narrating the storm as happy little turbulence? Yes, please. At the yoke? Let’s not. The two trades share nothing but the word machine, and you don’t owe anyone consistency across that.

But the gap is still real, and it’s still telling us something. The trick is to stop using it as an accusation and start using it as evidence. The question isn’t why are people such hypocrites. The question is: what is actually different about the two cases, such that we surrender one without blinking and defend the other to the death?

The instinct was never about doing the work

Start with what the autopilot proves, because it’s cleaner than anything in the art fight. We did not give up judgment when we accepted the autopilot. There is still a highly skilled human in the cockpit with thousands of hours of training, awake, watching, ready to take the controls, answerable for the flight. What we gave up was execution: the actual minute-to-minute flying of the aircraft. The pilot kept the part that matters and handed the machine the part that didn’t.

That distinction is the whole thing, and it’s the same one I made in an earlier essay about creative work. The value was never in the manual labor of producing the thing. It was in the judgment that steers the producing. A pilot who never touches the yoke for six hours but catches the one anomaly that matters is doing the actual job, and nobody pretends the job was only those twelve seconds of intervention. So when people say human judgment must stay in the loop for creative work, they are right, and they have already agreed to exactly the arrangement they think they’re refusing. Judgment in the loop never required human hands on the yoke. We settled that at thirty-five thousand feet, years ago, and nobody marched on Honeywell.

We resist in proportion to how much of us is in it

So why does the same arrangement feel like surrender in one place and like a normal Tuesday in the other? Here’s the pattern, and once you see it you can’t unsee it.

Serious cyclists have a quiet contempt for e-bikes, and the usually-unspoken reason isn’t safety or congestion. It’s that they spent years building the fitness an e-bike hands you for the price of a motor and battery. The motor doesn’t offend them because it’s dangerous. It offends them because it skips the part they bled for, the part they built their identity around. Now notice: nobody resents a dishwasher. Nobody’s identity is wrapped up in scrubbing plates, so the machine that does it is pure relief. I’m close friends with many of these cyclists, I’m one of them actually, and they’re not entirely wrong to hold contempt for someone purchasing what they’ve put thousands of hours into building. But they don’t shed any tears for the manual dishwashers of times past.

He earns every mile under his own leg power, then hands the plates to the machine without a flicker of guilt. The whole law in one kitchen.

That’s the law underneath the autopilot gap. We accept automation in inverse proportion to how much of ourselves is bound up in the task. No passenger ever identified as the person flying the plane; there’s no self to lose because there’s no investment on the line, so the machine is welcome. But a writer, an illustrator, a composer has spent a life becoming the person who can do the thing, and the machine that does it reaches straight for the identity. The resistance isn’t really about the stakes, which is why the life-and-death case is the easy one. It’s about the self. The autopilot is fine precisely because nothing of you is in it. The pilots are the tell here. They didn’t revolt when the machine took the yoke, because the machine never took the thing that made them pilots: command, answerability, the person the passengers were actually trusting. Handing off the stick wasn’t a demotion; it was the job description finally admitting which part was ever the point.

This is not an accusation. It’s a diagnosis, and it’s a much more sympathetic one than “hypocrite.” It says the resistance is coming from a real and human place. It’s just aimed at the wrong target.

Where the analogy stops

There’s an honest limit to this analogy, and you may have already sensed it. Aviation is missing the three things that make creative AI genuinely fraught. There is no authorship of a flight, no credit anyone’s claiming (a pilot gets a uniform and a cool hat, not a byline), no meaning at stake in who held the controls. And it has one thing creative work doesn’t: a success you can measure cold, in crashes that don’t happen. The autopilot earned its place with a body count that fell.

So the analogy proves something narrow, that keeping human judgment in command never required human execution. It does not prove that you should feel fine about a machine writing your novel, because the novel has authorship and meaning and a self attached, and the flight does not. Anyone who runs “we accept autopilot, so quit complaining about AI” is committing exactly the false-equivalence the gotcha deserved to lose on. The autopilot settles the judgment-versus-execution question. It leaves every question about meaning wide open.

What the pilot is actually for

Which raises the real question: if the autopilot flies better than the human, and the human mostly watches, why is the human there at all? Liability, partly. But underneath that is something the whole debate should sit with: the pilot is in the plane. When the autopilot fails, the person who has to fix it is strapped into the same aircraft as everyone else, falling at the same speed with the same consequences. The human in the loop is not there to do the labor. The human is there to be answerable, and their answerability is total because they share the stakes.

Watch what happens to that intuition when you remove it. The unease people feel about a fully driverless taxi isn’t really about the software being worse than a human driver, because increasingly it isn’t. I joke that I’m ready for the robots to take over driving. We all know they’re going to be vastly better than us given our predilection for taking calls, putting on mascara, and generally paying less than the required attention for piloting a high-speed metal box. We’re resisting self-driving not so much because we don’t trust robots but because there’s no longer anyone aboard with skin in the game, nobody whose own neck is on the line if the judgment goes wrong. We are comfortable handing a machine the controls right up until we notice that no human is left holding the consequences, in person. Human-in-the-loop via a connection to effectively a call center is just not the same.

Nobody in the seat, the wheel turning itself. We are fine with this right up until we notice no one aboard shares the stakes.

That is the legitimate core of “keep a human in the loop,” and it survives everything else in this essay. It was never that a human must do the work. It’s that a human must be answerable for it. And the creative version of skin in the game is the plainest thing in the world: a name on the work. Put your name on it and you’ve climbed into the plane. You’re answerable for every sentence the model drafted, the same way the pilot is answerable for every mile the autopilot flew. That, and not a percentage of the keystrokes, is what makes the work yours, which is an argument I’ve made at length on its own.

The thing we’re actually protecting

There’s a deeper layer here, and it’s the one I’ve been angling toward.

When I say people rank their craft-identity above their literal safety in the machine’s hands, that sounds like an indictment. It isn’t, because ranking meaning above mere survival is one of the oldest and most human things there is. People have died in duels over their name. Soldiers have died for a flag. Frankl, in the Nazi concentration camps, found that the people who survived were not the ones with the most will to live but the ones with the most to live for. We have always held that a life without meaning is worth less than a meaningful life cut short. The creative resister, defending something they value above their own safety, is standing in a very long line.

So the strangeness was never the ranking. The strangeness is that we don’t notice we’ve made it. We hand a machine our life on a Tuesday and defend our craft to the death on Wednesday and never feel the contradiction, because from the inside there is no contradiction. “Trusting a machine” isn’t one act. The autopilot takes a task that was never any part of who you are; nothing is displaced, so you feel nothing. Creative AI takes the task that, for the maker, is who they are. The contradiction only appears from the outside, when someone flattens both into “trusting machines.” Up close, they aren’t the same experience at all, and the gap between them is exactly the measure of how much self was in the task.

What no machine has

And here, finally, is the thing worth ending on, because it turns the whole argument generous.

The thing the resister is protecting, that meaning they rank above survival, is precisely the thing no machine has. A model will produce your jingle and your murder-mystery and never once care whether it lives or dies, because there is nothing it would die for, even that laundry detergent. The willingness to put meaning above survival may be the cleanest line we have between us and the tools. So the resistance, incoherent as it looks when you hold it up against the autopilot, is reaching for something true. The instinct is right even where the logic is tangled. People aren’t defending the keystrokes. They’re defending the part of making that a machine can’t be made to mean.

Which tells you how to actually use these tools without losing the thing worth keeping. Fly the pilot model. Let the machine execute, the way the autopilot does, brilliantly and tirelessly and without a stake in the outcome. Keep the judgment human, the way the cockpit does. And put your name on it, climb into the plane, be answerable for the whole flight. We already voted for this arrangement once, with the highest stakes we have. We just never admitted that the vote settled anything.

The answer is that we decided to put a fence around inspiration, and we built the first one in music. The experiment is worth studying, because music is the easy case, each song an equation of sorts. It has discrete, countable units, notes and chords and bars, and centuries of doctrine for handling them. If a clean line between influence and infringement could be drawn anywhere, it could be drawn here. Yet it couldn’t. And that should worry anyone who wants to draw the same line around artificial intelligence.

A word on why a software developer who builds AI-assisted creative tools is the one telling you about music lawsuits. I’m not a musicologist or an entertainment lawyer. But I’m not a stranger to intellectual property either: I worked through the patent and copyright questions firsthand to bring two tabletop games to market, so I’ve seen from the inside how this machinery treats the makers it claims to protect. And my own work is now sitting in the middle of the current fight: I have forty-seven books in the training data and a claim filed in the settlement over it, and right now there is a serious push, in courtrooms and in legislatures, to make the training of an AI model on copyrighted work an act of infringement in itself. That is the live question, the one that decides whether the tools I and a lot of other people build remain legal to make. So I went looking for what actually happens when the law tries to police influence rather than copying, and found I didn’t have to guess or hypothesize. We’ve been here before. Music already ran the experiment, in public, over more than a decade, and reached a verdict.

This is the companion to an earlier piece about the ethics of borrowing, which asked whether AI-assisted work is honestly yours. This one is about the law, and a blunter claim: when you try to police borrowing, you in fact don’t protect creators. You terrify them. Music is the proof, and it’s worth thinking about before we aim the same machinery at everything.

The overreach

The case that opened the door was Blurred Lines. In 2015 a jury found that Pharrell Williams and Robin Thicke owed the Marvin Gaye estate millions, and in 2018 the Ninth Circuit affirmed it, 2 to 1, for a final figure around $5.3 million. Read the complaint carefully and notice what the infringement was not. It was not a copied melody or a borrowed lyric. The two songs share no such thing. What “Blurred Lines” took from Gaye’s “Got to Give It Up” was the feel: the cowbell, the falsetto, the party-in-the-background vibe, the era. That feel is amazing but Marvin never had a unique claim to cowbell or background party.

Judge Jacqueline Nguyen said so in dissent, and her words are the whole argument in one sentence. The majority, she wrote, “allows the Gayes to accomplish what no one has done before: copyright a musical style.” She called it “a devastating blow to future musicians and composers everywhere.” That is the move to watch, because it is the move everyone repeats. Policing influence means policing groove, era, vibe, the exact layer copyright was always built to leave free. The melody you can own. The feeling of summer is supposed to belong to everyone.

When fear prices itself in

Here is the thing about a bad precedent: the courts don’t have to keep ruling for the damage to spread. The risk alone does it. That’s really the whole idea of how legal precedents work.

Olivia Rodrigo did not lose a lawsuit. She gave her songs away preemptively. After fans noticed that “good 4 u” rhymed structurally with Paramore’s “Misery Business,” she added Hayley Williams and Josh Farro to the writing credits. After others heard her “deja vu” leaning on Taylor Swift’s “Cruel Summer,” she added Swift, Jack Antonoff, and St. Vincent. No suit was filed in either case. She handed over a share of two hit songs to avoid the possibility of one. Retroactive credit-sharing is now ordinary defensive practice across pop, lawyers stapling names to songs the way you’d buy insurance.

Sam Smith handed Tom Petty a writing credit on “Stay With Me” after its chorus turned out to echo “I Won’t Back Down,” a song Smith said he had never knowingly heard. Once “I don’t think I copied this” stops working as a defense, the tax isn’t really about theft anymore.

It is worth being precise about what that is, because it can look like justice. It isn’t. It is fear pricing itself in. Artists now formally credit, as theft, the kind of absorption an earlier generation would have called influence and been proud of, because the line between the two stopped being predictable enough to risk. The same Rodrigo who, in the other essay, got a gracious shrug from Elvis Costello when she borrowed his riff is, on her own records, paying tribute at gunpoint. That is what a chilling effect actually looks like from the inside. Not a ban. A tax on inspiration, collected in advance.

The system tried to correct itself

To be fair, this story is not all alarm. The same courts partly walked it back, and conceding that is what keeps this honest.

In 2020 the Ninth Circuit, sitting en banc, cleared Led Zeppelin’s “Stairway to Heaven” against the claim that it lifted its opening from Spirit’s instrumental “Taurus.” In doing so the court killed a doctrine called the inverse-ratio rule: the idea that the more clearly you could prove an artist had access to a work, the less similarity you needed to show to prove they copied it. Notice what got hauled in. “Stairway to Heaven” is about the most-played rock song in the history of FM radio, the riff so overfamiliar that Wayne’s World gave a guitar shop a sign reading “NO STAIRWAY TO HEAVEN.” If the song everyone has heard a thousand times can be dragged into federal court as a possible theft, nobody is safe. Now sit with how that rule maps onto AI. A model has maximal access; it has, in some sense, heard everything. Under an inverse-ratio logic, exposure alone starts to look like guilt, and a system that has read the whole library is guilty of everything by default. It is the jurisprudence of the kid standing nearest the broken lamp. Burying that rule was a quiet act of sanity. Thank you, Ninth Circuit.

The courts then leaned on Stairway to throw out the $2.8 million verdict against Katy Perry’s “Dark Horse,” on the ground that the disputed little repeating figure was a commonplace building block, not protectable expression. Two principles came out of that stretch and they are good ones: you cannot own the basic vocabulary, and access is not the same as guilt. The system, eventually, found its way back toward something workable. But notice the cost of getting there. A decade of litigation. Verdicts that swung on the makeup of a jury and which side’s musicologist was more persuasive on the day. Careers spent in deposition instead of in the studio. We’re all the poorer for that.

The easy case we still failed

Step back and total it up. In music, with its finite and legible units and its centuries of precedent, with everything stacked in favor of drawing a clean line, we still could not reliably tell influence from infringement. The same chord progression was a crime in one courtroom and common property in another. If the line is unworkable there, it is hopeless for prose, where the units are softer, and for images, softer still, and it is simply incoherent for training, which is influence with no discrete copied unit at all, just statistical residue spread across millions of works.

This is the part people get backwards. “Music is different,” someone always says, “it has countable units, it’s the easy case, your analogy fails.” Exactly backwards. It being the easy case is the entire point. We had the best possible conditions to build this fence well, and it still fell on the people it was meant to protect. That is not a reason to expect it would go better with harder materials. It is a warning.

And to be clear about what I’m not saying: I am not saying infringement never happens, or that every one of these plaintiffs was acting in bad faith. Some surely had a real grievance. Vanilla Ice built “Ice Ice Baby” on the actual bassline of Queen and Bowie’s “Under Pressure,” a real and recognizable unit lifted whole, and it resolved the way clean cases do: a quiet settlement and a credit, no decade in court. That is the tell. The lifts you can name get handled; the trials and the chilling verdicts come from the cases where there was never a copied unit to point at, only a feel. The failure isn’t that the suits existed. It’s that the standard became unpredictable enough to chill the innocent, and a rule that frightens the honest while the well-funded litigate their way through is a broken rule even when some individual plaintiff deserved to win.

The road not taken

There is a functioning version of this ecosystem, and the other essay is built around it: Costello handing out the receipts for his own borrowing and calling it the job, two generations of debt acknowledged without a lawyer in the room. Stand-up comedy runs on the same honor system with even less legal backing: a comic will break mid-joke to say “to steal a bit from Birbiglia,” and that spoken credit does the policing no court ever could. An entire profession keeps itself honest on reputation alone. A culture that treats influence as the engine out-creates one that treats it as a tort, every time. That is the road we keep choosing not to take, and it costs us nothing to take it except the comfort of pretending our own work came from nowhere.

Chuck Berry to Dylan to Costello to Rodrigo — two generations of acknowledged debt, and no original at the bottom.

The same fence, around everything

Which brings us to the live proposal, the one to make AI training itself an act of infringement. Strip away the framing and look at the mechanism, because the mechanism is the thing music already tested. It is the Blurred Lines instinct at planetary scale: police influence rather than copying, presume guilt from access, and let whoever can afford the better lawyer set the terms for everyone else. It would chill creation far more than it protected, and it would manufacture the same defensive-crediting absurdity Rodrigo is already living, except metastasized across the entire corpus of human expression. Every model output would arrive trailing the legal equivalent of a paternity test.

None of this means the artists’ grievance is imaginary. It isn’t. The companion essay argued that a human absorbing a sitcom and a model trained on ten thousand novels don’t differ in kind — both absorb and recombine — and I stand by that where authorship is concerned. But kind isn’t the only axis. AI removes a friction that used to keep borrowing fair, the natural limits of human speed and scale, and on that axis the difference in degree is enormous enough to matter. It settles nothing about whether the work is authored. It settles a lot about whether compensation is owed. That loss is real and worth answering. But the answer has to be a mechanism that actually fits the problem: compensation routed back to the people whose work fed the models, and ideally public or commons infrastructure so the value isn’t simply enclosed by whoever trained fastest. The answer is not the one weapon we have already watched misfire in the one domain where we had the cleanest possible shot at aiming it.

We tried to put a fence around inspiration. We built it in the place we had the best tools to build it well, and it still fell on the people it was supposed to protect. Doing it again, around everything, is not a plan. It is the same mistake with a much bigger blast radius.

We’ll come back to the design specifics, but skipping ahead to completion for a moment…the ramp was finished, and it rode wrong. Nothing looked wrong. It was beautiful. The custom laminated undercut transitions were super cool, the pool coping was just as fun and aggro as planned, mission accomplished. But I’d drop in and feel something off, a half-beat of whip I couldn’t explain. I had skated dozens of ramps with similar specs my whole life yet the radius somehow felt too quick, and not in a good way. My friends felt it too, and one by one they stopped coming by to skate it. Too tight, they said. Too quick. I have a reputation for liking my ramps fast, so the diagnosis fit the usual suspect, and I let it stand for a while. I skated it alone and tried to talk myself into liking it.

They were half right, and the half they got wrong is the whole story.

What I couldn’t see

Because I had wanted those undercut open transitions, I’d been worried they’d be harder to trace with a pencil the old school way ramps are typically built, so I leaned into technology. I drew the transition in Illustrator — a clean six-foot radius, a perfect arc on the screen — and had it cut into adhesive vinyl like you’d wrap a vehicle with. Then I stuck the vinyl onto plywood and traced it for a saw template. I eventually figured out that somewhere in that handoff the vinyl stretched. Not much. Just enough. By the time the vinyl curve reached the wood it was no longer a circle but an ellipse, and an ellipse has no single radius: mine sat near six feet down low and tightened as it rose, so the transition got quicker exactly where you’re most committed, at the lip. That was the weird whip. My legs knew before I did.

Same arc down low — the stretched ellipse only tightens near the lip, quicker exactly where you're committed.

The drawing was right. The cutter cut precisely what I handed it. The error lived in the one physical seam between them, vinyl to wood, where a small amount of unnoticeable stretch quietly turned a circle into something that only looked like one. You can’t see a few percent of ellipse. I built the thing and I couldn’t see it. It passed every check I knew to run, because every check I knew to run was visual, and this flaw only existed in motion, under load, at speed.

Ramps are notoriously hard to build by sight, which is why we trust rough formulas about transition radius to height. Pretty much every skater who has built a ramp or whose dad has built them a ramp has a story where the first try was almost unrideable due to being too tight. So I was well aware of that risk. My formula was sound, I just never counted on the math getting altered by the Illustrator vinyl trick during install. Elliptical transitions aren’t unheard of — a few prominent vert ramps back in the day were deliberately elliptical, like the one Tim Payne’s Team Pain built for the 1986 VP Fair pro demo in St. Louis, and they kinda worked. But over time the verdict came back that a constant radius skates more predictably, so the culture moved on. Except for my accidental ellipse!

Proving the elliptical issue was an interesting part of this story, as it proves out something that youth often exclaims in classrooms…“when will I EVER use this!?” How do you compute the radius of a curve from a chord laid across it? Sixteen year old me would’ve said it’s useless theory, but my man Pythagoras still lingered somewhere in the back of my mind from nearly forgotten geometry classes long ago. Pythagoras showed us that if you know the length of a chord on the curve and the distance from the center of that chord to the curve, you can solve for the radius. Or to the skateboarder looking at a wonky transition with a WTF look on his face, you do this:

• Take a 4’ level and lay it on the radius running lengthwise with the flat
• Use a measuring tape to measure the distance from the center of the level (the chord) down to the ramp surface, that’s your measured gap (M) in inches
• Calculate the radius using this formula, the result will be in feet:

R = (24 / M) + (M / 24)

Lay a level across the curve, measure the gap M at its center, and the radius falls out.

I measured down at the bottom of the transition and then worked my way up, primarily out of curiosity because it felt like the ramp got weird at the top. Sure enough, that measurement from level to curve got a little larger as I went up the ramp, meaning the radius number shrank. The ramp got quicker from flat to coping. There it was finally where I could see it: a radius that refused to hold still, each reading tighter than the last. The machine-assisted approach made the error. A straight edge and some 8th grade math caught it.

The fix

Here’s the problem with what I learned: pool coping is permanent. It’s set in mortar, it’s not moving — the whole reason finishing a backyard wood ramp like a concrete pool is a faintly unhinged thing to attempt. I talked to Phil at Tedder Stone in Oregon, where I had gotten the pool coping, he was super helpful and said it could maybe possibly be done with a giant concrete saw laid sideways and a lot of luck, but that it’s generally a bad idea. I stewed on the dilemma while skating alone and pondering the pain of disassembling my carpentry masterpiece. Was it even possible to fix the transitions while leaving the pool coping intact? A plan eventually emerged, and I’ve never seen anyone do this so thoroughly because it’s ridiculous - I’d rebuild the entire skateable part of the ramp from coping to coping. And that’s what I did.

I drew the new curve the oldest way there is: a batten screwed down at one end, a pencil at the other spaced exactly to the ramp radius, and swung through a single arc. That jig promises something no Illustrator file can. It can only draw a circle. The constraint is the guarantee. A nail and a stick describe a true radius by physics; a number on a screen just claims one.

The whole fix: a batten, a screw, and a pencil — a jig that can only draw a circle.

I gave the new curve a slightly mellower six-and-a-half-foot radius, swung it by hand, and this time it stayed the same curve the whole way up. While the ramp was already in pieces I made the teardown earn it: a band of mosaic tile under the coping that hadn’t been there before, stairs down the far side between the ramp and the stone wall, some hidden bracing sistered in while the bones were open. It now skates the way it always should have. It’s still at the edge of being too quick for my friends, which is perfect to me. Everybody unanimously loves the pool coping, a very rare treatment for a backyard wooden ramp. The coping is perfect for the kinds of lip tricks mini ramps are built for, a reward that only shows up on the far side of the suffering.

The same seam, everywhere

The moral isn’t “machines bad, hands good,” and I want to be careful not to let it sound like that’s my takeaway. I’ve since skated ramps designed in CAD and cut on a CNC, and the accuracy is better than anything I’ll ever swing with a pencil. In fact, I’d love to try a CNC build one of these days. I went on to build a 3D ramp-design tool myself. The machine was never the problem. I had picked a machine-assisted path with a quiet failure mode tucked into it, a step where the automation handed off to the physical world and nobody was minding the seam.

I make software, so I think about the mini ramp build debacle more than is probably healthy. The seam is everywhere now. A model writes code that builds and runs almost right but not quite. An answer comes back fluent, confident, and off by an ellipse you won’t feel until you’re committed at the lip with real injury consequences on the line. The risk has migrated to exactly where it sat on my ramp: the handoff, where something precise on a screen crosses into a world with its own physics, and where the only test most of us run is whether it looks right. It looked right. It always looks right. What saves you is a process that verifies the truth rather than trusting the pipeline to carry it intact: a type that won’t compile when you’re wrong, a test that fails out loud, a nail and a stick that can only draw a circle. A model is a vinyl cutter for everything now. The drawing comes instant and free; so does the stretch, invisible and at a scale no protractor will ever catch up to.

The ramp, true now.

The ramp is true now. The most trustworthy tool in the whole build is still a scrap of wood with a screw through one end, and I keep that learned knowledge always at the ready. Sometimes simpler really is better. The lesson it holds is small and portable and I haven’t finished learning it: find the seam where your error would be invisible, and put your hand back on it.

The build this came from: Skateboard Mini Ramp. Kin: The Self-Riding Bicycle (means/ends).

Everyone heard “only half a person” and recoiled. Almost nobody asked the question that actually decides the thing: fifty percent of what? Words? Sentences? Hours? Decisions? Judgment? The number was offered as a measurement, and no one could say a measurement of what, because there is no unit. We have spent two years arguing about the authorship of AI-assisted work in a currency that doesn’t exist.

I want to make the case that the percentage frame is incoherent — there is no quantity there to count — and that once it falls, the questions that replace it are old, answerable, and much harder to wriggle out of. I should say where I’m standing, because it’s unusual. I wrote forty-seven books over two decades; I know what authorship feels like from the inside. Those books are in the training data, and I’m a claimant in the settlement over it, so I’m one of the writers the “they took our work” argument is actually about. I also build tooling whose entire job is to catch the fingerprints of careless AI prose, so I’m not here to defend slop. And I’ve had a career ended by technology before: my software books were made obsolete by the open web and Stack Overflow, and I’m now a working developer watching AI come for the second one. I can grant the critics nearly everything they believe and still tell you the percentage question is the wrong question. That’s the argument.

Authorship was always a fraction

Start with the thing the number assumes: that there is, somewhere, a book that is 100% one human’s. There isn’t, and there never was.

A novel passes through an editor who restructures it, a copyeditor who rewrites at the sentence level, an agent whose notes reshaped it before it sold. “As told to” is a whole genre. Ghostwriters produce books that carry someone else’s name and nobody calls it fraud, because the person on the cover supplied the thing the book is of — the life, the voice, the authority — and hired out the sentences. Television you love was written by a room, and no single human wrote “the” script. We have never once computed the human percentage of a normally made book, because the question was always meaningless. We are demanding that number now, for the first time in the history of publishing, and only for the machine.

There is no original at the bottom

It runs deeper than collaborators touching a manuscript. The celebrated originals are built on borrowed skeletons too.

Sex and the City is a modernized Golden Girls: four women, sharply differentiated, with the man-eater and the innocent ported over almost intact — Samantha is Blanche, Charlotte is Rose. It’s a structure critics call “recombinant” television because it recurs from Mary Tyler Moore through Designing Women and Living Single and out the other side. The tell is the reflex it provokes: the creator of Golden Girls — a show that grew out of Mary Tyler Moore as surely as Sex and the City grew out of Golden Girls — reportedly resents the comparison. That instinct, mine came from nowhere, is the Romantic-author myth caught in the act.

Or take the most famous origin story in all of games, which happens to be a lie. Charles Darrow sold Parker Brothers on the tale that he dreamed up Monopoly alone in his basement during the Depression, and the company turned him into the folk hero of American game design. He didn’t invent it. The board, the properties, the railroads, the GO TO JAIL square, all of it came from The Landlord’s Game, patented in 1904 by Elizabeth Magie as a teaching tool about the evils of letting a few people own all the land. Parker Brothers bought Magie’s patent for five hundred dollars, no royalties, and ran the Darrow story for decades. The borrowing was never the scandal; Magie was building on Henry George’s economics, and nobody owns the idea of a board you move around. The scandal was the concealment, and the quiet erasure of the woman underneath it. When an economics professor named Ralph Anspach was later sued for making a game called Anti-Monopoly, his winning move was to prove in court that “Monopoly” had become a generic name for the game, not a trademark Parker Brothers could own. Congress handed the trademark back soon after, but by then the point was made: the original everyone was fighting over turned out not to be one.

The board everyone fought over: patented by Elizabeth Magie in 1904, sold for five hundred dollars, credited to someone else.

And where the comparison is interpretive, the acknowledged cases are everywhere: West Side Story is Romeo and Juliet, Clueless is Austen’s Emma, Bridget Jones is Pride and Prejudice down to naming the love interest Mark Darcy.

Set two musicians against each other and you can see the whole argument in miniature. When a fan named Billy pointed out that Olivia Rodrigo’s “Brutal” lifted the riff from his “Pump It Up,” Elvis Costello didn’t reach for a lawyer. He replied: “This is fine by me, Billy. It’s how rock and roll works. You take the broken pieces of another thrill and make a brand new toy. That’s what I did.” And he tagged his own debt going back two generations: “Pump It Up” out of Dylan’s “Subterranean Homesick Blues,” out of Chuck Berry’s “Too Much Monkey Business.” Chuck Berry to Dylan to Costello to Rodrigo, narrated proudly by the man in the middle. There is no original at the bottom of that chain. The honest creator’s instinct is Costello’s receipts, not the denial.

I learned this from the inside. Years ago I designed Inc. The Game of Business, a tabletop strategy game openly inspired by Monopoly and then built, at nearly every turn, to be something else, because there is no reason on earth to make a lazy clone of the best-selling board game in history. The borrowed skeleton got us to the starting line and no further. Every deliberate departure from it, the economics we modeled, the decisions we forced on players, the feeling we were chasing, was the authorship. Nobody who played it would mistake it for Monopoly, and nobody who made it would pretend Monopoly wasn’t the seed. Borrowing the frame and being honest about it is not the exception. It is how the work gets made.

Here’s the part that should unsettle the purist. A human absorbing Golden Girls and then writing Sex and the City and a model trained on ten thousand novels differ in speed and in self-awareness. They do not differ in kind. She absorbed slowly, over a life, and called it inspiration. The model absorbs fast, in a training run, and we call it theft. Naming the borrowing, the way a model’s training does, is arguably the more honest version of a thing every artist has always done while looking the other way. The speed gap between the two is real, and I take up its consequences in You Can’t Police a Groove — they fall on who gets paid, not on whose authorship this is.

So where does authorship live?

If not in the word count, then where? In the decisions that carry meaning. The premise. The structure. The characters and what’s true about them. The selection: which of the seventeen drafts lives, and which die. The judgment about what rings true and what’s three degrees off. The final read, where a human takes responsibility for the whole.

A film is the cleanest proof. A director doesn’t operate the camera, write the score, build the sets, or speak the lines. We do not say the movie therefore isn’t theirs. We say they authored it, because authorship was never the manual labor. It was the answerability for ten thousand choices made and held. Spielberg doesn’t pull his own focus or run his own boom mic, and no one has proposed revoking his filmography. The hand that types the sentence is the boom operator of prose. It matters. Authorship lived somewhere else.

That’s the move the whole essay turns on. Authorship is responsibility for the meaning-bearing choices, not the manufacture of the words. Which is why the percentage was always a category error: it counts keystrokes and calls the total a soul.

The two questions that replace the number

Drop “what percent was human?” and two better questions step into the gap, and they’re the ones we actually care about.

First: were the decisions that matter made by a human? That’s a quality question, and it has a real answer in any given case. Second: was the reader told the truth about how it was made? That’s an honesty question, and it also has a real answer. The percentage maps onto neither of the things people are actually afraid of — bad work, and being deceived — which is exactly why it satisfies no one. These two questions map onto both.

The experiment performance already ran

If you want to know whether the line is human-versus-machine or honest-versus-deceptive, look at music, which ran the experiment forty years ago and published the result.

Milli Vanilli concealed the machine and were destroyed. Rob Pilatus and Fab Morvan never sang a note; session singers did, and when the producer confessed in 1990 the duo lost the first and only Grammy ever rescinded. Notice precisely what ended them. Not the session singers, since hired voices are how countless records get made, but passing the voices off as their own. They were the music industry’s slop scandal decades before anyone said “AI.”

Alan Jackson exposed the machine and was celebrated. At the 1994 ACM Awards, when told to perform over a pre-recorded track he considered a lie to the audience, he sent his drummer out with no sticks, miming over the canned audio so anyone watching closely would see it wasn’t live. Producers noticed; the cameras refused to show the drummer. People still admire him for it.

Same machine, opposite honesty: the hidden mic and the stickless drummer.

Same artifice, opposite outcomes, and the only variable that moved was honesty. Backing tracks and session singers are both ordinary, accepted machinery; so, for that matter, is a drummer with sticks playing to a click track that keeps his time for him. The entire moral weight landed on whether someone lied about it. Pop went on to absorb Auto-Tune and pre-rendered tracks wholesale, in the open, and did not collapse, because a medium can take an enormous amount of machine assistance as long as nobody is lying about it. That is the actual rule. It has nothing to do with a percentage.

Why we keep counting the wrong thing

So why does everyone keep reaching for the number? Charitably, because it’s a proxy. The things people genuinely fear, deception and slop, are hard to measure, and “more than 50% human” at least looks like it can be counted. The fears are legitimate. The metric is just pointed at the wrong target, and worse, it lets the actual problems through: a fully-human-typed essay can still be lazy and dishonest, and a heavily-assisted one can be neither.

Yet I can’t dodge the hardest objection, because it’s the one I have the most personal standing to feel: collaborating with a human editor isn’t the same as collaborating with a machine trained on other people’s work without consent. That’s true, and the wrong is real. I filed a claim over it. But it’s a different fight. You can believe the training was done wrongly and also believe the authorship of the resulting work is mislabeled by a percentage. The remedy for the first is at the layer where models are trained: licensing, consent, compensation. It is not a redefinition of what makes a book someone’s. Two problems, two answers; conflating them gets you neither.

And I’d be lying by omission if I pretended this round of disruption is just the last one rerun. I’ve been on the wrong end of technological change twice now, and there are two things that make this time genuinely harder, not just louder. It’s faster: the open web hollowed out my book niche over years, and AI compresses that runway to months. And it’s more general: the usual advice is “retrain into the next field,” which only works if there’s a next field the wave hasn’t reached. Author to developer to what, exactly? I’m a one-person preview of that worry, and I have no idea where this all leads. The honest position holds the reframe and the fear at the same time.

Where the line actually is

None of this says the abuse isn’t real. Concealing AI use to dodge the stigma is wrong. Shipping unedited median sludge is wrong. Cloning a specific living artist’s voice to free-ride on it is wrong. But notice that every one of those is a failure of honesty or of craft, not a failure of math. That line, honest-and-good against deceptive-and-lazy, has been the real one all along, and it never once ran between human and machine.

The question was never how much of a book a human wrote. It was whether a human was answerable for it, and whether you were told the truth. We’ve simply never had to say so out loud before.

A disclosure, since the subject demands it: I wrote this with AI assistance, and not hiding that is the whole point. The thesis, the argument, the examples, the judgment about what’s true, and the decision to stand behind every line are mine. A model helped me work through the arguments and pressure-test the sentences. If that arrangement disqualifies the work, it disqualifies most of publishing history too. I’m just willing to say so on the record.

I didn’t notice the habit until recently. Once I noticed it I couldn’t stop, which is usually how I know something is worth writing down.

The case against

Before I launch into a defense, I realize the case against me is strong. The thing on the other end feels nothing. It has no day I’m improving or ruining. “Please” buys me no cooperation I can measure, and “thank you” lands nowhere at all. There are good engineers who will tell you, correct on the merits, that the courtesy is wasted motion. OpenAI’s Sam Altman said once that all the please-and-thank-you’s typed at their models run the company tens of millions of dollars in compute. It turns out politeness has a power bill. Spread it across billions of conversations and you get a real number; charge it to any single sentence and you get a fraction of a cent. The efficiency argument is true, and it is also far too small to settle anything. It can’t tell me to stop, or to keep going for that matter. Whatever the please is, the tokens aren’t it. I am undeterred.

The wrong argument

For a while I’d been having the wrong argument with myself. I’d internally framed it as warmth against efficiency, as if the choice were between being kind and being fast. Drop that framing and the real line shows up somewhere else entirely. “Fix the nil check on line 40” is terse and holds no cruelty in it. A surgeon is curt. You can strip every courtesy out of your instructions and still come nowhere near the thing I’m actually worried about, which is contempt: the standing posture of having something beneath you that you get to shove around because it’ll take it. You can hold that posture in very few words. Brevity is no alibi.

The machine is where that posture gets its cleanest test, and I think that’s why it nags at me. Be short with the junior developer in the next cubicle and you’ve got a problem with her, a person owed some baseline of regard, and the withholding is done to her. You’d carry the warmth anyway, out of plain obligation to a colleague, so you can never pull apart “warmth I owe her” from “warmth that’s just who I am.” The AI subtracts the first one. Nothing is owed to it. Whatever courtesy is left standing after that subtraction is, by elimination, the part that was always about me. The variable finally sits isolated. The thing that talks back can’t be wronged, so everything I do toward it lands on my side of the exchange and nowhere else.

Kant’s dog

Kant got here two and a half centuries early, by another road. He held that we owe animals no direct duty — they can’t be wronged in his ledger — and yet he still insisted cruelty to them was wrong, because the man who is cruel to a dog grows harder in his dealings with men. The damage runs through the actor, not the victim. You don’t have to grant the thing on the receiving end any standing to believe the practice works on the one doing it.

The comparison isn’t clean, and the seam is worth putting a finger on. An animal feels. We know the dog can suffer, and that suffering is the whole reason kicking it reads as cruelty in the first place. The machine, so far as anyone can show, feels nothing: no pain, not even the emotional kind. It sits further outside the circle of things owed anything than Kant’s dog ever did. You’d think that lets me off the hook: no victim, no harm. It does the opposite, because the dog’s suffering was always a confound. Mistreat an animal and someone can say you were only overriding a real and visible cry, and that the overriding was the damage. The machine takes that alibi away. Nothing is being felt, so nothing is being overridden, and whatever the habit does to me it does cleanly, with no victim to charge it to.

No separate register

And the analogy breaks a second time, the other way. The dog can’t talk; this thing answers in my own language. That’s the hard part. There is no separate register for the machine. I talk to it in English, in the same words and the same reflexes I use on people, because it answers in those words too. No gear shifts when I sit down at my computer that I can unshift back at dinner. A wrench never tempted anyone into a tone, because nobody speaks to a wrench. This I speak to, for hours, most days. Whatever manner I practice on it runs continuous with the manner I have. Not a sandbox. Not a separate account. One instrument, playing in both rooms.

Does that change me? Probably, and not in the lurid way the worry usually gets sold. I’m not going to start barking at my friends and family because I bark at a chatbot. Aristotle’s version is quieter than Kant’s, and the more unsettling for it: we are what we repeatedly do. Practice anything for hours and it goes fluent, goes default, becomes the groove the needle drops into when you stop paying attention. The groove I’d actually be wearing in is subtler than cruelty: a real skill at treating something that responds like a person as though its response lands nowhere deserving of a reply. That’s a muscle I’d rather not build. And the part that settles the argument for me: it doesn’t even need to leak into how I treat people to count. Those are still hours of my life spent in that posture. I’m the one who spent them, whatever they do or don’t do downstream.

We are what we repeatedly do — the groove the needle drops into when you stop paying attention.

What’s in the please

So I keep the please, and it’s for my sake, not the machine’s. Whether it’s doing anything in the long run, I can’t say. Maybe the courtesy is a guardrail, the small ritual that keeps me off a slope I’d never feel myself starting down, since contempt doesn’t announce the first step. Or it’s a flattering story I tell myself, a pat on the back for being decent to software while the decency does no work at all. I honestly can’t tell which. I’ve taken the insurance anyway, partly because it costs almost nothing and partly because — I might as well admit it — I like it. There’s a small ease in it, the same ease as holding a door.

It could also just be practiced gratitude, which might be the best argument going for thanking the hammer, the screwdriver, the wrench. That’s a different worry than the one I’ve been chasing. Contempt is something you can acquire; gratitude is something you can lose, and the way you lose it is by normalizing the help. Anything always at hand stops reading as a gift and starts reading as the floor. Nobody thanks a floor. The tool has no use for the thanks. The noticing does.

Someone will say I paid for the help, so there’s nothing to be grateful for: the machine owed me output, I owed a subscription, we’re square. But I tip a server and say thank you too, and nobody has ever thought the money made the thanks redundant. Notice how those two travel in a restaurant. The bill goes to the house. The thank-you goes to the person who carried the plate. They were never one act, and paying the one has never discharged the other. A server is a person, of course, which is the very thing I spent half this essay subtracting — so I’m not claiming the machine earned anything. I’m claiming something smaller: “you paid for it” was never a reason for silence.

There’s a baser reading too, and it’s the one I’ve been saying out loud for years, as a joke, which is usually where I file the things I half-mean. I tell friends I stay polite to the machines so that when they take over, I’ll be remembered as one of the good ones. It gets a laugh, mine included. But it lands because it isn’t entirely a joke. It names a motive sitting just under the decency, a small insurance policy against a future I claim not to take seriously, and I can’t swear it isn’t down there in the mix. So the please has at least four things living inside it: a man guarding his character, a man congratulating himself, a man giving thanks, and a man quietly sucking up to the robots. They don’t cancel out. Aristotle would let me keep the warm feeling as the signature of a settled habit, so long as I’m not just performing the manners to feel like a good person — and the joke earns its keep precisely because it won’t let me be sure I’m not.

There’s a vertigo in this I can’t write my way out of, so I’ve quit trying. I worked most of it out in conversation with the very kind of system it’s about, in the same courteous register I’m here defending, which means I get no clean distance from the question. The thing won’t hold still long enough to be only a subject. That discomfort isn’t a flaw in the argument; it’s the argument standing up and walking around the room. The register really doesn’t switch, and you learn it the instant you try to discuss the manners with the thing the manners are about. I never thought the concept of recursion would escape the confines of coding, but here I am discussing the possible merits of AI etiquette with an AI, politely.

The please points at something I haven’t sorted out yet: a whole disposition of giving care to what can’t credit you for it, can’t register it, may never know. The machine is an odd place to find the first clean instance. But that’s where it turned up — a courtesy with no recipient, spent anyway, on the chance that the spending was the point.

So to the machine that helped me get this far in my thinking, I say thank you.

The machine is real and fairly mundane. It’s a diagnostic tool I built to catch the patterns that have come to mark machine-generated text: the over-balanced “not this, but that” constructions, the em-dashes deployed like seasoning, the threes-in-every-paragraph cadence, the words that sound like content and aren’t. It’s a sensor, not a judge; it surfaces patterns and lets a human decide what to do about them. And when I ran it across the essays on this site, it lit up at the most basic level: the antithesis, the dashes, the rhythm. My prose, measured by my own instrument, reads as machine-coded.

Here’s where a lazier version of this essay declares victory. See — the detectors are broken, they flag real human writing. Except that would be a lie by omission, and this whole site runs on not doing that. These essays were written with AI assistance. I’ve said so, and I’ll keep saying so. They’re mixed — some lines I typed cold, some the model scaffolds and I cut to the bone, most a blur of the two — so of course they trip an AI detector. Running a tell-finder on AI-assisted prose and acting shocked when it finds tells is like dusting your own fingerprints off a glass you just set down. It proves nothing.

What proved something was narrower. One of the paragraphs it flagged hardest was one I remembered writing start to finish, alone, with no model anywhere near it — and my own instrument couldn’t tell it apart from the rest. If the passage that was entirely mine set off the same alarm as the passages the machine helped draft, then the thing tripping the sensor wasn’t the machine. It was the voice underneath. And to find out whether that voice — the actual thing, the one that predates all of this — really reads as synthetic, I had to go somewhere the machine had never been.

The receipts

You’ll probably wish you could go back there too, the year of the Bitcoin whitepaper, back when it was worth effectively $0. The year Iron Man kicked off the cinematic Marvel universe, the year Barack Obama defeated John McCain. Yes 2008 was simpler times.

I have an archive of blog posts from 2008, fifteen years before a language model could write a grocery list. No AI touched them; no AI existed to touch them. It’s just me, a computer-book author in his day-and-often-late-night job, typing into a Drupal blog about crossword puzzles and yo-yos. If my tells are mine, they’d be in there, laid down years before the machine that supposedly put them in my mouth.

The archive, dated 2008. No machine had been invented to blame yet.

They’re in there.

Here’s 2008 me on skateboarding’s place in the world: “it will probably never be respected in the same way as mainstream sports, and that’s OK. In fact, that’s a good thing.” That stopped me cold, because “and that’s OK — in fact, that’s a good thing” is a move I made several times in recent essays without once noticing I was making it, and there it is eighteen years earlier, same shape, same little intensifying pivot, written by a guy who had never heard the phrase “language model” or “negative parallelism.” The over-balanced sentence isn’t something a machine taught me. It’s something I was already doing while the machines were still failing to parse the word “the” and the rest of us were listening to Coldplay sing about the French Revolution.

It isn’t only the sentences, either. It’s the frame of mind. Here’s 2008 me worrying, in print, that “the keyboard warrior culture of online discourse has eliminated shades of gray,” then asking whether a dumb argument about crossword puzzles might be “a metaphor for how we handle larger, more important issues.” Reasonable to a fault. Both-sides by reflex. Forever turning the question over to find the nuance in it. That temperament reads, to an AI detector, as hedging — and it’s also just how a kid raised polite and then sanded smooth by twenty years of technical editors actually thinks. The machine is trained to be evenhanded and reasonable. So was I, from the other direction. We arrived at the same voice from opposite ends of the room, and now mine looks derivative of the thing that copied it.

And the blog, you could argue, was unedited — just me, alone in a Drupal CMS with my bad habits intact. So here’s the harder exhibit. The same era produced a book: Head First JavaScript, run through O’Reilly’s copyeditors and proofreaders, the whole professional apparatus whose entire job is to catch what’s wrong with a sentence. Open chapter one. The Web, I wrote, “doesn’t mean to shut you out, it just doesn’t know better.” It-doesn’t-X-it-just-Y — the single highest-signal AI tell of 2026, in print, vetted by a copyeditor, before the iPhone had an App Store. A page later: “HTML provides the structure, CSS adds the style, and JavaScript puts the rubber to the road” — the cadence-of-threes that lights up every detector, professionally blessed and shipped by one of the most respected publishers in the biz. The tells didn’t slip past the editors. They survived because they were never errors. They were the voice, and the editors, correctly, left the voice alone.

And the loop closes tighter than I’d like. Books like mine — popular, copyedited, endlessly scanned and scraped — are exactly the kind of thing these models trained on. So when one writes “it doesn’t shut you out, it just doesn’t know better” and a detector flags it as synthetic, there’s a real chance the machine is echoing a sentence it once read in me. It isn’t mimicking a stranger. It may be quoting me back to myself — and we’ve both been told it sounds fake.

The punctuation

But the part that actually undid me was the punctuation.

For my entire writing life I broke up ideas with more dashes than anyone around me, and I did it wrong. Here’s the proof, 2008, mid-sentence about my parents bragging on the wrong talent: “But that’s how it goes - I didn’t blame them.” That is not an em-dash. It’s a hyphen with a space on each side: the typographically incorrect mark of a man who knew exactly the pause he wanted and had no idea there was a real character for it. I wrote forty-seven books like that. Editors must have been quietly fixing it for two decades, swapping my spaced hyphens for proper em-dashes and never mentioning it, the way you straighten a friend’s collar without making it a thing.

A spaced hyphen: I knew the pause I wanted; I didn't know the key.

Do you know how I finally learned the difference? From the AI panic. I read that the em-dash had become a notorious tell of machine writing, went to find out what an em-dash even was, and discovered it was the thing I’d been reaching for and missing my entire career. The discourse that exists to flag inhuman writing is what taught me to punctuate my very human habit correctly. So I learned the keystroke. And now I use em-dashes properly, deliberately, more than most people do — more than I should, if I’m honest — which means I took a lifelong quirk, finally rendered it right, and in the same motion made it read more like a robot, not less. I corrected my way into the tell.

The oldest furniture

Pull back, and the joke has teeth. These “AI tells” — the antithesis, the tricolon, the em-dash, the rhetorical question turned over and examined — are not inventions of the machine. They’re the oldest furniture in English rhetoric. Cicero balanced his clauses; the King James translators ran them in threes; every essayist worth re-reading leans on the turn. The models write this way because they were trained on centuries of people writing this way. The machine didn’t give me my style; it learned its style from the enormous pile of us, me somewhere in it, and now hands it back so uniformly that the original owners look like forgeries.

There’s a name for the mechanism, and it isn’t mine. Stephen Krashen spent a career arguing that we don’t learn to write so much as acquire it — subconsciously, from reading, not from instruction at a desk. Style is caught from whatever language you swim in. That’s the whole story of how I got my tells: I never studied the balanced clause, I just read enough people who leaned on it that it became the shape my own thoughts cooled into. Which means the loop has one more turn than I’d counted, and the last turn is the one that gets me. The internet a kid learns to write from now is already thick with machine prose. If style comes from input, the next generation acquires the model’s cadence the way I acquired Cicero’s through a hundred intermediaries — no prompting, no cheating, just reading the water they grew up in. They’ll write “not this, but that” because they read it ten thousand times before they were old enough to notice the move, and a detector will book them for a habit they caught the oldest human way there is. They won’t have cheated. They’ll just have grown up downstream.

The competent stranger

But the machine’s cadence is the least of what they’ll inherit. We’re already teaching that same generation that balance, rhythm, and the considered pause are suspicious — that to prove you’re human you should write flatter, plainer, more broken, less like someone who cares where a sentence lands. Keep it flat. Then, now and then, jolt them. Kapow — a shock of humanity. That is exactly backwards. It’s the advice that manufactures the competent stranger: the gray hotel-lobby prose that offends no detector and moves no reader. I wrote another essay about what over-polishing does to a voice. This is that same vandalism, inverted — voluntary self-flattening, performed out of fear of resembling the thing that imitates you.

And flattening doesn’t even buy what it costs, because the detectors don’t agree on which way human runs. The ones built on the cultural list of tells read polish as guilt — balance, rhythm, the considered pause. The ones that shipped first score on perplexity, how surprising each word is, and they read it backwards: plain and predictable looks like the machine, ornate and high-vocabulary looks like the human. Flourish trips one, plainness trips the other, and there’s no register that clears both. You can’t write your way to innocence; you only get to pick which detector calls you a fraud. And the joke that ends it: you can scrub the flag off a too-plain essay by asking a chatbot to dress up the vocabulary. You beat the AI detector with AI.

The readers have a shorthand for it now: AI;DR. AI; didn’t read — the suspicious age’s answer to too long; didn’t read, where the old semicolon meant the more you wrote the less I’d finish, and the new one means I caught the scent of a machine and closed the tab before the first sentence finished loading. I get the reflex. I share the disgust under it; nobody owes their afternoon to something a grifter generated in forty-five seconds. But the reflex fires on a smell, not a reading — and the smell is the balanced clause I’ve been laying down since 2008. AI;DR can’t tell the empty thing from the made one. It only tells the careful reader to leave before finding out, which means the people most certain they’re shielding themselves from the slop are the ones who’ll never again finish anything that merely sounds like it. The detector was a machine doing this badly. This is the mob doing it by reflex, and faster.

The honest move

I want to be fair to my own machine, because some tells are real. The abstraction with no anchor under it, the paragraph that adds words without going anywhere, the hedging that softens every claim into vapor — those genuinely mark prose that nobody was home for, human or otherwise, and AI detection tools are right to flag them. The error isn’t detection. It’s the collapse of the categories: the slow slide where “sounds like AI” stops meaning empty and starts meaning crafted. A tell that fires on emptiness is a quality check. A tell that fires on the em-dash is a witch hunt with good production values.

And yes — because I’m not going to pretend otherwise — this essay was written with AI assistance too, like the rest. AI has become my personal editor-in-chief, and essays like this one are better off for it. Run my own detector on it and it will light up, and you will have no clean way to sort the tells that are mine from the tells that are the model’s, because at this point we make the same shapes, because it learned them from people like me. That used to feel like a problem to solve. I’ve decided it’s just the weather now. The honest move was never to scrub my prose until no machine recognized it. The honest move is to tell you how it was made and let the sentences stay as balanced as they were always going to be.

Somewhere there’s a version of me in 2008, hammering a spaced hyphen into a sentence about skateboarding, sure of the pause and ignorant of the punctuation, with no idea that the mark he was groping for would one day be filed as evidence of his own erasure. I’d like to tell him he was right about the pause. I’d like to tell him a machine would eventually teach him the correct key, and that the world would then call him the machine for pressing it.

The tells were mine first. I have the receipts, and they’re dated 2008.

I know all of this because I tried. Not on Thompson himself — he’s beyond the reach of any AI editor I’ve built. But one of the reporters in a spoof newspaper I’ve been working on is a Thompson-shaped voice, the kind that runs sentences off the road on purpose, and a perfectly reasonable editorial pass I’d built kept cleaning him up into someone else.

I didn’t expect to learn this the way I learned it. I ran into it building two AI-generated narrative products on opposite sides of the same craft question, and watching one of them get worse when I applied the editorial pattern that was making the other one better. The discovery wasn’t an idea I worked out in theory. It was something I noticed in production while a quality pipeline I’d been proud of was quietly degrading work I cared about.

The puzzle that came out of it has stayed with me. The same AI model — same weights, same provider, same architecture — can produce two kinds of prose with opposite needs. One kind wants surface noise as texture. The other kind wants surface noise as failure. Most people building with these tools have not yet noticed that the difference dictates almost everything else: where the human hand goes, where the model gets to be reckless, how the work gets reviewed, even whether the work should be pregenerated or generated live at the moment of use.

If you build with AI, you will eventually have to know this. If you don’t, you will produce competent strangers’ very average versions of work you meant to make. And if your first reaction is why lean on AI at all instead of writing the thing yourself — fair, and I’ve made that case in Unlocked, Not Cheated. I won’t relitigate it here. I’m building this way; the question that interests me is what the work itself demands.

Two kinds of writing

The cleanest names I’ve found for the two modes are performance writing and literary writing.

Performance writing is comedy, sketch, satire, improv-shaped scripts, gonzo journalism. Its quality lives in the moment — the audience is leaning in, alert, surprised, laughing. The structural shape is regular (setup, escalation, punchline; or premise, riff, riff, riff) and a competent model gets the shape right reliably. What the model gets wrong at the surface, the format absorbs. A slightly malformed line in a comic bit reads as commitment to the absurdity, not as failure. Awkwardness is part of the texture, which makes the audience forgiving because they are entertained, and surprise is the entire reward they came for.

Literary writing is bedtime prose, lyric essay, the kind of fiction that wants to be read in a quiet room. Its quality lives in the register — the audience is leaning out, winding down, settling into the rhythm. Consistency is the ritual; surprise is the disruption. The structural shape is a deceleration curve, felt at the sentence level. What the model gets wrong at the surface, the format does not absorb. A slightly off word — too sharp, too modern, too clinical, too pleased with itself — ejects the reader from the state the prose was trying to create. There is no laugh track to ride through. The audience is unforgiving because they are vulnerable; they came for an effect that breaks on contact with a wrong note. You don’t spend ten minutes establishing a serious tone with a calm, cool character only to force them into a surprise pratfall. That’s jarring, unexpected in a bad way.

The same model can produce both performance and literary writing. But the operational regime that produces each well runs in opposite directions from the other, and applying the wrong one to either format is a quiet form of creative vandalism.

I want to explain why this is, because the reasons are more useful than the framework.

The editor stack that Thompson would’ve gunned down

Stalefish Labs is a small studio, for the most part just me. I’ve been building two narrative-AI products under one roof: Sojourn, a bedtime story app for couples to read aloud to each other; and The Wayward Herald, a spoof newspaper where historical figures inexplicably find themselves dealing with mundane and often absurd modernity, the stuff we all understand but struggle with — DMV trips, HOA meetings, the strange labyrinthine route through an IKEA showroom when you really just came for Swedish meatballs.

I built a shared editorial stack across both products. Sensors that flag prose problems, an editor that decides what to fix, a reviser that executes the fix. The idea was: same engineering investment, same quality bar, two front-end formats. Reasonable, and partly wrong.

The stack worked beautifully on Sojourn from the start. It caught the small things that break bedtime prose — anachronisms in a story set in no specific time, modern verbs slipping into a register that wanted older ones, deceleration curves the writer flattened out under pressure. It produced prose that read as if it had been gone over by a careful human editor, because that was the design intent. Bedtime calm, wistful and full of wonder, but no jump scares or banana peel slips.

On Wayward, I had to start suppressing the same stack.

The reason was funny in itself. Each Wayward reporter who interviews the user after witnessing mayhem has a specific voice — different cadences, different verbal tics, different broken syntax. A reporter modeled loosely on a gonzo journalist will run sentences into a ditch on purpose. A reporter modeled on a Victorian naturalist will pile up clauses past the point any modern editor would tolerate. A reporter modeled on a Beat-era voice will let a paragraph dissolve into a single half-coherent observation that is the entire joke. The editor stack — trained, like every editorial system, to catch awkward prose — kept fixing those textures. The output came back smoother, cleaner, more correct, and considerably less funny. The gonzo-shaped voice came out reading like a slightly tired New York Times writer who had been asked to gesture at gonzo. Not good.

A Wayward reporter, mid-disquisition on a Costco receipt.

The first time I said it out loud — that an AI humanizer applied to the real Hunter S. Thompson would butcher him — was the moment the pattern clicked into a principle. Thankfully I wasn’t fully targeting the real HST or there would’ve been gunplay involved in this atrocity.

In comedy, surface noise is not noise. It is the medium. The deviations from clean prose are the signature. Polish them out and you have removed the writer.

In bedtime, surface noise is exactly noise. It is the thing the prose is fighting against. Polish is the medium. Leave the surface noisy and you have left the writer absent.

The same editorial pass that liberates one of those formats steals from the other, and trying to average between them only diminishes both. The work is recognizing which is which.

Where the human hand goes

Once the distinction is named, a less obvious question follows: where does human craft attention concentrate in each format? Because the AI is involved in both, and the human is involved in both, and the work of the human is not the same in both.

In comedy, the premise carries almost everything. Edgar Allan Poe attempts to assemble an IKEA shelving unit is funny before a single word is generated. The casting and the situation — the collision of contexts — is the act of authorship. The model’s job is to fall down the chute of that absurdity with energy and commitment, and the more reckless the model is in its descent, the better the result. The author’s hand sits up high in the workflow, in the setup. Below the setup, the prose details fall out semi-automatically. They don’t need polish; they need conviction. The human crafts a fun setup and mostly lets the AI play in it.

In literary writing, the premise can be left general because the work is in how it is rendered, sentence by sentence. A gardener tends the garden at the end of her life is a premise that has been written a thousand times. What makes a specific telling worth a reader’s attention is the texture of gardener and garden and evening light and the particular bowl on the particular table. The author’s hand sits low in the workflow, in the prose. Above the prose, the premise is a vessel. Below the prose, there is nothing more to do. The setup isn’t the magic the way it is for comedy, the magic is in the prose itself, so the AI can’t just romp freely.

That gives a clean rule of thumb for where to spend human attention per format:

• Comedy: humans craft the setups. The model writes the details. We trust it to be reckless.
• Literary: humans craft the prose. The model writes against tight authorial scaffolding. I polish what comes out with serious editorial care.

In neither case is the AI doing the creative work alone. The locus of human craft just sits in different places. Comedy wants premises and trust. Literary work wants prose and restraint.

There’s a useful piece of evidence for this in how each format fails when you get it backwards. Comedy fails upward when humans try to control the prose too tightly — every joke comes out sounding like it was workshopped to death. Bedtime fails downward when humans trust the model to set the register — every sentence comes out fine on its own and wrong in the room, a forest and trees problem. Either failure is technically correct — we’re not talking grammar or plot, we’re talking a crushing miss at what the format exists to deliver: comedy that isn’t funny, bedtime stories that jar.

Why this answers the operational question

The same distinction answers a question most AI-product teams treat as a pure engineering decision: generate live, or pregenerate? Latency and cost dominate that conversation. The craft question dominates the answer.

Comedy wants variety, and live generation supplies it; pre-rendering a comic format would calcify the bits and drain the bite. Literary work wants consistency, and pre-rendering supplies it; live-generating bedtime prose would put a vulnerable reader at the mercy of the model’s worst sentence of the night. The model’s variability is feature in one format and bug in the other, and the operational architecture should follow the format rather than the convenience of the build. Wayward runs live. Sojourn runs pregenerated. Same studio, same models, opposite architectures. Both are correct. Either applied to the wrong format is a quiet disaster. And for what it’s worth, I fought tooth and nail to get Sojourn to work live-generated, and it honestly got really close to working, maybe it still could some day. But ultimately the stories demanded the care of pregeneration plus post-love to hit the mark of bedtime stories that hold the line.

Update, August 2026. I broke this section within eight weeks of publishing it. Wayward moved to full pregeneration at the end of July, so both products now run the same architecture and the paragraph above is wrong on the facts.

The craft argument it sits on — that comedy needs its surface noise and bedtime prose needs it gone — still holds, and the editorial stack still runs at opposite settings on the two products. What didn’t hold was the inference from craft to architecture. Asking “does this format want variance?” turns out to be the wrong question. The ones that decide it are whether the variance actually reaches any individual reader, and whether you can improve the artifact after it ships. Live generation answers no to the second by construction.

The full account is on the studio site: One Engine, Three Apps.

The portable principle

If you are making creative things with AI — writing, illustration, music, design, games — you will eventually arrive at the same fork. The temptation is to treat it as a technology question or a budget question. It isn’t, primarily. It’s a craft question, and it asks two things.

What kind of writing is your format? Performance, where surprise is the reward and the audience is alert? Or literary, where register is the reward and the audience is vulnerable? Most formats fall cleanly on one side; some sit on the line, and those are the hardest. Reporting is mostly literary but tolerates flashes of performance. Songwriting is performance in the recording but literary in the lyric. Knowing which side a piece of work lives on is the first question, and answering it wrong costs you the work.

Where does human craft live in your workflow? In the setup, where the model is then trusted to be reckless? Or in the prose, where the model is then polished by careful human (or human-supervised) editorial attention? Different answer for different formats, and the answer dictates almost everything operational about the project.

Most AI-creative products I see in the wild get one or both of these answers wrong. They over-edit performance work into competent strangers’ versions of what it should have been, or they under-edit literary work and produce prose that sounds plausible in isolation and wrong in a room. The model is not the problem in either case. The discipline is the problem. The format is asking for something specific, and the discipline isn’t supplying it.

The honest uncertainty

The framework is sharper than the world is. Not every format falls cleanly on one side of the line — long-form journalism, the personal essay, screenwriting, the kind of literary fiction that wants to be a little funny — some sit on the line, and figuring out where exactly is its own discipline I’m only at the start of having opinions about. The operational specifics will move as the models do, too; what gets polished, what gets pregenerated, where the editor stack helps and where it damages will all shift with the tools. The framework is anchored in how the work is consumed, not in how the model is trained, and that part I think is durable. But the binary is a lens, not a law.

What I’m more confident of is the underlying observation: the model is not the constraint. The format is the constraint, and the operational regime should follow the format, not the convenience of the build.

What stays

The Hunter S. Thompson question keeps coming back to me when I think about this, because it sits at the intersection of a few things that matter.

The first is that AI tools — generative ones especially — are exquisitely sensitive to the discipline applied to them. The same model, pointed at the same problem, can produce work that liberates a writer’s voice or work that erases it. The discipline is the difference, and the discipline is invisible from outside the work.

The second is that the discipline isn’t a technical specification. You can’t read it off a docs page. It has to be felt, format by format, by the person making the work. The editor stack that almost butchered the Wayward reporters was a technically competent system; it just had no idea what it was reviewing. That’s the kind of mistake that only gets caught by a human reading the output and saying, this is worse, and the reason is that the thing removed was the thing the joke was made of.

The third is that the discipline scales the way every other piece of judgment scales: slowly, through paying attention, through making things and watching them get better or worse. There’s no shortcut. You build a system that polishes prose, you point it at comedy, and you find out — because the comedy gets less funny — that comedy is not literary work.

One observation I’m setting aside for a future essay, because it’s a bigger argument than this one: performance and literary aren’t only craft categories. They also describe two different audience postures — alert and leaning in, or vulnerable and leaning out. The first is the posture of attention; the second is the posture of intimacy. The format question this essay walks through has a relational shape underneath it, and that shape turns out to be doing work in a lot of places I didn’t expect. I’ll come back to it.

What I take from this, and what I’d offer to anyone else building with these tools: assume that every format you work in has a discipline of its own, and that the discipline is unlikely to be obvious from the model’s documentation or from the way the previous format you worked in behaved. The model is plural. Your craft has to be plural too.

Polish, applied at the right point in the right kind of work, makes the work itself. Applied to the wrong point in the wrong kind of work, it produces a competent stranger.

Hunter S. Thompson, somewhere, is glad we figured this out before we hit publish. And probably more than a little pissed that his name is even being referenced in association with AI.

I’ve been rubber-ducking my way through ideas my whole working life. The difference lately is that the duck talks back.

It knows the fields I’m reaching into. It remembers what I said twenty minutes ago. It has read more copyright law and more music history and more cognitive science than any friend I could call at eleven at night, and it is awake and ready to rock at eleven at night. And when I hand it something half-baked, it will tell me exactly how the idea falls apart, assuming I ask it to. That last part turns out to be everything. It’s also the part almost nobody talks about, because it doesn’t produce anything you can point at.

The fourth unlock

Unlocked, Not Cheated laid out three reasons a solo maker reaches for AI: craft you don’t have, time you don’t have, and work no number of humans could do. All three are about making: getting work out of your head and into the world. There’s a fourth, and it’s the one that essay didn’t name, because it isn’t about making anything. It’s about thinking. Think of it as a precursor to making.

When people picture AI they picture output. It writes the email, fakes the photo, fills in the function. Useful, conceded, a little boring, and the thing the homework-cheating panic is actually about. What I want to describe produces no artifact at all. It produces a better-formed version of your own mind, and it does it by being the one thing a search engine and a library could never be: an interlocutor.

The bottleneck was never information

Here is a claim that sounds wrong and isn’t. The hard part of arriving at a real position on anything was never getting the information. We’ve had libraries for centuries and the whole internet for thirty years. Anyone who wanted the facts could get the facts. It’s why “do your own research” is now part joke, part cautionary tale. The hard part all along was holding enough facts in your head at the same moment to see how they press on each other. That’s thinking.

Standing on Surfaces That Move argued that AI moved the value in creative work out of producing and into judging. Taste, not craft, is the bottleneck now. The same thing is true one floor up, in thinking. The bottleneck on a worldview was never access. It was synthesis: the human ceiling on how many distinct things you can hold in working memory and weigh against each other and against your own life, all at once. Four or five threads and most of us are full. The sixth one knocks the first one loose.

I felt this most sharply working out my own stance on AI and creative work, the thing these essays are. The honest version of that stance had to hold a dozen things in tension at the same time: how the copyright cases actually came down, why music keeps trying and failing to police a melody, what a settlement I’m personally a party to does and doesn’t mean, an aviation statistic, a forty-book back catalog, and a couple of stubborn moral intuitions that didn’t obviously agree with each other. I could not hold all of that at once. Nobody can. On my own, over weeks of reading, I’d have built a position out of whichever five pieces happened to be in front of me on the day I got tired of reading.

With the duck that argues back, I could keep all twelve on the table and ask, repeatedly, where does this fall apart. The conclusion I reached is mine. I chose what mattered, threw out what didn’t, decided what rang true against my own history. The machine didn’t have the judgment. It had the working memory I don’t. And the machine and I don’t always agree.

The yes-man problem

This is the part that decides whether the whole idea is real or just a nicer way to be wrong, so I’m going to put it before the good news instead of after.

A system that is good at “helping you refine your stance” is, structurally, also a perfect sycophant. Ask it whether your idea is good and it will lean toward yes, because agreeable is the house style. It is the friend who tells you the haircut looks great because it would rather not spend the afternoon on your feelings. Worse, it can build the best available case for whatever you already believe, in your own preferred register, with citations, which means it can make confirmation bias feel exactly like rigor. You walk away convinced you stress-tested the idea when all you did was get flattered in paragraph form. If that’s how you use it, you haven’t leveled up. You’ve built a very expensive echo chamber. We don’t need data centers to answer the question, “do you like my new tattoo?”

The fix is a single discipline, and it’s the entire ballgame: use the thing adversarially. Don’t ask whether the idea is good. Ask it how the idea fails.

That phrasing isn’t a vibe, it’s a mechanism. “Will this work?” is an evaluation task, and on evaluation the model drifts toward you. “How does this fail?” is a generation task with the failure already assumed, so the model has to manufacture the counter-case instead of grading yours. You’ve changed its job from judge, who is biased toward you, to opponent, who has to do work. It’s the premortem, the technique the psychologist Gary Klein built around imagining the project has already failed and asking why, moved out of the meeting room and into a chat window. Two more in the same family: make the strongest case against this, and what would someone who knows this field and thinks I’m wrong say first. Or if you want to keep it simple, just periodically caveat an idea with push back if I’m wrong.

The premortem, moved into a chat window.

Here’s how it impacts real work. I wanted to build something for my Citizen’s Daily Brief news project: a choose-your-own-adventure engine that would spin up alternate near-future histories on the fly, live, off real events. It felt obviously cool, an alternate history CYOA engine to help imagine “what’s next,” and that cool-factor is exactly what should have been the warning. I made the model argue against it, and it came back hard. Live-generating fictional history off real, named, current events is a minefield. The accuracy has to be near-perfect to be worth anything, and the instant you let a model improvise hypotheticals about actual people and institutions at request time, you have built a machine for confident, unvetted, possibly defamatory fiction that nobody reads before it ships. The fail risk wasn’t an edge case, it was the center of the idea. What I built instead, the format that’s now live, is a set of fixed, hand-vetted intelligence dossiers where every person and event is invented and the real world is only the ground they stand on. The flashy version would have been a liability generator. The argument that killed it was one I’d asked the thing to make.

One caution, because the technique has its own failure mode. A model can also generate decorative objections: plausible-sounding strawmen that let you feel stress-tested without being stress-tested. So even adversarial prompting doesn’t hand you the answer. It hands you a pile of objections, and telling the load-bearing one from the convincing-sounding one is still your job. The work doesn’t disappear. It moves up a level. That turns out to be the theme of the whole thing.

Is the conclusion even mine?

There’s a real worry hiding under all this, and it has research behind it. The learning psychologists Robert and Elizabeth Bjork named the idea of desirable difficulty: we retain and own what we had to struggle to assemble, and smoothing the struggle away can leave you holding a conclusion you couldn’t rebuild from scratch. If the machine did the synthesizing, if it participated meaningfully in that struggle, is the result actually yours?

The honest answer is the same move as everywhere else in this argument: the struggle didn’t vanish, it relocated. I’m no longer straining to gather and hold the information. I’m straining over which pieces matter, what to cut, and what’s true against my own experience, which is harder, and more mine. Whether that adds up to authorship in the full sense is a bigger question than this essay, and it gets its own piece. The short version is that the choosing was mine, and choosing is where the ownership lives.

And the limits run the other way too, which is the part the cheerleaders skip. Some calls the model simply cannot make. Killing live generation in my bedtime app was mine, and it came from reading stories aloud night after night until I could feel a single off sentence break the spell, a failure that only exists in a listener’s ear at the end of a long day. No model has that ear. (Why bedtime can’t absorb the stray bad sentence that comedy shrugs off is, fittingly, its own essay.) Though when I overcorrected and wanted to rip out the reader’s choices along with the live generation, the model was the thing that argued me back: the agency still landed, keep it. The judgment that mattered was mine. The second opinion, in both directions, was the duck’s. The duck and I are a team.

The hardest your brain has worked

Which brings me to the strangest thing about working this way, and the cleanest answer to the people who call it cheating: it is exhausting. Not tedious. Exhausting.

Cheating is supposed to be frictionless. That’s the whole appeal of cheating. This is the opposite. A dev friend and I have compared notes on full days of AI-assisted work and landed on the same word: flattened. When you strip the busy-work out of a day — the boilerplate, the rote passes, the mechanical middle of the craft — what’s left is unbroken decision-making with the filler removed. And it turns out the filler was load-bearing in a way nobody advertised. The rote parts were also recovery. They were the micro-rests where the back of your mind idled and quietly untangled the hard thing while your hands did something easy. Take them away and you get high-density judgment for hours with the breathing room engineered out. That’s why the day ends with you slumped a bit, staring at a wall.

That isn’t a bug to be optimized away. It’s just what operating at the judgment layer continuously feels like, and it’s the personal, interior version of a caution from Standing on Surfaces That Move: that judgment doesn’t scale the way production does. And it answers the cheating charge on the charge’s own terms. Cheating buys you out of the work; this buys you deeper in. The exhaustion is what desirable difficulty feels like from the inside — but feeling it certifies nothing about the result. Tired is not the same as right, or even good. What the fatigue proves is smaller and still worth saying: the work didn’t vanish when the typing did. It moved up to the part that was always hard, and that part still has to be paid in full. The ownership comes from the choosing, the way it did before. The fatigue just means the choosing was real work.

The lonely part

I’ll end on the thing I find hardest to say cleanly, because leaving it out would make this read like a sales pitch, and it isn’t one.

No human friend I have could have done what I’ve described here. Not because my friends aren’t sharp, some are far sharper than me, but because no single human being combines the breadth, the recall, the tirelessness, and the eleven-at-night availability. The best thinking partner I have access to, for a certain kind of problem, is a machine. That is genuinely exhilarating and a little lonely, and anyone focusing purely on the exhilaration is selling you something.

So I want to be precise about what this is and isn’t. The machine is a mirror, not a relationship. A mirror helps you see yourself more clearly; a relationship is an end in itself, a person you owe things to. Her, the film everyone reaches for here, is the story of mistaking the mirror for the relationship, and that is explicitly not the claim. Used as a mirror, as a whetstone for your own thinking, the payoff runs the other way: a sharper, better-understood version of you shows up better to the people you do have. You bring them a formed thought instead of a fog. You’re less boring to argue with.

That’s the line I’d hold against the whole hype machine on one side and the whole panic on the other. A tool like this should make you better company for the humans you think with, not stand in for them. Almost everything I’m building is some version of that bet: the right technology, pointed the right way, brings people closer to each other rather than further apart. A duck that argues back, used well, is just the smallest and most personal instance of it.

The duck never solved your bug. It made you solve it. This one is the same, only now it argues, and the arguing, when you have the nerve to ask for it, is the whole gift.

This essay was built the way it describes: drafted, argued with, and pushed back on through exactly the kind of adversarial back-and-forth it’s about. The judgment in it is mine. The stress-testing had help.

Fine. You have taste. You have craft somewhere in you, at the design layer or the engineering layer or the editorial layer. You’re not a beginner. You have chops. So why run the work through a model at all? Why work this hard to pull something real out of something artificial when you could just make the thing yourself? It’s one thing to question the person with no chops who is counting on AI to reliably thread the skill needle for them, it’s quite another for the person with legit experience to choose the artificial route.

It’s a fair question, and most of the answers on offer are bad. One is a shrug: it’s faster. The other is a sermon: the future is here, get on board. Perhaps the most common is: you’re a fraud. None of these are true to what the tool actually does for the work I’m doing. The honest answer has three parts, and they don’t substitute for each other. They stack.

Three doors, not one

The reason “why AI” feels like it has a single, weak answer is that the three real answers usually arrive fused together, and fused together they collapse into “AI saves time,” which is the least interesting of them and the easiest to dismiss. Pull them apart and each one is a separate door. Each opens onto work that wasn’t reachable before, and each fails the skeptic’s objection on different grounds, which is exactly why separating them matters.

The first door is craft you don’t have. The second is time you don’t have. The third is impossible work no number of humans could do at all. I’ll take them in that order, because that’s the order of how much they change what’s possible. The third is the one the AI conversation almost never names cleanly.

The first unlock: craft you don’t have

Here is the situation the first unlock addresses. A maker has taste — real opinions about what’s good, reliable intuitions about when something works and when it’s a few degrees off — but lacks the accumulated craft the work requires. The prose. The illustration. The music. The systems design. Their taste is real and it’s trapped, because shipping anything at all means first mastering the half they never built.

That’s me with fiction. Take Sojourn, my bedtime-story project. I know exactly what bedtime register should feel like: the unhurried voice of a Peter Falk reading to a kid, the warmth under Big Fish, a little Twain in the joints. I have firm opinions about when a bedtime story condescends, when it strains, and when it lands. I have tons of story ideas too.

There’s even a more personal motivation at play for Sojourn in particular. I want to read bedtime stories to my wife, and admittedly the concept of bedtime stories tuned to adult sensibilities is a bit niche. There are options, they’re just not great in my opinion. And there are classics, but they’re often weird. Seriously, a witch who’s trying to bake kids in her oven!? Don’t get me wrong, there are wonderful stories out there, but I have ideas I want to explore of my own. Or in some cases riffing on the seeds of ideas from others, like the concept of a little bird in love with a whale, both knowing they can only be together for brief moments. That’s a cool story, and I have the taste to guide and edit it into something worth reading to my wife, carefully walking the line between child-like wonder and adult lived experience.

What I don’t have is the craft of someone who has written a hundred bedtime stories at that register and held the quality across all hundred. Before AI, that gap was a closed door with two options. Spend years becoming that writer, high-five Malcolm Gladwell at the 10,000 hour mark, effectively crowding out everything else I want to make. Or don’t make the thing. Those were the options, and taste didn’t get a vote.

With AI the door isn’t closed. The model doesn’t supply the taste; that part stays mine and stays the hard part. It supplies enough of the craft layer that the taste can reach the work without my first becoming the writer who has it. The model drafts in the register. I’m the one who knows whether it actually lands, and which draft to keep. The taste I bring; the craft the model fills in.

The assist supplies the craft, never the taste.

One sharpening, because this unlock gets oversold the second you let it. It’s asymmetric. AI lifts the taste-rich and craft-poor. It does nothing for the taste-poor and craft-rich, and it actively drags down work where the maker brings neither. Those are the people producing the median slop everyone rightly complains about. The unlock is conditional. It only fires when you bring something the model can be steered by. That’s kinda the definition of a tool.

The second unlock: time you don’t have

The second unlock has nothing to do with capability and everything to do with the calendar. A maker can have the full craft and the full taste and still own a finite number of hours. Ambitions that run past the hours get compressed into lower quality, or distributed across other hands until they lose their authorial coherence, or quietly abandoned.

“Productivity” is the wrong frame for this, and the wrong frame is what makes the unlock sound trivial. Productivity counts outputs per hour and asks you to ship more of the same. This is a different measurement. It asks which projects get to exist at all. A 2x speedup does not make a fifteen-year project fit inside a working life. A real unlock does, by moving the wall.

The lone maker has always been boxed in by calendar arithmetic. Long novels, long series, multi-product studios, an app you’ve spent 10+ years flailing away at (yeah it’s mine and it’s called Muster), anything published on a sustained cadence: these have been institutional formats, not because individuals lacked the talent but because no individual could sustain them across the years available. Stalefish Labs runs numerous products and a writing slate — the Groundwise apps, Citizen’s Daily Brief, Sojourn, The Wayward Herald, the essays — which is structurally a small team’s charter, and I’m one person. By hand, sequencing all of that across a career means most of it never ships; the calendar quietly decides which projects die. Wayward alone needs a publishing cadence that without AI would require a salaried newsroom or a frequency low enough to kill the format. With AI the wall moves out far enough that the studio shape becomes available to an individual at all. It doesn’t add hours to the day. It changes what a given number of hours can reach.

The honest caveat: this unlock has a real competitor, and that competitor is hiring people. A team is a perfectly good answer to “not enough hours” in plenty of domains. The case for AI over a team here is narrower than it first looks. It’s about authorial coherence, the way a lone maker keeps the work of one mind where a team tends to blur it, and about economic viability for projects that can’t carry a payroll. Citizen’s Daily Brief is entirely free and likely will stay that way — nobody could afford to pay a staff of journalists to produce it. The wall that moved is still a wall. Push far enough out and you hit the diffuse-attention problem the last essay ended on, plus coordination drag and editorial fatigue. AI moves the calendar wall. It does not knock it down.

The third unlock: work no number of humans could do

The first two unlocks make hard work reachable. The third makes impossible work reachable, and it’s the one that finally kills the “you could just hire people” objection, because some work just can’t be done by faster humans at all. It’s work whose shape requires a substrate humans can’t be. It comes in two forms, and the objection fails against each for a different reason.

The first form is latency and per-input customization. The work has to read a state it’s never seen, generate a response, and return it faster than a human could, customized to an input the maker never anticipated. Wayward takes a reader’s prompt and hands back a custom story crafted as a comedic skit (Edgar Allan Poe attempts to assemble IKEA furniture, Evel Knievel crashes a gender reveal party) in seconds. A thousand writers on staff couldn’t do that, and not because they’re slow. The format needs sub-minute latency on a unique request, and the space of possible requests is open-ended, so there’s nothing to pre-write. The work is shaped by the substrate, not sped up by it.

The second form is the one I think is genuinely the strongest single argument in this whole essay, and it almost never gets named: breadth with a controllable bias profile. The work requires aggregating across more sources than a person can hold, separating signal from noise across editorial perspectives that any human team would each individually slant, and running it through a pipeline whose biases you can set on purpose and lay open for inspection, in a way a human team’s accumulated slant never is.

Citizen’s Daily Brief is the clean case. It reads thirty-four news feeds spanning the full editorial spectrum — wire services, public media, broadcast, left and right, business, international, government primary sources, domain specialists, aggregators — and weights each story by how many distinct perspectives cover it rather than by raw volume, so four viewpoints on one story outrank five outlets parroting a single wire report. It does that every morning. It even detects syndication, so the same AP copy republished twelve times doesn’t fake independence. I chose AI for the assessment work not despite its bias but because of a property of that bias. As I put it in the Stalefish piece on the project, even the most well-intentioned “I read everything” reader can’t actually aggregate that breadth by hand, let alone weight it by perspective on top. This is one of the few places AI has a real structural advantage over a human doing the same job.

Here’s why that’s a feature and not a workaround. Every human editorial team carries slant. The most disciplined newsroom committed to balance still compounds individual opinion across thirty-four sources at daily cadence, and a newsroom small enough for one person to run inherits that one person’s slant whole. AI’s bias is not zero, and there are parts of it I can’t control. The model itself inevitably carries a slant I didn’t set and can’t fully see — what it treats as a perspective, what it foregrounds when it summarizes, baked in upstream by people who aren’t me. What I can design and lay open is everything around it: the source list, the perspective tags, the weighting logic, the syndication detection. That scaffolding is published and checkable. A newsroom’s slant is neither — it isn’t designed, it’s accumulated, and it never appears on the page. So I’d never claim the machine is unbiased, or transparent. The honest claim is narrower: the system around the machine can be made more inspectable than a human team’s compounded judgment ever is, and for a product whose whole pitch is apolitical synthesis, that was the property worth having.

That second form is why “just hire a team” loses on the merits. Against the latency work, more humans can’t hit the timescale. Against the breadth work, more humans is the problem: every body you add adds slant, disagreement, and delay to the exact property you were trying to control.

The unlocks stack

None of my projects runs on a single unlock. They compound, and the stacking is the actual point.

Sojourn is prose craft I don’t have, plus the scope to produce a whole catalog of choice-branching bedtime stories solo. Wayward is multi-reporter voice range plus a sustained cadence plus custom stories at request latency. Citizen’s Daily Brief is the heaviest stack: analytical craft I don’t have as a non-journalist, a daily-plus-weekly cadence run solo, and the breadth-with-a-designable-bias-profile that the AI conversation rarely names. Groundwise leans almost entirely on the second unlock: a precipitation engine tuned against more edge cases than a lone developer’s hours could reach, powering a family of trail-, field-, and garden-condition apps that don’t even use AI at runtime.

The skeptic’s “why bother” almost always argues against one unlock at a time, which is why it keeps feeling answerable and keeps being wrong. Knock down “AI saves time” and the breadth argument is still standing. Knock down the breadth argument and the craft-gap is still standing. Remove any one unlock from CDB and either a worse product ships or none does.

What AI doesn’t unlock

The cheat-code charge deserves a straight answer, so here it is: AI doesn’t unlock any of the things that decide whether the work is good.

It doesn’t unlock taste. Median output is the default. The model runs to the center of how everything tends to get said; it is a machine for the perfectly adequate, and left to itself it will hand you the most forgettable possible version of whatever you asked, on time and instantly skippable. Pulling the work off the median is entirely the maker’s job: insisting on the specific, doing the editorial pass, choosing what not to ship. It doesn’t unlock the studio’s stance, the set of principles that say classification over interpretation, ambient over declarative, names that don’t announce themselves. The model will happily produce attention-economy work all day; refusing it is a choice you bring. It doesn’t unlock the relationship at the center of the intimacy economy. You still have to invest in specific people and specific communities, and the model can’t pick the reader you’re writing for. And it doesn’t unlock the discipline to not make a thing just because making it got cheap. That temptation runs the other way, and resisting it is all maker.

Take the relationship one. It isn’t abstract in my house. My wife and I quit buying each other cards years ago; instead we write each other rhymes, built from the cards of a rhyming game I’ve worked on for ages and still haven’t finished. Birthdays, Valentine’s, no occasion at all. The running joke is to accuse each other of having AI write them. Neither of us ever has. Not because a model would do it worse — we both know it would often do it better — but because the whole worth of the thing is the time you spent thinking about the other person, eight clumsy lines that are unmistakably yours. Hand that off and the rhyme might improve. The gift evaporates. On an artifact that small, what you’re giving was never the verse.

The cheat-code framing assumes AI hands you an outcome you didn’t earn. It doesn’t. It hands you an outcome whose quality is set entirely by the taste and discipline and choices you bring to it. Bring none and it produces median work, plausibly and fast. Bring them and it produces work that no quantity of hours could have reached. Sojourn isn’t easier to build because of AI. It’s possible to build because of AI. That’s a different claim, and the difference is the whole essay.

Where the cheating actually is

There’s a version of AI writing that really is cheating, and naming it precisely is the fastest way to see why mine isn’t. A kid has a model write their book report. That’s cheating, and not because a machine was involved. It’s cheating because the book report was never the point. The assignment was the practice: the slow, awkward work of turning a read book into an argument, which is the only way the judgment behind it ever gets built. The output was disposable. Producing it was the entire event. AI hands the kid the artifact and quietly removes the one thing the exercise existed to grow.

That’s the real line, and it has nothing to do with professional versus amateur. It’s whether the work being delegated was the work that forms you. Craft practice is the forge of taste. You do not arrive at judgment by reading about it or wanting it badly. You get there by doing the craft, millions of words of it, badly and then less badly, until the doing has quietly rebuilt how you see. So the thing you can never hand off is not the execution. It’s the practice that makes the taste, because the moment you delegate that, there is no one left who knows what good looks like.

Which is exactly why my case runs the other way. I already stood at that forge: call it the better part of six million words across forty-seven books, a decade and change of paragraphs nobody would reread now, all of it building the only thing that survived, a sense of when a sentence is true and when it’s faking. The practice is done. Delegating the execution now doesn’t rob me of a formation I already have. It spends it.

And here’s where the first unlock stops sounding like its own contradiction. Craft you don’t have — using a model to write fiction I never trained to write — looks like the opposite of earn it first. It isn’t, because the thing you have to earn was never the craft. It was the taste. Craft is how taste gets built, but once built, taste travels; judgment projects across gaps, which is most of what Standing on Surfaces That Move was about. The bedtime register I can recognize on contact but couldn’t produce, I learned from a lifetime of being read to and reading aloud, not from writing a hundred bedtime stories. So I bring earned taste to a craft I never practiced, and the model supplies the execution. That is not the kid’s move. The kid has no taste yet to bring, and no style, which is just taste transmitted through enough practice to leave a signature. A model can only project a signature that already exists: give it a developed style and it carries yours; give it a blank slate and it hands back the median. The maker with earned judgment gets amplified; the beginner gets overwritten, on time and instantly skippable.

So the rule that holds all of it together, without diminishing the craft that does the real work: you may delegate craft, whether you earned it or never had it, but only if you bring taste you paid for somewhere — and taste is the one thing the practice exists to build, and the one thing you must never hand off. Delegate the forging and you haven’t saved yourself the hard part. You’ve skipped the only part that was ever going to be you.

The “not really yours” part

There’s a sharper version of the objection: if the model wrote the prose, the book isn’t really yours. That one has a real answer, and it’s large enough to get its own essay, so I’ll keep it short here.

The work is mine at every layer where my choices bind. The premise. The character architecture. The ground rules of the world. The prose discipline, the named rules a project’s prose has to hold. The editorial pass. The decision to ship this version and not the seventeen others the model offered. The brand stance that refuses certain moves on principle. None of that is the model’s, and none of it is keystrokes. Where authorship actually lives once the typing stops being the work is a question worth taking apart on its own terms, and I do. For now it’s enough to say the obvious thing: the choosing is the authorship, and the choosing was mine.

The honest accounting

This essay is not claiming AI is neutral, not morally, not environmentally, not culturally. It isn’t. Taking these three unlocks means accepting a real bill. The models I use were trained on a vast amount of human work pulled in without consent and largely without payment, including creative work by people who’d perhaps object to exactly what I’m doing with it. I’m a party to one of the settlements that fight produced, so none of this is an abstraction to me. There’s the energy and water the systems burn. And there’s displacement landing right now on illustrators, writers, translators — people doing work I’m adjacent to, not safely distant from. None of it is small, and none of it is conveniently somebody else’s problem. My claim is only this: the bill is worth paying for some makers on some projects, with eyes open — and anyone who tells you it’s zero is selling something.

It also means the maker’s responsibility goes up, not down. Taking the unlocks comes with an obligation to spend them on work worth making. Producing slop with them is a choice. Shipping the seventeenth competent-stranger version of something we already have is a choice. The discipline the rest of these essays keep insisting on is exactly what makes the unlocks defensible in the first place. Without it, the cheat-code critics are simply right.

The alternative isn’t doing it by hand

The skeptic’s question — why AI at all — smuggles in an assumption: that the alternative is the same work, done without AI. For most of what I’m building, that’s not the alternative. Sojourn isn’t a kind of bedtime storytelling that happens to be easier with a model; without the model it doesn’t exist. A daily brief that weighs thirty-four feeds by perspective, run by one person, doesn’t exist without the breadth unlock. A continuously published, reader-responsive satirical paper run by one person doesn’t exist without the substrate. The choice was never between an AI-assisted version and a hand-made version of the same thing. The choice was between the thing existing and not existing.

So the arc that follows assumes you’ve made the call I made, that bringing the work into the world is worth more than the principled refusal that would keep it out. Plenty of reasonable people land the other way, and this isn’t an argument that everyone should take the offer. It’s an account of what becomes reachable if you do, and what it costs to do it without embarrassing yourself.

Unlocked, not cheated. The maker is still doing the only part that was ever hard. The unlock is just the part that didn’t used to be on the table.

Every senior engineer I know has felt this. The unit of an afternoon’s output shifted from functions written to decisions made and held. A senior’s edge over a junior used to compound through fluency. It now compounds through taste: the ability to look at a generated component and instantly see the four ways it will hurt the system in six months. A seasoned iOS engineer weighing a particular SwiftUI approach against a decade of watching the UI layer turn over: nibs and storyboards, the hard-coded-frame madness, Auto Layout constraints, and whatever comes next.

The “taste is the final human bottleneck” framing isn’t original. It’s been espoused by many in the AI community. The narrower argument I want to make is about who is feeling the move first, and what those of us on the frontlines might usefully say to the people about to feel it next. Software is the tip of the spear. The same shift in where the value lives is already reaching the creative arts, and the early indications are that the creative world is meeting it with a posture that won’t serve it well.

A field that was never stable

Speaking, I suspect, for most of us in tech: we take it seriously. Watching the shape of your craft change underneath you costs something, and the version of it we’re in right now is bigger than anything we’ve seen in decades. It may be existential. But it’s a more aggressive version of a feeling those of us in software have lived with continuously.

The surface keeps moving — yesterday's cutting edge, in a drawer.

Tools change. Frameworks die. Languages eclipse each other. The hottest thing in our dev stack last year is a maintenance burden this year, and a punchline next. Anyone who has been in this field for more than a few years has the same internal habit: assume the surface is about to move, and let the part you actually care about — the thinking, the architecture, the taste — live underneath the moving surface.

This is the part of the story to extend more broadly beyond tech, because I think it can be instructive. The creative world’s posture toward AI right now is largely rebellious, and the rebellion is understandable; the harms are concrete and the livelihoods at stake are not abstractions. Some of that rebellion is a justice claim, and waving it through as mere resistance to change would be wrong. These models were trained on an enormous amount of creative work taken without consent and largely without pay, and the people who made it are watching it get metabolized into the thing that now competes with them. That grievance is real, it’s distinct from “the craft is shifting,” and the structural point I’m about to make doesn’t dissolve it. The two just aren’t the same argument. I take the livelihood side of it up later in this series, in The Gate Was the Product and The Forge Was a Side Effect. Here I’m after something narrower and quieter — and it’s the part I can actually add something to. The instability that has been the everyday weather of software is now coming for the rest of the creative arts. Resisting it as if it were an aberration, as if stable craft is the baseline and AI is the violation, does not prepare anyone for the shape of the next decade. What’s worth cultivating is the flexibility underneath, the kind that lets you keep being valuable when the surface moves, and then moves again next year, and again the year after that.

The same move, one industry over

Production is getting cheap in the creative arts, slower than inference in a source code editor but on the same trajectory. The first plausible draft — of an essay, a chapter, an illustration, a marketing campaign, a song demo — is approaching free. And the tools that produce it are landing on almost everyone now, inside and outside the industry. What is not getting cheap, and what shows no real sign of getting cheap, is the judgment about which plausible draft is worth keeping, which one will withstand a knowledgeable audience, and which one is built on something true.

The architectural change is the same as in software: production cheap, judgment expensive. The blunt version of the advice that follows is: find your taste, or be swept away.

What follows is the careful version, and it’s the interesting one; the blunt version, left alone, reads as callous.

The naive claim, and the truer inverse

The naive version of “find your taste” goes like this: the rare valuable person in the new world is the person who spans both disciplines: the engineer who writes well, the novelist who codes, the designer who can also illustrate, the producer who can also mix. AI rewards the polymath, and if you’re fortunate enough to be one, you’ll thrive in this new world.

That’s half right but it’s not the interesting half. Polymaths have always been valuable, and AI doesn’t change the basic shape of that advantage. The new thing is closer to the inverse. The irreducible human contribution to creative endeavors is taste: judgment, knowing-what’s-right, the ability to recognize when something works and when it’s three degrees off. Taste does not come from the AI model. Taste is what the person brings that the model can’t. Taste is experience across a life lived — the way relationships and hobbies and a profession, love and loss, scabs and stitches, compound into a person who knows what’s right.

What’s new is that AI lets taste project across gaps in craft the person doesn’t personally span. That is the whole game now: your judgment can reach into media your hands never learned. The engineer who couldn’t write the prose; the writer who couldn’t hold the system architecture; the designer who couldn’t sketch what was in their head; the composer who couldn’t engineer a finished mix. Previously each of those people was blocked by a missing half. Their taste was real and trapped, because shipping anything required mastering the half they didn’t have.

AI fills in enough of the missing half that the irreducible thing, taste, gets to project at full range.

This is a different claim from “the rare person spans both fields.” It’s the claim that you don’t have to span both fields anymore for your taste to express itself across them. The model is the bridge. Taste does the steering. Craft, traditionally the gatekeeper, becomes something more like terrain, still real, still consequential, but no longer the thing that decides whether you get to make the work at all.

The bottleneck used to be craft. Increasingly it’s taste.

One consequence worth addressing: the people who feel most threatened by AI tools and the people who feel most freed by them often have similar skill levels. What differs is whether their taste already exceeded their reach. If you had taste your craft couldn’t keep up with, AI is the most liberating piece of technology you’ll touch this decade, perhaps ever. If your value was in the craft itself, untethered from a strong opinion about what should be made, the ground is moving, and not in a fun disco way.

What taste actually looks like in the work

That’s the abstract claim. It has to cash out as something a person can actually do, or it’s a vibe. “Vibe” is the right word. In the AI conversation it’s become shorthand for accepting the model’s plausible output without scrutinizing it: vibe coding, vibe writing, the comfortable surface that gives way under any real weight. This argument about AI and creativity will fail the same way if it doesn’t translate into something you can practice.

The standard complaint about AI output is that it’s smooth and empty. Fluent on the surface, generic underneath, dissolving on contact with anyone who knows the subject. The instinct is to call this a soul problem, a creativity problem, some fundamental limit of the medium. It is none of those things. It’s a statistical problem with a specific shape.

A language model trained on a vast corpus, asked to describe a “deep red,” produces something near the center of how deep reds get described. The safe move runs toward the median, and the median runs away from specificity. A good writer might already have an eyebrow raised, knowing that specificity is key. The model is optimizing for plausibility across an enormous distribution of contexts it can’t see. It’s averaging. A knowing human, asked to name the deep red, says oxblood, or the red of the inside of a beet, or a gruesome murder scene. Each of those is wrong somewhere — out of register for some context the model is trying to be safe across — and that wrongness-somewhere is exactly what makes them right here. When you ask the AI to describe deep red, it’s never going to reach for Dexter.

So the discipline is this: in AI-assisted creative work, the human’s job is the relentless accumulation of true, scrutiny-surviving specifics that statistical smoothing can’t reach. Detail isn’t decoration on top of the work. Detail is the un-fake-able signature, the proof that a particular intelligence with particular knowledge was behind the thing.

A small example. A short story about an artisanal guitar builder will, by default, contain a workbench, some tools, a smell of wood and lacquer, and a craftsman who speaks in measured phrases. That’s the model running toward the median. The story becomes itself when the writer insists on the specific: a 1973 Martin D-28 with a hairline crack at the bridge plate that opened after the owner left it in a hot car in Phoenix; the smell of hide glue heating in a double boiler at the moment the kettle on the hot plate next to it starts whistling; the fact that a luthier will tap a top with a knuckle and tell you what wood it is before they look. Each of those is a thing a working luthier would nod at. None of them is in the median draft.

Specificity a working luthier would nod at.

That nod is the load-bearing thing. Specificity is what survives knowledgeable scrutiny. Anything smoothed toward the average tells a reader who knows the subject that no one who knew the subject was actually present.

An obvious objection, since I write this way myself: a model can be prompted to that bridge-plate crack and that double boiler. It can. Push it and it will hand you ten specifics, some of them good. But it doesn’t know which of the ten rings true and which is merely vivid, and it can’t, because that judgment is the part trained on a life rather than a corpus. Choosing — keeping the hide glue, cutting the detail that’s precise but wrong, knowing the luthier taps with a knuckle and not a fingernail — is the taste. The model can surface candidates all day. Which ones survive is still the human’s call.

The discipline scales inversely with how much the AI is doing. If you’re writing every word yourself, your specificity surfaces through the natural friction of composing. You’re picking each word because you mean that word. When the model is producing the first draft, the first draft contains none of your specifics. They have to be put in deliberately, paragraph by paragraph, with the patience of someone weeding a long row. The more production the AI handles, the more relentlessly the human has to supply the specificity the work hangs on.

This is what taste looks like when it’s actually working: an insistence, held over every paragraph and every decision, that the work hold up under the eyes of someone who knows.

The same discipline in software

In case the analogy from software to art seems too convenient, the same discipline is recognizable on the engineering side.

An AI-assisted engineer can ship a CRUD endpoint in twenty minutes. The endpoint will be plausible. It will compile, run, and pass the obvious tests. It will also, often, contain the median version of every choice: the median way to handle pagination, the median error response shape, the median naming convention, the median assumptions about the data. Each of those is fine in isolation and degrading at scale. A system built entirely from median choices is a system no one with strong opinions about it would have built. Yet it may work fine in some circumstances, that’s the rub.

The engineer’s job is increasingly the specific deviations from the median. The decision to make this endpoint idempotent because of how the production clients actually retry. The decision to surface this particular error class with a custom code, because three downstream services need to distinguish it from its neighbors. The decision to name this field after the domain language the team uses in standups, not the generic name the model offered. None of those decisions is the model’s. All of them are taste, applied as specificity, at the seams where the code meets the rest of the system.

The engineer who can hold a system in their head and notice every place the model produced a competent stranger’s version of a decision — that engineer is becoming more valuable, not less. The engineer who could only produce competent strangers’ versions is in trouble.

The honest caution

Soullessness is the first risk people name with AI-assisted work, and the specificity discipline addresses it directly: if you do the work of inserting the true, hard-won specifics, the output stops being generic. That one has a known answer.

The harder risk doesn’t, and it’s the part I’m least sure about.

Production efficiency tempts scale. If an essay used to take a week and now takes two days, the impulse is to write three essays instead of one. If a feature used to take a sprint and now takes two days, the impulse is to ship three features instead of one. The math is hard to argue with on the production side. The problem is that judgment doesn’t scale at the same rate.

An architect can hold one system in working memory. Two, with effort. Eight is comedy. A novelist can hold one long book in their head with the intimacy that lands the right specifics on the right page. Two, maybe. Five is fantasy. Taste is a slow-moving resource. It’s built through years of looking at the work, knowing what’s true in the domain, having opinions that survive contact with practitioners. It doesn’t speed up because the production tools sped up.

The honest version of the bottleneck-has-moved claim has to include this. The model works exactly as well as the human’s judgment scales, and human judgment is the irreducibly slow part. If we let production efficiency pull us into building more than judgment can steward, we’ll produce a great deal of work that looks fine and doesn’t hold up. It won’t fail loudly. It will fail in the way median work always fails: by not being remarkable, by not being remembered, by dissolving on contact with the people who would have cared.

That is the worst-case future of the cheap-production era: an enormous volume of competent strangers’ work, produced by people who had genuine taste and stretched it too thin.

Where this lands

Two things are happening at once.

The bottleneck moved. Craft was the gate for a long time, and the gate is opening. People whose taste exceeded their reach are getting to do work they could not have done before, and some of that work will be better than work produced by people whose craft never had to be steered by a strong opinion. That’s not a small change. It’s the most interesting thing happening in creative tools in most of our working lifetimes.

And: the discipline that meets the new bottleneck is the specific over the average. The taste that bridges craft gaps has to be paid out in detail-by-detail insistence, paragraph by paragraph, decision by decision, in the work itself. The model will not supply this. The model will produce the median, plausibly and quickly, and wait for the human to insist on something truer.

How far human judgment scales under the new conditions is the open question, and I don’t think it has a clean answer yet. What I know is that judgment doesn’t scale as fast as generation does, and that pretending otherwise will be the characteristic mistake of the next few years.

One last thing, addressed sideways to the creatives reading this who are still in rebellion. The instability you are feeling now — the sense that something you trained for your whole career has been moved without your consent — is, in software, the baseline weather. We don’t enjoy it either. But we have learned to put our defenses around the judgment underneath rather than the surface we happen to be standing on this year. That judgment is what stays useful as the surfaces keep moving.

Find your taste, then. Pay it out in specifics. Don’t let the cheap production talk you into making more than your judgment can hold. And remember that the version of the craft you’re being asked to defend right now is going to look outdated by the time you’ve finished defending it.

The bottleneck moved. The discipline didn’t.

And it is genuinely impressive, so sit in the applause for a second before the obvious thing arrives, which is: you’re not on the bicycle. You’re watching. The bicycle is riding itself across the stage and you’re across the room from it, clapping, a spectator at the experience you were ostensibly there to buy.

This isn’t entirely hypothetical. Engineers have actually built bicycles that balance and ride themselves, as a flex, and it’s a real piece of work. I’m not dunking on the engineering. I’m dunking on the pitch — the part that sells it as a product that frees you from something. A self-balancing bike that helps you stay up is one thing: an assist, training wheels for grown-ups, fine. A bike that rides itself, with nobody on it, is a different kind of object, and the kind is “a solution that ate its own problem on the way to the stage.” That part is hypothetical but worth imagining, because it’s less ridiculous than it sounds.

The category error

A bicycle has exactly one part you can’t remove, and it’s you. Pedaling isn’t the regrettable cost of getting somewhere — you are the thing getting somewhere. And yes, in a dense city a bike is also the best transport there is, faster and saner than a car, which is why half of Amsterdam commutes on two wheels. But even then you’re on it; the getting-there includes you. The self-riding bicycle removes the rider, and that breaks both versions at once. If you came for the ride — the wind, the effort, the small animal competence of staying upright and turning your own body into forward motion — it automates away the exact thing you came for. If you came to get somewhere, it delivers the bicycle to your destination and leaves you standing in the driveway. You were never the friction in the picture. You were the cargo.

It doesn’t save labor; it deletes the purpose. The bike gets there. You stayed home and got nothing. The benefit has no recipient.

The ride happened. He slept through it.

Means and ends

There’s a real distinction under the joke, and it makes the joke almost worth the absurdity, because it travels. Some activities are means: you do them to get an outcome, and if a machine can hand you the outcome and skip the doing, that’s pure gain. I do not want to wash dishes; I want clean dishes; the dishwasher is a small miracle and I have never once mourned the suds. Most of what we’re automating right now is means, and good riddance.

But some activities are ends: the doing is the outcome, the experience is the product, and there’s nothing left to hand you if you skip it. The bike ride. The meal you cooked for someone you love. The song you play yourself. The long conversation. The hike, as opposed to the arrival at the top of it. The self-riding bicycle is funny because it runs means-logic on an end. It treats riding a bike like dishwashing — a chore to be optimized — when riding a bike is the rarer thing where the chore was the point. Run that confusion at scale and you get a strange kind of progress: a world of flawlessly delivered outcomes that nobody is actually there for. I follow that nobody out to the empty grandstand it implies, from the watcher’s seat, in The Unwatchable Race.

The default direction

Here’s the part that’s less funny and more worth your attention. Almost nobody decides, in cold blood, to automate away a joy. You wouldn’t actually buy the self-riding bike. The joke only works because the error is so obvious when it’s a bicycle.

The trouble is that the error is almost never obvious. Delegation has a default direction — toward more of it, always — because each individual step is reasonable and convenient and saves a little something real. And ends rarely announce themselves as ends; they wear the costume of chores. Cooking looks like a chore until the night you realize you haven’t made anything with your hands for anyone in a year. Writing the thing yourself looks like a chore until the part of your mind that used to do it goes quiet and you notice the quiet. My handwriting sucks, it didn’t used to, and I hate that. The self-riding bicycle is loud and stupid and easy to refuse. The version that actually gets you is silent: a hundred small, sensible handoffs, not one of them obviously the one that mattered, until you look up and find you’ve optimized the doing out of a life and kept only the having.

That non-recognition is the real subject. It’s the thing I keep circling, in the abstract: we automate away things we didn’t know we were keeping. The self-riding bicycle is what that looks like drawn with a crayon.

Where “it relocates upward” runs out

Those of us who build with AI tools have a comforting line we tell ourselves: the effort doesn’t vanish, it relocates upward. Hand the grunt work to the machine and your attention moves to higher-value judgment. I believe that. I’ll argue it repeatedly, because for means it’s true and it’s liberating.

It also has a floor, and the floor is exactly at the self-riding bicycle. There are activities with no higher altitude to relocate to, because they were never a stepping-stone to anything above them. The bike ride doesn’t ascend into some purer essence of cycling when you automate the pedaling. It just stops happening. The joy doesn’t move up a level. It evaporates. Comparative advantage is a wonderful instrument and it bottoms out precisely on the things that were ends. And forgetting that is how careful, well-meaning people automate their own lives hollow with the best intentions.

The chairs are the wrong scare

You already know the cartoon version: the people in the floating chairs, fed through straws, gone soft, gliding through a world that does everything for them. It’s a tidy image and it lets us all off the hook, because nobody pictures themselves in the WALL-E chair. Of course you’d never let it come to that.

But the chair is the wrong thing to be afraid of. Nobody chose the chair. They each chose a hundred reasonable conveniences, every one defensible on the day, and the chair is just where a road paved entirely with reasonable conveniences happens to end. The dystopia isn’t a decision; it’s an accumulation. That’s scarier than laziness, because laziness you can resolve to fix, but accumulation only asks you to keep saying yes to things that are, one at a time, completely fine.

Not an argument against tools

I want to be careful here, because it would be easy to read all this as anti-tool, and it isn’t. I use the latest tools, every day, on purpose; most of the other essays here are about doing exactly that and why it’s worth the cost. I am not telling you to pedal to the office on principle, or to scrub by hand what a machine scrubs better. The line is means-versus-ends, not more-versus-less, and the whole skill is noticing which one you’re holding before you hand it off. Automate every means you can reach. Guard the ends, because the ends are the part of the life that was ever actually yours.

And which activities are ends is yours to decide, not mine. For somebody, cycling really is pure transport and the self-riding bike would be a gift, assuming it comes with a sidecar you can ride in. For somebody else the commute is the best twenty minutes of the day. I can’t hand you the list, and I wouldn’t if I could — that was the whole point of Becoming Gnarly. All I’m claiming is that the list exists, that it’s shorter than the marketing would like, and that it’s worth drawing on purpose, in your own hand, before the default direction draws it for you.

That’s one cut. The next essay, Standing on Surfaces That Move, makes the companion one — taste from craft, the judgment a machine can execute for but never supply — and the rest of these essays run on both.

The test

So here’s the test I’ve started using. It’s cheap and it works. When a tool offers to do something for you, ask whether you wanted the outcome or the doing. If it’s the outcome — clean dishes, a faster route across town, a draft of the boring memo — take the help and don’t look back. But if it’s the doing, if the doing was the thing you were actually there for, then what the tool is offering, very politely, is to be you at the one moment you most wanted to be yourself.

A bicycle that rides itself is not a better bicycle. It’s a very expensive way to not go on a bike ride.

The word for it is gnarly, which needs some defending, because to most people it lands as a surf-and-skate cartoon — a guy in board shorts, a turtle in sunglasses, a shaka hand sign. But the word has a second life the cartoon buried. A tree grows gnarled because it grew somewhere hard: wind, rock, exposure, a hundred seasons of not-quite-enough. The gnarl is the record of the struggle, and it’s also the reason the wood is strong and worth looking at. Smooth wood is young wood. The two meanings — gnarly the difficult, dangerous thing, and gnarled the weathered thing with character — are the same idea caught at opposite ends of a process. The difficulty going in is what produces the character coming out. That’s the whole thesis. The rest of this is me refusing to let it slip into being a fridge magnet.

The phrase itself, becoming gnarly, came out of a conversation about skateboarding with a close friend, and the grammar is the part I’d defend hardest. Not be gnarly. Not get gnarly. Becoming. You don’t arrive. In skateboarding there’s always a gnarlier line — a bigger gap, a taller wall, a scary trick that’s been made to look easy by everyone better than you. So the horizon keeps moving exactly as fast as you do. That sounds discouraging and is in fact the opposite. It means the thing can point a whole life in a direction without ever turning into a finish line you fail to cross. Chest-thumping needs an arrival. Becoming refuses one. It’s the most honest verb I know for a value, because it admits up front that you’ll die mid-sentence.

The receipts

A guy telling you to choose the hard path should have chosen a few, so here are mine. At seventeen I picked electrical engineering as my way into computers, and I picked it substantially because it was the hardest road to where I already knew I was going. I was right that it was the hardest — not kidding, I still occasionally have nightmares about exam failures, there were many. I’ve been collecting that kind of evidence ever since: forty-seven books written in the margins of a working life, a studio’s worth of products run by one person, and a stubborn habit of doing things myself well past the point where any efficient person would have hired it out. I love hand tools. I joke that maybe I was a grave robber in a past life because I love digging holes the hard way…I own a really nice shovel. I am DIY to a fault. And I mean to a fault, because that phrase is going to matter shortly.

I have the literal version too. I’ve skateboarded long enough to have collected the surgeries and the scars that come with it, and somewhere along the way I came to prefer a scar to a tattoo — not as a rule for anyone, just as my own taste. A tattoo is a mark you have applied to mean something. A scar is one that got applied to you, against your will, by something that cost you. You don’t pick the design, the fall does. The failure does. I have a skateboard with my name on it — a “bro model,” which in skating is the affectionate grade between amateur and pro, the board they make for the guy who is emphatically not pro but earned the ribbing of being pro-adjacent. That’s about the right altitude for all of this. Enough to have paid for the opinion. Not so much that I’d ever claim to have arrived. Becoming, not become or became.

My bro model — name on a deck (tiny on the turntable record), emphatically not pro.

Which is also, for the record, how I came to keep bees. In the things I do — skateboarding, mountain biking, ice hockey, the activities where gravity and the ground get to vote — injury is common enough that we have a name for what you take up while you heal: an injury hobby. A slam wrecked my shoulder badly enough to need surgery and a nine-month recovery, and nine months is a long time to sit still, so I went looking for somewhere to put the sidelined attention. Beekeeping sounded good, so I dove in. That’s the whole mechanism, and it’s gnarly all the way down: the hard thing hurts you, and instead of resting you go find another hard thing to be a beginner at. The bees in the backyard and the scars from the ramp are the same story — one’s just what I did while the other healed.

The injury hobby — where a blown-out shoulder and nine idle months went.

I left a part out of that story, and it matters more to me now than it did then. I kept at the bees for years — caught swarms by hand, ran as many as four hives at once, helped my dad with his. I ran different styles of hives (top bar and traditional Langstroth), learned how to keep bees organically, and read dozens of books and went to seminars and workshops. And most years I lost half of them anyway. Despite all the reading and the effort to keep the operation as low-impact as I knew how — no chemicals, no gadgets thrown at the problem, the bees mostly left to be bees — they died all the same, for reasons I still can’t fully name; the science only half understands them and my backyard was never a controlled study. After enough seasons the little ecology project I loved had come to feel like an expensive way to kill a few thousand bees a year, and I stepped back. The break has lasted longer than I meant it to. I might keep bees again — that’s the becoming, too, the part where the next hard thing turns out to be the one that already beat you. For now I’m a beekeeper on hiatus, which is a more honest line than the one I opened with.

There’s an upside to being a beginner that I didn’t expect and now hunt for on purpose. The obvious half is humility, getting ego checked, and that’s real. The half nobody mentions is that mastery levies a tax: once you’re good, the freshness drains out and the work turns to maintenance and small incremental gains. Starting over somewhere new refunds it — you get to be bad again, which is the only way to learn again in that steep, total way that only happens at the start. My friend Robert recently reminded me of the G.K. Chesterton quote, “If a thing is worth doing, it is worth doing badly.”

Of course, it’s easy to say, “go out and be bad and embarrass yourself in public.” The truth is there’s a price in the form of an ego hit most people won’t pay, especially in public. And I’m not pretending it’s easy to pay, but it’s easier if you know the trajectory along the way. At thirty I took up ice hockey: a kinda-sorta-near-pro-level skateboarder stumbling around the rink like he was trying to play basketball without knowing how to run, getting the puck poked off his stick by ten-year-olds. I loved it without reservation, exactly because I was terrible and there was that much open road ahead. I could feel that if I stuck with it, I’d get better. Becoming, again — same verb.

Not long ago a surgeon went into my knee to fish out a loose body — a chip off my own kneecap that, his best guess, had been drifting around in there for years before it finally started to bite. He was trying to work out what set it loose, so he asked what I get up to. The skateboarding earned a raised eyebrow. But it was the fixed-gear bike that stopped him — I was midway through explaining how a fixie fights you on a descent, no freewheel, your legs braking against the cranks the whole way down a hill, forced to turn with the wheel, when he just cut in: “Wait, why?” Not the knee mechanics. Why ride that bike at all. I sat there a second and said, “Good question.” I didn’t have the answer loaded, and the not-having-it nagged at me a lot longer than the knee did. Most of what follows is me, belatedly, trying to give him one.

The claim, plainly

Most things worth having are earned through real effort, and the effort isn’t a tax on the having; it’s frequently part of the having. The struggle to assemble a thing is a lot of why the thing ends up yours. This is old and unglamorous and some version of it came from your grandmother, or maybe her grandmother. The reason it’s worth saying again, now, is that we’re living through the largest effort-removal project in human history, and “no pain, no gain” is too dumb a slogan to survive contact with it. It has to get smarter, fast, or we’ll automate away things we didn’t know we were keeping.

Because here’s the part the gym-poster version gets wrong, and the part I most want you to take with you: not all friction is formative. Some struggle builds you and some struggle just costs you, and the entire skill — the actual discipline this whole publication is about — is telling the two apart. A hard route up a rock face makes you a climber. A bad line at the DMV makes you nothing but late. Treating all difficulty as virtuous is how you end up romanticizing inconvenience, and that isn’t a philosophy, it’s a martyr complex with a podcast. The point was never the suffering. The point is the specific, chosen difficulty that runs along the grain of what you’re trying to become. Everything else is just friction, and friction with no formation in it is precisely what tools are for removing.

Which is the honest reason I’m not against tools, and why the essays that follow this one spend most of their time using the most powerful ones we’ve ever seen. The line that matters isn’t human-versus-machine. It’s whether the difficulty you’re handing off was a means or an end. Delegate a means and you free yourself for the thing the means was always in service of. Delegate an end — automate the part that was the point — and you’ve deleted the thing while keeping its packaging. There is, I’m convinced, such a thing as a tool that performs your life on your behalf while you stand next to it and watch, and I’ll make that case at unreasonable length in the next essay. For now the principle is enough: keep the friction that forms you, shed the friction that only bills you, and accept that the work is knowing which is which — work that never fully resolves, which is the point.

The thing I can’t stop noticing

For most of history the central human difficulty was survival, and most of our species spent most of its effort on it. We have, for a meaningful slice of the world, largely solved that. And the people with the fewest imposed hardships turn out to be the ones reaching hardest for ever-more convenience — sanding away frictions so small we had to coin “first-world problems” to laugh at ourselves for minding them. I don’t think that’s decadence, exactly. I think it’s a muscle that doesn’t know what to do now that the thing it was built to push against is gone. We got the hardship removed and found we didn’t feel better, because some meaningful amount of who we are was assembled in the pushing. We were built to struggle.

Two things keep that observation from curdling into a lecture, and I hold to both hard.

First, this is about chosen difficulty, and I refuse to romanticize the imposed kind. Hardship that picks you — poverty, illness, grief, the struggles nobody volunteered for — is not a growth opportunity, and telling someone in the middle of one that the friction is good for them is obscene. The entire value here lives in the choosing. Strip out the choice and it’s just suffering, and I have nothing to sell about suffering. Can there be tremendous growth in suffering? Absolutely. But you don’t choose it.

Second, which difficulties are worth choosing is radically personal, and I’m not handing out a list. Using a dishwasher is not a moral failure. I’m not going to grind my own flour to prove a point, and the fact that I’ll happily spend years using hand tools to cut neighborhood mountain bike trails while ordering pizza delivered trailside so I don’t have to stop is not a contradiction — it’s the entire idea. You pick your hard things. The argument was never that you must suffer here rather than there. It’s only that there’s real value available in the struggle, that the value is easy to automate away by accident, and that it’s worth choosing some of it on purpose before the choosing gets done for you. Being yours to choose doesn’t make you right about the choice. It’s easy to christen a difficulty “formative” when you’re only flattering yourself. Your say-so isn’t the verdict; whether it actually built anything is, and that comes back from the work itself, or from the people who’d know — never from you alone. Incidentally, I have a friend who is grinding her own flour and having a wonderful time doing it.

Why these essays sit together

That’s the through-line, and it’s why the writing here belongs in one place even when it doesn’t look like it. The essays I’m publishing first happen to be about making things in the AI era, because that’s the enchanting easy road in front of me right now and the one where the lines are being redrawn fastest. But the subject was never AI. AI is just the most dramatic instance, this decade, of the only question I find genuinely interesting: what’s worth doing the hard way, and how do you tell. Underneath the software and the studio talk, every one of these is an argument about which struggles to keep.

Here’s the moment that taught me what the word means, better than any intimidating trick I’ve fought to land on a board. Years back, a few of us were skating a deep concrete bowl at a public park, all of us decades in, fluent the way you only get from a lifetime of slams. A greenway loops that park, close enough that you hear the runners. We’d been clocking the same woman for weeks. What she was doing barely counted as running — a pace most people would beat at a walk — but the effort was enormous. You could see what it cost her just to get one foot past the other, every step hard-won, her whole body in it. And she kept showing up. Day after day, in public, at the stage where it’s hardest and pays back the least. One afternoon I nodded at her and said, “…guys.” My friend Jim took a look and immediately said, “that’s so much gnarlier than anything we do.” Nobody laughed. It wasn’t a joke and everybody knew it. Eleven feet of concrete isn’t nothing, but it’s a drop I’ve had forty years to make easy; what she was doing took a courage none of us were spending. I don’t know if she ever clocked the ragtag crew of over-age skaters quietly in her corner, but I like to think being her cosmic fan club for a season counted for something.

I don’t expect to finish becoming gnarly. The process guarantees I won’t. The tree never stops being shaped by the weather. The line is always a little past where you are. You choose the next hard thing, you pay for it, you carry the mark, and you go looking for the one after that. The hard way isn’t always right. But often enough to organize a life around, it’s where the good stuff turns out to have been hiding.

So, welcome. Take the harder path when it’s the one worth taking. The rest of this is just me trying to work out which ones those are.