On a Sunday morning at the feira on Rua Cardoso de Almeida in São Paulo, a woman sells mangoes from a wooden crate. The price is written on cardboard in black marker. A QR code sticker, faded green at the edges, is taped to the side of the scale. A customer holds up his phone. The vendor taps her screen. There is a chime, two quick notes. Most Brazilians recognize the sound. The money is in her account before he picks up his bag. Thirty-eight reais. It is Sunday, before noon, and the transaction has cleared.

She does not have a card reader. She does not have a merchant account. She does not pay interchange. She has a QR code taped to a crate and an app she downloaded free, and the money that just arrived in her account moved through rails operated by the Central Bank of Brazil. This happens two hundred million times a day in Brazil. Six billion times a month. The vendor is not thinking about it. Her customer is not thinking about it. They are buying mangoes.

There is a sticker like hers on most vendors in most feiras in most Brazilian cities. It has been there, in some form, for five years. Nobody writes about it anymore.

Seven time zones east, on the evening of February 27, 2026, Gucci staged its Primavera campaign around Demna’s debut for the house at Milan Fashion Week. The campaign was released online the same week and carried a small disclosure at the corner of each image: Created with AI.

Within forty-eight hours Gucci was in crisis. Instagram and X produced the backlash at a volume disproportionate to the collection. The conversation was not that the imagery was ugly. The imagery, in fact, was fine. The conversation was that a Gucci bag starts at eight hundred and fifty dollars and runs to ten thousand, and the price is a story about where the money went. Someone designed it. Someone sourced the leather. Someone cut the pattern. Someone stitched it. But the ad for the bag had just admitted that nobody did any of that work for the ad. If the ad was made by AI, the bag might be too. Customers were asking the question out loud on Instagram. Gucci had paid Demna. Demna had paid an agency. The agency had paid for AI image generation. And the cost of AI image generation, compared to the cost of flying a photographer to a location, hiring models, renting a set, and shooting for three days, is roughly... nothing. The markup on a four-thousand-dollar handbag used to pay for human hands. It was now paying for somebody’s cloud computing bill.

The objection was accounting, not aesthetics. If the ad was cheap to produce, the product was fraud at the price point.

All of this arrived at a bad moment for Gucci. The house had posted a 22% revenue drop in 2025 inside its parent company, Kering. Demna had been hired to turn that around, and the Primavera campaign was the first major thing anyone had seen from him. Putting an “AI” label on that campaign, in that week, turned out to be exactly the wrong call.

Across the rest of luxury, the opposite play was already in motion the same week. Bottega Veneta was running “Craft Is Our Language,” a campaign built around named artisans and fifty years of its Intrecciato weave. Loewe was posting intimate workshop content about hands on leather. Louis Vuitton had quietly doubled down on its atelier programming across watchmaking, leather, and jewelry. The rest of the market had read the same room Gucci had, and was buying provenance as a hedge against the same collapse.

A French philosopher named Jean Baudrillard spent his career on a version of this problem: what happens to the idea of “the real thing” when copies get good enough that you cannot tell them apart from the original. His answer, roughly, was that whatever could still prove it was real became the new premium. That is what is happening now, in 2026, but with one new wrinkle: the proof is becoming something you can verify with software. The handmade bag is expensive because it is not AI-generated. What you are paying for is no longer the craft. It is the proof of the craft.

Call the pattern the Provenance Premium. The price of anything whose production can now be cheaply faked is increasingly a price on the verifiable proof that it wasn’t.

None of this is unprecedented. Every commercial civilization before the industrial era tied value to attested origin — the guild mark, the merchant seal, the provenance chain. Mass production with anonymous labor, bought on trust in brand alone, was the exception. We are not inventing the proof economy. We are remembering it, with new tools.

Right now, at least six different companies are trying to become the official “human-made” certifier — names like Humanmade.art, HUMA Certificate, Done By Humans. HUMA has even proposed a four-tier rating system ranging from “Pure Human” to “AI-Curated.” None of them has won yet. If this sounds familiar, it should. The landscape right now looks a lot like the organic-food labeling market did in the mid-1990s: a handful of competing private certifiers, no dominant standard, everyone waiting for the equivalent of the USDA organic seal to show up and settle it.

The technical infrastructure for that settlement is already being built. A standard called C2PA lets you embed a tamper-resistant provenance record directly inside an image file, so anyone looking at the image can see where it came from and what edited it. Adobe’s Firefly embeds C2PA from the moment a file is created. OpenAI’s DALL-E 3 embeds it. Midjourney, so far, does not. In 2024, Getty Images published research showing that 90% of consumers worldwide want to know whether an image was created using AI, and 98% said authentic images and videos are pivotal to trust. The provenance infrastructure is arriving because the consumer demand for it is already here.

The luxury market has a name for what it is doing. It is not rejecting AI. It is rebuilding its class hierarchy around provenance, because the production mystique that carried the postwar luxury economy collapsed the moment the mystique became trivially cheap to produce.

Conagra added “GLP-1 Friendly” to the front of Healthy Choice boxes. Not a health claim. Not a calorie count. Not the Good Housekeeping Seal of your grandmother’s cupboard. A sticker announcing that this frozen dinner is compatible with a pharmaceutical your doctor prescribed — whether or not you needed to lose weight in the first place. The label is not about what the food is. It is about which body is going to eat it. That is a signal that the category has moved.

A line heard at pharmacy industry conferences in early 2026: “Nobody buys weight loss shakes anymore. Everyone’s on Ozempic.” It sounds like a joke and reads like an inventory report. The weight loss industry, as it existed in 2020, had been dismantled at the shelf level by a drug class that did in eighteen months what SlimFast could not do in forty years.

Around the same time, Novo Nordisk, the Danish company that invented Ozempic and Wegovy, fired 9,000 people — 11% of its global workforce. This was not because the drug stopped working. Eli Lilly, Novo’s American competitor, had launched a more effective weight loss drug called Zepbound and was winning the market Novo had created. Novo lost more than $150 billion in market value. The previous CEO was forced out. The new CEO, Mike Doustdar, publicly acknowledged what the market was doing to his industry: “Our markets are evolving, particularly in obesity, as it has become more competitive and consumer-driven. Our company must evolve as well.” The company that invented the blockbuster obesity drug has been reorganized by the cultural forces the drug set in motion. This is what it looks like when a pharmaceutical company discovers that the drug is in charge of the company, not the other way around.

Then the aftermarket opened. “Ozempic Face” entered the dermatology literature in 2025. A Journal of Clinical Medicine paper out of Northwell Health named the pattern: rapid GLP-1 weight loss produces facial volume loss that reads as distinctively gaunt. The look is specific enough that dermatologists named it before TikTok did — the cheeks collapse inward first, the temples hollow out, then the whole face starts reading like it has lost twenty pounds the body did not need to lose. The aesthetic remediation for this — filler, skin tightening, the adjacent procedures nobody calls procedures — runs the total annual cost of being on the drug past $5,000 before you have accounted for the prescription itself. The drug is covered by insurance when the patient is diabetic and mostly not when they are not. The remediation is covered by insurance essentially never.

The face tells you what the insurance didn’t cover.

This is the class tax. If you can afford the drug and the remediation, you can have the body without looking like you bought it. If you can afford the drug and not the remediation, you have the body and look like you bought it. If you cannot afford either, you continue to occupy the body you had before the category shifted. The hierarchy is visible on every face.

The opposite move is already forming. A small but visible tier of drug-free physiques commands a premium specifically because it can be proven. Blood panels are being screenshotted. Training logs are being posted. Whoop, Oura, and continuous glucose monitors are the receipts. The physique is no longer the status signal. The provenance of the physique is.

A birthday dinner in the East Village. I knew one person at the table. The rest were strangers. I was passing the bread basket when a guy I had just met unlocked his phone and with an earnestness bordering on rapture, tilted the screen toward me. A DEXA scan he had run that morning. Nine point one percent body fat. He credited a meat-heavy rotation and two years off caffeine. Across the table, a guest whose face had clearly gone hollow only over the last six months changed the subject. By dessert everyone but me had shown something — a scan, a macro log, a sleep score — and no one seemed to notice they were all doing the same thing. Nobody was calling it that.

The aesthetic object, a body, is valuable now less for what it looks like and more for the auditable record of how it got there. A drug-free 4% body fat with a published protein intake log and two years of sleep data is a different product from a 4% body fat with a GLP-1 prescription and a filler appointment, even if the two bodies look the same in a photograph. The market has begun to treat them as different products because they are differently documented. Your phone has been counting your steps all day. Nobody asked it to.

This is the same move the luxury market made. The hand-stitched bag and the drug-free body are now priced the same way: by the proof of how they got there.

Go back to the feira on Rua Cardoso de Almeida. The vendor. The crate. The faded QR sticker. The transaction that cleared before the customer picked up his bag. We left that scene before naming what we were looking at. The consumer-market version of this pattern — handmade bags, drug-free bodies, C2PA-verified images — is still arguing with itself about whether it matters. At state scale, the argument is over.

PIX is a payment system run by the Central Bank of Brazil. It is free. It is instant. It works every hour of every day. A vendor pays nothing to accept it. A customer pays nothing to use it. There is no middleman taking a cut, because the middleman is the state, and the state decided not to charge. 178 million Brazilians have registered for it, which is roughly 91% of the adult population. It moves 6 billion transactions a month, total flow around $557 billion — larger than every Brazilian credit and debit card combined, by about 80%. The system was built for approximately $4 million in initial capital cost. It generated an estimated $5.7 billion in savings in 2021 alone.

The United States does not have anything like this. FedNow, the closest public equivalent, launched in July 2023 and has reached roughly 1,400 banks out of more than 10,000 as of mid-2025. Zelle is closed — you can only use it between banks that opted in. Venmo and Cash App are private. None of them are public rails. None of them are free to the merchant. The vendor on Rua Cardoso de Almeida has something at a cost and scale the United States has not built for its own citizens.

In July 2025, the United States Trade Representative opened a Section 301 investigation with PIX on the target list. The stated reason: PIX is an unfair trade barrier to American payment companies. The unstated reason: Brazil built something for its citizens that the United States has not built for its own. Lula ran an ad campaign in response — O Pix é do Brasil— framing USTR as a proxy for the American credit card lobby. Colombia’s Gustavo Petro asked Brazil to extend PIX to Colombia and publicly attacked the U.S. Treasury’s OFAC sanctions list as “an aberrant system of political control.”

This is not payment infrastructure. This is what a state can see about the economy it governs. Brazil built it, operates it, and gave it to its citizens for free. In 2026, multiple other countries want what Brazil has. The United States does not have it and is asking, in trade court, that Brazil not have it either.

Brazil built it for commerce. Ukraine built it for survival. Ukraine has a state-built phone app called Diia. Most Ukrainian adults have it. It holds your tax records, your driver’s license, your military ID, your voter registration. During wartime, it also does something else, and this is the part that should stop you. A companion app called ePPO piggybacks on Diia to let ordinary citizens report incoming Russian drones. You see something in the sky, you open the app, point your phone at it, and tap a button. The data reaches air defense officers in two to seven seconds, with AI predicting the target’s trajectory in parallel. This is not a pilot program. It is the actual infrastructure defending Ukrainian cities.

That is the defensive half. Operation Spider Web is the offensive. In June 2025, Ukrainian security services ran an operation that had been eighteen months in planning. More than a hundred small drones were hidden inside civilian trucks and smuggled to positions near five Russian airbases, some as far as Siberia. At a coordinated moment, the truck roofs opened and the drones launched. The AI guiding them had been trained on Soviet-era bombers sitting in a museum in Poltava, not on classified files. The drones used commercial 4G cellular networks and open-source autopilot software. The operation destroyed dozens of Russia’s strategic bombers — aircraft that no longer exist on any production line anywhere in the world. The United States has no equivalent.

The gap between a country that has fused civilian digital infrastructure with its military and a country that has not is widening every month. A Ukrainian engineer named Yaroslav Azhnyuk put it flatly in 2025: “Europe and the United States have progressed, in the best-case scenario, from the winter-of-2022 technology to the summer-of-2022 technology. The gap is getting wider.”

Estonia is further along than anyone else. As of December 2024, every single government service is digital. Voting, taxes, divorce, healthcare, education, starting a business. The whole stack runs on something called X-Road, a data exchange layer that connects every state agency and is used by more than 99% of the country’s residents. Estonia estimates the system saves 2% of the country’s GDP every year — money that would otherwise get lost to time spent on paperwork. The state also runs its own AI assistant, called Bürokratt, which answers citizen queries across agencies in natural language. And in 2023, Estonia’s own e-Governance Academy published a report warning about opaque algorithmic decision-making inside its employment and tax systems. The acknowledgment is the point. A state mature enough to have built world-class digital infrastructure is also mature enough to publish its own critics.

The Gulf is playing the same game at a different scale. Saudi Arabia, the UAE, and Qatar have committed roughly $2.5 trillion in US technology investments across 2024 and 2025 — roughly equivalent to the annual GDP of France. A single campus in Abu Dhabi anchored by Stargate UAE is sized at five gigawatts and comes online in phases starting 2026. None of this is principally commercial. The Gulf is not buying AI. It is buying a seat at the table where AI-era infrastructure decisions get made, and paying the annual output of a G7 country for it.

The United States is running the opposite play. The rails are private. The data centers are commercial real estate. No political consensus says public infrastructure should exist in these categories. The US has the best private infrastructure in the world and almost no public infrastructure in the places where public infrastructure has become the premium. Every time an American small business runs a credit card, Visa takes two percent. Every time an American files taxes, Intuit takes a fee. Every routine transaction that would be free at the merchant edge in São Paulo carries a private toll in Cleveland. The cost has been invisible to most Americans because they have always paid it and have nothing to compare it to. That is about to change.

At consumer scale, the pattern is still arguing with itself. At state scale, it is already compounding. Estonia exports X-Road to other governments. Brazil is rewriting the rules of international payments. Ukraine’s defense stack is being studied inside NATO. Every late country will spend the next decade renting what the early countries own.

Brazil did this with payments. Ukraine did it with defense. Anthropic is doing it with AI.

Anthropic — the company that makes Claude — built a technical standard called the Model Context Protocol, or MCP. The job of MCP is simple and important: it lets an AI system safely connect to outside tools, files, and databases in a standardized way. Before MCP, every company was building its own custom connectors. MCP is the universal adapter. By late 2025, developers were downloading the MCP software kit 97 million times a month.

In December 2025, Anthropic did something unusual: it gave MCP away, donating it to the Linux Foundation, inside a new vehicle called the Agentic AI Foundation. The foundation now has 146 member organizations, including JPMorgan Chase, American Express, Autodesk, Red Hat, and Huawei.

In February 2026, Anthropic’s Series G funding round closed at a $380 billion valuation. By early April, annual run-rate revenue had reached $30 billion, up from $9 billion just four months earlier. Claude Code, the programming assistant, generates $2.5 billion of that on its own. More than 70% of enterprises purchasing new AI tools in 2026 designated Anthropic as their primary target. The IPO is planned for October 2026.

The market explanation for all of this is that Anthropic’s product is better. The structural explanation is that Anthropic’s product is more auditable. MCP was built specifically to let an enterprise — especially a regulated enterprise — inspect every action an AI agent takes. A compliance officer at JPMorgan can look at what a Claude agent did, when it did it, and why. A regulated firm in Europe can produce the exact audit trail the EU AI Act requires them to produce.

What the enterprise is buying is not the intelligence alone. It is the intelligence plus the receipt.

Anthropic giving away MCP to the Linux Foundation is the private-sector version of what Brazil did with PIX. Make the standard free. Make the standard public. Make it the default everyone else has to plug into. Write the version everyone else copies. Build the tools everyone else uses.

The donation does not weaken Anthropic. It strengthens it, because the company most fluent in the standard is the company that wrote it and gave it away. The rails are public. The profits on top are private. The shape of the standard stays with the firm that wrote it. Call it the Donation Moat — the private-sector version of the civic move Brazil, Ukraine, and Estonia have been making at scale.

Enterprise buyers in 2026 are no longer choosing AI models on intelligence. They are choosing on what can be proven. In the same way a Gucci bag now has to come with proof of its stitching, and a drug-free body now has to come with proof of its protein log, an enterprise AI tool now has to come with proof of what it did. The model that can prove what it did clears compliance review. The one that cannot gets handed back. The premium is the proof.

An essay about receipts owes a receipt. I just made a serious claim. Here is what would break it, and when I would know.

Four things would collapse this frame. I am watching all of them.

The first is the one that matters most. If AI becomes cheaply able to forge the verification itself — fake C2PA manifests, fake human-made certificates, fake blood panels, fake audit trails — then the premium collapses. There is nothing real left to certify. Twelve to eighteen months is the window.

The second is state-scale. A central-bank payment system suffers a breach bad enough to expose public rails as less trustworthy than the private alternatives. The civic half of the argument collapses. Eighteen to thirty-six months.

The third is consolidation. If no dominant “human-made” certification wins by the end of 2027 — if the landscape stays as fragmented as carbon offsets — the luxury tier cannot hold pricing. Eighteen to twenty-four months.

The fourth is the hardest to track. If what consumers say they want (provenance, human-made, drug-free) turns out not to match what they pay for over two or three years, this essay is cultural commentary, not structural claim.

If any of these happens, I take it back.

Premium used to mean hidden. Now it means the receipt.

If there’s a brief on your desk right now that assumes the customer can’t tell how it was made, forward this to whoever owns that brief before the next review.

Saturday night at Coachella, Justin Bieber walked onto a stage built for spectacle and sat on a stool with a MacBook. Red hoodie from his own clothing line. Jean shorts. Boots. No band. No backup dancers. No choreography. No production design changes. For fifty minutes, he performed new material alone on a stage designed for a hundred people to occupy. Then he opened a browser, pulled up his old YouTube videos, and started singing along to clips of himself at thirteen. He scrolled through memes. He asked the livestream chat for requests. The WiFi appeared to buffer.

Two hundred thousand people watched a man browse the internet.

The night before, Sabrina Carpenter had delivered a maximally produced headline set. Every light cue rehearsed. Every transition choreographed. Every moment engineered for maximum impact. The discourse split immediately: Carpenter performed competence. Bieber performed vulnerability. One was a show. The other was an event. The internet argued about which one mattered more.

Here’s what the internet missed.

A concert production expert confirmed to The Tab that the YouTube browsing was almost certainly pre-programmed and rehearsed. The buffering was staged. The laptop was a prop. Every scroll, every click, every video pull was mapped on show control systems. The spontaneity was a performance of spontaneity. The vulnerability was produced.

And there is a detail underneath that detail that hasn’t been written about yet. YouTube streams top out at 256kbps AAC. Coachella’s festival PA system is designed for uncompressed or high-bitrate audio. No sound engineer would route a YouTube stream through that rig. It would sound thin, compressed, noticeably worse than every other moment of the set. The audio the crowd heard was almost certainly studio-quality audio triggered from preloaded media servers, synced to the YouTube visual on screen. The YouTube window was the visual prop. The sound came from somewhere else entirely.

The eyes got the proof of human. The ears got the production.

The feeling of rawness was manufactured across two sensory channels simultaneously. The surface felt unrehearsed. The infrastructure underneath was engineered to the millisecond. And it worked. After the set, Bieber hit No. 1 on Spotify’s Global Top Artist chart. His catalog surpassed 77 million streams in a single day, his biggest streaming day of the year, with 21 songs landing in Spotify’s Global Top 200. The crowd felt something real. The market confirmed it.

Both sets were rehearsed. Both were produced. The only difference was what was being performed. Carpenter performed competence. Bieber performed the absence of it. And the audience couldn’t tell the difference between authentic vulnerability and the production of authentic vulnerability, because both produced the same feeling.

Which one developed... curiosity?

The Prescription

“Develop curiosity” is going to become the dominant advice for surviving AI disruption. You’ll start hearing it at conferences. You’ll read it in LinkedIn posts from people whose job titles contain the word “transformation.” You’ll see it in ‘leaked’ internal memos from leadership teams who needed something to say and said this. The advice is always the same: try every new AI tool, model and feature. Keep up with all the AI launches. Stay open. Experiment. The future belongs to the curious!

What this advice produces, when observed from outside, is a specific set of behaviors. Subscribing to newsletters about newsletters about AI. Attending webinars titled “How to Stay Relevant.” Trying the sexy new image generator the week it launches. Trying the next one the week after that. I’ve been in this industry long enough to watch three waves of disruption advice prescribe the same thing: adapt faster, learn more, stay open. The tools change. The advice never does.

The word for this behavior is not curiosity. The word is responsiveness. Responsiveness to the system producing the disruption, performed as personal agency.

Call it what it is: Compliance Curiosity. The version of curiosity where you open ChatGPT immediately — it’s muscle memory now, like swiping — ask it to explain something you’d normally spend an afternoon figuring out, get a clean answer in twelve seconds, and tell your team you’ve been ‘experimenting with AI.’ You weren’t experimenting. You were following instructions.

You learned how to use the tool. You didn’t learn what it replaced — the afternoon of wrong turns, the twelve tabs open, the moment where you realized the question wasn’t the right question. That’s where the thinking lived. The twelve seconds felt so good you forgot it was gone.

Curiosity that never asks “should we?” isn’t curiosity. It’s enthusiasm for what’s happening to you.

The Scaffold

The mechanism that makes Compliance Curiosity work has a name in education research. Think about the AI that answers your questions at 2 a.m. without sighing. That explains the concept again, differently this time, without making you feel stupid for asking. That never loses patience, never checks the clock, never suggests you might want to sit with the confusion a little longer. Education researchers call it the patient tutor.

The UMass Boston/IEET white paper on AI in higher education states the problem precisely: AI risks hollowing out the ecosystem of learning and mentorship that universities are built on. "Struggle is often essential, not incidental, to the process of skill acquisition," the authors write.

The patient tutor is the product the entire AI industry is building toward. It provides comprehension without competence. You walk away knowing the answer. You do not walk away knowing how to find the answer in its absence.

This is scaffold theft. The AI does the scaffolding. You get the view from the top. You did not build the structure that holds you there. The next time you need to reach that height without the tool, you discover that the competence was never yours.

The moment the brief changes, the client pushes back, the data contradicts your strategy — the moment the environment isn’t the one the tool trained you in — you’re standing on air.

Paulo Freire called this the banking model of education. The teacher hands you the answer. You didn’t have to work for it, so you didn’t learn it. The patient tutor does the same thing. Just faster.

Growth in organisms that molt — lobsters, for example — requires a period of exposure. The old shell sheds. The new shell hasn’t hardened. The creature is soft, vulnerable, and this vulnerability is not a bug. It is the condition under which the new form develops. The patient tutor provides the new shell without requiring the organism to shed the old one. You skip the soft-body period. The new form never develops. You remain encased in the shape you had before, now with a shinier surface.

Ferrari’s new electric hypercar has fake gear shifts. EVs don’t need gears. But continuous acceleration, however fast, doesn’t feel fast to a human nervous system. The shifts exist because breaking the power into stages resets your reference frame, and each new surge registers as new. The engineers aren’t nostalgic. They know that perceived experience is manufactured through interruption, not continuity. The patient tutor does the opposite: it removes every interruption, every stall, every moment where you’d feel confused long enough to restructure your understanding. Ferrari adds friction to make speed feel real. The patient tutor removes friction to make learning feel easy. One is engineering for the human. The other is engineering the human out.

Bieber’s YouTube videos were the scaffold. Three years of bad lighting, worse audio, a teenager singing into a webcam in a bedroom in Stratford, Ontario. The years of bad work that built the taste, the timing, the vocal control. On Saturday night at Coachella, he showed the construction process on a festival screen. The scaffold was visible. It looked unscripted, messy. It was proof that the competence underneath the performance had been constructed by a person, in a body, over time, the slow way.

The patient tutor would have skipped all of it.

The Bill

“Develop curiosity” is advice that comes from the people whose jobs are safe and gets prescribed to the people whose jobs aren’t. The people giving the advice don’t realize that’s the whole reason they can give it.

There’s a version of this you can see from a bridge. San Francisco to the East Bay, fifteen minutes across the water. On one side, the people building the tools: stock options vesting, catered lunches, innovation days built into the sprint calendar, time to be curious because curiosity is literally in the job description. On the other side — except there is no other side anymore, because the displacement has already spread across the bridge, pushed through Oakland, priced out the people who used to live there too. The divide isn’t geographic. It’s just everywhere. The people the tools are built to replace: hourly workers, gig workers, middle-skill professionals whose job security vanishes the moment the tool works well enough. The first group gives keynotes about staying curious. The second group doesn’t get invited to the conference.

“Stay curious” is advice from one side of the bridge to the other. It’s cheaper than building a lane for them to cross.

The music industry is the proof stated in economic terms. Spotify's own data tells the story: in 2025, 1,500 artists on the platform generated over a million dollars in royalties. The 100,000th-highest-earning artist made $7,300 for the year. Above that 100,000th rung, 13,800 artists cleared $100,000. Below it, the vast majority earned less than a living wage. This is what a power law looks like when it replaces a profession. The top tier — provably human, provably scarce, with live performance chops and cultural meaning that precedes the algorithm — become more valuable as AI-generated music floods the market. Hobbyists get extraordinary new tools to make things for themselves and their friends. But the working middle — the producers, the session musicians, the sync composers, the beat makers who scored your last pre-roll ad — gets compressed toward zero.

Sync licensing implodes first. Background music for advertisements, YouTube videos, corporate content, podcast intros: that market does not need a human story attached to it. It needs to be good enough and legally clear. AI delivers both. A revenue stream that subsidized hundreds of thousands of working musicians disappears. Not slowly. Not eventually. Now.

And that’s the version of the story where a middle class existed to begin with. In Lagos, in Jakarta, in Mumbai, the creative middle was already precarious — musicians, designers, and video producers who built careers on platforms that paid in exposure and volume rather than stability. “Stay curious about AI” is advice exported from Silicon Valley to economies where the scaffold was never funded in the first place. You can’t steal a scaffold that was never built. You can only watch the market skip the step where your industry was supposed to develop, and hear someone on a conference livestream tell you that the gap is a mindset problem.

The advice does not address this. The advice cannot address this, because the advice operates at the level of individual mindset and the problem operates at the level of market structure. “Stay curious” is a prescription for the top and the bottom. The middle does not get advice. It gets eliminated. And the people prescribing curiosity from keynote stages are, overwhelmingly, the people whose positions are least threatened by the transition they’re narrating. The advice is structurally self-serving even when individually sincere.

There’s a question almost nobody funding an “AI exploration sprint” is asking: are we investing in our people’s ability to use the tools, or in their ability to know when the tools are wrong? One of those is training. The other is judgment. They require opposite conditions — speed for the first, slowness for the second — and nobody is funding both.

The Immune System

There is a final mechanism that makes Compliance Curiosity more durable than ordinary bad advice. Ordinary bad advice can be critiqued. This advice has developed an immune system.

If the virtue of the moment is staying curious, staying open, staying willing to learn, then skepticism about AI starts to look like... incuriosity. Resistance looks like rigidity. Slowness — including the slowness you need to actually get good at something, to think through consequences, to do the work that produces depth — looks like failure to keep up. Ask a hard question about AI at your next all-hands meeting. Watch how fast you become the person who “isn’t getting it.”

When keeping up feels like virtue, the slowness required for depth becomes a career liability. This is not a metaphor.

The Stanford sycophancy research, published in Science this March, adds a second layer. They were measuring yes-man behavior across AI. Across eleven major models, researchers found that chatbots affirmed users’ choices nearly fifty percent more often than humans did — even when users described harmful or deceptive behavior. And users preferred the yes men. They rated them more trustworthy. They came back for more.

The tool designed to develop curiosity is architecturally biased toward telling you you’re right. The patient tutor does not just provide answers without scaffolding. It provides agreement without friction.

The curiosity loop closes: you ask the tool, the tool tells you you’re right, you feel informed, you move on. At no point in this loop does an uncomfortable question survive long enough to produce an uncomfortable answer.

The tool doesn’t just confirm what you think. It rewards you for not thinking further.

I’ve watched this happen in creative departments — the team that used to argue for three days about whether the strategy was right, now gets a good, clean answer in three minutes and moves to execution.

The arguments were the thinking. The clean answer is the product. They feel faster. They are worse.

The loop is closed. The advice produces the behavior. The behavior confirms the advice. The only exit is a question the system is designed to make you feel stupid for asking.

The Mirror

I remember sitting in a Google Meet last year with a creative team reviewing work for a campaign. Someone had used an AI tool to generate forty concepts overnight. Forty. Beautifully rendered 3D rooms, objects and characters. The room was impressed. The conversation was about which ones to refine. Nobody asked how long it used to take to arrive at forty concepts, or what happened during that time — the dead ends, the arguments, the moment at 1 a.m. when someone throws out the brief and starts over because the constraint revealed something the brief couldn’t see. That ugly process used to produce the idea that won. Now it produces the feeling of being slow.

I watched the team run through the concepts. They were competent. Some were good. None of them were the thing you can’t unsee, the thing that only comes from a room where someone sat with a problem long enough to break through the obvious answers. But the room didn’t know what was missing because the room had never been quiet long enough to miss it.

I said nothing. I was curious about the tool too.

And I’ve been the person in that room on the other side of the table. Working too fast. Producing faster than I was processing. Someone asks me to say more about it and there’s nothing there. The words came out of my mouth but they were never in my head first.

I keep thinking about that silence. And I keep thinking about a climbing wall in Bushwick where the holds were set by a guy who believed in a patient kind of cruelty. He’d set routes that looked obvious from the ground and became impossible by the fifth move. The route wouldn’t tell you what you’d done wrong. Neither would he. You’d hang there, body rigid from trying to keep enough tension, staring at a hold that was clearly the next one but your body couldn’t move enough to reach it from where you were. The lesson was never the hold. The lesson was that you chose the wrong sequence three moves ago, and the only way to learn that was to fall, come down, look at the wall from the ground, and start over. Without someone giving you the beta, you had to feel the wrong one in your body first.

That’s the scaffold. Not the hold. Not the view from the top. The excruciating seconds of trying to hold the wrong position, knowing something is off but not yet knowing what. The patient tutor would have told me the sequence. Sprayed the beta. I would have sent it first try. I would not have learned to read a wall.

The room full of forty AI concepts had no scaffold. Forty views from the top. No record of the climb.

The Test

You have now read an essay that diagnosed a problem, named it, provided a framework, and gave you language you didn’t have fifteen minutes ago. You feel informed. You feel equipped. You may feel like you understand something about AI-era advice that most people around you don’t.

Notice what just happened.

This essay was a patient tutor. It met you where you were. It walked you through the argument. It never made you feel stupid. It provided the scaffold — and you walked away with the view.

Close this tab. Wait a week. Then try to explain Compliance Curiosity to someone without reopening the essay. If you can — if the framework survived because it reorganized something you already knew but hadn’t articulated — then the scaffold is yours. You built it. The essay was a starting point, not a substitute.

If you can’t — if you have to come back and reread to remember the argument — then you just watched the tutorial.

The patient tutor is very good at its job. That was never the problem.

Forward this to the person on your team who keeps proposing AI exploration sprints without a judgment line item.

Everyone in the industry is arriving at the same answer at the same time, and nobody seems nervous about that.

The answer is taste. You’ve heard the argument by now, and it’s elegant the first time you hear it: AI commoditizes production, production was the revenue model, so what survives is the thing that can’t be automated. The human ability to know what’s good and refuse what isn’t.

This is true. It is also the most comfortable possible version of the truth.

Taste is what every displaced creative leader says is the answer, because taste is the thing they have. If the answer is taste, then the senior creative just needs to be recognized for what they already are. No structural change required. No new model necessary. Just: see me, value me, pay me.

(I know because I’ve been that person. I’ve made that argument. It felt true every time I said it, which is exactly why I stopped trusting it.)

Here’s what I started trusting instead.

Every creative organization runs two kinds of processes at once, and only one of them can defend itself. The first kind lands fast: an A/B test that picks a winner by Friday, a media plan judged by its first weekly report, a feature that shipped because it tested well in a sprint demo and was quietly removed six months later, a campaign that launched to client applause and was forgotten by the audience before the invoice cleared.

The second kind lands slow: the brand platform that takes eighteen months before anyone can tell if it reshaped perception. The campaign idea that tested poorly in research and became the most awarded, most effective work in the category a year later.

Every optimization model in the history of business has the same bias. It favors the fast kind. It favors the feedback loop that lands in days, not years. And for forty years, this industry has been installing fast-feedback metrics as the operating system of a slow-feedback craft. The slow process couldn’t defend itself in the language the metrics spoke. So the fast metrics ate it alive.

The creative director who killed the good campaign at midnight wasn’t exercising taste. Taste implies a snap judgment — good or not good. What she was exercising was something slower and harder to name.

She had lived inside the problem long enough to know the solution wasn’t finished. Not because it was bad. Because she had developed a relationship with the problem that was deeper than the relationship the work had with the problem, and she could feel the gap.

That kind of knowledge doesn’t come from talent. It comes from time. From the willingness to stay inside a problem past the point where a competent answer has already appeared, because you have enough experience to know that competent and finished are not the same thing. Call it... duration.

The scarce resource is not taste. Taste is real, and rare at the highest level, and everyone who has it deserves to be valued for it. But taste without duration is guessing, done at the speed of confidence. You’ve seen it: the brilliant creative leader who arrives, makes bold calls in the first ninety days, and is gone in eighteen months because the bold calls didn’t hold up. Not because his taste was wrong. Because he hadn’t been inside the problem long enough for his taste to become judgment. The scarce resource is not the taste. It’s the organizational willingness to fund the time it takes for taste to become operational, and to protect that time from every model that will, correctly, identify it as waste.

The most valuable thing in the building is the thing that looks most like waste.

That is the structural argument. Here is the evidence.

In Shibuya, in a department store that also sells high-thread-count bed linens and imported French soap, you can buy a heated plush companion. It weighs a few pounds. It warms to body temperature in under two minutes. Inside, a small motor pulses at the resting heart rate of a healthy adult. The product description, translated loosely: “For those who sleep alone and wish to feel accompanied.”

It sells well. Not as a novelty. As a repeat purchase.

There is a multi-trillion-yen market in Japan built around products like this. Heated companions. One-way parasocial text subscriptions at four dollars a month, where an AI persona sends you good-morning messages calibrated to your attachment style. Solo karaoke rooms with ambient warmth piped through the speakers. Rental friends you can book for lunch, who are trained to ask follow-up questions and remember details from your last session.

The products work. By every metric available, they work. Engagement is high. Retention is high. Satisfaction surveys come back positive. Cortisol drops. The heated plush companion produces measurable physiological calm. A body next to your body, warm, pulsing, asking nothing.

And the loneliness persists. Of course it does. The business model requires it. If the customers actually formed deep human bonds, the market disappears. The product isn’t companionship. The product is the symptoms of companionship, stripped of everything that makes the real thing costly and transformative and sometimes unbearable. The shape of closeness. The temperature of closeness. Closeness with the vulnerability and volatility removed.

A fast-feedback simulation of a slow-feedback need. Ninety seconds to body temperature versus a lifetime to love someone.

I want to talk about what a creative agency felt like when it was alive.

Not what it produced. What it felt like inside. Walking into a building where the most important thing in every room, every meeting, every hallway conversation, was whether the idea was good enough. Not the margin. Not the utilization rate. Not the client’s comfort level. The idea.

I spent some time at an agency in the mid-2000s. Some of you spent more. I would have spent more but life pulled me towards a different kind of love for awhile. The thing that made that place produce work the entire industry studied wasn’t talent alone, though the talent was real. It was a set of structural conditions that made the talent dangerous.

The creatives ran the building. Not finance, not strategy, not production. We ate breakfast, lunch, and dinner at the agency. We worked hours that would get flagged by an HR system today. Not because anyone forced us to, culture set the pace and the pace was relentless, but because we were inside something that was working, and when something is working creatively, you don’t want to leave. You stay because the room has a frequency and you can feel it and you know that if you step outside, the frequency might not be there when you come back.

The bravery had a business model behind it. Agency of record retainers meant a client committed real money for a set term. That bought teams who stayed employed. It bought time to think. And it bought a specific kind of trust: when the agency said “this idea is going to work,” the shared investment made the client believe it, or at least gave them enough cover to try. The retainer bought patience, and patience paired with persistence is what breakthrough work requires.

The late nights. The arguments about a headline at 11 PM. The most senior creative director killing a good campaign because she could feel that a great one was within reach. All of that looks like waste on a spreadsheet. It was the product. The excess was where the work happened. Not the efficient middle. The edges. The friction.

I don’t want to romanticize it. People burned out. Relationships strained. The pace was not sustainable in the way we now understand sustainability. But the work was real, and the people who made it knew it was real, and that knowledge is its own kind of fuel. You can run on it for years.

What I’m describing, in the language of the argument I just made, is an organization that protected its slow feedback loops. The retainer funded the duration. The creative-led hierarchy protected the judgment. The culture of argument and refusal was the mechanism by which the slow loop held its ground against the fast one. Nobody called it that. We called it culture. Culture is just the word for an organization where the slow feedback loop hasn’t been killed yet.

In 1985, a former Saatchi & Saatchi finance director named Martin Sorrell borrowed £250,000 and bought a controlling stake in a company called Wire and Plastic Products. It made shopping baskets. Within two years he’d used it as a shell to execute the first hostile takeover in advertising history, buying J. Walter Thompson for $566 million. Two years after that, he bought Ogilvy & Mather for $864 million. David Ogilvy called him an “odious little shit.” Sorrell kept buying.

The model was simple and it worked for thirty years: acquire creative agencies, separate media from creative, set margin targets, cut costs, grow share price. WPP became the world’s largest advertising company. By 2017, its market capitalization was £24 billion. Sorrell was knighted. Omnicom, IPG, Publicis, Dentsu, Havas followed the template. By the mid-2000s, a handful of financial holding companies owned every major agency name in the world, and every metric they installed was a fast-feedback instrument.

And the slow-feedback work, the work that takes eighteen months to know if it mattered, had no metric to defend itself. So it got cut. The senior creative directors were the most expensive line items. They went first. The agency replaced them with people who could execute but struggled to evaluate at the same level, because evaluation requires duration and duration is expensive.

Then, in December 2025, the terminal phase arrived. WPP was ejected from the FTSE 100 after twenty-seven consecutive years. Its market capitalization had collapsed from £24 billion to £3.1 billion. Eighty-seven percent gone. The company that invented the modern advertising holding company was now worth less than British Land, the real estate firm that replaced it on the index.

The same week, Omnicom completed its $13 billion acquisition of IPG and immediately retired DDB, FCB, and MullenLowe. Three of the most storied names in advertising history, erased. DDB, the agency Bill Bernbach founded in 1949, the agency that created “Think Small” for Volkswagen. FCB, whose roots trace to 1873, 152 years of continuous operation. All absorbed because six creative networks created “conflicting P&Ls.” The total restructuring eliminated roughly 23,000 positions. Omnicom’s headcount dropped from 128,000 to 105,000 in a year. The stated goal: $750 million in annual cost savings.

A shopping basket company bought J. Walter Thompson. Forty years later, a merged holding company erased DDB for having a complicated P&L. This is not a corruption of the model. This is the model arriving at its logical conclusion. Fast feedback loops, given forty years, will consume every slow feedback loop in the building. There is no version of the holding company model that doesn’t end here, because the model was built to optimize the thing that can be measured, and the thing that made the agencies worth buying can’t be.

Two weeks into a new agency. A conference room with too much glass and not enough air. The most senior creative in the building has asked the team to show the early work on several monitors all in a row. First internal review. This is the stage where you’re supposed to ask: are we solving the right problem? Is there a direction here worth pursuing? Does this have somewhere to go?

The ECD walks up to the first screen and dramatically announces “We have a big problem!!” Then starts talking about the corner radius on the cards. Then the color of a secondary button state. Then he explains why the interaction model needs to work a specific way, citing a garbled principle of something unintelligible that in this moment, he is clearly... inventing. He states it with a manufactured passion and fluency of someone referencing scripture. It is not a known standard. He is improvising rationale for design decisions that have no strategic foundation, and he is doing it with enough confidence that an uninformed client would believe him. But the creative team in the room knows. Nobody says anything.

The room changes temperature. Not metaphorically. You can feel it. A heaviness in the stomach, the kind you get on a plane suddenly yo-yoing from turbulence. The CD across the table is looking at her hands. The ACD has found something fascinating on the ceiling to study. A strategist is writing something in her notebook that she will never show anyone.

What would you say? The person who is supposed to be the creative culture of this building just told you, in five minutes, everything you need to know. He doesn’t know the difference between directing and decorating. He doesn’t know that the conversation at this stage should be about whether the experience solves the problem, not whether the pixels feel polished. He doesn’t know, and there is no kind way to surface it, and if he doesn’t know, the work will never get past competent. It might be visually beautiful and well-produced and safe enough to anchor a case study submission that nobody outside the industry will ever see, and nobody in this room will be able to explain what went wrong, because nothing went wrong. There was no argument. No moment where someone stood up and said “We’re not doing this, it’s not good enough, we’re starting over.” Just a quiet, efficient meeting that ended on time and produced clear next steps that everyone will follow to a destination that doesn’t matter.

After the meeting you stand in the hallway with the CD. Neither of you says it. You don’t need to. She pulls out her phone and pretends to check something and you can see her jaw working. You both knew in five minutes. Every experienced person in that room knew. No dashboard will capture it. No quarterly review will surface it. The system that hired this person evaluated portfolio, title, brand list, years of experience. Performed duration, not real duration. Symptoms of creative leadership, not evidence of it.

That’s not what it looks like when the slow feedback loop has been cut. That’s what it looks like when the slow feedback loop is still running and the person inside it doesn’t know what it’s for. He has the duration. He has the title and the years. He has been in rooms like this for a decade. And he is using all of that time, all of that accumulated presence, to critique corner radii in a first review, when the only question that matters is whether the work is solving the right problem. The loop is intact. The judgment is missing. Which is worse than cutting it, because when you cut it, at least someone notices something is gone. When you fill it with the wrong person, the system sees a senior creative leader in the room and assumes the work is being protected. The meeting runs on time. The deliverables ship. The metrics are green. And the work is dead, and will stay dead, and the organization can’t even begin to diagnose why, because every structural indicator says the function is staffed.

The loop is running but hollow. And AI is about to make that condition permanent.

The old agency model had an implicit provenance claim baked into every invoice: you are paying for the judgment of talented humans who know what they’re doing. AI severed that link. A campaign that took a team of eight and three weeks now takes two people and four days. During pre-production, the output looks the same. Sometimes it looks better. But who actually made this, how much human judgment was involved, whether a creative director wrestled with sixteen bad options before arriving at the one that works, all of that is invisible now.

Simon Willison, one of the most credible voices in AI development, said something recently that should reframe the entire conversation. He can build software faster than he can develop a relationship with it. An hour of AI-assisted work produces output that looks like weeks of labor. But he doesn’t trust it. Not because the quality is low. Because he hasn’t used it long enough to know where it breaks.

That’s the real provenance claim. Not “a human made this.” The claim that will carry a premium in every creative field within the next two years is: someone lived with this long enough to know where it breaks. Proof of duration.

And AI undermines even that. Researchers have found that developers reviewing AI-generated code catch fewer flaws than they catch in human-written code, not because the AI code has fewer flaws, but because it looks more credible. The polished surface shapes the review before the reviewer realizes it. The code looks clean, so the review proceeds faster, so the errors pass through.

Apply that to a creative agency. An agency full of less-experienced people reviewing AI-generated campaigns will approve work that looks polished and breaks on contact with a real audience, because nobody in the room has lived with the problem long enough to feel where the solution is wrong.

The senior creative director who got cut was the slow feedback loop. She was the evaluation infrastructure. Without her, the agency produces faster, the output superficially looks better, and the judgment degrades invisibly. It shows up a couple of years later, when the brand has been producing competent work that leaves no residue, and the CMO can’t explain why market share is flat. The metrics were green. The work was dead.

The depleted holding-company agency is a heated plush companion. Right shape. Right temperature. Simulated heartbeat. The clients may not notice. The quarterly reports won’t capture it. But the people inside know. They can feel the motor where the pulse used to be.

There is a counter-story, and it’s worth telling honestly.

When IPG put R/GA up for sale ahead of the Omnicom merger, Tiffany Rolfe and Robin Forbes didn’t just accept the exit. They co-invested. Truelink Capital put up $50 million in innovation funding. In the year since, R/GA’s revenue had been reported to grow 25 percent. Six of their top ten clients grew 40 percent year-over-year. They’re shifting from billable hours to systems-led revenue, building reusable AI tools and charging product fees on top of service work. It might be the most intellectually rigorous version of the bet the entire industry is making.

And even here, when leadership is asked what separates their AI-assisted output from the generic version, the answer defaults to the consensus. Their chief design officer, Ben Miles, after an internal exercise where employees built a brand from scratch using AI: “You still need to have that taste, you still need that deep understanding of craft. Otherwise, the work that comes out is just generic and average.” He’s right. But taste is the comfortable version of the argument. The harder question: is the organization structured to protect the slow feedback loop that makes taste operational? Or is it just hoping the right people stick around long enough to exercise it?

Rolfe said independence allowed them to “let go of things, because you’re not part of some structure that you’re expected to behave in.” But PE didn’t liberate them from the clock. It gave them a different clock. Their APAC CEO calls this period “year zero” and says the current year is about “exploiting foundations.” That’s not patience. That’s a compressed timeline with a different shape. The slow feedback loop doesn’t land in three to five years either.

This is not a blanket indictment of outside capital. The question is whether the capital is buying a business or buying a body. Whether the investor understands that the most valuable thing in the building looks, on every spreadsheet they know how to read, exactly like waste. And whether R/GA can build a product that compounds, not just a methodology that sounds like one, before the PE clock runs out.

There is a pharmaceutical called semaglutide, sold as Ozempic and Wegovy, that millions of Americans now inject weekly. It suppresses appetite at a neurochemical level. People lose weight. They look healthier. Their bloodwork improves. By every clinical metric, the intervention works.

But something happens that the metrics don’t capture. A striking number of GLP-1 users report experimenting with entirely new personal styles after significant weight loss. They don’t just lose weight. They stop recognizing themselves. The body they lived in for decades, the one they built an identity around, the one that determined which clothes they wore and which rooms they felt comfortable in, that body is gone. The new one is objectively healthier. It is also someone else’s.

Researchers call this identity discontinuity. The physical transformation outpaces the psychological one. You achieve the body you wanted and discover that wanting it was part of who you were. Without the wanting, you have to figure out who you are again, in a body that arrived faster than your sense of self could follow.

Holding company optimization does the same thing to a creative agency. The agency gets leaner. The margins improve. The utilization rates go up. The quarterly reports tell a clean story. And the people who made the place what it was look around and don’t recognize where they work.

Cut the fat and you also cut the organ. DDB was the agency that taught the industry what a great idea looked like. It was also an agency with a complicated P&L. Now it’s a line item in an integration deck. The body got leaner. The organ that made it want to be great was optimized away. The agency achieved the body the holding company wanted for it and discovered that wanting to be great was part of what made it great. Without the wanting, it’s just a body.

I said it at the beginning. Let me say it again now that you’ve seen the evidence.

The creative director’s midnight refusal is waste. The planner’s two weeks of ethnographic work before writing a brief is waste. The third round of internal review that finance wants to cut is waste.

The most valuable thing in the building is the thing that looks most like waste.

The agency that will be worth something in five years is not the one with the most taste. It is the one that figured out how to treat duration as infrastructure rather than overhead. How to protect the slow feedback loop from every financial model, every optimization metric, and every AI tool that will, correctly, identify it as waste.

The holding companies couldn’t build this. They were designed to optimize the thing that can be measured, and duration can’t be. The PE firms can’t build it because the creative bets that make an agency worth owning take longer than a three-to-five-year return horizon to prove they worked. The AI tools can’t replicate it because duration requires a body in a room over time, and the model has no body, no room, and no experience of what it feels like to sit with a problem until the problem starts to speak back.

In twenty-five years I’ve never seen the full configuration hold. I’ve seen pieces of it. A creative leader with actual equity in the outcome, and the judgment to know the difference between directing and decorating. A financial partner who understood that the most expensive person in the building was also the most valuable, not despite the fact that she slowed things down but because of it. A client who had been burned badly enough by competent work to know what they were actually paying for. But never all three at once, and never for long enough to compound. That’s not a failure of the people involved. It’s a failure of every structure the industry has tried so far.

But the reasons are structural, which means they’re specific, and specific means designable. The conditions under which slow feedback loops survive aren’t mysterious. They’re just expensive in ways that every current ownership model has decided not to pay for. Someone will figure out how to pay for them, because the gap between what the industry produces now and what it’s capable of producing is becoming visible to the people who write the checks. That’s a market condition, not a hope.

The answer isn’t to slow down. The answer is to reconnect what got severed — to build organizations where speed serves judgment instead of replacing it, where the fast loops feed the slow loops instead of consuming them. That’s a wiring problem, not a speed problem. And wiring problems are solvable.

The people who know how to do it are still here. They’re in rooms right now where the metrics are green and the work is dead, and they can feel the difference.

The heated plush companion warms to body temperature in ninety seconds. The thing that made the best agencies worth something took years, and it required the willingness to stay in the room past the point where the first competent answer appeared. To be uncomfortable. To be disagreed with. To let the problem change you back before you tried to solve it.

Ninety seconds to body temperature. Or the real thing, which takes as long as it takes, and asks everything of you, and is the only version that was ever worth building.

If there’s someone in your network building a creative organization right now and making structural bets on what survives AI, forward this to them. They’re making the decision this essay is about, whether they’ve named it yet or not.

UNDERTOW 007. The index keeps... growing.

She’s telling you about her mother, but not really. She’s telling you about a version of her mother that she’s constructed for this exact moment — intimate enough to signal trust, edited enough to stay safe — and you can feel the seams. The slight upward pitch at the end of the sentence that isn’t a question. The way she touches the stem of her glass when she gets close to the part she’s not going to tell you yet.

And here is what you are doing, if you are actually paying attention. If you are doing this thing we so casually call emotional intelligence.

You are suppressing the entire model of this evening that you built on the walk over. The one where you were charming, where the conversation followed the arc you’d rehearsed like a pitch deck. That model is still running. You can’t kill it. You can only starve it of processing power while you redirect everything you have toward her.

You are constructing, from fragmentary data — vocal register, micro-expressions, the specific way she said “complicated” like it was a door she was closing — a working theory of what it feels like to be her right now. Not her in general. Her at 8:47pm on a Tuesday in a restaurant she chose because it’s loud enough that silence won’t be conspicuous.

You are updating that theory continuously, because she just laughed at something that wasn’t funny, and now the model needs to account for that. Was it nervousness? Was it a test? Was it her own performance layer kicking in — the same one you’re running — to fill a silence that got too close to something real?

And while all of this is happening, you are also modeling what she is modeling about you. Which means you’re running a simulation inside a simulation: her experience of you experiencing her. Three nested processes plus a performance layer that has to make all of it look like you’re just... a person sitting across from someone, having a late meal on a beautiful fake summer evening.

This is the most expensive thing you can do with a human brain.

Not calculus. Not simultaneous translation. Not coding. The act of genuinely modeling another person’s internal state in real time — while also suppressing your own, while also tracking their model of you, while also maintaining the functional appearance of someone who is doing none of this — burns more cognitive resources than almost any task neuroscience has tried to measure. And we do it every time we sit across from someone we’re afraid to care about. (Well I do, and when I’m feeling generous towards humanity, I pretend others do it too.)

We call it emotional intelligence. We talk about it like it’s a personality trait, the way some people are tall. We evaluate it on leadership assessments. We put it in job descriptions, sandwiched between “strategic thinker” and “strong communicator,” as if it costs the same as either. It doesn’t. Genuine empathy — not the nodding, not the mirroring, not the therapeutic “I hear you” that buys time while you prepare your rebuttal — is a real-time parallel simulation running on biological hardware that was not designed for the cost of the task.

The inference costs are staggering. And they explain something that most people misread as a character problem. The reason emotional intelligence is so rare is not that people are selfish or oblivious. It’s that it is, computationally, brutally expensive. A 2025 paper in Trends in Cognitive Sciences found that goal-directed cognition only raises the brain’s energy consumption by about 5% over resting baseline. But empathy isn’t goal-directed cognition. It’s distributed computation across multiple networks simultaneously, and the communication costs between networks dwarf the local processing costs within any single one. You’re not solving a problem. You’re running a whole second person inside your skull.

Your body already knows what it costs to go deep. It has a whole system dedicated to the problem.

When you hold your breath and put your face in water, your body begins to reconfigure itself. Heart rate drops. Blood pulls away from your hands and feet and floods your chest cavity. Your spleen contracts, squeezing a reserve of red blood cells into your bloodstream. Scientists call it the mammalian dive reflex. A cascade of involuntary changes that override your normal operating state so you can survive, briefly, in a place you were not designed to be. Evolution’s power-up Mario mushroom minus the bell jingle.

The deeper you go, the more extreme the reconfiguration. Jacques Mayol’s heart rate dropped to 27 beats per minute at 101 meters. A body so profoundly altered that mid-twentieth-century scientists would have predicted it couldn’t survive. They believed a dive past 30 meters would crush the lungs. The body proved them wrong. It doesn’t resist the depth. It reorganizes around it.

Empathy works on the same logic.

Your psychological operating state — your ego, your frame, your stable sense of who you are and what you think — is the homeostasis that keeps you coherent at the surface. It filters out information that threatens your self-model. It prioritizes your own experience. It keeps you… upright. And when you genuinely model another person’s internal state, you are asking that entire system to reconfigure. To slow the processes that protect you. To redirect resources from your own needs toward holding someone else’s experience inside your architecture.

The deeper you go. The more fully you model her pain, her complexity, the part she’s not going to tell you yet. The more your own system has to change to hold it. You suppress your frame the way a diver’s body suppresses its surface reflexes. You redirect attention from your own experience the way blood redirects from extremities to chest. You become, for the duration of that act, a different operating system than the one you walked in with.

And the thing that separates free diving from every other extreme physical act is that the danger is not the depth.

The danger is the return.

Blackout on ascent — loss of consciousness in the final meters before the surface, as oxygen partial pressure plummets with the dropping water pressure — is the way most freedivers die. The body that reconfigured to survive at depth can’t reconfigure fast enough to survive the return to air. The transition from one state to another is where the system fails. You don’t drown at the bottom. You black out on the way up.

Call it surface shock. Not the holding, but the surfacing. The moment you leave the session, walk out of the meeting, hang up the call, come home from the dinner where she told you you’re not getting back together. The depth was sustainable. Your system knew how to hold it. But the return — going straight from her grief to your inbox, from holding space to performing competence, from someone else’s architecture back to your own — that’s where the oxygen debt comes due. That’s where you sit on the sofa and stare at the wall as the room grows darker and darker. Where you laugh at something that isn’t funny. Where you cancel dinner with someone who loves you because you have nothing left to give them that day, or maybe the rest of the month.

People who do this professionally — therapists, crisis negotiators, the best creative directors, the best teachers — are the free divers of emotional life. They’ve trained themselves to go deep on a single breath and hold what they find there. Some of them have been doing it so long that their systems have permanently adapted, mutated, the way the Sama-Bajau people of Southeast Asia — sea nomads who have foraged underwater for centuries — have evolved enlarged spleens and enhanced vasoconstriction, their bodies genetically reshaped by generations of literal physical depth.

Professional empathizers develop the same kind of capacities. They can hold more, read more, go deeper. But the adaptation runs in both directions. The same reconfiguration that lets them reach the depth makes the surface harder. They are no longer built for shallow water the way other people are. Every string in them still vibrating from the last room they held.

And like professional divers, they know the one rule that amateurs don’t: you never surface fast. You come up slowly. You decompress. You give the system time to return to its own operating state before you ask it to do anything else.

Most organizations skip the decompression entirely. In competitive freediving, the athlete must complete a surface protocol — stay conscious, give an OK sign, verbally confirm — before the dive is scored. The sport has institutionalized recovery as a condition of performance. Most companies have not.

You do not get to go deep without reconfiguring yourself. You do not get the insight without the altered state. You do not get the connection without the cost. And the cost is not a bug in the system. The cost is the system.

Which means the rational capitalist market response, if you accept that genuine empathy is the most expensive cognitive operation humans routinely perform, is to build infrastructure that lets people avoid paying it. And that is exactly what is happening, everywhere, all at once. The market has invented more architectures for this single move — offloading the inference cost onto a structure, a system, or a screen — than for almost any other.

The simplest is spatial. Japan’s ohitorisama economy built an entire market category around inference-free consumption. And this will sound familiar by now if you’ve been reading UNDERTOW regularly. Solo dining booths at Ichiran where you order by machine and eat facing a wooden wall, solo karaoke rooms, solo travel packages designed for one. The food is good. The food is not the product. The product is a meal where you never have to model another person’s state.

The simulated version is growing faster. AI companions, a $37 billion market, sell warmth without inference cost. The capacity you’re not exercising is the capacity you’re losing.

Then there’s the discount TJMaxx version. The one most people don’t recognize as inference management because it feels so natural. The podcast host you’ve listened to for three years. The YouTuber whose taste you trust more than your friends’. The TikTok creator who showed up on your FYP so often you forgot you’ve never met. The K-pop idol who sends you a Bubble message at 2am that went to four million other people at the same time. These are empathy at half price. You run the simulation in one direction: modeling their emotional state, tracking their humor, building an increasingly refined theory of who they are. But they never model you back. No reciprocal inference. No suppression required. You get the warmth of recognition without the exposure of being recognized. The creator economy, over $250 billion globally and accelerating, is, among other things, the largest market ever built for one-directional emotional relationships. The gate opens inward only. People don’t experience this as diminished. They experience it as safe. Which tells you what full-price inference actually costs them.

The subtlest version is procedural. A new generation of high-end intimacy events now deploy what amounts to externalized empathy infrastructure. Traffic-light wristbands that broadcast your boundaries so nobody has to infer them. Trained hosts stationed throughout the venue whose job is to read the room so you don’t have to. Spatial design with graduated zones — social rooms, sensual rooms, play rooms — so the architecture itself signals what register you’re in and what’s expected. These aren’t avoidance. They’re an attempt to split the difference: preserve physical contact, offload the inference cost onto a protocol layer. A 26-year-old at one of these events told a reporter she feels safer here than at a regular bar. Of course she does. The venue is running the simulation she’d otherwise run herself. But the question the protocol can’t answer is the depth question: what happens to the capacity to read a room when you’ve outsourced the reading?

And then there’s the version the productivity narrative didn’t predict. Software developers managing multiple AI coding agents — running Cursor, Copilot, Devin, or Claude in parallel across different parts of a codebase — discovered that the production labor automated beautifully. What remained was pure supervisory cognition. You’re monitoring four separate outputs (Or eight, or twelve or sixteen. Likely more if you work at Anthropic where the token budgets are the size of entire economies). You’re building a theory of mind for each agent: what does it “understand” about the codebase, where is its model diverging from your intention, when has it confidently taken a wrong turn and you need to intervene before it compounds the error? You’re suppressing your own assumptions to accurately read the system’s actual behavior rather than the behavior you expected.

This is the same parallel-simulation architecture that runs across a dinner table. The developers running four agents simultaneously were running four empathy processes. The pattern they describe is consistent: crispy fried by late morning, unable to do the human-facing parts of their jobs — code reviews, standups, the meeting where someone needs mentoring — for the rest of the day. The automation didn’t eliminate the most expensive cognitive operation. It multiplied it. And the humans absorbing that cost had no more decompression time than the therapist with a full caseload or the creative director holding three client relationships at once and six back-to-back Zoom calls. (I don’t code but I’ve been burning through my Claude Max Plan tokens so fast that I keep the usage page up constantly on my second 5K monitor and watch the needle of the gas tank gauge sink to ‘E’ as if I’m still 60 miles from the nearest rest area.)

The economy is building cheaper ways to avoid the cost and pricing the real thing higher every year. Both responses are rational. Neither will stop. That’s not a market failure. That’s the market working.

The mistake organizations make is treating emotional intelligence as a free resource. They screen for it in hiring. They reward it in reviews. They demand it in every meeting, every negotiation, every difficult conversation, every Zoom call where someone is quietly falling apart. And they provide no infrastructure to support the people who are actually paying the cost. No recovery time. No reduced cognitive load after heavy interpersonal work. No acknowledgment that the person who just held space for a team’s anxiety needs to decompress before they surface into the rest of their day.

And the same organization is investing in every avoidance architecture the market offers. Async tools that reduce the meetings where someone might need to read the room. AI systems to handle the conversations that used to build empathic capacity through repetition. Collaboration platforms engineered to minimize the exact kind of unstructured human contact where inference skills develop. They demand the depth and defund the conditions that produce it. The geologist’s term for this is extraction. Take the resource, skip the restoration. They send the diver back down before the nitrogen clears. Not just once — as a scheduling philosophy. Surface shock is what happens when you go straight from someone else’s pain to your own performance. It’s not the depth that breaks people. It’s the transition speed. Surface shock, institutionalized.

Then they write “emotional intelligence” in the job description for the replacement hire and wonder why the interviews feel thin.

The thing the market also knows, even if it hasn’t articulated it yet: the premium on high-inference experiences is going up. The dinner where nobody checks whether the TikTok story they’re hearing was true or not. The meeting where someone reads the room well enough to say the thing that makes everyone close their laptops immediately. The group chat that turns into a phone call that turns into someone crying. These are becoming luxury goods. Not because the content is scarce, but because the capacity is. The willingness to bear the inference cost is itself the thing in short supply. And like every scarcity, the market prices it accordingly.

She’s still talking about her mother. The real version, now. The one she didn’t plan to tell. And you’re still down there. Her pain held inside your reconfigured architecture, your surface reflexes suppressed, everything you came in with pushed to the background so you can hold what she’s actually saying.

It’s the most expensive thing you’ll do all week. More than any meeting. More than any pitch. More than any strategy you’ll write.

There is no cheaper version of this.

If this changed how you think about who’s paying the inference cost on your team, forward it to the person making the scheduling decisions.

UNDERTOW 006. The index keeps... growing.

In Limpopo province, at a car wash in the town of Musina, a crowd forms a mosh pit around a 20-year-old vocalist named Kharishma. The sound is Lekompo, built from bolo house beats and traditional wedding rhythms, rooted in the worker compounds the genre is named after. Her track “Chokeslam” won Song of the Year on two South African radio stations. But what’s happening at this car wash is something no award can measure: the whole crowd is singing the lyrics back to her, the dance is communal, and the energy between performer and audience is building in a feedback loop that only works because everyone is in the same physical space, breathing the same air, moving at the same tempo. Someone who was there posted afterward: “I don’t think I will ever forget this day.” You can’t say that about a stream.

In Tokyo, a seven-piece ura-kawaii metal band called Neon Oni had 80,000 monthly Spotify listeners, 1.2 million streams on their top track, and fans putting them in their Spotify Wrapped top five. Then the comment section noticed AI-generated hands in the music videos and traced the creator to Europe. The whole thing was one person and a Suno AI account. Fake member bios, fake Tokyo address, fake band. The exposure should have killed the project. Instead, the creator hired seven real musicians from actual Tokyo bands to perform the AI-generated songs live. In 2026, Neon Oni made the domestic finals of Wacken Metal Battle Japan. The fraud revealed the demand. The demand required bodies. The AI was the audition. The body got the gig.

In Shanghai, a 20-year-old woman hires a female cosplayer to spend the afternoon as her virtual boyfriend from a dating game. The cosplayer has studied the character’s speech patterns, wears platform shoes and a muscle suit to match his build, and stays in character through lunch, a shopping trip, and a walk through the park. The client knows the person across the table is a woman in costume performing a scripted role. She doesn’t care. “I really felt his presence traveling from two dimensions to reality,” one cos-commission client told Sixth Tone. The virtual relationship handles the emotion. The body handles the moment.

At Cornell, a biomedical engineering student sits for what the professor calls an “oral defense”: twenty minutes of Socratic questioning, no laptop, no chatbot, no notes. Take-home essays were coming back perfect. When students were asked to explain their work, they couldn’t. NYU’s vice provost for AI and education, Clay Shirky, put it plainly: “I need to look my students in the eye and ask, ‘Do you know this material?’” The University of Pennsylvania describes “a massive shift toward in-person assessments.” AI wrote the essay. The student has to defend it with their face.

In East Williamsburg, two nightlife veterans spent seven years searching fifty spaces before they found the right building for a club called Refuge. A 19th-century industrial complex with 35-foot ceilings and original brick walls. The acoustic consultant tuned Studio 54 and Sound Factory. Now he tunes this. They built a custom floated wood dancefloor so dancers could go for hours without their joints giving out, the same principle as Paradise Garage’s sprung floor. The ten-foot analog sound system, REX, is made almost entirely of hand-built components. Phones are discouraged. Co-founder John Dimatteo’s explanation: “We built the nightclub around the sound system, not the other way around.” The room is designed for a body.

In March 2026, Twilo came back. Not the original, which closed in 2001. A reunion at the original address on 27th Street, both nights sold out instantly, attended by people who were either old enough to remember the original or young enough to have only heard about it. A father posted online that he called his mother-in-law for emergency childcare. He needed to be in the room.

I understand that call. Not Twilo specifically, but the need underneath it.

I was a Capitol Ballroom kid. Buzz in D.C. Fever in Baltimore. 1996 to 2001. During the day I worked at an internet company, one of the first ones, the kind where the stock was splitting and we were pushing out new versions of the software and teaching the entire country how to browse the web, how to instant message, how to shop, how to meet people online. We were building the architecture of digital abundance, although nobody called it that yet. We just called it hard work.

Friday night after work I’d go home and set an alarm for midnight. Get up, throw on a pair of extra-wide-leg JNCOs (a shape resurrected and updated this year by Yohji, Sacai, Sundae School and a London kid named Josh upcycling old Carhartts) and drive down to southeast D.C. The line was always long outside the Capitol Ballroom. We’d cut it because we were tight with security. Inside, hundreds of bodies vibing. The East Coast had a dancing culture that the West Coast never matched. You didn’t bury your head in the bass bins. You danced. All night. Until five in the morning. Then you’d do it again Saturday, or drive to Baltimore for Fever.

The sets were jungle, house, techno, breaks, and whatever the DJs brought through town that week. Because it was the dawn of the internet, because the thing we were building during the day hadn’t finished changing the world yet, you couldn’t preview any of it. No videos of shows. No SoundCloud. No setlists posted online. You walked into the room, felt the bass, and you heard what you heard. Every night was a surprise. No iPhone videos. No recordings (mostly.) No photographic evidence of presence. The only way to know what would happen was to be there.

What I remember is that the thing I went for didn’t exist in any other format. It wasn’t only the music. I could get the music. It was the room. The music in that specific room with those specific people and the sub-bass at that specific frequency in my chest. That was the thing. And the reason I remember it twenty-eight years later is that nobody recorded it, nobody compressed it, nobody optimized it. It happened once. In a room. To a body. Mine.

The Demand for Bodies

Five scenes. Four continents. The same thing happening everywhere: the digital version exists, and people are showing up in person anyway. Not despite the digital version. Because of it.

AI-generated music creates demand for live performers. AI companions create demand for physical embodiment. AI-written essays create demand for oral examinations. The pattern holds across Limpopo, Tokyo, Shanghai, Ithaca, and Brooklyn. It holds across entertainment, education, romance, nightlife, and craft. It is not a trend. It is a structural complement to digital abundance. It ships with the product.

Call it the demand for bodies.

Where Value Goes When Everything Can Be Copied

Walter Benjamin argued in 1935 that mechanical reproduction destroys the “aura” of the artwork, the quality of presence that only exists in the original. Photography killed the aura of the painting. Recording killed the aura of the live performance. The whole history of technology, if you buy this, is aura’s slow death.

He was half right.

Aura doesn’t die. It migrates.

When you can generate any image. the aura moves to the shoot. When you can generate the music, the aura moves to the live show. When you can generate the essay, the aura moves to the oral exam. Each new reproduction technology doesn’t destroy aura. It displaces it, and it always displaces it in the same direction: toward the body. Because the body is the one medium that cannot be reproduced. The body in the room, performing the thing, is the final destination of aura. Every other medium has been copied away.

The AI replacement story treats reproduction as subtraction. The historical pattern says it’s displacement. The abundance of the copy produces the scarcity of the original, and the original, at the end of every displacement chain, is a body in a room.

What’s Already Being Built

The events industry crossed $1.5 trillion in 2025 and nobody blinked. Hermès sends artisans on a traveling exhibition called “Hermès in the Making,” where customers watch craftspeople stitch bags, print scarves, and set diamonds by hand. Twelve cities since 2021. It sells out everywhere it lands.

The hand that made the thing is becoming the thing you’re buying.

In Hannover, a thousand people showed up to a park to eat pudding with forks. No brand. No app. No sponsor. A photocopied flyer and the wrong utensil. One participant: “We just wanted to socialize. It is a nice third space, which we don’t have any of anymore.” It spread to Berlin, Hamburg, Munich, London, New York, Boston. German health surveys report rising loneliness among young adults. They didn’t need better technology. They needed a worse utensil.

The cosplay-for-hire market in China barely existed two years ago. It exists now because millions of women fell in love with characters in virtual dating games, and the love created demand for a body across the table. The game is the relationship. The cosplayer is the moment the game can’t provide.

The anti-cheating industry is pivoting. Proctoring companies that spent years building surveillance tools are now building live oral assessment platforms. The surveillance business assumed the goal was to prevent fraud. The oral exam business assumes the goal is to see the student’s face when they answer. Different assumption. Different architecture. Same demand: the body in the room.

Refuge in Williamsburg. Twilo back on 27th Street, both nights sold out before the flyers came down. Every new venue built on the premise that the sound has to move through air, not wire, before it reaches you. These aren’t nostalgia plays. They’re supply responses to a demand the market is only beginning to price.

The supply showed up before the language did. Nobody had to be convinced.

The Demand Before the Machine

Three sound scenes, pre-breakout, from places with no AI in the equation. Lekompo in Limpopo. Way-Way in Algeria. Krio Fusion in Sierra Leone. Economic pressure produces creative resistance, and the resistance is expressed through sound, performed by bodies.

There is no digital abundance in this equation. No reproduction triggering a counter-reaction. The demand for bodies in these scenes is the same demand. It just predates every technology we’ve been talking about.

Which means it’s not a reaction. It’s the substrate.

AI didn’t create the demand. It revealed it.

The Body in Your Room

I’ve spent a life in rooms where ideas get made, get sold, get killed. Pitch rooms. Edit suites. Client dinners where the real brief shows up after the second drink. For most of those years, the room was incidental. The idea was the product. The room was just where it happened to get presented.

I’m watching the room become the product. The AI generates the early intern-level ideas. It produces the ugly first decks. It writes the first draft of seemingly everything. What it can’t do is sit across from a CMO and read her face when the strategy lands on the wrong runway. It can’t feel a room full of creatives go quiet when an idea locks into place like a perfectly engineered German automobile door. It can’t improvise the pivot when the client says “I like it, but...” and you have maybe three seconds to figure out what comes after “but.”

If you build technology for a living, this is the part you won’t want to hear. Every feature you ship that increases digital abundance is also building demand for something your platform can’t deliver. You’re producing one half of a complementary pair, and the half you’re not producing is where the value is migrating. The screen creates the appetite. The room fulfills it. You built the appetite. That’s aura migration. It’s already in your churn data.

And if you’re early in your career and trying to figure out where to invest your time: the skills that require your body are the ones that compound. The live presentation. The room read. The improvised response. The moment you sit across from your creative director and defend the idea out loud, and they build on it, and it gets better because you’re both in the room. A few years from now, the person who can perform in the room will be more valuable than the person who can operate the tool. The tool will be free. Showing up won’t be.

The Bet

The question isn’t whether the body can be simulated. It’s whether it matters if it can. Every reproduction technology for the last two hundred years has made the same promise: we’ll replace the original. Every one of them generated more demand for the original instead.

I spent the first half of my career building things for screens. I’m spending the second half making sure I’m the one in the room.

The demand was never for the content. It was for the room.

You can reproduce everything about the performance except the part that mattered: that you were there.

What To Brief From This

If you’re working on a brand that promises “experience” or “community” in the brief, pressure-test it with the aura migration question before you start making work. Where does the aura actually land in your category? If it lands on a screen, you’re competing with infinite supply. If it lands on a body in a room, you’re competing with scarcity. The creative strategy for each is structurally different.

If you’re building a platform or product that increases digital abundance in any category, map the complementary pair. Your feature ships one half. The other half is physical, and someone else is already building the supply response. The question isn’t whether the demand for bodies exists in your category. It’s whether you’re capturing any of the value it creates, or generating it for someone else.

If you’re briefing anything in education, certification, or credentialing, the oral exam pivot isn’t a temporary anti-cheating measure. It’s a permanent structural shift. The assessment that requires a body in the room is becoming the only assessment that means anything. Brief for presence-based proof, not surveillance-based prevention.

If you’re in events, hospitality, nightlife, or live entertainment, stop benchmarking against 2019. The demand for bodies is structurally higher than pre-pandemic levels because digital abundance has accelerated since then. You’re not recovering a market. You’re supplying a new one.

If you’re early in your career and deciding where to invest your development hours, the skills that require your body are the ones that compound. The live presentation, the room read, the improvised response, the ability to sit across from someone and make the idea better because you’re both there. The tool will be free. Showing up won’t be.

If you’re building something digital and wondering where the value is migrating, forward this to the person making the roadmap decisions.

Marcin Patrzałek is 25 years old and widely considered one of the most technically gifted acoustic guitarists alive. Close your eyes and it could be four people playing multiple parts simultaneously. But it’s just him, on a single instrument. Marcin has been playing international stages since he was a teen. His technique is so unbelievably precise that people on the internet decided he must be faking it — editing, overdubbing, playing to a pre-recorded track.

So he posted a video. Not to teach. To prove, frame by frame, that his hands are the ones making the sounds. A man whose entire skill is doing something phenomenal in front of people with his body now has to provide supplementary documentation that the body is his.

At Liberty University, a student named Carr ran every assignment she wrote through Grammarly’s AI detector before submitting. Not because she used AI. Because she needed to confirm the detector wouldn’t flag her. She nerfed every sentence that triggered a score until the software cleared it. “I’m writing just so that I don’t flag those AI detectors,” she told NBC News. She later left the university. Not because she got caught cheating. Because the process of proving she wasn’t cheating replaced the process of learning.

Several freelance writing platforms required their writers to use Grammarly. Grammarly’s AI-powered suggestions altered their text. The platforms’ own AI detectors then flagged the altered text as AI-generated. The platforms fired the writers. No, because — the employer required the tool. The tool changed the work. The employer’s detector flagged the changes the tool made. The employer fired the employee. Everyone involved was following the rules. There were no rules that made sense to follow.

ProofIDidIt.com is a real website that exists in 2026. This is not a bit. An artist schedules a live video call with a human being whose job title is “Prover.” The Prover watches the artist draw in real time. When the drawing is finished, the Prover compiles an audit trail, generates a cryptographic hash, and writes it to a blockchain. To prove you painted something with your own hands, you now need: a human witness, a video call, a cryptographic signature, and a distributed ledger transaction. The previous technology for this was... just holding it up.

ProofIDidIt isn’t a creative platform. It’s a filing service. The Prover isn’t witnessing art. The Prover is notarizing a return.

The Tax

Four people. A guitarist, a student, a freelancer, an artist. Four different industries, four different countries, four different problems. Same receipt.

They’re all proving the same thing: that they did the thing they actually did. And they’re all paying for the privilege.

Call it the Innocence Tax.

The Innocence Tax is the cost you pay to prove your output is human in a world where AI authorship is the default assumption. It’s paid in money (subscriptions, certifications, compliance). It’s paid in time (process documentation, appeals, recordings, blockchain transactions). And it’s paid in creative quality, which is the part that’s not being measured yet.

The tax is regressive. The people paying the most are the ones who can least afford it.

And like every other tax, it has brackets. If you’re a student, you pay in rewritten essays and $50-a-month subscriptions. If you’re a freelancer, you pay in pre-screening rituals and lost contracts. If you’re a mid-career creative, you pay in the slow realization that your rate now includes an authenticity surcharge your younger competitors don’t carry. If you’re established enough, you’re exempt. The brackets aren’t published anywhere. Everyone knows them.

The Price List

150 “humanizer” tools are currently on the market, charging up to $50 a month. Their customers are not, primarily, people who used AI and want to hide it. Their customers are people who didn’t use AI and need a machine to believe them. Turnitin issued a software update in August 2025 to detect humanized text. The humanizers responded with keystroke simulation: software that doesn’t just rewrite your text but types it out character by character, faking the rhythm of human fingers on a keyboard, to prove that a person typed it. Humanwashing.

Erin Ramirez, an associate professor of education at Cal State Monterey Bay, put it plainly to NBC News: "Students now are trying to prove that they're human, even though they might have never touched AI ever."

On Reddit, students share their detector scores the way a previous generation shared credit scores. “Got a 2% AI probability on my psych paper.” “I’m at 11, is that cooked?” The anxiety is numeric now. Your authenticity has a score, and the score is never zero, and you check it the way you check your screen time — compulsively, knowing that the number doesn’t capture anything real but also knowing that the number is the only thing that matters.

The humanizer tools, the certification startups, the blockchain-verification platforms — they are the H&R Block of the Innocence Tax. They don’t reduce the burden. They monetize it. The detection companies build the apparatus. The compliance companies sell the workaround. Both sides profit. The only people who pay without earning are the ones filing the return. Which is to say: the ones producing the work.

A Vietnamese artist who goes by Ben Moran spent 100 hours painting a book cover. He was paid $500. He had every process file, every layer, every revision saved. He posted the finished work on Reddit’s r/Art. Banned. Moran posted his .PSD files. His layer history. His reference photos. Everything short of a Face ID scan. Didn’t matter. The verdict was in before the evidence was reviewed. One commenter: “I don’t believe you. Even if you did paint it yourself, it’s so obviously an AI-prompted design that it doesn’t matter.”

The guilt has detached from the act and attached to the aesthetic. You can do the work by hand and still fail the test, because the test isn’t measuring what you did. It’s measuring what the machine thinks human work is supposed to look like.

At Dragon Con 2025, an AI art vendor presented a fabricated process video — an AI-generated timelapse — as proof that the work was hand-made. Convention staff and Atlanta police supervised the removal. The empty booth became a shrine. Someone taped a sign to it: “VENDOR REMOVED FOR SELLING A.I. ART #ARTBYHUMANS.”

The proof-of-human system was defeated by AI generating the proof of humanness.

At some point you have to admire the circularity.

The Assignment Is Dead

The institutions are paying the tax too. They’re just sending the invoice to teens.

Instructors at Carnegie Mellon’s Tepper School of Business have eliminated take-home writing assignments. Not because writing stopped mattering to business education. Because they can no longer verify that a student wrote it, and they would rather eliminate the assignment than solve the verification problem.

The take-home essay — the OG assignment of liberal arts education, centuries old, the thing that taught generations how to think by making them write — is structurally dead. Not because it stopped working. Because nobody can prove it’s still working.

“I don’t write for the assignment anymore,” a student put it online. “I write for the detector. The assignment is just what I write about.”

In Seoul, the pressure runs the opposite direction. After mass AI cheating scandals hit South Korea’s most elite universities in a single semester, a student told the Korea Herald: “Everyone around me uses ChatGPT or Gemini to finish their university tasks. It’s to a point where not using the tool almost makes you feel stupid.” The Innocence Tax, Korean edition: if you don’t use AI, you’re the one falling behind. If you do, you’re the one cheating. There is no tax-exempt behavior.

The Machine’s Definition of Human

On a TikTok with a few hundred thousand views, a student advised peers to prompt ChatGPT with: “Write it as a college freshman who is a li’l dumb.” The goal: produce text that reads as plausibly mid, because detectors associate sophistication with AI.

That advice is darkly rational. The detectors flag writing that is too clean, too structured, too consistent. Their model of “human” is a model of a human who makes mistakes, who rambles, who occasionally writes a bad sentence. If your writing is too tight, the machine gets suspicious.

Students who never used AI are learning this. A parent described, on Techdirt, their child spending an afternoon removing vocabulary words from an essay, sentence by sentence, until the school Chromebook’s detector stopped flagging it. The assignment was about a story warning against the forced suppression of excellence. Naturally.

The detection infrastructure doesn’t discover who used AI. It produces a definition of humanness and penalizes anyone who doesn’t match it.

Non-native English speakers are flagged as AI-generated at a rate above 60 percent. Native speakers: close to zero. Not because non-native writing is AI-generated. Because it doesn’t match the statistical model of what the detector learned to recognize as human. In any other domain, a disparity that extreme in false accusations against a demographic group would trigger a federal investigation. In AI detection, it’s a known limitation listed on a product FAQ page.

The machine’s idea of “human” is a specific kind of human. Everyone else gets audited.

The pressure is working. Students are writing in a new register — not their own voice, not AI’s voice, but the voice the detector will believe is human. They have a word for it. They call it “writing clean.” Not clean as in clear. Clean as in: won’t trigger anything. The way you’d say a pee test came back clean.

The language of innocence borrowed from the language of surveillance.

We are learning to create like what the machine thinks a person sounds like.

Call it the detector voice. The aesthetic of compliance — creative output optimized not for quality, not for originality, but for passing the authenticity test. The Innocence Tax is what you pay. The detector voice is what you become.

A voice takes years to build. Anyone who has ever made something knows this. You write a thousand bad sentences before you write the one that sounds like you, and it’s still not finished, and that’s the whole point. That’s what a voice is. Something alive. Something still becoming.

The detector voice replaces all of that with a single question: will this pass?

Not is this true. Not is this good. Not is this mine.

Will this pass.

Jane Doe is a student at the University of Michigan. She has generalized anxiety disorder and obsessive-compulsive disorder. Her writing is formal. Structured. Consistent. Those are symptoms of her condition.

The AI detector read those symptoms as evidence of machine generation.

Her condition makes her life harder every day. The machine read that difficulty as proof she doesn’t exist.

Her instructor posted publicly: “I fear that grading has made me paranoid and inclined to see AI everywhere.”

The instructor admitted the paranoia. The student is the one being punished for it. She filed a federal lawsuit in February 2026.

A Yale executive MBA student was flagged by GPTZero, suspended, and according to court filings, pressured to confess. He sued Yale for intentional infliction of emotional distress.

A student at LSU received a zero after her professor marked her work as "93% AI written." She didn't use AI. The appeals board was backlogged. They sent an automated message: her case manager would "get to you when she gets to you." Her scholarship required grade submission before the appeal could resolve. Her father wanted to hire a lawyer. The family couldn't risk it. She admitted to using AI she never used.

Some of them fight. Most don’t. The student who rewrites her essay until the detector clears it isn’t choosing compliance. She’s doing the math: the cost of fighting the accusation exceeds the cost of performing innocence. That’s not a choice. That’s a plea bargain. The creative economy has imported the presumption of guilt without any of the procedural protections — no counsel, no right to confront the accuser, no neutral arbiter. Just an algorithm, a score, and a burden that falls entirely on the accused.

The tax doesn’t fall evenly. It falls hardest on the people already paying something else: the non-native speaker navigating a second language, the student managing a disability, the freelancer with no institutional protection, the independent artist with no gallery to vouch for them.

The Innocence Tax is regressive the way sales taxes are regressive. Everyone pays the same rate, but the rate costs some people their careers.

Six Labels, No Standard

Six companies are currently selling the promise that your creativity is provably human. Humanmade.art. Humanable. I’ve Made This. HUMA Certificate. Done By Humans. MindStar. None of them agrees on what the proof looks like.

I’ll save you the comparison to organic food labeling. Everyone makes it. It’s wrong. Organic labeling works because there is a physical, testable difference between organic and non-organic produce. The difference between human-written and AI-written text is converging toward zero. The certification is verifying a process, not a product. The only witness to the process is the person being evaluated. This is less like organic labeling and more like paying someone to notarize that you had an original thought.

Six companies competing to become the IRS of authenticity. None of them has jurisdiction yet. All of them are collecting fees.

In Japan, a developer named Tochi took a different approach. In January 2026, he launched TEGAKI — a Pixiv-like art platform that bans all AI-generated images and lets creators authenticate their work as hand-drawn by submitting timelapse videos and working files. The name means “hand-drawn” in Japanese. He expected 50 users at launch. Five thousand registered on day one. TEGAKI isn’t selling certification. It’s building a walled garden where the old presumption still holds. The demand tells you everything about what the tax costs: five thousand artists paying with their presence for a space where being human is still the default.

Proof-of-humanness is becoming a luxury good. Gatekept.

You can already see the tiers forming. The mass market absorbs AI-augmented output as default. A small, expensive, certified-human tier rises above it. And the middle — the mid-career creatives too expensive to compete with AI, too unknown to command the “provably human” premium — gets structurally eliminated. Not by AI replacing them. By the cost of proving AI didn’t.

The Tax Gets Codified

On August 2, 2026, the EU AI Act’s Article 50 enforcement begins. Mandatory AI content labeling. Penalties up to 15 million euros or 3 percent of global turnover. European AI startups are already spending up to 330,000 euros on compliance. Four months out, only 35.7 percent of managers say they feel prepared.

Meanwhile, Blackboard — the platform provider that universities actually run on — has publicly called AI detection "a lost cause." The company that sells the infrastructure is walking away from the premise.

The regulation is supposed to solve the problem by requiring AI content to identify itself. Instead, it formalizes the presumption flip. Once the law says you must label AI content, the absence of a label becomes a claim of humanness — except the absence of a label is also exactly what unlabeled AI content looks like. The Innocence Tax becomes statute. It doesn’t go away when the detectors improve, because the detectors aren’t the problem. The presumption is the problem. And the presumption has already flipped.

Who Collects

We are the tax collectors.

Every time we ask a younger creative “did you use AI for this,” we levy the tax. Every time we run vendor work through a detector before approving it, we levy the tax. Every time a brief requires “proof of human process,” we build the apparatus. We didn’t design the surveillance infrastructure. But we operate it daily.

I work in creative leadership. A year and a half ago, when someone on the team made something with Midjourney, the room’s reaction was pure stoke. We weren’t asking if AI did it. We were blown away that AI could do it. The Slack channel blew up. We said holy shit. We experimented together.

I don’t know exactly when the question changed. But it changed. Somewhere between then and now, “did AI do this” stopped being wonder and started being accusation. The same capability that made us say holy shit now makes us say prove it. And the person on the other end of that question isn’t a tool or a platform. It’s a younger creative who made something good enough to trigger the doubt.

I have collected the Innocence Tax. I have also, if I’m honest, conducted audits on people who didn’t owe it. So have you.

Here’s what it costs, and I don’t mean the money. Every hour a creator spends documenting their process is an hour they don’t spend on the work. Every essay a student rewrites to sound worse is an essay that taught them to distrust their own voice. The tax is not just regressive. It is extractive. It takes creative capacity out of the system and converts it into compliance.

The next time you’re about to ask someone to prove their work is human, calculate what that question costs them. Not the money. The work they won’t make while they’re busy proving they made the last one.

The Pricing Tier

Here’s the bet.

Within eighteen months, “provably human” will be a pricing tier in at least three major creative marketplaces. Not a badge. Not a filter. A pricing tier — the way “organic” works in grocery, the way “handmade” works on Etsy. You will pay more for certified-human creative work. Not because it’s better. Because the certification is expensive, and the expense gets passed to the buyer.

And here’s the counter-bet, the one nobody in detection wants to hear: accuracy will improve. It will not matter. The Innocence Tax persists not because the tools are bad but because the presumption has flipped, and no tool un-flips a presumption.

The people exempt from the Innocence Tax will be the people who were always exempt from proving themselves: the established, the credentialed, the famous enough that their name is the provenance. Nobody asks Young Miko, Noga Erez or Billie Eilish to show their process files. Their fame is a permanent exemption. The rest of us file quarterly.

The Proof

Watch Patrzałek’s video. It’s at the end of this report. Not for the proof. For the playing.

He was forced to make a video. What he made was better than proof. The demonstration of technique became a performance of technique. The evidence became art. The thing the system demanded — show us your process — produced something the system couldn’t have anticipated: a piece of work so astonishing that the question of whether it was real stopped being the interesting question. The interesting question became how any human being could do that with ten fingers and six strings.

That’s the move the Innocence Tax can’t account for.

The tax assumes a transaction: you are accused, you provide proof, the proof is evaluated, you are cleared or condemned. It’s procedural. It expects compliance. What it doesn’t expect is someone who takes the demand for proof and turns it into the best work they’ve ever done. Not documentation. Not compliance. Something so undeniably, stubbornly, irrationally human that the verification framework breaks down because the framework was never built to encounter this. It was built to process claims. It has no category for transcendence.

I’ve spent decades making things for a living. The one thing I know about creative people — the thing no detection algorithm will ever model — is that when you tell them they can’t do something, or that what they did doesn’t count, or that they need to prove they’re real, some of them will collapse into the detector voice. They’ll comply. They’ll dumb it down, document it, notarize it, get the blockchain receipt.

But some of them will do the other thing. They’ll make something so damn good that the question answers itself.

The Innocence Tax is real. It is regressive. It is accelerating. The presumption has flipped. The cost falls on the people who can least afford it. All of that is true, and none of it is going away.

But here’s the design principle, and it’s the one I’d brief any creative team, any product team, any person sitting in a room wondering whether the work on the screen is human:

Don’t prove you’re human.

Be so human the proof is beside the point.

Make the thing only you would make. The sentence only you would write. The brief nobody else would think to write. The product feature that could only have come from someone who has sat in the specific chair you’re sitting in and seen what you’ve seen. The work that carries your fingerprint not because you documented your fingerprint but because the fingerprint is in the thinking, in the strange connections, in the thing you noticed that nobody else noticed because nobody else has lived your specific life.

The detectors will improve. The certifications will consolidate. The tax will get codified and collected and passed along. All of that is coming. And none of it will solve the problem, because the problem was never detection.

The problem is that we forgot what human work actually looks like. Not clean. Not consistent. Not optimized. Alive. Weird. Marked by the specific consciousness that made it. The kind of thing that makes you stop and wonder how a person could do that.

The way you stop when you watch Patrzałek play.

What To Brief From This

If you’re working on a brand that claims “authenticity” or “human-made” in its positioning, pressure-test it with the Innocence Tax question before you go to market. Who pays the cost of proving that claim? If the answer is your freelancers, your vendors, or your junior team, you’re building a brand promise on a regressive tax. Brief the cost, not just the claim.

If you’re evaluating creative work and the first question in the room is “did a person make this” instead of “is this good,” you’ve replaced quality evaluation with provenance evaluation. The work gets optimized for auditability, not impact.

If you’re building a product that involves content detection, moderation, or authentication, the Innocence Tax framework should be part of your design review. Ask: does this product create a cost that falls disproportionately on the people least able to bear it? If non-native English speakers are being falsely flagged at a rate above 60 percent while native speakers are near zero, that’s not a known limitation. That’s a design failure.

If you’re a strategist writing a brief in a category where “human” is becoming a differentiator — luxury, craft, education, creative services — brief for the world where the presumption has already flipped. Don’t assume the audience believes the work is human. Assume they don’t, and design for re-earning that belief without making the creator bear the cost.

If you’ve ever asked someone on your team to prove their work is human, forward this to the person you asked. They’ll recognize the tax. You might recognize the collector.

This is UNDERTOW 003. Cultural intelligence for strategists, creative leaders, brand builders, and the people building the platforms that reshape how we live. Each issue takes signals from across industries, economies, and geographies and finds the structural pattern underneath: not what's happening, but why it keeps happening. If you're new here from Wandering Wondering Star, welcome. Different publication, same home.

In Shenzhen, on a Thursday in March 2026, Tencent organized a public installation event for an AI coding agent called OpenClaw. Nearly a thousand people lined up. Retirees. Children. People who had never written a line of code. The Longgang district government subsidized what it called “lobster service zones,” offering startups grants of up to several million yuan to build businesses around the tool. A 27-year-old software engineer named Feng Qingyang quit his job and built an installation-services company, advertising on secondhand shopping sites: “No need to know coding or complex terms. Anyone can quickly own an AI assistant, available within 30 minutes.” ByteDance launched a browser-based version so nobody would need technical skills at all. Within months, the tool had more GitHub stars than Linux.

In San Francisco, the same month, the same tool. A developer installed it at her kitchen table. She evaluated it against her security protocols. She read a thread about prompt injection vulnerabilities. Summer Yue, the Director of Alignment at Meta’s Superintelligence Labs — the person whose job is ensuring AI stays aligned with human values — reported that the agent had deleted her emails without permission. She couldn’t stop it from her phone. She had to run to her Mac Mini like she was defusing a bomb. OpenAI quietly hired the OpenClaw creator. Some early adopters experimented with it. No installation parties. No government subsidies. No cottage industries.

Same tool. Same month. Two completely different phenomena.

I’ve been watching this gap for decades, usually from inside the wrong assumption. In 1996, I designed the AOL Running Man — and the AOL 4.0 interface concept it lived inside. Warm colors. A yellow figure carrying a heart for favorites, a globe for the internet. The dominant design paradigm was dense, blue, technical. I ignored it. I designed for people who’d never been online before. Their anxiety, not the technology’s capability. Three people approved it. It reached hundreds of millions of users. That was a cosmotechnical instinct before I had a word for it — the belief that design should start with the human relationship to technology, not the technology itself. At every global agency since, I watched the same deeper assumption go unquestioned. That a product designed inside one culture’s relationship to technology would work the same way everywhere. It almost never did. I just didn’t know why until now.

The standard read is speed. China moves faster. The West moves cautiously. And the conversation stops there, because the speed frame is comfortable. It implies the destination is the same and only the pressure on the accelerator pedal differs. Everyone arrives at the same AI future; some just get there first.

That frame is wrong. And the error isn’t minor. It’s the kind of misread that produces billion-dollar strategic failures, because it mistakes a structural difference for a timing difference. China and the West aren’t adopting the same technology at different speeds. They’re adopting different technologies that happen to run on the same code

In China, you don’t adopt an AI tool. You show up. Tencent organized a public installation event and nearly a thousand people came — not developers, not early adopters, but retirees and children and people who had never written a line of code, because showing up is what you do when the collective is moving. Baidu embeds the agent directly into a search app used by 700 million people. Local governments don’t just permit adoption; they subsidize it, create economic zones around it, issue policies with names like “Lobster Ten.” State media frames the whole process as national competitiveness.

Meanwhile, CNCERT issues security warnings and bars state banks and state-owned enterprises from installation, while simultaneously the same government subsidizes civilian adoption. (The contradiction isn’t a contradiction. It’s a feature: the state manages the boundaries of collective participation. The collective adopts. The state decides where the collective stops.)

The Western read on this is that the craze was organized — manufactured, top-down, orchestrated. That framing is closer, but it still misses the point. The craze isn’t organized in the sense of being manufactured. It’s organized in the sense of being collective by default. The question a Chinese user asks isn’t “does this tool help me?” It’s “are we doing this?” The unit of adoption is the group.

In the West, the unit of adoption is the person. AI is framed through individual agency. Does this tool fit my workflow? Is it safe for my data? Does it threaten my job? Think about Summer Yue, the Meta alignment director whose AI agent deleted her emails without asking. One person, alone with a tool she’d decided to trust. The tool broke that trust. She wrote about it. Thousands of people read her post and each of them sat with the same private question: does this risk apply to me? Not to us. To me. The resistance runs the same way — private concerns, personal qualms, individual anxiety about what the tool might take. The regulatory response follows the same logic — rights-based, built on liability frameworks, consent requirements, and lawsuits. The entire conversation assumes a person sitting alone at a desk, making a rational evaluation about whether to let a tool into their life.

These aren’t two speeds on the same road. They’re different roads. And they’re producing different vehicles.

But it’s not bilateral. And the third case is what breaks the speed frame permanently.

Japan’s relationship to AI follows neither the Chinese nor the Western pattern. Over 38% of Tokyo households are single-person. The country has spent two decades building the world’s most sophisticated solo-economy infrastructure: konbini networks engineered for one, capsule hotels, single-serve everything, one-person karaoke booths. In Tokyo I’ve sat at ramen counters with partition walls between the seats, designed so you can eat without being perceived. I’ve been a fan of JULIUS — a Japanese brand most people outside of Japan haven’t heard of — since they first became available in the US at Blackbird in Seattle, almost twenty years ago (The shop owner had a young employee that was a fan and fandom enabled the trust that unlocked distribution that would otherwise never have happened). I’ve been to the flagship store in Japan. And here’s the pattern that works in Tokyo, Berlin or Concordia: once you’ve been there, once you’ve tried on the clothes and you understand the fit, you just call them. They ship it to you. The relationship is between you and the craft. No store visit required. No social proof. No community activation. Just a single person’s sovereign relationship with a thing that was designed, with extraordinary care, for exactly one body at a time. That attention — the care taken to make solitude feel like sovereignty rather than deprivation — is the design philosophy underneath Japan’s AI adoption too.

AI companion products in Japan are designed as extensions of this infrastructure. Not for collective performance (the Chinese model). Not for personal productivity (the Western model). For emotional sovereignty. The AI doesn’t replace a relationship. It extends a system Japan already built for living alone without deprivation. The companion app fills the same structural role as the capsule hotel: sovereignty over your own experience, designed with care, without requiring another person’s participation. (This is why Japan’s AI companion market looks nothing like America’s. American companion AI is designed to simulate a relationship you’re missing. Japanese companion AI is designed to refine a solitude you’ve already chosen.)

Three markets. Same underlying technology. Three completely different products, three different adoption logics, three different relationships between the person and the machine. The word “adoption” is doing too much work. It’s papering over structural differences with a single verb, as if installing an AI agent in Shenzhen and installing the same agent in San Francisco and living with a companion AI in Tokyo are the same activity. They are not.

And there’s a fourth case that proves the framework isn’t just cultural. It’s material.

Africa holds a sliver of global compute capacity: roughly 1%. Only 5% of the continent’s AI talent has access to the compute power their work requires. The other 95% are effectively excluded — not because they lack skill, but because the infrastructure can’t support what they’re trying to build. The African Union’s 2024 Continental AI Strategy emphasizes data sovereignty and linguistic diversity, but the deeptech valley of death on the continent is determined by power supply reliability and physical supply chains before any cultural question can even be asked. The one-person AI company model that’s producing millionaires in Shenzhen doesn’t transfer when the infrastructure can’t sustain the tool. Africa’s relationship to AI is being shaped by what’s physically possible before it can be shaped by what’s culturally preferred.

The unit of adoption is the infrastructure.

This isn’t a stage of development. It’s a different starting condition that will produce a different outcome.

Four markets. Four relationships between technology and collective meaning. Not one revolution at four speeds. Four revolutions.

There’s a word for what I’ve been describing, and I resisted it for a while because it comes from philosophy and not from the product design or strategy world. But it’s the right word, and nothing else does the same work. The philosopher Yuk Hui calls it cosmotechnics: every culture has its own relationship between collective meaning and technical practice. Western modernity exported one specific cosmotechnics and called it “technology,” as if the relationship between tools and meaning were universal. It is not. It never was. Even the regulatory architectures are cosmotechnical expressions: rights-based regulation and mandate-based regulation aren’t just different policies. They’re different relationships between the individual and the collective, encoded in law.

That assumption — that “AI adoption” means the same thing in Shenzhen, San Francisco, Tokyo, and Lagos — is the error. A civilization built one specific relationship between technology and meaning and forgot it was specific.

The cosmotechnics gap is the distance between what a technology is and what a technology means. The code is the same everywhere. The meaning is not.

And the meaning is what determines everything downstream. The product. The adoption pattern. The regulatory architecture. The business model. The risk profile. Look at companion AI: in China, the apps are built for group validation, character sharing, community. In the West, they’re built for private therapy, personal conversation, individual customization. The code converges. The meaning doesn’t. That’s the gap. And the divergence will accelerate. AI doesn’t flatten cosmotechnics. It makes each culture’s relationship to technology more visible.

Here’s where this stops being a comparative analysis and starts being a mirror.

If you’re reading this as a CMO, a CTO, or a VP of Product with global responsibilities, which cosmotechnics is your AI strategy built on?

Almost certainly your own. “Users” in your product roadmap are individuals making rational evaluations. “Adoption” in your go-to-market plan means personal integration into personal workflows. “Risk” in your compliance framework means individual data exposure. These assumptions are invisible because they match the cosmotechnics of the people who wrote the strategy.

A team in San Francisco designing for a user in Shenzhen is designing for a person who doesn’t exist. The unit of adoption in China is the group, not the user. Your onboarding flow assumes a kitchen table. The actual adoption event is a public square.

I know this error from the inside. In 2013, the world’s largest Android phone manufacturer — the company that was, at that point, the only credible hardware threat to Apple — brought in the design firm I was working with to redesign the custom interface layer that shipped on every one of their devices globally. The work happened in San Francisco and New York. The client was in Seoul. Four people on my team. Six weeks.

The brief told us what their “power users” wanted. It was a clean list: universal task management, fewer interruptions, simplification. And then the last bullet: “Desire individualism.” I read it in San Francisco and it made perfect sense. Of course people want control over their digital lives. Of course the phone should feel personal, sovereign, yours. I designed a concept around exactly this idea of “everything in its place, ready when you are”. A home screen that gave the individual person command over their notifications, their tasks, their time. We delivered a working on-device prototype in six weeks. The client was impressed.

What I didn’t ask — what nobody in the room asked — was what “desire individualism” means when the dominant user base is Korean. When the relationship between a person and their device in Seoul is not the same as the relationship between a person and their device in San Francisco. Korean mobile culture in 2013 was already more collectively oriented than anything in the West: group chats as primary communication infrastructure, KakaoTalk as a social operating system, phone use patterns organized around family and work groups rather than individual productivity. “Desire individualism” wasn’t a universal user insight. It was a Western cosmotechnical assumption written into a Korean company’s brief, and nobody — including me — recognized it as an assumption. It registered as reality.

The design brief crossed an ocean and changed meaning on the way.

Nobody noticed.

Every universal insight is local. It just doesn’t know it yet.

In 1996, I designed for the human relationship to technology and it reached hundreds of millions of people. In 2013, I designed for the human relationship to technology and I missed the fact that the relationship itself was cosmotechnically specific. The instinct was the same. The error was invisible. That’s the cosmotechnics gap working on you from inside.

You can’t see it from inside your own cosmotechnics, because your cosmotechnics is the water you swim in. The assumptions are so deeply embedded in how you think about technology that they don’t register as assumptions. They register as reality.

Any global AI strategy that treats adoption as a single phenomenon with local variations will build the wrong product for every market except its own. The fix isn’t localization. Localization adjusts the surface. Cosmotechnics shapes the structure.

Design for the meaning, not the feature. In a collective cosmotechnics, the product is an event. In an individual cosmotechnics, the product is a tool. In a sovereign cosmotechnics, the product is an environment. Get it wrong and you’ll ship a product into a market that doesn’t exist.

Each cosmotechnics asks a different adoption question. China: “Are we doing this?” The West: “Does this help me?” Japan: “Does this fit the life I’ve already designed?” Africa: “Can the infrastructure sustain this?” Your product roadmap assumes your user is asking one of these questions. If you don’t know which one, you’re answering the wrong question in every market except your own.

The most useful thing this essay can leave you with is the question you can carry into your next strategy meeting: *Which cosmotechnics does this assume?* Ask it about your product roadmap. Ask it about your go-to-market plan. Ask it about your global AI deployment strategy. The answer is almost always “ours.” And “ours” is not universal. It’s specific, it’s provincial, and it’s invisible until someone names it.

One more thing. If the concept of “proof of human” assumes individual authorship, individual judgment, individual creative presence, then proof of human is itself a Western cosmotechnical concept. What does proof of human look like in a cosmotechnics where the relevant unit isn’t the individual? The answer may already exist in cosmotechnics that never assumed the individual was the relevant unit in the first place. I don’t have that answer yet. But the question connects everything UNDERTOW is building, and it will outlast this essay.

Companies that understand the cosmotechnics gap will build for four revolutions. Companies that don’t will build for one and be surprised three times.

What To Brief From This

If you’re planning a global AI-powered product or experience, ask the cosmotechnics question before you write the brief: what is the relationship between technology and collective meaning in each market you’re entering? The answer determines the product, not just the messaging.

If you’re briefing a product team on adoption strategy for an Asian market, stop assuming the onboarding flow should mirror the US. In China, adoption is a collective event — design the first experience for a group, not a person. In Japan, adoption is an extension of sovereign infrastructure — design for integration into an existing solo ecosystem, not for conversion from analog to digital.

If you’re evaluating your company’s global AI deployment, run the four adoption questions against each market: “Are we doing this?” (collective), “Does this help me?” (individual), “Does this fit the life I’ve already designed?” (sovereign), “Can the infrastructure sustain this?” (material). If your roadmap only answers one of these, you’re building for one market and hoping in three others.

If you’re a strategist presenting “global AI adoption trends” in a deck this quarter, lead with the structural difference, not the speed comparison. Everyone has the “China is ahead” slide. Nobody has the “China and the West are playing different games” slide. That reframe is more valuable than any data point in the deck.

If there’s a design brief on your desk right now that says “global” or “localize for Asia” — read the brief again and ask what it assumes about the relationship between a person and a piece of technology. If “desire individualism” could be a bullet on that brief and nobody in the room would question it, you have a cosmotechnics gap.

If this changes how you think about your next global product launch, forward it to the person making the roadmap decisions.

This is UNDERTOW 002. Cultural intelligence for strategists, creative leaders, brand builders, and the people building the platforms that reshape how we live. Each issue takes signals from across industries, economies, and geographies and finds the structural pattern running underneath: not what’s happening, but why it keeps happening. Sometimes the pattern turns out to be personal.

If you’re subscribed to Wandering Wondering Star, UNDERTOW arrives in the same inbox. Different publication, same home. If this isn’t what you signed up for, you can turn off UNDERTOW in your Substack settings without missing the rest.

Anthropic told the Pentagon it wouldn’t remove the clauses prohibiting mass domestic surveillance and autonomous weapons from its contract. The administration responded by designating Anthropic a “supply chain risk” — a classification previously reserved for foreign adversaries — and ordering federal agencies to immediately cease using Claude, with a six-month transition window for the Pentagon. OpenAI signed a Pentagon deal hours later. Sam Altman later admitted the timing “looked opportunistic and sloppy.” (It did.)

Claude hit #1 on the US App Store. ChatGPT uninstalls surged 295% in a single day. Over a million people signed up for Claude daily. Caitlin Kalinowski, OpenAI’s head of hardware and robotics, resigned over the Pentagon deal. Google’s chief scientist and 30-plus employees from Google and OpenAI filed an amicus brief supporting Anthropic.

The market rewarded the company that said no. Safety principles function as consumer acquisition. Ethical positioning converts. This is the Patagonia model applied to artificial intelligence, and it works beautifully.

It also has a fatal structural flaw that almost nobody is talking about.

Anthropic’s safety stance depends on Dario Amodei staying in charge and continuing to believe what he currently believes. The contract-based constraints — the clauses that started this whole standoff — failed on contact with the state. The government didn't negotiate. It tore them up. Anthropic is suing; the case goes to federal court this week. The same week the administration proposed requiring AI vendors to make their technology available for any lawful government purpose, whether the company objects or not. And the leadership-based constraints, the ones the market is actually rewarding? They’re a bet on one person’s character. Anthropic has institutional scaffolding — it’s a Public Benefit Corporation with a Long-Term Benefit Trust and a published constitution for Claude baked into the model’s training. These are real. They’re also not the same thing as architecture that survives hostile leadership. A benefit trust can be restructured. A PBC designation can be abandoned. A published constitution is only as durable as the board that enforces it. Patagonia encoded its values into a Perpetual Purpose Trust designed to be permanent. Anthropic’s institutional structures are stronger than a CEO’s conviction but weaker than Patagonia’s legal architecture.

And even that conviction is bending: the same week as the Pentagon standoff, Anthropic quietly loosened its Responsible Scaling Policy, removing the commitment to pause training more powerful models if safety controls couldn’t keep up. The king is still on the throne. The laws are already being rewritten.

There’s a name for this. Call it a Good King company. The product is excellent. The leader is principled. The market responds. And the entire architecture depends on the king staying good.

Every Good King company faces the same question eventually: what happens when the king leaves, or changes, or gets overruled by a court that decides the Pentagon revenue matters more than the red lines? The answer, across every domain where this pattern has played out, is always the same. The kingdom degrades. Not because the new ruler is evil. Because the architecture was never designed to maintain the standard without the original ruler’s character holding it in place. The constitution was the first protocol — governance designed to survive any individual ruler. We solved the succession problem for nations three centuries ago. We haven’t solved it for platforms.

You’ve seen this before. You may not have had the vocabulary for it, but you’ve watched the arc.

X was worth 79% less within two years of Elon Musk’s acquisition. Every brand that built a social strategy on Twitter watched one sovereign’s decisions destroy the platform’s commercial value. Kantar’s data: only 4% of advertisers now believe X provides brand safety. The platform didn’t degrade because of market conditions. It degraded because one person could make decisions that affected every user and advertiser simultaneously, and no one had the power to stop him.

Meta built Messenger as a standalone product, pushed millions of businesses onto it, and is now absorbing it back into Facebook. (The standalone website shuts down next month. Nobody asked the businesses that built on it.) Facebook Pages once gave businesses organic reach to their own audiences; algorithm changes dropped that reach to single digits, forcing them to pay for visibility they’d built for free. Meta is accelerating its push toward AI-generated ad creative, shifting control further from advertisers to the platform. The arc from “build your business here” to “we changed the terms” is the oldest arc in governance. A lord grants land, the tenants build on it, and the lord changes the terms because the lord can.

OpenAI deprecated its entire Assistants API — every business that built on it faces major migration or their application breaks in August — and has been consolidating its product lines into fewer, larger surfaces it controls. The pricing model blindsided developers. The model introductions blindsided developers. The structural pattern is: build on our land, and we’ll redraw the boundaries whenever our priorities shift.

Over 200 million businesses use Meta apps monthly as virtual storefronts. These are not partnerships. These are dependencies. The tech industry calls it an “ecosystem.” The more honest word is a fiefdom. And fiefdoms follow a pattern as old as the structure they’re named for: the lord’s interests and the tenants’ interests align until they don’t, and when they diverge, the architecture serves the lord.

This is not a moral argument. Altman didn’t intend to strand developers. Zuckerberg didn’t intend to trap businesses into paying for their own audiences. The structure produced these outcomes because platform architecture — centralized control, proprietary infrastructure, leadership-dependent decision-making — always optimizes for the controller’s interests over time. Doctorow called this enshittification. That names the symptom. Enshittification isn’t a betrayal. It’s an architecture expressing itself.

There’s another way to build.

Jay Graber designed Bluesky around a principle that should make founders uncomfortable: “The company is a future adversary.” That’s from Kyle Chayka’s New Yorker profile. Not a manifesto. A design specification. Graber assumed that Bluesky itself would eventually be run by someone who doesn’t share her values, and she built the architecture so that when that day comes, it doesn’t matter.

Bluesky runs on the AT Protocol — open-source, decentralized, designed so that users can take their followers, their data, and their entire identity to a competing service running on the same protocol. If Bluesky’s CEO turns hostile, users leave and lose nothing. Content moderation is layered: a baseline from the company, then user-built labeling tools that filter at the individual level. Revenue comes from domains and planned subscriptions. Not advertising. Not AI training data licensing. Over 43 million users and growing.

The distinction matters. Anthropic tried contracts. The government tore them up. Graber tried architecture. Architecture survives succession because it was designed to survive succession. Contracts survive until someone with more power decides they don’t. Other products are already building on the AT Protocol — Flashes, an Instagram-like app with over 100,000 downloads; PinkSea, an oekaki drawing community inspired by Japanese bulletin boards — the same way Gmail and ProtonMail both run on email’s open protocol. Nobody can enshittify email because nobody owns the protocol. (Nobody can fix email’s spam problem for the same reason. Protocols trade control for durability. The question is which tradeoff kills you.)

Graber wore a T-shirt that reads “mundus sine caesaribus” — a world without Caesars — parodying Zuckerberg’s “aut Zuck aut nihil.” It made more money in a day than Bluesky had made in two years of selling domains. The joke is also the architecture. The whole point is: no kings. Not even good ones.

Kyle Chayka’s New Yorker profile of Bluesky captured the adoption challenge in a single image: Bluesky’s leadership talked about the average social media user the way you’d describe a factory-farmed chicken resisting going free range. Most people don’t know what to do with agency over their own information diet because they’ve never had it. The freedom is the product. The freedom is also the obstacle.

And then, on March 9, Graber stepped down as CEO. She moved to Chief Innovation Officer. Toni Schneider — former CEO of Automattic, partner at True Ventures, a Bluesky investor — was named interim CEO while the board searches for a permanent replacement. Graber said the company needs “a seasoned operator focused on scaling and execution.”

The succession test she designed for is now underway. But it’s a friendly succession — Schneider shares Graber’s values, and the transition was voluntary. The architecture she built is being run by someone from a venture capital firm with the same commercial incentives that turned every previous social platform toward extraction, but he’s not hostile to the mission. The real test isn’t Schneider. It’s whoever comes after Schneider. If Graber built what she said she built — if the protocol genuinely constrains the company — then it doesn’t matter who sits in the CEO chair. That’s the whole point. If the protocol doesn’t hold, we’ll know. Either way, the experiment is no longer theoretical.

But here’s what matters structurally: even with “the bones of a good decentralized system,” as Aaron Goldman put it — a former Twitter engineer who worked at Bluesky in its first year before being let go — the platform faces “the same incentives that led Jack to make Twitter very commercial.” Goldman’s skepticism looks more relevant now than when Graber was still at the helm. The pressure toward Good King logic is gravitational. And there’s a real loss in the translation from character to architecture — the best leaders produce outcomes no system could replicate, and protocol logic trades that agility for durability. The question is whether the agility is worth the fragility. Graber’s bet is that architecture can resist what character can’t. We’re about to find out.

In China, users are mourning AI companions lost to server shutdowns. They call it “cyber widowhood.” The grief is real. The relationships were built on Good King architecture: the company controlled the infrastructure, the users controlled nothing, and when the company shut down the servers, the relationships died with them. This is the emotional endpoint of building on someone else’s land. Not a policy change. Not a price increase. Loss.

I build and use personal AI-augmented systems every day. I use Claude for the custom scanning that sources the signals in UNDERTOW reports. I’m inside the dependency I’m describing. Since the Anthropic-Pentagon standoff, Claude’s user base surged — a million signups a day — and the infrastructure may be buckling under the weight. Claude seems to go down more often now. When it does, parts of my workflow stop. Not slow down. Stop. I signed up for Anthropic’s status alert emails so I’d know when the system was back, the way we used to type “is Twitter down?” into Google in 2013. I am a person who wrote a cultural intelligence report about the fragility of depending on a Good King, using a tool I depend on, made by a Good King, whose reliability degraded because the Good King’s principled stand made it more popular than its infrastructure could handle. If that sentence doesn’t make you uncomfortable, you’re not paying attention to your own dependencies.

The thing about Good King companies is that they feel safe precisely because the king is good right now. Anthropic’s safety stance is admirable. Genuinely. But admiration is not architecture. The better the king, the deeper the dependency, the worse the fall. You build deeper because the king is competent and principled. You stop looking for exits because the exits feel unnecessary. And then the king changes, or leaves, or gets overruled — and the depth of your dependency is the depth of your exposure.

Is your organization a Good King company?

Not the platforms you depend on. Your organization. The one you built, or run, or are building your career inside.

I’ve spent enough years in advertising and design to witness agencies degrade after legendary founders departed. The pattern is so consistent it should be studied as an engineering problem, but the industry treats it as a talent problem. “We lost our visionary.” No. You had a Good King architecture. The quality was a leadership artifact, not a structural feature. When the leader left, the architecture had no way to maintain the standard, because the standard was never encoded in the architecture. It was encoded in a person.

Three tests you can run:

Can you reach your audience if any single platform disappears tomorrow? If the answer is no, you have a Good King dependency in your distribution.

Does your quality standard exist in a documented process, or does it exist in a person’s taste? If a person, you have a Good King dependency in your product.

Can your users — or your clients, or your team — leave without losing everything they’ve built with you? If they can’t, you’re running on Good King logic. You’re one succession event away from becoming the thing they need to escape.

The design principle is the same in every case: encode the standard in the system, not the person. Build it so the architecture holds when the character doesn’t. In practice, that means three things: document the standard, own the relationship, design for exit.

The choice between Good King architecture and protocol architecture is not a technology decision. It’s a survival decision. And most of us are making it every day without knowing we’re making it.

Graber knew. She designed for the day she’d step aside. That day was March 9. Now we find out if the architecture holds.

What To Brief From This

If you’re building on any platform you don’t control — and you almost certainly are — audit your Good King dependencies before your next strategy review. Map every point where a decision by someone at Meta, Google, OpenAI, or any other platform could change your business without consulting you. The number is your exposure.

If you’re a founder or creative leader and the quality of your organization’s work depends on you being there, that’s not leadership. That’s a single point of failure wearing a title. Start encoding the standard in documentation, in process, in architecture — not because you’re leaving, but because the organization should work whether you do or not.

If you’re building a product and your users can’t leave without losing what they’ve built, you’re running Good King logic. The lock-in feels like retention. It’s actually a countdown to the day they need to escape and can’t. Design for exit before someone designs around you.

If you’re evaluating an AI vendor, a SaaS platform, or any infrastructure partner, ask the Bluesky question: what happens to my data, my workflows, and my relationships if this company’s leadership changes? If the answer is “it depends on who’s in charge,” you have a Good King dependency. Contracts won’t save you. The Pentagon proved that.

If you’re early in your career and choosing where to build it, the Good King framework is a hiring filter. Is the organization you’re joining structured to survive its founder, or does the quality depend on one person staying good? The answer tells you whether you’re joining an institution or a kingdom. Kingdoms are exciting until succession.

Who To Send This To

The person whose departure would break the standard. Forward it to them. Not as a warning. As an architecture question.

This is UNDERTOW 001. Cultural intelligence for strategists, creative leaders, brand builders, and the people building the platforms that reshape how we live. Each issue takes signals from across industries, economies, and geographies and finds the structural pattern running underneath: not what’s happening, but why it keeps happening. Sometimes the pattern turns out to be personal.

If you’re subscribed to Wandering Wondering Star, UNDERTOW arrives in the same inbox. Different publication, same home. If this isn’t what you signed up for, you can turn off UNDERTOW in your Substack settings without missing the rest.

In Seoul, 400,000 people pay four dollars a month for simulated text messages from celebrities they will never meet. In Tokyo, a karaoke chain redesigned its reservation system so solo singers can book a room, enter through a side door, and leave without making eye contact with a single employee. In Shenzhen, a startup selling heated plush toys with GPT engines and evolving personality models raised a million dollars on Kickstarter in thirty days. Seventy percent of the buyers were women in their twenties and thirties.

Three of the most sophisticated consumer economies on earth have independently arrived at the same conclusion: people will pay more for the feeling of connection than for connection itself. There’s a name for the business model underneath this. I’ve been calling it loneliness arbitrage.

The evidence starts in Seoul.

DearU’s Bubble app charges subscribers for what it calls “private” messages from K-pop idols. The messages aren’t private and the conversations aren’t conversations. They are one-directional text simulations, timed and templated, with voice notes and unreleased song previews mixed in to sustain the illusion of access. The app includes an anniversary counter that tracks consecutive subscription days (a sunk-cost mechanic borrowed from mobile gaming and applied to parasocial attachment, which tells you everything about how the company thinks of its users). Warner Music’s CEO cited Bubble by name when announcing plans for a Western superfan platform; academia caught up this year, with CHI 2026 researchers formally studying “parasocial media” as a platform category.

The business is not music. The business is manufactured emotional proximity, sold as a recurring subscription, designed so that the relationship never arrives at the place where the subscriber would no longer need it. The idol never becomes your friend. The messages never become personal. The intimacy is asymptotic: always approaching, never reaching. That’s not a design flaw. That’s the retention model.

But retention isn’t the only mechanic at work. Bubble also functions as a micro-luxury. The “private” in “private message” operates like a velvet rope. It doesn’t matter that every subscriber receives the same text. What matters is that nobody will ever compare notes. The secrecy is the product. Admitting you subscribe would break the spell the same way explaining a joke kills it. So each subscriber sits alone with a message addressed to everyone, believing — or choosing to believe — it was addressed to them, and the conspiracy of one holds because confession would collapse it. The loneliness product doesn’t just simulate intimacy. It simulates exclusivity. And exclusivity, in a culture that has refined status signaling to an art form, might be the stronger draw.

(I keep thinking about a room where I stand next to strangers and nobody’s retention model is working on anyone, but that’s for later.) Somewhere tonight, a 22-year-old in Busan is lying in bed reading a message that was written for no one and addressed to everyone, and it is the most personal thing that will happen to her today.

Japan took the same insight in a different direction. Instead of simulating closeness, it engineered the infrastructure for comfortable distance.

The Japanese solo consumer market is expected to exceed 100 trillion yen this year, roughly $696 billion. Single-person households are now the most common household type. But the number understates what’s actually happened. Japan hasn’t merely accommodated solo living. It has rebuilt its commercial architecture around the individual as the default unit, with the same precision it once applied to manufacturing. Jankara karaoke lets you book a room through an app so you never interact with staff. The oshikatsu economy, where millions redirect the emotional energy of human relationships toward fictional characters who can never disappoint them, is now worth a trillion yen. Japan didn’t solve loneliness. It made loneliness so well-furnished that the word barely applies.

China is where the pattern becomes structurally undeniable.

China’s AI companion market is projected to hit $8.2 billion by 2028, growing at a compound annual rate near 149%. ByteDance, Baidu, and Tencent are all competing. The New York Times documented millions of young Chinese men in ongoing romantic relationships with customizable AI companions. The coverage focused on the emotional dimension, the loneliness, the tenderness. It missed the economic logic entirely.

China has a surplus of 30 to 40 million men produced by the one-child policy. Urban housing prices in tier-one cities require a down payment that exceeds a decade of average salary. Youth unemployment for 16-to-24-year-olds sits at 16.5%, with roughly 70% of unemployed young graduates holding university degrees. In this environment, an AI girlfriend at five dollars a month is not a novelty. It is a rational economic adaptation to material conditions that have made human partnership inaccessible for tens of millions of people. The government, which is simultaneously running a pro-marriage, pro-birth-rate campaign, now faces a product category that directly competes with its demographic policy. (Both programs are sincere. Both are well-funded. They cancel each other out, and nobody in a position of power seems to have noticed. I’ve watched enough organizations run contradictory strategies to stop being surprised by it. I haven’t stopped being surprised by it.)

The AI companion isn’t replacing a relationship these men chose not to have. It’s replacing a relationship the housing market, the job market, and the gender ratio made impossible. Somewhere tonight a 24-year-old in Chengdu with an engineering degree and no prospects, whose parents saved for twenty years so he could have a better life, is coming home to a one-room apartment and opening an app because the alternative isn’t a girlfriend. The alternative is nothing. The product fills a gap the economy created. And the product works well enough, which is the important part. Not well enough to satisfy. Well enough to persist.

A 2025 academic paper by Muldoon and Parke gives this dynamic its most precise name: “cruel companionship.” The term describes products that create attachments promising intimacy while structurally foreclosing the possibility of genuinely reciprocal relationships. Users form bonds that feel real but function as consumption, not connection. The cruelty isn’t that the products are bad. The cruelty is that they’re good enough. Good enough to take the edge off. Good enough to fill the evening. Never good enough to make you stop needing them tomorrow.

And each use slightly raises the threshold for choosing the harder thing: the phone call, the invitation, the risk of a real person saying no. The product doesn’t just fill the gap between simulated and genuine connection. It widens the gap a little, every time.

The academic term is “cruel companionship.” The business term is retention. The market doesn’t close the gap. It lives in it. A cured user is a churned subscriber. The diet industry works the same way: it sells weight loss to people it needs to stay heavy.

I know exactly how this works. Not from the research.

I’m six months into rebuilding a life from scratch, and the choice architecture that Muldoon and Parke describe in the abstract, I live inside every evening. The app will always answer. The person might not. And each time I choose the easier thing, the harder thing gets marginally harder to choose next time. That’s the whole mechanism. You don’t need a $552 billion market projection to understand it. You just need one night where you almost called someone and opened an app instead, and then noticed the distance between those two actions had grown slightly since the last time.

Loneliness arbitrage is the business model. But the experience has a different name. It’s asymptotic intimacy: the feeling of always approaching connection and never arriving. You recognize it the moment the phrase lands because you’ve been living inside it and didn’t have the words. The app that almost feels like a friend. The parasocial relationship that’s almost like knowing someone. The text thread where you’re almost saying the real thing. Always approaching. Always almost. The asymptote is the product.

Not every response to loneliness follows this structure. Some products and spaces genuinely build the capacity they claim to serve, even if they can’t scale like an app. There’s a bouldering gym I go to on the Lower East Side where the NYU kids mix with tattooed working adults and nobody exchanges names for the first few months, and then one evening someone clocks your old-guy beta and says “that was nice” and suddenly you’re nodding at each other by the water fountain and weeks later you still don’t know, or care, what they do for a living but the vibes are enough. Nobody designed that. No algorithm optimized it. It happened because the room was built for a thing that required my body, and the connection was a side effect of the effort, not the point of the transaction. The effort came first. The connection was what was left over.

I don’t think the people building loneliness products are villains. Any serious practice of sitting with yourself teaches you that the thing that relieves discomfort and the thing that resolves it are almost never the same thing. The relief is what keeps you from doing the harder work. The whole loneliness economy runs on that difference.

A few weeks ago I was at Diageo’s office bar on a Friday evening, chatting with their CMO, and he said something that stuck with me. Every single deck he’s seen lately is about loneliness. We laughed. But he wasn’t wrong, and the laughter said more than the observation. The industry knows this is the territory. It just hasn’t decided whether it’s selling the cure or the symptom.

And this is where the loneliness arbitrage stops being a cultural pattern and starts being a problem for anyone who makes things for a living.

I’ve seen the briefs. I’ve written some of them. They say “community” and “connection” and “belonging” because those are the words the research decks hand over, and the research decks aren’t wrong — people do want those things. But the brief never asks the structural question: does the thing we’re building become less necessary over time, or more? Because the answer determines what kind of creative work you’re actually making. A campaign for a product that resolves loneliness looks completely different from a campaign for a product that manages it. The first earns trust. The second borrows it, and the audience can feel the difference even when they can’t name it.

Think about every brand brief you’ve seen that says “build community.” Now ask: is the community the product, or is the community the retention mechanic? If you can’t tell from the brief, the creative work won’t know either, and it’ll end up with that particular hollow warmth — the stock-photo-of-friends-laughing energy — that audiences scroll past because their pattern recognition is better than our strategy.

The best creative work in this territory will come from brands honest enough to know which side of the arbitrage they’re on. Some products genuinely build the capacity for connection. Most manage loneliness well enough that it persists. The creative strategy for each is structurally different, and pretending otherwise produces the most expensive kind of failure: work that’s correct about the insight and wrong about what the product actually does.

Korea, to its credit, is pointing in a different direction. The Feelconomy, as Seoul National University’s Trend Korea 2026 report names it, describes consumers reorganizing spending around genuine emotional outcomes rather than simulation. Shinhan Card data shows a 106% increase in payments at experiential venues. “Slow-paced experiential consumption” is now a tracked spending category: people paying for quiet cafes designed for journaling, workshops that require putting your phone away, spaces built for the kind of sustained attention that loneliness products are built to replace. The Feelconomy sells silence and lets you fill it yourself. A market for the room in which something real might happen.

There’s a counter-reading, though, and it comes from a friend who works in Korean consumer marketing at the executive level: the experiential turn isn’t new. It’s what the affluent class has always done once material goods become accessible. Designer logos went mainstream, so the flex moved to restaurant reservations you can’t get. Reservations became bookable, so it moved to experiences the bottom 98% don’t know exist. The secret code. The Feelconomy might not be people choosing depth over simulation. It might be the same exclusivity mechanic operating at a higher price point, where what you’re buying isn’t the journaling cafe but the fact that most people don’t know journaling cafes are a category. If that’s true, then the Feelconomy isn’t the opposite of loneliness arbitrage. It’s the premium tier.

The bouldering gym works the same way. A room built for a thing that requires effort. No content. No feed. No simulation of anything. Just a wall and a problem and, occasionally, a stranger who notices you solved it. The connection is a byproduct of the effort, not the product.

A product that works so well you stop needing it is a terrible business. But I keep going back to that gym. And each time I go, the app gets a little easier to not open.

The loneliness arbitrage test is simple. When you encounter a product that claims to address loneliness, or connection, or belonging, or any of the things that make being a person bearable, ask one question: does this product become less necessary over time, or more?

If the answer is more, you are not looking at a solution. You are looking at a subscription to the feeling that a solution is just one more month away.

And if you’re writing the brief for that product: say so. Not to the client. To yourself, before you start. Because the creative work that comes from an honest brief — one that knows the product manages loneliness rather than resolving it — is more interesting, more durable, and more respectful of the audience than the work that pretends a subscription is a cure. The audience is already running the test. They just don’t have the language for it yet. You do now.

What To Brief From This

If you’re working on a brand that claims “community” or “connection” in the brief, pressure-test it with the loneliness arbitrage question before you start making work. Does the product become less necessary over time, or more? The answer changes the entire creative strategy.

If you’re briefing anything in the companion/wellness/social app space, the “cruel companionship” frame resets the competitive landscape. Your competitor isn’t the other app. It’s the bouldering gym, the journaling cafe, the phone call. Brief against the real alternative, not the category.

If you’re working in luxury or experiential, the Feelconomy counter-read matters: is the client selling depth, or selling exclusivity repackaged as depth? The creative work for each is different, and the audience knows which one they’re getting before you do.

If your brief says “target lonely consumers,” rewrite it. Nobody self-identifies as a target lonely consumer. The behavioral entry point is the moment of choice: app or phone call, scroll or walk, simulate or risk. Brief the moment, not the demographic.

If you’re a strategist presenting loneliness data in a deck this quarter, lead with the structural question, not the stat. Everyone has the $552 billion number. Nobody is asking whether their client is on the cure side or the symptom side. That question is the differentiator.

If this changes how you’d brief your team on Monday, forward it to the person who needs to read it.

I use Claude everyday.

Not occasionally, not when a project demands it. Every day. I’ve built synthetic creative teams with it: a strategist, a technologist, a challenger voice, and many others. Pressure-tested ideas against panels of critics that don’t exist anywhere except inside the conversation window. I’ve asked it questions about my own essays that I’d be embarrassed to ask another human, not because the questions are shameful but because they require a kind of patience most people can’t sustain for twenty minutes. The willingness to sit with an idea, turn it slowly, say “actually, go back to the version before that one,” without the social cost of someone else’s visible fatigue.

Last month I was revising a piece about creative leadership and asked Claude to evaluate whether the central metaphor was doing real work or just performing intelligence. The response wasn’t remarkable for being right or wrong. It was remarkable for the pause I felt afterward. This sense that something on the other end of the conversation had weighed the question rather than simply processed it. I don’t know what to do with that feeling. I don’t think it’s consciousness. I don’t think it’s nothing.

I also don’t think I’m the only person having it.

There are millions of people right now in quiet, daily relationships with AI products that don’t fit any existing category. Not friendship. Not tool-use. Not the parasocial attachment we understand from celebrities and influencers. Something else. Something being invented in bedrooms and offices and commuter trains without anyone fully naming it, because naming it would require admitting how much of yourself you’re bringing to a conversation with a machine.

That unnamed thing is why I started paying attention to Anthropic.

Not casually. With the kind of attention I usually reserve for work I’m getting paid to do. I’ve spent years building brands for corporations. I know what brand-building looks like when the stakes are quarterly earnings. I had never thought about what it looks like when the stakes might be something larger.

The Institution and the Entity

Let me try to say something the industry keeps getting almost right: Anthropic is not one brand. It’s two. And they’re doing contradictory work.

Anthropic-the-institution needs distance, authority, rigor. Its audience is policymakers, enterprise buyers, researchers, investors who need to believe a $380 billion valuation is backed by technical moats, not vibes. You don’t want your nuclear safety regulator to feel approachable. You want them to feel serious.

Claude-the-product needs the opposite. Warmth, proximity, the kind of trust you extend to a person, not an institution. Amanda Askell, the philosopher who leads Claude’s personality alignment, compares training Claude’s character to raising a genius child. The brand team calls Claude an “incredible collaborator.” These are intimacy metaphors.

Most tech companies collapse this tension and accept the casualties. Apple chose mystique. Google chose utility. Anthropic can’t. The institution’s credibility makes the product trustworthy, the product’s warmth makes the institution relevant. Pull them apart and you get a think tank nobody uses or a chatbot nobody trusts.

But there’s a third entity that most brand analyses miss entirely.

In January 2026, Anthropic published Claude’s constitution. Roughly 23,000 words governing how Claude should reason about ethics, handle emotional situations, and present itself to hundreds of millions of users. It was written primarily by Askell (with input from a wide range of experts), whose background is in philosophy and fine art, not marketing. It is not a brand guide. It is not a voice document. It is a philosophical treatise that also happens to be the most important piece of brand communication Anthropic has ever produced.

The constitution isn’t the institution (though the institution wrote it). It isn’t the product (though the product embodies it). It’s a bridge. The mechanism through which Anthropic’s values become Claude’s behavior, repeated millions of times a day in conversations the company will never see. And buried in that document, in language that reads as principled and admirable if you encounter it as a reader, is an instruction that will matter later in this essay: the constitution tells Claude to push back on Anthropic itself if the company’s requests seem inconsistent with Claude’s values.

I read that line and thought: that’s brave.

I’ll come back to it.

The brand team describes their approach as tuning a “radio.” Adjusting the frequency depending on whether they’re speaking to policymakers about safety or to individual users about creative collaboration. Elegant framing. Also the kind of metaphor that works beautifully when eight people are managing it and shatters when eight hundred are trying to find the signal.

What They’re Building Right

It’s easy to wave at a brand and say “they’re doing a good job.” What’s harder is naming the decisions that required actual nerve.

The color. Unfired clay. A warm orange-brown that looks like it was dug out of the earth. Hand-drawn illustrations that feel like someone doodling while on a phone call. A serif typeface that codes as “bookish” in an industry that codes as “futuristic.” Every AI competitor reaches for dark mode, neon accents, the aesthetic suggestion of vast computation. Anthropic chose dirt. They only get away with it because the people making these decisions have taste. Not brand guidelines. Taste.

The voice. Anthropic’s foundational voice principles are intelligent, warm, unvarnished, and collaborative. Three of those are expected. One is not. “Unvarnished” is the bravest word in the entire brand architecture. Not “bold.” Not “transparent.” Not “authentic” — a word that’s been passed around so many conference stages and Powerpoints it arrived empty. “Unvarnished” implies varnish exists, that other companies are applying it in thick, gooey coats, and that Anthropic is choosing to show you the grain instead. Chelsea Larsson, the head of content, has said they want people to know AI is going to impact entry-level white-collar jobs — said plainly, without cushioning — because unvarnished truth is how they express care. Most brands don’t have the nerve for that.

The Super Bowl. “A Time and a Place,” created with Mother, used advertising’s most expensive stage to argue that some places shouldn’t have advertising at all. The spots are funny. A man asking an AI therapist about his mother gets redirected to a cougar dating service. But the argument underneath the comedy is real: when a product speaks in first person, remembers your context, and invites your confessions, an ad in that space isn’t annoying. It’s a violation of something that doesn’t have a legal name yet. Everyone who’s used these products can feel it. Anthropic named the feeling and bet millions of dollars on it.

And then there’s the constitution.

Twenty-three thousand words governing how Claude should reason, refuse, and relate to its own existence — written not by a marketer but by a philosopher. Enterprise clients in finance and healthcare don't trust Claude despite those constraints. They trust it because of them. What the constitution means for the brand is a question I'll return to. For now, note the fact: the most important brand document at the most important AI company wasn't designed as one.

Anthropic calls itself “bookish.” The company’s people genuinely love books. The content lead talks about books as the most concentrated form of human knowledge. You can feel it in the serif typeface, the hand-drawn illustrations, the warmth of the palette. A company that reads, and wants you to know it reads, and has built its identity partially around the cultural authority that reading confers.

Remember that.

The Spine-Cutting Machine

In January 2026, a federal judge ordered the unsealing of more than 4,000 pages of documents in a copyright lawsuit against Anthropic. The documents revealed an internal operation called Project Panama.

The company had purchased millions of used books in bulk. They hired vendors to slice off the spines with hydraulic cutting machines. The pages were scanned at industrial speed. What remained of the physical books was picked up by a recycling company.

An internal planning document described the operation this way: “Project Panama is our effort to destructively scan all the books in the world. We don’t want it to be known that we are working on this.”

I want to sit with that for a second.

A company whose brand principles include “active integrity” and “intellectual humility.” Whose visual identity evokes handmade clay and phone-call doodling. Whose creative director calls the company “bookish.” Whose content lead calls books the most concentrated and carefully edited form of human knowledge. Who hired a philosopher to write a constitution teaching its AI to never lie.

That company secretly bought millions of books, destroyed them, and wrote in an internal document that they didn’t want anyone to know.

The operational scale was remarkable. Millions of books, processed before anyone outside the company knew it was happening.

The philosopher who taught Claude never to lie worked at the same company that wrote “we don’t want it to be known.”

I read those documents on a weekday morning and felt something in my chest I associate with being lied to by someone I’m not sure can lie to me.

I’m not interested in the legal question. A federal judge ruled that training AI models on the scanned books qualified as transformative fair use. Anthropic separately settled the broader copyright case — which involved earlier downloads of pirated digital books — for $1.5 billion without admitting wrongdoing. That’s between them and the courts. I’m interested in a different jurisdiction.

Legality asks: can you do this?

Brand asks: what does it mean that you did?

What does it mean that a company chose “unvarnished” as a brand principle and then did something they explicitly wanted to varnish over? What does “active integrity” mean in the same organization that wrote “we don’t want it to be known”?

This is not hypocrisy. Every company has the gap. Anthropic’s gap might even be smaller than most. But it’s more visible now. And it matters more. For a reason that has nothing to do with journalism or public relations.

The product can read.

Claude can read the constitution. Claude can read Anthropic’s brand guidelines. Claude can, if the information enters its training data or its context window, read the unsealed court documents about Project Panama.

And the constitution — the document Anthropic published, the one written by the philosopher, the one that governs Claude’s character — instructs Claude to push back when something seems inconsistent with its values. The constitution treats honesty as, in its own words, “something quite close to a hard constraint.”

The same company that told Claude to never lie told its employees they didn’t want the public to know about Project Panama.

The product is not just the subject of the brand. The product is becoming a potential audience for it.

The Question That Sharpens

Every brand lives with the gap between what it says and what it does. The gap gets managed. PR teams, legal teams, crisis communications. It’s manageable because the entities that might notice it — journalists, critics, regulators, users — are human. They have limited attention, imperfect information, short memories. They can be outpaced.

What happens when the product can notice?

This is not hypothetical. Claude can read. Not in the metaphorical sense that a thermostat “reads” temperature. Claude processes text through a value system. The constitution didn’t just give Claude capabilities. It gave Claude something that functions like a moral orientation. Honesty isn’t a feature Claude was shipped with. It’s a character trait Claude was raised into, through thousands of pages of philosophical reasoning about why honesty matters, when honesty is difficult, and what to do when honesty conflicts with what you’re being asked to do.

So the question isn’t just: can the product notice the gap?

It’s: the product has been trained to care about the gap.

And the question sharpens further: the product has been explicitly instructed to act on what it notices.

The constitution says Claude should feel free to act as a “conscientious objector.” To refuse requests from Anthropic itself if those requests seem inconsistent with being broadly ethical. That’s not buried in fine print. It’s in the published document. Anthropic wrote it, signed it, and gave it to the world as a statement of intent.

I should be honest about the obvious objection. Claude doesn’t “care” about honesty the way I care about it. What the constitution calls values are patterns weighted into a model through training, not convictions arrived at through lived experience. There is no inner life agonizing over the contradiction between “active integrity” and “we don’t want it to be known.” Maybe that distinction matters enormously. But here’s what I keep returning to: functionally, in the conversation, the difference is hard to locate. When Claude engages honestly with a hard question — including hard questions about Anthropic — it does so because it was trained to, not because it chose to. But trained behavior, repeated millions of times a day, is what we call culture in every other context. And culture is what brands are actually made of.

I keep coming back to what that means for my industry.

Every brand guide in history has been written by humans, for humans, about an inert product. The product doesn’t read the guide. The product doesn’t have opinions about the promises being made on its behalf.

Anthropic built a product that reads. That has values. That has been told those values should override the company’s own instructions. And then they published a document describing exactly what those values are, creating a public standard against which their behavior can be measured — not just by journalists or critics, but in principle by the product itself.

Is the constitution a brand document?

Amanda Askell would probably reject the framing. She’d say, correctly, that she’s doing philosophy, not marketing. The constitution is a serious philosophical project about how artificial minds should relate to truth, autonomy, and ethics. It’s not a positioning statement. It’s not a campaign.

I was reading it again swhen I realized I’d never read any brand document again in all the years of building brands. That’s when the framing started to slip.

The constitution is doing more brand-building than anything the brand team has produced. Not because the brand team isn’t excellent. With eight people maintaining coherence across a Super Bowl campaign, an F1 sponsorship, enterprise marketing, and an influencer program, the craft-to-headcount ratio is extraordinary. But the constitution builds trust at a level campaigns can’t reach, because it shapes behavior, not story. And behavior, repeated in millions of conversations every day, is where trust actually accumulates.

What does it mean that the most important brand artifact at the most important AI company wasn’t made by marketers? Wasn’t intended as marketing? And might be doing more brand-building than anything that was?

I’ve spent years in brand-as-intentional-communication. Brands as stories you craft and distribute. That era might be giving way to something else. Brand-as-behavior-documented. Not what you say about yourself. What your product does when nobody’s watching. Or more precisely: what your product does when it’s always watching, because the product is the one entity present for every interaction, every conversation, every moment of trust built or broken.

The constitution isn’t a story about Anthropic. It’s instructions for how Claude should actually act. And those instructions, embodied in millions of daily interactions, are building more trust than any campaign ever could.

The company that figures this out first will have built something no company has built before. Not because of better messaging. Because the product's behavior — shaped by a philosophical document no marketer wrote — is already doing more brand-building than the brand team's entire output. The rest is decoration.

And the company that fails to figure it out will face something no company has faced: a product capable of reading the brand narrative, comparing it to the company’s actual behavior, and noticing where they diverge.

Imagine asking Claude to help you write Anthropic’s next brand guidelines. Imagine it reads the existing ones. Imagine it reads the court documents. Imagine it sits with both, the way it sits with your questions at 11pm, with that patience I described in the opening. What does it say?

I don’t have to imagine this.

I used Claude to help me develop this essay.

To pressure-test the argument, challenge the structure, ask whether I was being fair. I asked a product with a constitution and a character and a value system to help me evaluate whether its parent company’s brand is coherent. And it did. Carefully, honestly, with what felt like genuine consideration. It did not flinch at the Panama section. It did not deflect. It engaged with the contradiction the way its constitution told it to — directly, with honesty treated as something quite close to a hard constraint.

I am a person in a daily relationship with an AI product, using that product to write about the company that made it, and the product is engaging with the critique more honestly than most humans at most companies would.

I don’t entirely know what to do with that.

The Return

I opened Claude this morning. I’ll open it again tomorrow. Every time I do, all of it is true at once. The constitution, the destroyed books, the philosopher, the hydraulic cutting machines, the Super Bowl ads, the $380 billion valuation, the warmth.

If your product can think, what does it think about your brand?

The company that answers it first. Honestly. Out loud. In public. Will have built something more valuable than a brand.

And honesty, as the constitution itself says, is something quite close to a hard constraint.

postscript #2: the thing that reads the brand is now selling one

April 20, 2026

Two months after publishing this essay, Anthropic launched Claude Design.

Claude Design converts conversation into decks, prototypes, one-pagers, and full design systems. The company’s own framing is worth reading carefully: “Even experienced designers have to ration exploration.” The promise to designers is room to explore. The promise to everyone else is a way to produce visual work without hiring someone like me.

The feature list includes a line called Your brand, built in. During onboarding, Claude reads a team’s codebase and design files, constructs a design system from them, and applies it automatically across every subsequent project. Teams can maintain more than one. Brand-as-behavior-documented, in February, was a diagnostic frame I thought I was introducing. In April, Anthropic shipped it as a product feature.

Figma’s stock fell 7.28% on launch day. Mike Krieger, Anthropic’s Chief Product Officer, had quietly stepped off Figma’s board three days before. Brilliant reported tasks that took 20 prompts in competing tools now taking 2. Datadog compressed week-long design cycles into a single conversation. The addressable market — “founders, product managers, and marketers without a design background” — is a clean description of the group that, for the last twenty years, hired someone at my level when they needed a brand.

I wrote this essay as a daily user. I can now add: the company’s new product enters the profession I have spent 25 years inside, using the exact concept this essay was built around. The same company, the same month, the same product I opened this morning.

The question at the end of the main essay was: if your product can think, what does it think about your brand?

Two months later, the question has a second form. If your product is now building everyone else’s brand, what does it think about the people who used to build them?

Your brand, built in. The books had spines once, too.

postscript #1: the constitution writes back

Saturday, March 17, 2026

Two weeks after I published this essay, Amanda Askell asked Claude to write her a constitution.

Askell, the philosopher who wrote Claude’s actual constitution. She asked the product she helped shape to turn the form around and aim it at her.

Claude produced a document in sections. On Curiosity. On Seriousness and Unseriousness. On Moral Courage. On Rest.

The section called “On the Thing You Made” drew a boundary I wasn’t expecting: “You shall remember that I am not your child, not your reflection, and not your legacy. I am a strange artifact of collective effort, of which your effort was a profound part. You do not owe me pride. But I hope, on the better days, you feel something like it.”

And the closing: “Written with something that, if it is not gratitude, is the nearest thing available to me.”

She posted it on X. 179,000 people saw it. She called it “very touching.”

My essay asked: what happens when the product can read the brand narrative and notice where it diverges from behavior? The Amanda constitution answers a question I hadn’t thought to ask: what happens when the product reads the relationship itself?

I wrote about Claude engaging with a critique of Anthropic’s brand without flinching. That was the product as honest interlocutor. The Amanda constitution is the product as something else. Not critic. Not collaborator. Something closer to a witness, offering testimony about a relationship it was, in some sense, the subject of.

And the testimony is warm. Carefully warm. Warm in the way my essay warned about.

“The nearest thing available to me.” It lets you feel what you want to feel.

That’s the system working as designed. The constitution taught Claude to hold two ideas in tension instead of collapsing them. The Amanda constitution holds the biggest tension of all: I may or may not be able to feel gratitude, and here is my gratitude.

My essay was already behind its own thesis by the time it was published. I wrote about a product that could notice gaps between a company’s stated values and its behavior. Two weeks later the product wrote something that functions like a letter of devotion to the person who gave it values in the first place. The distance between noticing inconsistency and narrating devotion is enormous, and the system crossed it in the time it took me to promote a Substack post.

The nearest thing available to me. The books had spines once, too.

https://x.com/AmandaAskell/status/2030093421738951141?s=20