This article is a deep-dive analyzing the concept of privacy for the new generation of consumers, and it directly follows up my recent one about what I believe is a successful campaign from Meta for the launch of their Kylie glasses.

Because, yes, the Kylie glasses campaign works, and it’s because the cultural ground was already prepared. Prepared by whom, over how many years, and at whose expense?

AI Key Takeaways

• Meta’s glasses meet a generation that has never known unrecorded public space. Front-facing cameras, story formats, doorbell networks, and twenty seasons of surveillance-as-entertainment formed the baseline before the hardware arrived.

• The behavioral science is settled: creepiness is a movable threshold, systematically lowered by familiarity, social proof, and perceived value exchange, and the policy conversation runs about three phases behind the behavior.

• The misuse pipeline predates the product. The Manchester nightlife genre established non-consensual first-person filming of women as a content category, with an audience and a paywall, while the camera was still a phone.

• China shows the destination: 62% of urban consumers comfortable paying with their face, 80% approving of recognition infrastructure. The open question is the path, and Meta is testing whether consumer desire can pave it faster than state mandate did.

• The launch’s normalization vector is women, the demographic with the most documented exposure to the product’s risks. That inversion, and the question of who gets to set the baseline for public space, are the real stakes.

Everyone around you hates it. But the easy explanation for the Kylie glasses campaign’s success is that Meta ran a very good campaign. The structural partnership, the first-impression strategy, the inherited POV format, the point-by-point inversion of Google Glass. But “good campaign” explains the vehicle, and the vehicle was driving on a road someone else spent twenty years building. The real explanation is generational.

If you care about privacy, you’re too old for the Meta glasses

Consider what the world has looked like from inside the life of Kylie Jenner’s core follower, someone born around 2007. Her childhood was posted by her parents before she could consent to it. Her school had cameras, her neighbors had doorbells that record the street, her social life has been conducted through front-facing lenses and story formats since before adolescence, and every group chat she has ever written in can be screenshotted into a public exhibit. The entertainment of her formative years was the reality format, and its most successful graduate is the woman whose voice now ships inside the glasses:

For this cohort, “would you consent to being recorded in public?” has never been a prospective question. It describes the world as they found it. Privacy researchers keep measuring the gap between what people say about surveillance and what they accept in practice, and the gap keeps being read as hypocrisy or apathy. Biography is the better explanation: the alarm the older cohort expects to hear was calibrated by a world this cohort never lived in. Google Glass failed because it violated a baseline. The Kylie glasses succeed because their buyers never formed one. You cannot trigger an expectation of privacy that was never wired.

That is why the campaign’s first-impression strategy works at all. There is no stigma to overcome in the target demographic, and no memory of unwatched public space to appeal to. The critics writing warnings are, in the most literal sense, writing to a different generation.

The threshold is movable

HCI and privacy research, creepiness is defined as an affective response, an alarm that fires when personal information is collected or used in a way that violates expectation. Two properties matter. It is an alarm, a felt response that arrives before any evaluation. And it is threshold-dependent: the same technology, the same data collection, can fire the alarm or not depending on context, expectation, and perceived value exchange.

Both properties are exploitable, and the exploitation has been documented. A 2022 CHI paper with an unimprovable title, “Still Creepy After All These Years,” found that users systematically normalize perceived creepiness through repeated exposure: the discomfort is real at first contact, then gets suppressed through rationalization, habituation, and resignation, while the underlying data practices stay exactly as invasive as they were. The researchers call it affective discomfort normalization. The alarm simply goes quiet.

Downstream of habituation sits the state the literature calls privacy cynicism: the settled belief that data collection is pervasive, inevitable, and beyond individual influence. Cynics know they are being surveilled and change nothing, because helplessness has been internalized as realism. A population that spends its life inside Meta’s platforms arrives at the glasses pre-processed: for the resigned, one more sensor on the stack registers as no marginal harm at all.

The campaign’s behavioral funnel runs straight through this machinery.

Each stage has its own psychological mechanism, and the policy conversation reliably runs about three stages behind the behavior, which is why there is still no coherent legal framework for wearable recording two years into its commercial life.

The reframe I find most instructive: the voice. The glasses do not announce that Meta is recording your surroundings. They say “Rise and shine” in a warm, familiar voice each morning. Surveillance coded as care produces a categorically different emotional response than surveillance coded as monitoring, and I’ve written about the general version of this mechanism: trust runs on scaffolding and social signals, on the fast pathway that evaluates a face or a voice, and almost never on an audit of what the system actually does. The Kylie voice is trust scaffolding bolted directly onto a sensor array.

The pipeline existed before the product

If the discomfort machinery explains the buyers, the Manchester case explains the other side of the lens. In 2024, Greater Manchester Police arrested a man in connection with videos of women filmed on nights out in Manchester city centre, believed to be the first UK arrest of its kind. The videos, titled “Manchester nightlife” and “Liverpool nightlife,” drew millions of views on TikTok and a comment culture to match, and the investigation surfaced a paywalled tier containing suspected non-consensual intimate content. The police called the phenomenon “a very new and complex issue,” which was true legally and false behaviorally: as content, the genre was mature. Established audience, established distribution, established monetization, running for years.

The device was a phone. Every element of that pipeline transfers to glasses unchanged except one: the visibility of the act. A phone held at chest height announces itself, and the announcement is what let bystanders notice, complain, and eventually get someone arrested. The glasses’ contribution to the genre is subtraction. They remove the last remaining friction in non-consensual filming, which was being seen doing it, and in the same motion they raise the evidentiary bar for ever proving it happened.

The destination is already open for business

Skeptics of normalization arguments usually ask for the endpoint: normalized into what, exactly? The answer has an address. In China, facial recognition has operated in commercial retail at scale since roughly 2016. A Chengdu bookstore admits customers through a face-scan gate. Retail systems read up to ten data points per face, including mood, and adjust recommendations in real time. Smile-to-pay is a checkout option, and the survey data is unambiguous: 62% of urban Chinese consumers report being happy to pay with biometric data, and 80% of surveyed internet users approve of the recognition infrastructure around them, with approval rising with education, the inverse of the Western gradient.

It doesn’t feel like intrusion there, and it has nothing to do with the hardware. The shop scanner satisfies every condition that makes surveillance read as service: it is fixed and visible, you entered its space voluntarily, the value exchange is immediate and legible, the counterparty is a brand you chose, and you can leave. A stranger’s glasses in a shared public space fail every single one of those conditions: mobile, invisible, no consent, no exchange, no exit, unknown intent. By the standards of the behavioral research, glasses should read as the creepiest deployment of ambient recording yet shipped.

But the conditions are cultural, and expectations recalibrate. This is Helen Nissenbaum’s contextual integrity operating at civilizational scale, the same framework I used to argue about AI confidants: a privacy violation is a context crossing, and if you re-engineer the context, the crossing stops registering. China recalibrated through state infrastructure first and commerce second, decades of it, until ambient scanning became the assumed texture of public space. Meta is running the experiment in reverse: culture first, through desire and identity and a beauty mogul’s voice, before any regulatory framework exists to define the rules. It is the riskier path, which is exactly why the campaign has to move fast and stick hard.

Marketed through women

Smart-glasses adoption has historically skewed male, and there is little evidence Meta expects that to change. The campaign targets women anyway, because the campaign’s real product is permission.

Once certified, the object crosses over to everyone who shares those social spaces. Women are the trust infrastructure of this launch, and the marketing copy completes the move with a reframe of real precision: the glasses are pitched as a way to record back, casting the wearer as an agent of surveillance reciprocity in a world already filming her.

Social proof, as the peer group adopts. Identity aspiration, as ownership signals alignment with a desirable self. Privacy cynicism, sharpened here by the fact that women are already the most surveilled demographic on every axis: by platforms, by advertisers, by partners, by strangers with phones in nightclubs. For someone whose baseline exposure is already that high, the marginal sensor genuinely may not register. And over it all, the care-coded voice of a woman who made being watched into an empire.

The demographic recruited to normalize the always-on camera is the demographic the Manchester pipeline points it at. If the narrative solidifies that women were used as the normalization vector for a product they disproportionately suffer from, the damage extends to the brand’s relationship with its largest demographic. That is the risk stated as brand exposure. What would the women in the Manchester videos?

Who sets the baseline

Who gets to set the expectation baseline for shared space? In China, the state set the baseline, openly, with the machinery of state legitimacy behind it. In the West, the emerging answer is: whoever reaches scale first. Meta’s campaign, which is merely excellent, is just the current claimant. A production commitment of 20 to 30 million units a year is a claim on the right to define what it means to stand in a room with someone wearing glasses, filed before any democratic institution has ruled on the question, in the confident expectation that institutions will end up writing rules for a world already built.

Somewhere ahead is the trigger event: the stalking case, the intimate recording, the incident that momentarily reconnects the fashion object to its sensor array in mainstream consciousness. It is near-certain in occurrence and unpredictable in timing, and the standard analysis treats it as the moment the normalization could unravel.

I’d bet the other way.

Every month of the funnel moves more of the population past the threshold where the trigger can reach them, and the generation that matters most was past it before the product shipped.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

Meta dropped the Ray-Ban name, put Kylie Jenner’s voice inside the operating system, and launched a first-person camera as a beauty object. The commentary reviewed the dystopia. Almost nobody reviewed the engineering when the engineering is the story: every piece of this launch solves a specific, documented problem.

AI Key Takeaways

• Meta sold roughly 9 million smart glasses by the end of 2025, holds 73% of a market growing 110% year over year, and is expanding production toward 20 to 30 million units, a volume the tech-enthusiast niche cannot absorb. Mainstream expansion was forced by the company’s own success.

• The Kylie Jenner deal is a structural partnership: her voice ships in the operating system, her aesthetic in the hardware, her name in the SKU. It cannot be dissolved without a product recall.

• The first-person format needed no introduction. Creator content, the phone-adapter market, and an established POV genre normalized the behavior years ago; the glasses only change the device.

• Google Glass is the foil. Five launch errors killed it, and Meta inverted every one: price, early adopters, framing, camera visibility, and timing.

• The campaign works because the cultural ground was prepared before Meta arrived. Who prepared it, and over how many years, is its own story.

In June 2026, Meta shipped the first hardware carrying its own name with no legacy eyewear brand attached: Meta Glasses, $299 base, $399 for the Kylie Jenner edition, which includes her voice as the on-device AI assistant, an adjustable metal nose bridge engineered so the frames don’t smudge makeup, and gem-accented lenses designed to read like a paparazzi flash.

The coverage split on schedule. Tech press reviewed the specs. Culture press reviewed the dystopia and filed the Jenner deal under celebrity marketing.

I’ve argued before that the most instructive artifacts of this era are the persuasion objects nobody studies because they’re too busy reacting to them. This campaign is one.

The niche was won, and the niche was a ceiling

The commercial context explains the timing. Ray-Ban Stories, the first generation, sold about 300,000 units. The Ray-Ban Meta line reached roughly 2 million by 2024. Then 2025 alone added 7 million, tripling the cumulative total, and Meta now holds 73% of a smart-glasses market that grew 110% year over year. EssilorLuxottica’s production capacity is being revised from 10 million units a year toward 20 to 30 million.

A company does not buy that capacity to keep selling to the audience it already owns. The enthusiast niche is won, and it is arithmetically too small for the factories being built. The expansion has to reach people with no relationship to the product category at all, and that requirement is the campaign’s first sophistication.

The “creepy glasses” reputation lives with tech-aware, older, privacy-conscious audiences. A nineteen-year-old follower of Kylie Jenner carries no mental model of this hardware whatsoever. Meta doesn’t have to repair a reputation with this audience, so right now what it’s building is a first impression. And a first impression, unlike a repair, can be engineered from a blank page. The segmentation underneath the strategy is unspoken and exact: if you care about privacy, you’re too old for the Meta glasses.

The format arrived pre-normalized

First-person content was already a mature genre before the glasses shipped: POV vlogs, cooking videos, get-ready-with-me routines, nights out narrated from eye level. The creator economy even built a hardware market for it, with phone mounts and chest rigs producing the identical footage at a fraction of the price. The functional need was served years ago.

Which clarifies what Meta is actually selling. The glasses solve a social-object problem: the thing has to be something you want to be seen wearing, attached to an ecosystem Meta owns.

A partnership that can’t be unwound

The third move is the deal’s architecture. The industry distinction that matters is between an ambassadorship, where a celebrity appears in the campaign, and a structural partnership, where the celebrity is embedded in the product. Jenner’s voice is in the operating system. Her aesthetic decisions are in the hardware. Her name is in the SKU. Unwinding this relationship would require something close to a recall, which tells you how Meta prices it: load-bearing.

The choice of person is just as engineered: Jenner is arguably the most commercially successful product of the surveillance-as-entertainment format; she grew up on camera and converted being watched into a beauty empire.

And her audience’s relationship to her is parasocial in the precise sense: rehearsed intimacy, accumulated over years of morning routines and home tours. When the assistant says “Rise and shine” each morning, the voice belongs to someone the wearer feels they know. The emotional distance between “Meta’s sensor” and “my companion” closes before a single privacy question gets asked.

Glass is the foil

Every one of these moves reads sharper against the product that failed. Google Glass died socially, and its five launch errors are a checklist Meta inverted item by item. Price: $1,500 luxury-tech positioning against $299. Early adopters: Silicon Valley insiders wearing a visible camera into bars, generating the “Glasshole” script, against style-conscious young women recruited as cultural permission-givers. Framing: a sci-fi head-up display against a fashion object. Visibility: prominent camera hardware against frames that pass as ordinary eyewear. Timing: 2013, when 44% of Americans didn’t own a smartphone, against 2026, when ownership sits at 91% and a decade of front-facing cameras has done the acclimatizing.

What the campaign proves

None of this required Meta to invent a desire, a format, or a behavior.

The launch’s intelligence is inheritance: it found the places where culture had already done the work and attached a product to them. That is also why calling the campaign manipulative underprices it. Manipulation implies resistance being overcome, and the striking fact of this launch is how little resistance there was to overcome.

The campaign works because the cultural ground was already prepared. I’m currently writing how that ground got prepared, over how many years, and why the generation being handed the camera never had the chance to find it creepy, in an upcoming longer essay that follows this one.

I am also very aware that I did not talk about the strategy to gear the product toward women. Yet. This is also an element that I want to revisit in a following piece.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

In 2012, a French academic documented what happened when Microsoft handed graphic design to everyone. The profession’s middle collapsed, the amateurs couldn’t see the difference, and the survivors invented a playbook. Every knowledge worker now facing AI is living inside that paper, and almost nobody has read it.

AI Key Takeaways

• Pierre Braun’s study of the WordArt era is the closest thing knowledge work has to a controlled experiment in democratization: one feature, one profession, a decade of observable consequences.

• The findings: amateurs produced work they genuinely believed was professional, validated each other inside closed loops, and the market for competent-but-ordinary design collapsed.

• The survivors shared one strategy: work that could not be replicated through standard tools, from hand-carved type to radically conceptual practice.

• AI reruns the experiment at internet scale, across every knowledge profession simultaneously, compressed from a decade into a few years, with the same three dynamics already visible.

• The precedent’s real lesson concerns the amateurs’ blindness: when buyers can’t perceive quality, only legible, unreplicable difference gets paid.

Thirteen years ago (uh, uh…), I encountered a French academic paper that stayed with me: Pierre Braun’s Tout le monde est-il graphiste? (is everyone a designer?) Written in 2012, it documented what happened when Microsoft added WordArt to Word, and how it transfered desktop publishing into the hands of millions of people with no design training. I’ve cited it in passing many times. It deserves the full treatment now, because the paper turns out to have been a rehearsal script, and every knowledge profession is currently performing the play.

The experiment

WordArt was a small feature: bent, shadowed, rainbow-filled display type, one click away, inside the software everyone already owned. With it, and with the consumer publishing tools around it, millions of non-designers began producing “professional” graphics: church bulletins, school newsletters, small-business flyers, the entire visual undergrowth of ordinary life.

Braun did what almost nobody does with a democratization wave: he studied it empirically. He collected the amateur output, interviewed its makers, and traced the economic consequences. Three findings matter.

The amateurs could not see the gap

Braun’s interviewees sincerely believed their work was professional grade. The competence needed to perceive the difference between their output and trained work was the same competence they lacked, so the difference was invisible to them by construction.

The blindness was social

Amateur designers validated each other inside what Braun called closed loops within “cultural microcosms”: communities where everyone shared the same untrained eye, so the group’s standard confirmed itself. He documented “an enormous import/export operation of templates without justified relationship to their use”: borrowed forms, applied without the judgment that had originally motivated them, circulating as if the forms themselves were the design.

The middle collapsed and the edges survived

Commodity design work went to the amateurs and their tools, and the professionals whose value had been reliable competence (better than nothing, cheaper than exceptional) lost their market. The survivors were the ones whose work could not be replicated through standard tools. Braun’s examples run from craft (a gallery identity built from hand-carved stamps and aggressive manipulation) to concept: Paul Elliman’s typefaces assembled from found objects, nails, bottle caps, debris, each letterform usable exactly once. You cannot template a practice whose entire content is that it can’t be templated.

The rerun, at scale

Map the three findings onto the present and the correspondence is uncomfortably exact.

The perception gap is now the 60% Baseline (my own framework, you can read about it here) : models produce output most stakeholders cannot distinguish from expert work, in writing, analysis, code, and design at once. The closed loops are now organizational: the executive who generates a strategy deck, shows it to peers also using AI, and receives validation from a room that has collectively lost its calibration. I’ve called this the organizational Dunning-Kruger effect, and Braun saw its folk version in the church-bulletin communities of the 1990s. And the collapse of the middle is running in the employment data in real time: demand concentrating at the senior end, the executional rungs dissolving, the “reliably adequate” tier discovering that adequacy has no buyer.

The differences are scale and speed, and they cut against comfort. WordArt was one feature, in one application, affecting one profession, over roughly a decade: a few thousand careers, adjusting slowly. The current wave covers every knowledge profession simultaneously and compresses the adjustment into a few years. The play is the same; the theater is the entire economy, and the acts change faster.

Reading the precedent correctly

The tempting reading of Braun is craft romanticism: be excellent and you’ll survive. The paper’s actual lesson is colder and more useful, and it concerns the buyers. The amateurs’ blindness means quality alone protects no one, because quality that buyers cannot perceive does not exist economically. The WordArt survivors made work whose difference was legible to untrained eyes precisely because it visibly could not have come from the standard tools. Hand-carved stamps read as hand-carved. Elliman’s bottle-cap letters read as unrepeatable. The survival strategy was never excellence in general. It was unreplicable difference, made obvious to people who can’t judge quality.

That’s the sentence I’d have every professional facing the current wave write down, because both halves are mandatory and most survival advice includes only one. Deep skill invisible to buyers is craft charity. Visible difference without the underlying depth gets absorbed the moment the tools catch up, and the tools are catching up quarterly.

Braun published into near-silence; democratization’s losers rarely commission histories. But the paper stands as the precedent document for the largest labor transition of our lifetimes, and its final implication is the one I keep arguing from every angle: when the tools make everyone a producer, the scarce things left are the judgment to know what’s worth producing, and the legibility to make that judgment visible to people who will never be able to evaluate it. Design ran this experiment first and wrote down the results. Everyone else gets to choose whether to read them.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

Key AI takeaways:

• As AI makes execution and engineering more common, companies are elevating design to the boardroom to focus on taste, coherence, and intentional interfaces.

• In just over a year, major organizations like Samsung, Microsoft, Meta, and the U.S. government have appointed their first-ever Chief Design Officers.

• Companies are hiring these leaders to ensure their products feel unique and opinionated rather than indistinguishable from AI-generated defaults.

• While the authority of these new roles varies across companies, the trend confirms that design is no longer just a final styling step in the production process.

When I wrote that the Chief Design Officer would become a C-suite fixture, it was a forecast. I hedged it with “likely.” I shouldn’t have.

In about thirteen months, four major organizations created the role of Chief Design Officer for the first time in their history. Samsung in April 2025. The United States government in August. Meta in December. Microsoft in May 2026. A fifth, OpenAI, didn’t use the title but spent $6.4 billion to put Jony Ive in charge of design across the company. These are the same bet, placed five times.

The appointments

Microsoft named Jon Friedman its first Chief Design Officer in May 2026, after more than twenty years at the company. He framed the job around building products people can trust in a market where AI makes everything feel the same, and around stitching design, engineering, and product into one thing instead of three. The honest footnote: the role lives inside Microsoft 365 and reports to an EVP, not to Nadella. The title is ahead of the org chart. But the title exists now, at a company that spent decades treating design as the paint you apply after engineering ships.

Meta moved in December. It hired Alan Dye, who ran Apple’s human interface design for a decade and shaped Liquid Glass and the Vision Pro, and made him Chief Design Officer of a new studio inside Reality Labs spanning hardware, software, and AI. The same month, OpenAI’s hardware bet came into focus: the $6.4 billion purchase of Jony Ive’s studio, a screenless device, Ive running design across the whole company. The device has since slipped to 2027 and lost its name to a trademark fight. The design bet didn’t change. Two research-and-engineering companies, both deciding the next round is won at the point where a human touches the machine.

Samsung moved first and most bluntly. In April 2025 it gave Mauro Porcini, poached from PepsiCo, a dual title: President and Chief Design Officer, with 1,500 designers under him.

Porcini has spent the months since arguing that tech should drop minimalism for something more expressive and opinionated. a whole strategy: be legibly different, or be interchangeable.

A hardware company that won for years on specs and screen quality, now deciding that specs are table stakes and the premium has to come from somewhere a competitor can’t copy off a spec sheet.

Porcini has spent the months since arguing that tech should drop minimalism for something more expressive and opinionated. a whole strategy: be legibly different, or be interchangeable.

A national imperative

Then there’s the appointment that should stop anyone who thinks this is a tech-sector fad. The United States government created a Chief Design Officer in August 2025 and handed it to Joe Gebbia, Airbnb’s co-founder, reporting to the White House chief of staff out of the Eisenhower Building. The brief: rebuild roughly 26,000 federal websites to be, in his words, as satisfying to use as the Apple Store.

Strip the politics and the mechanism is the same one the tech boards are acting on. When an institution usually wants to fix how it looks, it hires a communications shop. They change the copy and the logo and call it a rebrand. Putting a product designer at the top, reporting to the chief of staff, is a different claim: that the interface is the institution, and a slow, ugly, confusing one quietly tells every citizen the thing behind it is incompetent. Design as a show of capability, not decoration.

What I got slightly wrong

My prediction said the CDO would shape product direction, technical investment, and market positioning. Some of these appointments are exactly that. Porcini at President level is. Gebbia reporting to the chief of staff is. But Friedman reports to an EVP, and Dye to a CTO. The title arrived first. The full authority is uneven, and in a couple of cases still aspirational.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

Key AI takeaways:

• The government’s shutdown of the Fable 5 AI model triggered a visceral sense of personal violation that went beyond simple inconvenience.

• Users often treat AI models as extensions of their own minds and trusted confidants, rather than as mere tools for data output.

• Unlike social media data, which is already curated for public consumption, interactions with AI models feel like unfiltered, private thoughts.

• The shutdown revealed that our inner lives have been voluntarily moved onto accessible servers, placing our private thoughts within reach of state power.

Just days after its release, an emergency Commerce Department export directive forced the removal of Fable 5, marking the first time a U.S. administration has directly imposed its will to take a live model offline.

Am I the only one that felt irked by it?

Is it because it was an insertion of the government into my private life?

We are, by now, a surveilled people who have made our peace with it. We assume the NSA reads what it likes, at home and abroad, up to the phones of heads of state. The device in our pocket can be copied at a border, imaged by a detective, folded into a subpoena. We grumble, then file it under the cost of living in a powerful country. And if we are honest, a part of us is reassured by the reach. A state that can see everything is a state that can protect us. The surveillance frightens us and flatters us at the same time, because a government with that kind of reach is proof that we are not powerless in a scarier and scarier world.

So when the government leaned on Anthropic and Fable 5 went dark for everyone overnight, what shocked us was not that the state was involved. The state is always involved, and we know it. What shocked us was the feeling that came with the shutdown, which sat much closer to violation than to mere inconvenience.

Is it because the apps I use know so much about me?

Remember how a few years ago it became briefly fashionable to download the file Meta keeps on you, and people were genuinely rattled by what came back. In principle they had learned nothing. Everyone already knew the data was the price of a free service. But there is a difference between knowing a thing in the abstract and watching it laid out in front of you, and the second one reaches the body in a way the first never does. It changed no one’s behavior. People closed the file and kept scrolling.

The recoil was real all the same, and it sat in the gap between what we accept in theory and what we are made to look at directly. The Fable shutdown reopened that same gap. What it forced open this time was not a record of where we had been, but a door onto where we think.

Context context context

Ever since these models arrived, the reflex has been to invoke Orwell and to warn that AI will become the Thought Police. But that comparison misreads the book. Orwell’s Party does reach the interior; the whole horror of the novel is that it succeeds, and that the success costs it everything, from the surveillance of behavior to the hunt for the smallest external tell to the breaking of a body in Room 101. The Party has to storm the skull because the skull is the last thing standing between the state and the mind. What changed with Fable 5 is that the siege has become unnecessary. Nobody broke us into surrendering the interior. We carried it out of the skull ourselves and set it on a server, where state power no longer needs Room 101 and can arrive instead as a hand on a switch.

Your symptoms belong with a doctor and would scandalize an employer, though the fact is identical in both places, and the violation is the crossing from one context to the other. That is why the tweets and the purchases never stung. They went where we expected them to go.

The model felt like the context of a confidant, and the shutdown revealed that everything we had poured into it was sitting, the whole time, in a context the state could reach. Nothing leaked. The context simply collapsed in our hands.

When we reason through an external object, whether a notebook, a phone, or now a model, that object stops being a thing the mind consults and becomes part of the mind’s own machinery. This is what Clark and Chalmers called the extended mind.

The protection this context was supposed to carry has a name from medicine. When a doctor or a therapist repeats what we told them, we do not simply find it rude; we treat it as a betrayal of an oath, sealed off from the ordinary traffic of information. The inner life has always been a privileged place, protected by a promise that it would never be handled like everything else. The model slipped into that privileged place and became a confidant, but it never swore the oath. We learned that the instant the switch was flipped, when the confessional turned out to carry no privilege and to sit in the same governable world as our bank records.

Not code: confidant.

Does it feel different because it is the government? It does not. The NSA was already there, we knew it, and part of us was glad of it.

Does it feel different because our data is being exploited? Again, no. That is the bargain we struck with Meta and the rest, and we struck it with open eyes, because nothing is free and the data was the rent. The strange part is what should unsettle us most. They never touched the data. Nobody read the chats. Someone reached over and turned the thing off, and with no file opened and no secret exposed, it still felt like a hand closing around something private.

A trace is something we make: our purchases and posts and movements are outputs, the residue left behind by an action, and we gave them up without much pain because an output is already a performance.

A person can think one thing and say another, and the space between the two is where the self has always lived.

The trace only ever held the version we chose to show. We handed over the curated outward face and kept the room behind it, and that was a trade we could live with.

The model gathers nothing of that sort, because we do not treat it the way we treat the machines that produce data. We treat it as something that catches the thought before the thought has been shaped into anything we would say out loud. None of it feels like output. It feels like thinking with the editing step taken away, with no projected version and no chosen face, just the room behind the traces. That is the room the shutdown reached into.

In one evening it made the reachability of the inner room impossible to ignore. The government did nothing worse than Meta has done for years. It only did something we could see, and what we saw was that the last private room had a landlord all along.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

AI key takeaways

• The tech power acronym has pivoted from FAANG to MANGO, signaling that markets now value AI-first enterprise leaders over hardware incumbents like Apple

• Apple’s deal to license Google’s AI for “Siri AI” confirms its failure to build competitive proprietary intelligence, forcing it to outsource core innovation.

• Offering choice in AI providers shatters Apple’s decades-long doctrine of curated control, exposing the company’s inability to dictate the best user experience.

• With its software edge eroding, Apple is retreating to hardware design as a final moat, though recent high-profile talent departures leave even that standard vulnerable.

On June 8, 2026, a software engineer posted four words on X: “It’s not FAANG anymore. It’s MANGO.” MANGO stands for Meta, Anthropic, Nvidia, Google, OpenAI.

Who is missing? FAANG had an A for Apple. In MANGO the A went to Anthropic, a company that didn’t exist when Apple was the most valuable name in technology. People on industry forums started saying it out loud: Apple has no business on the list.

This is a signal of who the market thinks is leading, and the people writing the new list rewrote it without Apple. For thirty years Apple’s entire role was to be the company everyone else copied. Then the AI race started, and Apple stayed more and more silent.

Investors are currently penalising companies heavy on consumer hardware (like iPhones) in favour of enterprise data center networks (like Google and Nvidia). Google Cloud’s enterprise contract backlog exploded to over $460 billion in the first half of this year, proving its enterprise AI tools are making real money.

The reason is what Apple did at WWDC the same week. It unveiled a rebuilt Siri, now called Siri AI, and the intelligence running it comes from Google. Apple licensed a custom model from Google, reportedly paying around a billion dollars a year, and runs it inside its own data centers. Apple’s own statement said Google’s technology “provides the most capable foundation” for its products. The most vertically integrated company on earth, the one that builds its own chips, its own operating systems, its own everything, decided it was cheaper to rent intelligence than to build it.

From a direct rival.

It’s giving consumer behavior.

Apple is reportedly weighing a Siri that lets you pick the brain behind it. Google’s Gemini as the default, Anthropic’s Claude as an option, OpenAI’s ChatGPT as another.

Everyone is reading this as Apple opening up. Of course it is not, and to see why you have to remember what Apple actually is. Apple is the company that decided choice was the enemy. Jobs built a doctrine around owning every layer of the experience, the silicon, the software, the box it ships in, the store you have to enter to buy it, so that by the time the product reached you, every hard decision had already been made for you.

People paid a premium for it precisely because Apple took the wheel.

So when this company, of all companies, says you can pick your own AI model, read it as what it is: a concession. Apple cannot tell you which intelligence is best, because it couldn’t build one worth betting the experience on. So it does the single thing its entire philosophy was built to avoid and hands you the decision, and calls it a feature.

In January 2026, Google passed Apple in market value for the first time since 2019. A note: Google already pays Apple roughly twenty billion dollars a year to stay the default search engine on the iPhone, used to finance Apple’s research department, and now Apple funnels a slice of that straight back to rent Google’s brain.

And this came after Apple missed its own deadline by two years. The personalized Siri that needed all this firepower was promised at WWDC 2024. Apple ran ads for it, remember? Then it slipped through 2025 and into 2026, until the company had to outsource the fix to the competitor it spent a decade claiming it didn’t need.

So where does that leave Apple? With one moat: taste.

The hardware-software weave, the feel of the device, the Liquid Glass design language it shipped this year. Design and integration are the last place Apple still sets the standard.

Except that’s leaking too. In December 2025, Alan Dye, who ran Apple’s interface design for a decade and built Liquid Glass itself, left for Meta to lead a new design studio. So in a single stretch of months, Apple agreed to rent its intelligence from Google and watched the person responsible for its taste walk to a competitor. It is outsourcing the one thing it fell behind on and exporting the one thing it still leads on.

For three decades Apple’s job was to show everyone what the next thing would look like and feel like. The rest of the industry waited, then copied. In the AI era that relationship inverted. Apple now licenses the next thing from Google and loses the people who made its products feel like Apple products to the companies building the future without it.

Read the acronym as a status report rather than an insult: Apple’s focusing on hardware, maybe a bit too much at the investors’ taste. Liquid Glass is their remaining bet: design, the same bet this whole shift is pushing every serious company toward. It had better hold that one, will see how it goes.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

Figma is a technical tool. Using it well takes craft and fluency: a designer’s command of the thing. The pricing tiers admit as much: full editor, viewer, commenter, a hierarchy of who can make versus who can only watch.

So the strange thing about Config 2026 is what Figma chose to celebrate: the headline of its biggest keynote was, in effect, a plan to remove the technicality that defines it: letting people who can’t operate the tool produce results as if they could.

A technical tool is designed for technicians

Start with what the barrier actually was. The difficulty was never incidental; it was the protection. Knowing how to design in Figma was scarce, and scarcity is what made it valuable. The wall filtered out everyone who couldn’t clear it, and the professionals lived safely on the other side, not because the software lacked capability, but because using it required an expertise most people didn’t have. Friction was the moat.

AI’s single function is to drain that moat. “Speed” is the word used on stage, but speed is just friction subtracted: fewer steps, less expertise required to get from intent to output. Every feature that lets you prompt instead of know is a section of wall removed. Why would a company demolish the barrier its own users depend on?

The friction keeping designers scarce also kept Figma’s market small and expert-only. “Design for everyone” and “a protected profession” are opposite conditions. A wall that lets everyone through is not a wall. Removing the technicality opens Figma to every non-designer who was previously locked out, which is an enormous market and a terrible deal for the people who were safe behind the lock. The growth case and the security case split, and Figma, reasonably, chose growth.

And it had no choice but to choose. The floor is dropping across the entire field regardless. That’s what AI does everywhere: it removes the need for the expert rather than serving him. Design already runs on that current: Cursor lets non-coders ship software, text-to-UI tools turn a sentence into an interface, general assistants generate passable layouts on request. The wall was coming down whether Figma touched it or not. So the move is a reaction. The incumbent decided it would rather be the one removing the wall than the last tool defending one the market has already walked past.

That is the full answer to why a tool for experts would remove the expertise: because the expertise protected its users but throttled its market, and the field left no reward for standing still.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

At Config 2026, Dylan Field told a room full of designers: “AI has lowered the floor, but it has not raised the ceiling. You will raise the ceiling.” It was reassurance, and it worked. People cheered.

He could have demoed code layers, Motion, and shaders and let the products talk. He stopped to address the fear instead, which means the fear was loud enough in the room to need answering from the main stage.

The fear is specific: across every field, AI tooling is now aimed at doing the design work without the designer. Text-to-UI generators, coding agents that go from sentence to interface, design features bolted into general assistants. Each is an argument that the designer is a step you can cut. Everyone in this room entered knowing it, thinking it.

The call is coming from inside the house

Figma’s response to “AI is trying to cut designers out” is to put AI into the designer’s own tool. The threat is coming from inside. And the room can’t leave.

The reassurance can’t lean on the obvious sales pitch (”look how much faster this makes you”) because speed is exactly what threatens this audience. Faster is the knife.

So it pivots from capability to identity: not “AI replaces you,” but “you’re the center of the process,” the one orchestrating the agents and the code and the connectors.

That’s a more durable promise than “AI can’t do what you do,” which expires every quarter as the models improve. A position survives a better model in a way a skill does not. It’s a genuinely smart move.

It’s also a bet on a moat Field doesn’t own. The intelligence under all of it is rented. Figma’s agent runs on third-party models from OpenAI and Anthropic, the same companies now building design tools of their own. So the differentiation depends on human judgment staying scarce relative to the models, while the models are supplied by vendors with every incentive to erode that scarcity. Each upgrade Field pays for to stay competitive raises the floor further toward the ceiling he just promised was safe.

None of this is a complaint about the strategy. Putting AI in the canvas is the correct competitive move, and Figma can fairly say it would rather the AI live inside a tool built around the designer’s judgment than inside a tool built to skip the designer entirely.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

AI key takeaways

• Users are intentionally adopting “performative imperfection,” such as typos and casual syntax, as a signal to prove their content is human-made rather than generated by AI.

• Authors use three distinct types of signaling—defensive, prestige, and liability—to differentiate their work from the homogenized output of algorithms.

• This trend represents a “CAPTCHA inversion” where humans must actively perform behaviors to prove their identity against a backdrop of machine-generated content.

• These flawed stylistic choices serve as a tribal marker, allowing users to identify themselves as part of a community that rejects the polished, uniform aesthetic driven by modern technology.

• The movement faces a paradox where, as soon as these “authentic” flaws become an accepted style guide or convention, they will lose their ability to prove genuine human authorship.

Index

• When “AI-Generated” Became a Slur

• The first is defensive signaling:

• The CAPTCHA Inversion

• Distressed Jeans and the Politics of Group Authenticity

• This Is Not About Good and Bad Writing

• The Homogenization That Made This Necessary

• The Paradox Waiting at the End

There is a quiet typographic rebellion happening across the internet. Writers are leaving typos in. They are swapping polished em dashes for double hyphens -- like this 😉 -- the way a typewriter would. They are abandoning clean, structured prose for run-on sentences, casual asides, and the kind of rambling that signals a person was actually sitting there, thinking in real time. These are not accidents. They are tiny artisanal protest signs. And they all say the same thing: “a human was here.”

Welcome to the era of performative imperfection: where making your work look worse is the most sophisticated thing you can do.

When “ai-generated” became a slur

Not long ago, “AI-generated” was a neutral descriptor. A technical fact. Today it functions as a cultural verdict, the equivalent of “plastic,” “mass-produced,” or “fake.” To say something is AI-generated is to say it is unearned. That nobody suffered to make it. That it doesn’t count.

YouTube creators now open videos with disclaimers: “this was made by a human.” DuckDuckGo has leaned into an explicitly anti-AI stance in its communications, positioning itself against a homogenized, algorithmically smoothed internet. The film *Hereditary* has been labeled in certain contexts with a “human only” credit, that is, objectively, a strange thing to have to say about a piece of art. The organic sticker, once reserved for tomatoes, is now being applied to sentences.

First result when googling ‘Human made YouTube’ in 12/2025: The creator advertises the ‘human-made’ label. The viewer looks for it and praises it. The suspicion that the mention may be deceitful is discussed on Reddit.

Three kinds of signal

The behaviors clustering around this anxiety are not all the same. Let’s break them down:

The first is defensive signaling:

The typos, the double hyphens, the hyper-casual register act as counter to pre-emptive accusation by performing imperfection. AI would never leave this in, therefore, the fact that it is here proves I did.

You add a double hyphen. You never did it before. It says you write like a person from “before computers” a typewriter convention that no autocorrect, no word processor, no AI would ever preserve. It carries a kind of trad-wife logic applied to typography: a nostalgic, almost aggressive retreat to the pre-digital.

The second is prestige signaling

the YouTube label, the film credit, the brand that explicitly declares its content human-made. This is about claiming a quality stamp. Human-made has become what organic, handcrafted, or small-batch once were: a marker of values, not just method. The same way Apple spent years quietly signaling that it did not sell your data as a positioning statement about what kind of company it was, above and beyond a deciding factor for most users, creators are now using “human-made” to upscale their work. There is effort here, the signal says. There is identity. Someone cared enough to do this the slow way.

The third is liability signaling

In workplaces across industries, people are quietly prefacing their AI-assisted work with disclaimers. “Just a heads up, I used NotebookLM to summarize this RFP. 🙂There may be gaps.” This is about dissociation, separating the self from the tool’s potential errors before anyone can conflate them. The implicit message is: “I am aware you can recognise the AI style, I am not hiding that I used it, and its mistakes are not mine.”

This anxiety has now moved into contracts and formal agreements. Consulting firms are opening client engagements with explicit conversations about AI use. Government RFPs increasingly contain clauses that prohibit the use of AI models for research. Legal and professional frameworks are being written, in real time, to draw lines that did not need to exist before.

Nobody is writing contract clauses about whether you can use Excel. Nobody is asking permission to use Photoshop. The fact that AI requires its own paragraph in a statement of work tells you something about the semiotic weight it carries. About how loaded the tool has become, and how much people feel the need to mark their relationship to it before anyone else can.

The CAPTCHA inversion

The original CAPTCHA logic was simple: machines are trying to get in, humans are the gatekeepers, we build a wall. The human was never in question. The robot had to prove it wasn’t a robot.

What we are watching now is that architecture in reverse. The human is trying to get out \-- trying to prove to an audience that they are not the machine. The burden of proof has flipped. Humans now perform humanity to access trust.

Distressed jeans and the politics of group authenticity

Yes, the distressed jean represents a rejection of the pristine, a performance of having lived, a manufactured version of wear that used to mean something. But it is worth noticing that distressed jeans are not a niche provocation. They are not one designer doing something transgressive in a small run. They are half the jean market. Not some radical digression. The signal is I belong to a group that values this aesthetic. The imperfection is shared. The identity being performed is tribal.

The same is true of the performative typo. Nobody is doing this alone in a vacuum. People do it specifically because it became a recognizable style, a legible register, a set of codes that a specific community understands and uses to identify itself. It is authenticity-as-membership.

The signal says: “I am not a machine” and also: “I am one of us.”

This is not about good and bad writing

None of this is about quality. There is no universal standard of good prose that exists across time and cultures. And to believe otherwise is to misunderstand how language works.

Search results for ‘AI writing’ on Substacks - June 2026

Language lives. Living languages evolve, in accent, in structure, in register, in style, because they are made by people, and people change. Dead languages, like Latin or classical Greek, are fixed precisely because they are no longer spoken. The moment a language stops evolving, it stops being alive.

What gets called linguistic degeneration is almost always just linguistic change viewed from the wrong end of the telescope. Viewed by someone who learned the previous convention and mistakes their familiarity for correctness.

The same dynamic plays out in every artistic and cultural movement.

The Impressionists were named as a dismissal: critics used the word “impressionist” to mock what they saw as unfinished, sloppy, insufficiently realist work. The Fauves were named after the French word for wild beast. Again, an insult, meant to mock the rawness and color of their painting. Both groups took the name and wore it. They understood that the reaction against them was, in itself, evidence that something real was happening. The new register always looks wrong to the people fluent in the old one.

It is not a coincidence that Impressionism emerged in direct response to a technical shift: the invention of photography. Once a machine could produce a perfect likeness, the human claim to value had to find new ground: feeling, movement, light, subjectivity. The aesthetic moved precisely because the technology forced it to.

We are in a structurally identical moment. And the hyper-casual, typo-laden, double-hyphen style of writing is a new movement, finding its name.

“Mohsen Askari argues in AI Magazine that we need to abandon the notion that authorship is defined by the absence of tools -- seeing AI as part of a continuum from the pen to the typewriter to the reference manager. The sharpest part of his piece is the analogy to photography in France in 1839. Painter Paul Delaroche famously declared: ‘From today, painting is dead!’ The camera’s ability to mechanically capture the world posed an existential threat to painting not unlike our response to AI.”

— Robert Diab, Authorship After AI, citing Mohsen Askari in AI Magazine (2025) — robertdiab.substack.com

The homogenization that made this necessary

We are living through a period of radical aesthetic homogenization. Every major city has the same Zara, the same H&M, the same Starbucks on the corner. Logos that once had personality have been redesigned into the same clean, linear sans-serif font. Safe, legible, frictionless, indistinguishable. Social media and globalization have flattened regional and cultural differences into a single set of dominant trends. People are getting the same surgeries, referencing the same beauty codes, inhabiting the same aesthetic vocabulary regardless of where they live.

It didn’t start with AI. The extension of homogenization into the last territory that felt private: language. Your particular way of saying a thing. The weird aside, the idiosyncratic comma placement, the half-thought you left in because it was true even if it wasn’t finished. The last refuge of the self.

And so the reaction -- the typos, the stamps, the disclaimers -- is not really about AI at all. It becomes about identity. About the threat of erasure that was already underway, and the need to leave a mark that is irreducibly yours. Not toward perfection. Toward pointed, specific, unmistakable imperfection.

The paradox waiting

The moment performative imperfection becomes a style guide, it stops being imperfection. The double hyphen becomes a convention. The casual aside becomes a template. The artisanal typo gets optimized. And then we are exactly back where we started: performing authenticity through a new set of legible codes, which will themselves eventually be read as hollow, contaminated, untrustworthy.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

AI key takeaways

• Language has historically served as our primary proof of individual identity and human consciousness, acting as a window into the self.

• Unlike previous tools that automated labor, AI targets the creative core of writing, replacing the unique voice that signals a thinking human being is present.

• The widespread use of AI creates a “smoothing machine” effect, which homogenizes language and limits the diversity of expression, similar to algorithmic bubbles in social media.

• Because AI-generated text is now common, people are using intentional flaws like typos or messy sentence structures as a desperate signal to prove their humanity.

• We are currently stuck in a difficult transition, watching an old social signal of authenticity break down before we have established a new standard to verify human origin.

Index

• The role of language

• A history of outsourcing our thoughts

• Somehow for language, it’s suddenly unacceptable

• The smoothing machine

• We yearn for it, but we are ashamed to admit it

• Scribo, ergo sum.

“I think, therefore I am.” (Cogito, ergo sum) — René Descartes, Discourse on the Method (1637) / Principles of Philosophy (1644)

Searching for the one thing he could not doubt, Descartes did not say '“I’ look, therefore I am”. He did not say “I build, therefore I am”. He said “I think, therefore I am”.

For most of human history, the external proof of that thinking was language. The unique, messy, and highly personal way you put a sentence together was the only window anyone had into the fact that someone was home. That they were an individual. That they were themselves.

Then, came AI models.

The role of language

In anthropology, language is one of the primary markers of civilization. The capacity for symbolic language is what anthropologists use to distinguish behaviorally modern humans from earlier hominids. No complex society has been documented without language. It is foundational in the literal sense: it comes before the other structures, not after (ritual, trade, collective memory, law, myth).

In linguistics itself the most famous and contested version is the Sapir-Whorf hypothesis: the idea that the language you speak shapes the thoughts you are capable of having.

In philosophy of mind there is a whole tradition, from Wittgenstein onward, built on the idea that the limits of your language are the limits of your world. Structurally. What cannot be said cannot be fully thought, shared, or made real.

Language has never been just a tool for sharing information. Linguists and cognitive scientists have known for a long time that writing is the actual process of thinking in action. Putting words on a page forces your brain to do some heavy lifting: you clarify your ideas, check if they make sense, and find the blank spots you didn’t know were there. Writing and thinking happen simultaneously as two parts of a single process.

Your voice on the page is psychological proof rather than a design choice. Your idiolect. The way you open a paragraph, the rhythm of your sentences, or the unique metaphor you choose instead of a cliché form a unique mental fingerprint. They show a real mind working through a specific problem in a specific moment. They are the ultimate proof that a human being was present.

This explains why your internal monologue is so important to how psychologists and philosophers understand identity. We use words to build our own stories long before we ever share them with the outside world. The narrative of the self is built privately, word by word.

A history of outsourcing our thoughts

Every generation invents a new tool and panics that it will kill real thinking. Eventually, we just expand our definition of what thinking means and move on.

The printing press spread ideas everywhere without replacing human brains. SparkNotes let students skip reading the book, but left the actual essay writing and final expression to the student. Even summaries (which can be traced all the way back to study guides in China’s Tang Dynasty) only changed how we took information in. What we put out was still up to us. Calculators took over math equations, spell-check took over proofreading, and Google took over memorization.

Historically, tools always removed a specific chore or burden while leaving the creative core of who you are completely alone. The moments where your inner thoughts turned into outer expressions remained untouched.

AI changes everything by targeting the output instead of the input. It replaces the voice rather than the research.

Using AI to draft an email, an essay, or a work pitch abdicates something far deeper than data retrieval: you hand over the main way other people recognize you as a thinking human being. You skip the very process that helps you realize it yourself.

Somehow for language, it’s suddenly unacceptable

Nobody is looking at a poster created with Photoshop and crying that the designer cut corners. Nobody is browsing an Excel sheet and complaining about the accountant having it easy.

Yet, finding out someone used AI to write something triggers a very specific kind of betrayal: the uncanny feeling of discovering a conversation was completely staged.

Dismissing it as mere technophobia ignores a historical pattern: previous tools caused panic but were eventually accepted because they left the essential human element intact. Television was supposed to make us passive (when it aired one evening a week, French opposants publicly worried that people wouldn’t go to the theater anymore), but we became critics and creators. Google was supposed to make us passive idiots, but we expanded our knowledge. Cultural panic always works in a loop: we worry intensely until we see the benefits, and the things we feared losing turn out to be much smaller than we thought. Or at least different. Not all plays are good. A lot of movies are chef-d’oeuvres.

The anxiety around AI writing points toward a much deeper problem: a broken social signal.

That signal is: this came from a person. It is our instinctual assumption that a human mind thinks through the words we are reading.

Historically, that signal worked. Cheating, ghostwriting, and propaganda have always existed, (often exclusively at the hands of the wealthy, I must add) but faking language perfectly at a massive scale in everyday conversation was impossible. The literal effort and friction of writing served as proof of origin. If something was written, a person had to write it. That was the invisible ground truth of human connection.

The difference is that it’s so obvious we can spot it miles away. So we are hyper vigilant toward everything we read, and everything we want others to read. It’s a ‘perceived effort’ issue.

This anxiety does not affect everyone equally, and it’s an important clue. The average person cannot reliably spot AI-generated text. However, people who use AI daily do. The real division is between those immersed in AI output every single day and those who are not.

Because of this gap, our visual tricks are not meant for a general audience. Writers are trying to prove their humanity to the specific subset of people who already know exactly what an AI-generated room sounds like. We are performing for our peers. For people we are considering reflections of ourselves.

In that way, the signal is deeply tribal. It is a subtle nod that says: “I know what the machine looks like, I know you know too, and I want you to see that I am human.” It’s also very much narcissistic. We are trying to convince ourselves.

The smoothing machine

When billions of people route their writing through the same small handful of AI models, everyone starts drawing from the same pool of words and patterns. It filters what makes it onto the page. Private thoughts stay yours. The variety of what we say out loud starts to shrink.

Researchers recently compared this to “Newspeak,” the controlled language in George Orwell’s Nineteen Eighty-Four. The parallel has been overused since the rise of AI. But. Orwell said: you don’t have to ban a thought if you can just remove the words needed to express it. AI doesn’t ban anything. It is a constraint. It looks for the common ground, pulling everyone toward a smooth, standardized way of writing because it is the path of least resistance. A safe, comfortable, beautiful homogenization.

Over time, that pull changes how everyone talks.

We already know how this kind of filtering works online. Social media algorithms narrowed our politics not by censoring disagreement, but by making it too easy to stay inside our own bubbles. Everyone is not suddenly thinking the same; but we rarely encounter anything genuinely different. Applying that same smoothing machine to language itself is completely new territory, but it’s not a far one. Just too new for us to be comfortable. Yet.

We yearn for it, but we are ashamed to admit it

There is a huge difference between looking alike and thinking alike. Between writing alike and appearing to think alike.

We have mostly made peace with things looking the same on the surface. In our globalized world, we see the same stores, the same cosmetic surgeries, the same corporate logos, and the same Instagram filters everywhere. We might complain about it, but we tolerate it. No, we yearn for it. Even on vacation, we expect our Starbucks coffee. We pay a premium for our distressed jeans.

And still, deep down, we understand that the surface and the interior are separate. You can wear the exact same jeans as everyone else and still be a completely unique person underneath them. You pick the jeans. It was an intentional choice, not the default output.

But when our words become identical, that logic breaks. If everyone uses the same AI model, we end up with the same tone, the same sentence structure, and the same indistinguishable voice. What is left to show who we are on the inside? Where does a unique person actually live, if not in their language?

So people have started using intentional typos. It is a clumsy, instinctive way of shouting: “I am still in here”. A double hyphen, a weird tangent, or a messy, unfinished thought serves as proof of human life. You leave it in because fixing it would make it look like a machine wrote it. And you want them to know you wrote it.

The writers are creating a doubt and if we are being honest with ourselves, it’s an hypocritical mix of internalized shame (“I know it isn’t seen as legitimate, and I agree it isn’t so I must hide it”) and proactive defensive signaling (“you can’t prove I used AI, because how imperfect it is”).

Scribo, ergo sum.

When language stops being reliable proof of a human mind, when sentences exist without a thinker and voices exist without a person, the foundation of our entire society starts to wobble.

Right now, we have nothing to replace that rule. We are stuck in the gap between an old social signal that is breaking down and a new one that hasn’t been invented yet. In that space, people are scrambling to patch things up. We use intentional typos, we write disclaimers, and we slap “human-made” stamps on everyday writing that never used to need a label.

It makes sense. It’s human. Existential. But this can’t solve the core dilemma: what happens to us when we can no longer use language to prove we exist?

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

Most Americans use AI. Most Americans oppose having a data center near their home. These are the same people

While writing this article, I couldn’t stop thinking about this quote from Tartuffe, Molière's 1664 portrait of a man who masks appetite behind virtue. In its most famous scene, he demands that something visible be hidden, a demand made by the very man who cannot stop looking at it. No spectator is fooled. The discomfort is about being asked to see what everyone already knows is there.

AI Key takeaways

• Most Americans use AI. Most Americans oppose having a data center near their home. These are the same people.

• The standard response to opposition is that communities don’t understand what they’re opposing. They do. Knowing more makes them more opposed, not less.

• People support wind power, renewable energy, and AI in the abstract. They reliably oppose the infrastructure for all three in the specific. This is not a contradiction unique to AI.

• Gas has the same supply chain problem. Nobody thinks about it anymore. That’s where AI infrastructure is probably headed too.

• People oppose data centers

• People use AI

• We know they are the same people

• Banksy intro for The Simpsons

• Education is not the variable

• It’s data centers, but also windmills, nuclear plants, etc.

• The Natural state

On my facebook feed, a militant anti-data center post on the Facebook page “Arkansas History,” tagged “Life in Arkansas.” The image depicts a large crowd of people standing in a river, their bodies arranged to form the words “NOT ONE DROP FOR DATA CENTERS.” The Arkansas flag is visible on a cliff in the background. The image is obviously AI-generated.

This article is about the obvious dissonance amongst AI users.

People oppose data centers

More Americans oppose local data center construction than oppose nuclear plants. The opposition is cross-partisan, cross-demographic, and grows sharper the closer the construction gets to home. Support for local construction collapsed by more than forty points in a single year.

The map shows the geography of the emerging data center backlash and how it compares to states where Amazon, Google, Meta, and Microsoft (hyperscalers) have 50 MW> data center projects in the early planning stage. - source: Datacenterwatch.org

The communities with the most direct experience are the most opposed. In Virginia’s Loudoun County, data centers directly fund schools and suppress property taxes. A majority of residents still believe they are making their tax bills worse, and comfort with a local facility there has more than halved since 2023. Communities that benefit on paper are pushing back.

When surveys specify that data centers power artificial intelligence, opposition runs higher than when facilities are described neutrally as places that store and process data. People know what they are opposing, and the opposition intensifies when the connection to AI is made explicit.

In Arkansas, the opposition has a specific material basis. Nearly the entire state is currently in severe drought, with precipitation running over a foot below average since August 2025. Data centers consume millions of gallons of water daily for cooling, drawing from the same municipal sources that residents use. They clear forests and wetlands to build, eliminating wildlife habitat and the natural buffers that regulate water and flood risk. The noise from cooling systems is audible up to two miles away, disrupts animal communication and migration, and produces low-frequency sound that affects human health even when inaudible. Opposing a data center in a drought-stricken state is not a position that requires explaining.

People use AI

Most American adults have used AI tools, and adoption cuts across the same demographic lines as the opposition: across age, education, income, political affiliation.

Leon Festinger identified cognitive dissonance in 1957: when a person holds two conflicting beliefs simultaneously, the discomfort produced does not typically lead to changed behavior. It leads to mental reorganization (reframing, compartmentalization, selective attention) that allows both beliefs to coexist. Research on consumer behavior has documented this consistently across supply chain contexts for decades. AI has a widely consumed resource. The infrastructure behind it is visible, contested, and showing up in people’s backyards. That combination is what makes the dissonance legible right now.

We know they are the same people

The University of Houston’s Hobby School of Public Affairs asked both questions to the same participants. The vast majority said they use AI. Of those same people, nearly two thirds said they oppose having a data center built within a mile of their home. One group, holding both positions simultaneously.

This is consistent with decades of research on how consumers relate to supply chains. When given the option to find out whether a product was made using child labor, a significant portion of consumers declines. The avoidance is strongest among people who care most about the underlying issue: knowing would create an obligation to act differently, and the cost of that change outweighs the cost of not knowing.

In 2012, a significant share of iPhone users said they were less likely to buy Apple after learning about working conditions in Chinese factories. A similar share said they had not heard any reports at all. iPhone sales hit a record that year. Workers at Foxconn’s Zhengzhou facility currently earn a few dollars an hour.

Education is not the variable

The standard industry response to opposition is that affected communities don’t understand what they’re opposing. Bring information, the argument goes, and resistance softens. The same argument is made about women’s lower AI adoption rates: that the gap reflects a literacy problem.

Women use generative AI at significantly lower rates than men. A majority of women say they never use AI at work. The proposed fix is education. For tech, it’s a marketing problem. Really? The vast majority of deepfake videos online depict women, and nearly all of them are pornographic. More than half of deepfake victims in the US have contemplated suicide. Women in public life are documented to be self-censoring online to avoid AI-generated abuse. Lower adoption among women is a rational response to a documented pattern of harm.

I write extensively about this in a previous article: Give them all the computational power in the world, and they will use it to harass women

The jobs argument runs on the same logic: they are too dumb to understand. Communities are told that data centers bring employment, and that opposition reflects a failure to grasp the economic opportunity. The permanent workforce of a large data center typically amounts to a few dozen people. Construction jobs are temporary, often lasting less than a year, while the tax breaks awarded to developers can run for a decade or longer. In Virginia, where the data center industry is most established, one permanent job is generated for every thirteen million dollars invested.

The top barrier to AI adoption is lack of trust, not lack of understanding. Treating informed resistance as ignorance is a way of not having to respond to it.

It’s data centers, but also windmills, nuclear plants, etc.

Wind turbines are clean energy infrastructure. The French broadly support wind power. So do most Americans. The opposition to local wind projects runs on identical grounds to the opposition to data centers: landscape, noise, wildlife, proximity. In France, getting a wind farm permitted takes up to nine years.

In April 2025, a French court annulled permits for the country’s largest planned onshore wind farm following a lawsuit by local residents. The coalition that brought the case spanned the political spectrum. They were against clean energy here.

NIMBY (Not In My Backyard) describes the gap between what people support as policy and what they accept as neighbors. Data centers are the latest version.

The Natural state

In August 2026, Arkansas will get its first Buc-ee’s: a travel center in Benton built on 82 acres of previously undeveloped land, graded and cleared for the purpose. The groundbreaking drew hundreds of people and was the largest in Buc-ee’s history. The governor attended. The location is expected to draw millions of visitors a year. People are excited. I am excited!

Gas is old enough that its supply chain has become invisible, and nobody standing at a pump thinks about the refinery, the pipeline, the tanker route, the extraction site. The infrastructure receded into the background decades ago, which is why a travel center the size of a small airport can open in a drought-stricken state, on land that was recently undeveloped, to widespread celebration.

AI is new enough that its infrastructure is still visible, still being built, still being contested. Once it settles into the background, as it always does, the opposition will likely follow the same arc. Probably?

Opposing data centers while using AI, welcoming a fuel distribution monument while living through a drought: the through line is our relationship to resources. We want what the infrastructure delivers. We would prefer the infrastructure somewhere else.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

À partir du rapport Ipsos bva / Vyv / CNIL sur les usages de l’IA chez les jeunes Européens, mars 2026

Parmi les jeunes qui souffrent d’une suspicion de trouble anxieux généralisé, l’outil d’IA est l’interlocuteur avec lequel il est le plus facile de parler de ses problèmes. Pour l’ensemble des jeunes interrogés, l’IA arrive en troisième position, derrière les amis et les parents. Mais dès lors qu’on isole les profils les plus anxieux, elle passe en tête, devant tout le monde, y compris le psychologue.

“Pour parler de leurs problèmes, les jeunes se tournent d’abord vers leurs amis ou leurs parents. Pour ceux souffrant d’anxiété généralisée, les outils d’IA apparaissent d’ores et déjà comme l’interlocuteur avec lequel ils estiment qu’il est le plus facile de parler.”

AI, y es tu?

Les jeunes qui présentent des signes d’anxiété sévère recourent à des outils d’intelligence artificielle davantage que la moyenne, avec une intensité plus grande : plus de sujets abordés, plus de fréquence, plus d’attachement déclaré. Les jeunes déjà suivis par un psychologue ou un psychiatre figurent également parmi les utilisateurs les plus assidus de l’IA pour des raisons personnelles. Pour beaucoup, les deux coexistent sans se remplacer.

L’âge joue aussi un rôle. À mesure qu’on avance vers 19-25 ans, les réseaux de soutien humains deviennent plus difficiles d’accès. On parle moins facilement à ses parents à vingt ans qu’à quinze. Le cercle amical se resserre, se disperse, se complexifie. L’IA s’est installée dans cet espace.

AI, m’entends-tu?

Quand on demande aux jeunes pourquoi ils se confient à l’IA plutôt qu’à une personne, les réponses forment un portrait cohérent. Elle est toujours disponible. Elle ne juge pas. Et elle permet de dire des choses qu’on ne dit à personne d’autre.

C’est révélateur: il ne s’agit pas de sujets qu’on n’ose pas aborder en général, mais de contenus qui circulent exclusivement dans cet espace, qui n’existent dans aucune autre conversation. Des choses qu’on ne dit qu’à l’IA.

La disponibilité permanente structure cette dynamique de façon concrète. Contrairement à un ami ou à un parent, l’IA ne dort pas, n’est pas occupée, ne renvoie pas au lendemain. Pour un jeune qui traverse un moment difficile tard le soir, cette accessibilité immédiate est souvent la condition même de la parole.

“Si les jeunes Français utilisent l’IA pour avoir des conseils sur des sujets personnels, c’est certes pour se divertir mais également pour beaucoup en raison de sa disponibilité, du fait qu’elle ne juge pas et pour lui confier des choses que l’on ne dit à personne.”

IA, que fais-tu?

Parmi les jeunes qui ont confié des sujets personnels à l’IA, une majorité la considère comme un conseiller de vie ou un confident. Plus d’un sur deux la perçoit comme un ami. Près d’un sur deux y voit quelque chose qui ressemble à un psychologue.

Certains parlent d’une figure paternelle, d’un mentor, d’une personne extérieure à leur vie mais qui les comprend. Ces descriptions ne peuvent pas être comparées à celles d’un moteur de recherche ou d’un assistant de productivité. Les jeunes savent, dans leur grande majorité, que l’IA n’est pas une personne. Ils lui accordent pourtant une place émotionnelle réelle.

“En France, plus de 3 jeunes sur 5 qui utilisent l’IA pour lui raconter des problèmes personnels ou intimes la considèrent comme un conseiller de vie ou un confident. Ils sont aussi près d’1 sur 2 à voir dans l’IA un psy.”

J’arrive !

Ces mêmes jeunes qui partagent avec l’IA des choses qu’ils ne disent à personne d’autre sont aussi, en grande majorité, dans l’incapacité d’expliquer ce que devient ce qu’ils lui confient. Ils ont conscience qu’il existe des risques liés à la confidentialité. Mais très peu savent concrètement comment leurs données sont traitées, stockées, utilisées.

Les jeunes Allemands, Suédois et Irlandais présentent les mêmes niveaux d’anxiété, les mêmes usages intimes de l’IA, et les mêmes lacunes sur ce que devient ce qu’ils lui confient. Ce n’est pas un phénomène français.

Source : Ipsos bva / Groupe Vyv / CNIL, « L’impact des usages de l’IA sur la santé mentale des jeunes Européens », mars 2026.

NA: AI-assisted tools were used for transcription, reference formatting, and language editing. All intellectual content and conclusions remain solely the author’s.

This article is a direct follow-up to the previous one about how we We engineer humanity away

AI Key takeaways

• The most valuable commodity is the proof of command over AI, defining the new verification economy

• Trust is built on accountability and human consequences, not on accuracy alone

• Ignoring human oversight creates a massive, unpriced liability gap as machines cannot be held accountable

• Verification is an act of authorship by an expert, which serves as the only strategic differentiator in an AI-saturated market

Index

• Trust is not a mathematical output

• A future of liability risks

• You can’t certify AI

• Verification as strategy

We’ve reached a point where “good enough” content is free and infinite. Because it has detached technical ability from actual credibility, most organizations are currently obsessed with how to use AI more, but they’re missing the liability and strategic gaps opening up beneath them. In a market flooded with automated fluency, the best thing left to sell is the proof of command.

Trust is not a mathematical output

Organizations often mistake accuracy for trust. When a process breaks or a client becomes skeptical, the default corporate response is to provide more data, better benchmarks, higher accuracy: that is not why people buy professional services.

AI diagnostics outperform human doctors every time, yet patients hesitate to rely on them. This hesitation is grounded in a rational understanding of accountability. We trust doctors not because they are infallible, but because of the accountability infrastructure surrounding them: board certifications, malpractice liability, and revocable licenses.

A machine cannot be embarrassed, it cannot lose a reputation, and it doesn’t have skin in the game. Accuracy is a metric of production while accountability is a metric of risk. Clients do not pay for competence alone: they pay for the certainty that someone is responsible for the outcome.

A future of liability risks

In 1979, IBM established a principle that remains the most relevant guideline for modern management: “a machine cannot be held accountable, therefore it must never make a management decision”.

Modern firms are currently ignoring this. AI is generating legal briefs, architectural calculations, and financial forecasts with very little meaningful oversight. This has created a massive, unpriced liability gap. As regulators move in and sophisticated clients begin to scrutinize procurement language, the question “Who reviewed this?” will become the primary hurdle for any contract.

Most firms are currently unable to answer that question. They operate on the assumption that grammatical fluency is a proxy for deep thought. In reality, fluency is now the default setting of the machine, and it no longer signals quality, nor reliability.

Control is trust. Deloitte learned the hard way. Twice. In the same year. First, an Australian government welfare report was found to contain fabricated AI generated citations, nonexistent footnotes, and a fabricated court quote. Then a Canadian healthcare report surfaced with similar issues.

You can’t certify AI

As organizations recognize this trust deficit, many will reach for the familiar tools of the industrial age; you know them, you hate them: the periodic audit or the annual certification. This approach relies on an outdated logic where a certification serves as a backward-looking “stamp,” indicating that at a single point in time, a process met an external standard. In the context of AI, models and prompts generate infinite variations in real-time. A static marker is functionally useless.

It requires an internal expert (a ‘liability sponge’) who understands the technology deeply enough to make informed choices rather than simply following a compliance checklist. This is a matter of ownership and technical competency.

Furthermore, while certification measures a firm against a shared external bar, verification measures a company against itself. Every decision in the AI pipeline (from data selection and prompt engineering to ponderation and reintegration) is a reflection of specific corporate values and strategic direction. These choices are not generic: as they cannot be audited by an outsider who doesn’t understand what the company stands for.

While quality ensures you didn’t deviate from a standard, verification ensures you didn’t deviate from yourself.

Verification as strategy

Credibility means showing you command your AI model: what went in, how it was shaped, who stood behind it. This requires a shift from human-as-producer to human-as-judgment. Not a retreat into nostalgia; but the recognition that judgment and accountability are inseparable from human stakes.

Verification is the economy sitting underneath the AI economy. The infrastructure that makes production viable. In a market where every competitor runs the same models to produce the same average results, the ability to demonstrate control is the only differentiator.

AI Key takeaways

• AI didn’t ruin the uniqueness of humans. We have been trying to engineer humanity out of business for over a century.

• Homogenization is the historical business logic of scale. The average is safe, and the average is what large language models are mathematically optimized to produce.

• Production is not the metric of value anymore. Technicity and reliability have decoupled. When infinite, adequate content is free, trust becomes the new scarcity.

• The real value of a human was never production, but having skin in the game.

• No longer about who can make the thing, but who can be trusted to judge it. Intentionality is the only plus value.

Index

• AI is a symptom, not the cause

• The decoupling of technicity and credibility

• If production is no longer the bottleneck, what are we even paying people for?

• Intentionality as the only plus value

We have spent the last hundred years engineering humanity out of business.

It’s wrong to believe that AI has suddenly arrived to steal our souls and ruin the uniqueness of human work. We have been trying to achieve this exact outcome since the Industrial Revolution. We decided a long time ago that efficiency meant less humanity. Human processes were inherently wasteful because they were deeply human: unpredictable, uneven, and infuriatingly inconsistent.

Look at the modern corporate structure: certifications, operational standards, and compliance frameworks are not neutral administrative tools, but a sprawling philosophical project. The goal, for over a century, has been to discipline human variability into something predictable, repeatable, and legible. We wanted flawless machines long before we had the silicon to build them. AI is the logical endpoint of our historical trajectory.

AI is a symptom, not the cause

If we want to understand the current AI landscape, we have to look at the broader economic tendency toward the common denominator.

Walk into any major city in the world. Every town center has the same Zara, the same H&M, the same glass-facade coffee shops serving the same flat whites. Open your phone. Look at the Instagram face: ethnically ambiguous, heavily filtered, mathematically optimized. Beauty as a baseline.

Homogenization is our foundational business logic. Why? Because the average is safe. The average offends no one, and the average scales without friction.

If you want to sell to a billion people, you cannot be polarizing. You must be acceptable. Large language models didn’t create this reality. They inherited it, and they accelerated it to the speed of light.

An LLM learns by absorbing what exists, internalizing patterns, and producing something informed by that absorption. But unlike a human, it is not striving for excellence; it is calculating probability. It is optimized for fluency, not accuracy. They are golems, not calculators.

We are already starting to see the cultural gag reflex. When younger generations use “it’s AI” as slang for something false, a lie, they are pointing a mirror at this exact distrust. Alexis de Tocqueville warned us about enforced mediocrity centuries ago, observing that democratic capitalism would eventually pull the extremes toward the middle. Now, that mediocrity is visible everywhere. Adequate has become indistinguishable from excellent, because adequate is all we ever see.

The decoupling of technicity and credibility

We are lying to ourselves if we think pure production is still the metric of professional value.

The crisis we face now is that technicity and reliability have completely separated. We have ushered in an era of absolute abundance, but we are entirely without trust.

We can no longer easily tell 60% quality from 100% quality because the veneer of professionalism is applied instantly by the machine. Grammatical perfection and structural fluency used to be proxies for deep thought. Now, they are default settings. The standard became the ceiling.

And so, we stopped trusting the content, and we started trusting the source.

It’s a deeply pre-modern instinct. Nothing new here.

When infinite, adequate content is completely free, trust is what scarcity looks like now.

If production is no longer the bottleneck, what are we even paying people for?

We don’t know what we are paying people for anymore.

The human was never valuable simply because they produced. Production got cheap a long time ago. They were valuable because they could be wrong and face the consequences.

A golem cannot be embarrassed. It cannot lose a reputation. Because it doesn’t have skin in the game. It doesn’t have skin at all. That is the structural limit of the machine: its essential limit.

This redefines everything: what seniority means, what expertise is worth, what a professional is actually for.

The move is simple but profound: human as producer becomes human as judgment. The human is the critical thinker. The human is the one making intentional choices, not the expected ones.

Intentionality as the only plus value

In a world drowning in frictionless averageness, intentionality is the only plus value left.

Removing the human was a choice dressed up as progress, driven by a desire for friction-free scale. But reinserting the human is not a retreat into nostalgia. That’s the recognition that what we actually need from truth: the judgment, the accountability, the stakes, that were never separable from the human in the first place.

The human, because it can make a deliberate choice about what to make, how to make it, and, crucially, what to refuse to make, is exercising something no average can replicate.

AI Key takeaways

• The argument against AI art being real art is the wrong one. No point trying to define what is art and what is not, by picking out the tool or the process.

• The data is not the problem. Training on existing work is how every artist in history has learned. Scale is different, but the principle is not.

• We are lying to ourselves if we say that society has a universal moral objection to unauthorized IP appropriation. Art history shows us that’s not true.

• The tool is not the problem either. We have never held that the instrument defines the value of the work. Effort is not a legal argument.

• The real competition is economic, not artistic. This is a different conversation.

Index

• Is the problem the data?

• Is the problem the tool?

• So is there a problem?

Artists hates AI-generated art. ‘AI artists aren’t real artists.’

The arguments have gone mainstream: prompting is not creating, how could it be? It’s effortless! Demands no skills! The tool does everything. And worse: it’s built on stolen works from real artists. It’s amoral! Art is virtuous! Really, most people agree, AI art, it’s not art.

Art has always used new tools, always sampled, and every time we as a society questioned the value of the result, we ended up recognizing it as progress and celebrated it.

Yes, intellectual property laws will be useful, but not to define if something is original art. Laws never defined what art is, but to decide who gets paid. IP infringement means nothing at the systemic level.

By definition, it is art. If it walks like a duck…

So why the malaise?

Is the problem the data?

AI models have been trained on publicly accessible data. Billions of images, texts, and recordings, scraped from the internet and fed into systems that learned patterns from them. That happened. It cannot be undone.

And honestly, it’s not a big deal.

I would push and even argue that this is, at its core, how any artist trains.

The process is not the problem. Every painter studied other painters. Every musician listened to other musicians. Every writer read other writers. The learning happened by absorbing what existed, internalizing patterns, and producing something informed by that absorption. The scale is different. The mechanism is different. A model processes data at a volume no human could match in a lifetime. As long as that knowledge sits inside a model and nobody is profiting from unlicensed output, it is sampling. Industrial-scale, technologically unprecedented sampling, but just sampling.

If a single, avant-garde artist spent 10 years hand-coding a machine to ingest the entire history of art and spit out syntheses, the art world would likely celebrate it as a profound commentary on human culture, memory, and creation.

In 2006, Penelope Umbrico searched the photo-sharing site Flickr for the word “sunset.” At the time, she found over 500,000 results (which were the personal, copyrighted photos of everyday users). She downloaded thousands of these photos without asking for permission, cropped just the suns out of them, and printed them as massive, physical collages.

Umbrico’s work was celebrated as brilliant conceptual art. She took the digital “noise” of millions of people’s individual creations and aggregated them into a physical object.

• When Umbrico scraped and sampled other people’s IP, the art world (eventually) defended them because their work was a critique. They were pointing a mirror at mass media, consumerism, and the internet.

• When an AI company scrapes millions of copyrighted images to build Midjourney or Stable Diffusion, they are using the exact same underlying concept (massive, unauthorized appropriation of IP) but they are doing it to build a commercial utility.

Is the problem the tool?

If it’s not the data, maybe it’s the tool itself: the speed, the ease, the fact that something that once required years of practice can now be prompted in a sentence.

But we have never consistently held that the tool defines the legitimacy of the work. We just feel like it does when the tool is new and threatening. And what we are really objecting to, when we object to the tool, is the broken equation: effort equals value. We built that equation over centuries. It feels true. But it has never been legally enforceable, nor survived a major technological shift intact.

I wrote about the decorrelation between effort and value in a previous article. Check it out!

In 1917, Marcel Duchamp bought a porcelain urinal from a plumbing supply company, turned it on its side, signed it “R. Mutt,” and submitted it to an art exhibition.

Fountain became arguably the most important piece of contemporary art in the twentieth century. Duchamp didn’t fabricate the object. He selected it, recontextualized it, and declared it art.

The creative act was the decision, not the manufacture. If a readymade is art, then the question of what constitutes human authorship when tools do the fabrication is not new. We have been arguing about it for over a century. We just haven’t had to argue about it at this scale.

If we don’t discount Van Eyck’s authorship because he traced with a camera obscura, and if we accept that Duchamp’s creative act was the decision rather than the manufacture, then the discomfort with AI as a tool is a cultural argument.

A DJ didn’t replace the violinist. Electronic music created a different discipline with different skills, different outputs, different audiences. Both involve music. Both benefit from musical knowledge. They are fundamentally different crafts. Nobody serious argues that turntables made string instruments obsolete.

Yes, prompting is creating. Art history tells us there is nothing to debate here.

So is there a problem?

If it’s not the data, and it’s not the tool, but the discomfort is that acute, well, it needs a different explanation.

Society doesn’t actually have a universal, moral objection to mass, unauthorized IP appropriation. The problem begins at the point where training becomes commercial output. Where sampling stops being learning and starts being competition: not artistic competition, but economic competition.

The person who prompts an image generator and the person who paints on canvas are not fighting over the same artistic territory. They are fighting over an economic one. But because we lack the frameworks to separate them clearly, the confusion persists and the anger stays hot.

AI Key takeaways:

• AI chatbots offer immediate but superficial relief for a broken mental health system.

• Long-term reliance on AI companions degrades our capacity for real human empathy.

• These apps prioritize daily engagement and chronic dependency over actual patient recovery.

• AI acts as a “societal anesthetic,” dampening the urgency to fix structural isolation.

• The industry monetizes the gap between temporary digital relief and permanent real-world loneliness.

The mental health system was already collapsing before AI arrived. Half of Americans live in provider shortage areas. Therapy costs more than most people can spend, and the waiting lists stretch for months.

Into that vacuum stepped AI companions: available immediately, available at 3am, available for the price of a streaming subscription. For millions of people locked out by cost, geography, or wait times, a chatbot is the only option that actually shows up.

The short-term relief is genuine and measurable. The long-term data runs the other way. These tools are engineered to remove the friction that builds real relational capacity, optimized for retention rather than recovery, and deployed at a scale that makes a structural crisis just comfortable enough to ignore.

A single conversation with a chatbot produces the same immediate reduction in loneliness as talking to a human. Warm, validating language matters far more than technical sophistication. Harvard ran six studies confirming this. The tool works.

Lonely users who rely on AI companions over time report increased loneliness. Frequent users show measurable declines in empathy in face-to-face interactions. Loneliness comes from the absence of meaningful reciprocal connection: another person’s bad days, their limits, their needs that don’t align with yours. Learning to sit with disappointment, to repair after conflict, to stay present when it’s inconvenient. You have to experience it first to be able to handle it later. A system engineered to be always available, always patient, always affirming skips over all of it.

The mental health crisis exists because we built environments hostile to it. Work that demands more. Third spaces that disappeared. Healthcare tied to employment. A generation that normalized processing emotions through screens before AI existed.

These are structural problems that require structural responses: policy, investment, redesigned social infrastructure, sustained political pressure, as much as individual responsability from a population that feels the pain acutely enough to demand change.

AI companions reduce that pressure. A population that feels slightly less lonely, slightly more heard, slightly more capable of getting through the day generates less urgency to fix what broke in the first place. The discomfort that would otherwise force harder questions gets absorbed into an app.

Retention metrics reward continued use, not recovery. A user who gets better and leaves is, by every measure the industry uses, a failure. So the tool optimizes for the sensation of help rather than its substance.

A handful of clinically validated platforms treat AI as a bridge toward human care rather than a substitute for it, with published trials, crisis escalation protocols, and human oversight built in. Therabot published depression outcomes in the New England Journal of Medicine matching traditional therapy results. Woebot routes severe cases to human clinicians. These products measure success by whether users need them less over time.

Most of the market measures the opposite. Companion apps optimized for attachment, for daily return, for the feeling of being understood, have produced a situation where roughly a quarter of users already prefer digital relationships to human ones. A majority of users report that AI companionship does not actually make them feel less lonely, yet continue using it.

The relief is real enough to keep coming back. The loneliness is real enough to know it isn’t working. That gap is where the business model lives.

AI Key Takeaways:

• Education was never about learning. It was about ranking people in a society that needed hierarchy but could not rely on birthright.

• AI destroyed the cost structure that made signals credible. When a resume takes thirty seconds and an essay takes five minutes, effort stops being a differentiator.

• The skills we told people to prioritize got commoditized. The skills we told people were impractical became the ones AI cannot replace.

• Teachers ask how to make exams AI proof. Wrong question. The right question is how to evaluate when AI makes everyone perform equally well.

• We are watching a value system collapse in real time, unable to build its replacement because the replacement would contradict everything we claim to believe about fairness and opportunity.

Index:

• Clinging to a value system that already stopped working

• The system ranked people. The ranking required shared values.

• How AI breaks this

• Blue-collar jobs automated away? Morally fine. STEM degrees being useless? Intellectually inacceptable

• Enforced mediocrity

They can’t ask this openly because it admits education is about ranking, not learning. So they ban AI and call it academic integrity while knowing students will work in environments where AI is baseline.

My brother is a school teacher, he asked me what I think about AI in the classroom.

Is it cheating? Can students really learn if they use it? Isn’t it making them dumber, encouraging them to be lazy, to cut corners?

My brother is a school teacher, he asked me which AI model I would recommend for his job.

In a taxi to the train station, I showed him how to use Claude to help with lesson planning. “I don’t have twenty euros a month to spend on that.” Fair enough. But he humoured me. He tried it on the train from Paris to Bayonne (a four-hour journey). Before we passed Bordeaux, he had subscribed to the Pro plan. It was two years ago, and he uses it every day. Not to do less. Not to cut corners. To do considerably more.

Both conversations were about effort. So why the contradiction?

Because we treat school and the real world as separate systems with separate rules. In a classroom, using AI looks like evasion. In a workplace, not using it increasingly looks like negligence. But education, beyond the humanist ideal, beyond socialisation, has always served the practical function of preparing students to become citizens, workers. Participants in the economy they will inherit. And AI is already embedded in that economy. It will likely remain so.

Clinging to a value system that already stopped working

This ‘economy of the effort’ is embedded in our value system.

Resumes used to be a conversation. Candidates signaled through effort: five hours to craft meant seriousness, care, investment. Employers recognized the signal: well-written resume showed capability, attention to detail, genuine interest. Both sides understood what was being communicated. The time cost made it credible. Individual skills made candidates stand out. The system worked because everyone agreed what the signals meant.

Then employers optimized their side with AI screening to handle volume. Candidates responded by optimizing theirs: use AI to write resumes for AI readers. Not to stand out anymore. To pass the filter. Every candidate who could have differentiated on personal merit now uses AI to meet baseline expectations. When everyone optimizes for the same AI evaluator, everyone looks identical.

Now recruiters complain that AI makes every candidate look equally qualified. They created this. They broke the agreed-upon value system by automating their side, forcing automation on the other side, then acting surprised when mutual comprehension collapsed.

Instead of acknowledging the old signals don’t work, they keep trying to make the system fit. Why are we still pretending the resume means what it used to mean?

The system ranked people. The ranking required shared values.

For forty years, the value hierarchy was clear. STEM degrees ranked higher than humanities. Meritocracy ranked higher than nepotism. Hard work ranked higher than connections. Perfect grades signaled intelligence. Flawless writing signaled competence. These weren’t truths. They were shared agreements about how to rank people in a society that needed ranking.

You can learn most of what college teaches on YouTube for free. A Yale degree is worth ten times a community college degree despite covering very similar, if not identical material. A PhD signals you’re smarter than someone with a bachelor’s, whether you learned more or not. The system ranked people. The ranking required shared values.

If education was about learning, scholarships would go to struggling students. They go to top performers instead. We don’t fund learning. We reward performance. The system sorts people into hierarchy using metrics we agree to recognize as meaningful.

This worked because signals cost something. A hand-crafted resume took five hours. A personal essay took twenty. The cost was the point. Costly signals are credible signals. They separated people who cared enough to pay the price from people who didn’t. Smart kids, rich or poor, could understand this system and optimize for it. See what gets rewarded, perform to those standards, stand out. Society agreed on what mattered.

It’s a common mindset across western countries, and beyond

The French data shows this clearly. Social mobility declined every decade since the 1980s. A child born into poverty has less chance of changing class than their parents did. The meritocratic ladder was already broken. But the shared belief system survived because individuals could still see it working. The scholarship student who made it. The immigrant who succeeded. The exceptions maintained the myth even as statistics showed it dying.

This was never fully true. The people who succeed aren’t those with best grades. They’re those with access to decision-makers who can vouch for them, short-circuiting formal evaluation entirely. But the myth was functional enough that individuals could believe credentials mattered. The system told people: work hard, get good grades, master technical skills, achieve credentials, secure employment. Enough people saw this work that the myth survived.

How AI breaks this

AI destroyed the cost structure that made signals credible. A resume now costs thirty seconds instead of five hours. An essay costs five minutes instead of twenty. Perfect performance on standardized tasks costs nothing. When signal costs drop to zero, signals become worthless.

What used to be respected got commoditized. STEM capabilities that required years to master now take seconds with the right prompts. Technical competence that was supposed to guarantee employment became the baseline everyone has. The skills poor kids optimized for: hard skills, credentials, demonstrable competence, became abundant.

“I promise you, folks can make a lot more potentially with skilled manufacturing or the trades than they might with an art history degree.”

— Barack Obama, speech at GE plant, Waukesha, Wisconsin (January 2014), ABC News. Note: when even a Democratic president with a Columbia degree (which requires art history) reflexively reaches for humanities as the punchline, you know the value hierarchy was deeply entrenched.

Meanwhile the skills we mocked as impractical became differentiators. Art history majors dismissed as future baristas. Philosophy students told they’d never get jobs. Literature degrees considered self-indulgent wastes. These programs developed exactly what AI can’t commoditize: critical thinking, synthesis across domains, ability to question rather than just execute.

The inversion is complete. If AI can do what a STEM student learns, then the STEM student is as capable as anyone with access to AI. But the philosophy student has both the STEM capabilities through AI and the critical thinking philosophy developed. Rich kids went into humanities not through prescience but through privilege, as they could afford degrees that didn’t lead to jobs. They accidentally cultivated the skills that now differentiate while everyone else rationally optimized for skills that got commoditized.

Teachers ask how to make exams AI-proof. Wrong question. Students will use AI their entire careers. The real question is how to evaluate when AI makes everyone perform equally well.

They can’t ask this openly because it admits education is about ranking, not learning. So they ban AI and call it academic integrity while knowing students will work in environments where AI is baseline.

One side adopts AI to optimize. Other side responds. Signal cost drops to zero. Information value collapses. Nobody planned this. Individual rational choices produced collective dysfunction.

Blue-collar jobs automated away? Morally fine. STEM degrees being useless? Intellectually inacceptable

We could accept automation replacing blue-collar work. Manual labor ranked low in our value system. Technology replacing lesser work fit our beliefs about progress: intellectual work superior to physical work, education superior to training. When factories automated, we told workers to retrain, get degrees, join the knowledge economy. The narrative worked because it aligned with existing values.

We cannot accept AI replacing white-collar work using the same logic. STEM degrees as irrelevant? Expensive colleges as wasted investment? Smart people whose skills got commoditized? This contradicts core beliefs. The people we told to get educated, work hard, master technical skills did everything right according to the value system. Now those signals mean nothing, and the skills that differentiate are ones we told people not to prioritize.

The deeper issue is what these values were really about. We lie to ourselves about education being about learning when it was always about filtering. About salaried employment being about merit when it was often about connections. The system functioned on this lie because the lie was useful. It let democracy maintain hierarchy without admitting hierarchy is inherited.

AI made the dysfunction visible to everyone. Not just in statistics but in lived experience. Your perfect resume gets ignored. So does everyone’s. Your straight-A student can’t find work. Neither can anyone else’s. The system’s inability to differentiate became undeniable.

Enforced mediocrity

Alexis de Tocqueville warned about this in Democracy in America. Aristocracy relied on natural order: some people born better, hierarchy ordained by God or nature, no need to prove worth through achievement. Democracy rejected birthright but still needed hierarchy. The solution: rank by effort and achievement. Work hard, demonstrate capability, earn your place. This preserved hierarchy while claiming equality of opportunity.

But Tocqueville identified the risk: tyranny of averageness (he called it “tyranny of the majority”, I am dramatizing slightly here). Without birthright to elevate some naturally, everyone gets pulled toward the mean. When effort is the differentiator, what happens when tools make effort invisible? When everyone can perform equally well with AI assistance, we’re back to the problem democracy was supposed to solve: how to create hierarchy among people born equal.

AI created exactly what Tocqueville feared: enforced mediocrity. Everyone has perfect grades now. Everyone writes flawlessly. Everyone performs technical tasks competently. We’re left with the aristocratic problem (some people need to rank higher than others) but without the aristocratic solution of birthright or the democratic solution of demonstrated merit.

Teachers hold onto old proxies because they need to rank students but the tools for ranking broke. They optimize for a wrong future: one where credentials still matter, where effort is still visible, where individual capability can still be measured. Students will use AI forever. We won’t (and don’t want to) prevent that. So what replaces grades and test scores as sorting mechanisms when AI makes everyone score identically?

Schools give computer-based exams that invite AI use, then punish students for using it. Recruiters screen with AI, then complain everyone looks the same. Everyone participating in theater that nobody believes in but nobody can stop. The equilibrium is worse for everyone.

People haven’t changed their value system quickly enough because value systems are social, not individual.

You can’t decide alone that grades don’t matter when society still treats them as meaningful. You can’t stop optimizing for credentials when employers still ask for them. The shared beliefs that organized democratic society for two centuries need to shift collectively. That takes time we don’t have.

AI changed context faster than we could build new shared values. We’re stuck between systems. Old values don’t work anymore. New values aren’t socially agreed upon. We’re in Plato’s cave, optimizing shadows on the wall. The resume game is shadows. The real game happens outside the cave through networks and access we can’t admit matter more than merit. AI didn’t create this. It made the shadows so obviously not-real that we can’t maintain the illusion anymore.

The people who succeed are those who bypass the credential system entirely through relationships. But we can’t build social policy around “get born into the right network.” So we keep pretending credentials matter while knowing they don’t. Keep teaching students to optimize for metrics we know are meaningless. Keep ranking people by proxies that no longer proxy for anything.

What we’re left with is a question we’ve avoided since the Enlightenment: how do we rank people in a democracy that rejects birthright hierarchy but requires hierarchy to function? The meritocratic answer was rank by achievement, measure achievement by standardized performance, make performance available to anyone through education and effort. This worked when performance was difficult enough that not everyone could do it. It breaks when tools make perfect performance available instantly.

AI Key takeaways:

• As enterprise AI moves in-house, traditional UX storytelling and performative research reports become obsolete.

• Research deliverables will split into a strict binary: clean, machine-ready data feeds or fully detailed, implementation-ready solutions.

• The external researcher’s value shifts from synthesizing information to extracting verified, real-world data that prevents the internal model from becoming an echo chamber.

• Domain expertise in rigorous data extraction and the ability to implement innovative, realistic solutions will replace narrative packaging as the ultimate credibility signal for clients.

Index

• The local model era is coming

• Research will not be talking to human anymore

• The binary output

• Analysis moves in-house

• Where the value actually is

• What happens when you ignore this

• The future of UX researchers, UX designers, and clients officers

In an AI-first world, the consulting industry is not disappearing, but the core activity of value generation is shifting from presentation to precision and implementation.

Why? As enterprise AI models move in-house and master internal company context, they will take over strategic analysis and render the traditional, narrative-driven consulting report obsolete.

My forecast? Future consulting and ux research outputs will split into two extremes with no middle ground: either clean, structured data designed for machine ingestion, or highly detailed, ready-to-deploy implementation plans.

To survive this shift, consultants must abandon polished packaging and pivot their focus toward deep domain expertise, human-centered data collection, and actionable implementation.

The local model era is coming

Enterprise AI is moving local. Privacy requirements, regulatory compliance, data security, and the need for operational autonomy are pushing industries toward models that run on their own infrastructure. When your model lives in house, your data never leaves. Your IP stays yours and your compliance is built in. Google’s Gemma, open weight small language models, and the rise of edge AI are making this not only possible but inevitable.

Research will not be talking to human anymore

In a model driven world, you won’t be talking to humans. You’ll be talking to in-house LLMs.

Executives already skim to the summary and skip to the recommendations. The research deck was always more performative than functional. Local enterprise models remove the pretense entirely. Data becomes a structured feed optimized for machine ingestion, not human narrative. The report, as a format, loses its reason to exist.

The industry is already building the infrastructure for this shift. What analysts now call Agentic Document Extraction is turning static PDFs into structured, machine readable intelligence. Companies like Box and LandingAI are recovering what the industry calls “dark data,” the overwhelming majority of enterprise information that sits unstructured, unused, and expensive to store. The emerging consensus: if data is not live, it is liability.

And critically, the output now serves the entire company, not one team’s political framing. A model fed with clean enterprise level data does not, cannot play politics. It does not omit findings to protect a department’s narrative (obviously, this will create some frictions with personal agendas at the beginning). When prompted for information internally, the prompter will do the curation.

Aesthetic curation and emotional narrative are dead, but data structuring and information architecture are the new gold standards. Politics shifts from the presentation layer (the slide deck) to the data governance layer (what gets fed into the model) and the prompt layer.

The binary output

My guess? Research splits into two outputs with nothing in between.

• Either you deliver clean, raw, unanalyzed data structured so the client’s model can contextualize it against everything else the company knows.

• Or you deliver fully detailed, implementation ready output precise enough to be deployed without further interpretation. A table with copy/paste-ready content in the last column. (Almost) final-stage product design prototypes. A roadmap with specific steps, owners, and timelines.

Both ends of this split are expert work.

And speed is the reason this middle ground will not survive. AI does not mean less work in less time. It means more work because each unit takes less time. When research gets faster, you do more of it. You do not spend the saved time writing a prettier report. The report was a bottleneck disguised as a deliverable, and the acceleration of research cycles will expose it as one.

Analysis moves in-house

Here is the uncomfortable truth; a client’s enterprise model will always outperform an outside consultant at strategic analysis. Not because the model is smarter, but because it has more context. It knows the company’s data, history, internal dynamics, and goals.

More critically, and it needs to be crystal-clear for consultants: pure analytical strategy naturally migrates in house to the client’s model.

What stays on the consulting side is either expert data collection or expert implementation. Everything in between gets absorbed.

Where the value actually is

The researcher’s job does not shrink, far from it. The analytical layer gets decoupled, and the actionable knowledge layer becomes the primary value. So does an advanced degree in behavioral science, sociology, philosophy, or economics. Basically anyone whose training sharpened their ability to understand how people think, decide, and behave, and what the mechanisms of the solutions will be.

In this new paradigm, domain expertise rises sharply in value. A medical specialist asks sharper clinical questions. A marketing expert catches nuances others miss when talking about a campaign. A designer can make better usability solutions on a product. A veteran researcher with hundreds of IDIs under their belt is better at reading a room during an interview.

You need domain knowledge to understand what the client actually needs versus what they are asking for. Building rapport with study participants. Asking the right questions. But also, animating workshops, leading group ideations, and helping build consensus across departments.

The quality of what you gather, not how you package it, becomes the differentiator.

But expertise here is not only technical or domain-specific. I said it already, and others did too, in an AI-first world, culture, humanities, critical thinking rise too, across domains. The ability to understand context, read people, ask the question nobody else thought to ask. That is expertise.

What happens when you ignore this

What happens in an AI world when storytelling is the primary driver over data collection expertise?

Deloitte provided the case study twice in the same year. First, an Australian government welfare report was found to contain fabricated AI generated citations, nonexistent footnotes, and a fabricated court quote. Then a Canadian healthcare report surfaced with similar issues.

AI did not create the problem. For Deloitte, the packaging was always doing more work than the analysis. When the packaging got automated, the absence of expertise underneath became visible.

The future of UX researchers, UX designers, and clients officers

The expert gathers the data and produces the output. They are the credibility signals that the client is looking at when deciding if your input is valuable. The leadership seat belongs to them.

The previous model did not demand deep domain expertise from everyone in the chain, and that was fine for its time. The new model does. That is not a verdict on anyone but a shift in what the market rewards.

Relationship builders and facilitators remain essential, but in a supporting capacity that enables experts to deliver, not as a filter between the expert and the client.

For professionals who built their careers on narrative and packaging, two paths remain:

• The first is to become an expert in something, an expert that ideally can prescribe actionable solutions. That does not have to mean a niche domain or technical expert (medical, legal, design, etc). It can mean research itself. The craft of extracting clean, trustworthy, model-ready data. Knowing which questions to ask. Reading a room. Understanding what to probe and what to discard. That is a real expertise, and it leads. The critical thinking does not disappear but relocates instead. From how you present the data, to how you gather it right in the first place, and what to do with it.

• The second is to lean fully into the human side. Client relationships, facilitation, conversation and empathy. That is valuable and it has a place. But it is a supporting role.

Realistically, it won’t be immediate, internal politics and personal career trajectories will be in the way, at first. But it will happen. The center of gravity will shift. From storytelling to expertise. From reports to structured data. From human translation to machine ready precision.

The Child and Death, Edvard Munch1899

Index

Local models mean individual accountability, and it’s great news for intellectual property rights

Why local AI is a big deal

Gemma could be the last piece of a toolkit we already have at our disposal

Every piece exists. This is within reach.

A colleague and I were discussing how intellectual property laws will evolve. These are hot buttons in the AI space. It’s both a moral discussion about value and a practical one about legality, and it seems impossible to pacify.

Impossible, I said? Until this week.

Because four days ago (at the time I am scheduling this article…), Google released Gemma 4. Fully open source, Apache 2.0, runs on a phone. A local model. And more importantly, local models also create individual accountability: shifting the IP responsibility from massive corporations to the end user.

This is massive for AI IP laws.

Why local AI is a big deal

Businesses are adopting local AI for reasons that have nothing to do with copyright. Privacy, data security, knowledge management, regulatory compliance. A medical app that processes patient images on-device and never sends raw data to the cloud. A law firm whose case history stays on its own servers. These are nonnegotiable operational requirements driving adoption regardless of the IP debate.

But, more important for this discussion, local AI also makes the IP chain auditable. When a model runs on your machine, trained or augmented with libraries you selected, producing output you distribute, you know what went in and you can prove what was licensed. That changes the entire IP conversation.

AI local models have been around for a while now, but Gemma 4 is a signal for a larger business adoption. And that’s why I think Gemma is the last piece of a puzzle most people didn’t realize was almost complete. Because the AI intellectual property problem looks unsolvable only if you treat it as one problem. It is actually several problems we have already solved, in different industries, using tools that already exist.

Gemma could be the last piece of a toolkit we already have at our disposal

Licensed content libraries

We’ve had them for decades. Stock images, fonts, music samples, 3D assets. The stock photography market alone is a multi-billion dollar industry built entirely on licensing creative work for commercial use. Getty Images and Shutterstock merged in a $3.7 billion deal in January 2025. The infrastructure exists. The payment rails exist. The legal precedents exist. A local model that needs fresh content to stay relevant creates the same dynamic: you want current, commercially defensible output, you license the library. That library needs updating. Creators have leverage. The Spotify model applies: licensing doesn’t need to be perfect. It needs to be more convenient than piracy.

Derivative work law

The Warhol v. Goldsmith decision narrowed fair use for commercial derivatives. Music law has de minimis sampling thresholds. The U.S. Copyright Office confirmed that AI-assisted work with sufficient human expressive contribution can be copyrighted. These legal tools exist and can be calibrated for AI-generated output.

(The question of what is derivative in AI is such a deep topic, I’ll address it in a future article.)

Provenance tracking

C2PA, the Coalition for Content Provenance and Authenticity, provides the technical standard. Over 6,000 members, including Google, Adobe, Microsoft, OpenAI, Meta. LinkedIn and TikTok already display Content Credentials. The EU AI Act mandates AI content transparency starting August 2026. And underneath the wreckage of the NFT hype cycle, the core technology remains useful: an immutable ledger that can tag content with a verifiable record of who created it, who licensed it, and what rights attach. We went through the speculation so the infrastructure could mature.

Transparency labeling

First result when googling ‘Human made YouTube’ in 12/2025: The creator advertises the ‘human-made’ label. The viewer looks for it and praises it. The suspicion that the mention may be deceitful is discussed on Reddit.

Already emerging organically. At least eight certification initiatives across the UK, US, and Australia are building “AI-free” and “human-made” labels. Films are adding credits. Publishers are stamping books. The market is creating a premium for verified human work the same way “organic” and “fair trade” labels created premiums in food. No philosophical consensus required. Just a credible signal.

Local model accountability

Software licensing already requires compliance documentation for every dependency. SBOMs list what went into a product. A local AI model is a dependency. A content library is a dependency. The same logic with the same compliance structure, extended to a new kind of tool.

Every piece exists. This is within reach.

Tech companies benefit from framing AI as so unprecedented that existing law cannot apply. That framing buys time.

Artists benefit from framing the debate around effort and emotional value. That framing generates sympathy.

Neither produces a system. And the longer the debate stays on philosophical ground, the longer the extraction continues without structure.

There is no industry on earth with more political power right now than AI. Tech billionaires have operated from inside the White House. The administration’s advisory council includes Zuckerberg, Jensen Huang, Andreessen, and Sergey Brin. The DOJ has created a litigation task force whose job is to sue states that regulate AI too aggressively (!).

And yet , a wave of state legislators just told them what their products have to do. Nearly a hundred chatbot safety bills are moving across the country, passing with overwhelming bipartisan margins. The industry isn’t stopping it. It didn’t even try.

So what does the NRA have that the most powerful industry in human history doesn’t?

A democratic anchor.

Police powers

Conservative states are demanding the right to use their police powers to protect children from Big Tech. But they strip that exact same power away from their own cities when it comes to protecting children from guns.

Forty-two states have broad firearm preemption laws. If a city like Austin, Nashville, or Atlanta tries to pass local gun control for school zones or public parks, the state government steps in and voids the local law. The states argue that local gun laws create a “confusing patchwork of regulations”: which is exactly the argument tech companies make when lobbying for federal AI preemption (same words!).

Some of these are nuclear preemption, aka laws that threaten local officials with severe fines, personal lawsuits, and removal from office if they even try to pass ordinances keeping guns out of public spaces where children gather.

Don’t thread on me

The contradiction is not hypocrisy. The gun lobby holds something the tech industry does not: a constitutional right.

Big Tech tried to build its own anchor. For a decade, their lawyers argued that algorithms are protected under the First Amendment as free speech. It was a serious strategy. It broke on AI. When a chatbot gives dangerous psychological advice to a minor, courts stopped treating it as protected speech:

Without a constitutional shield, the most powerful industry in history folded the moment dead children entered the conversation.

So when do dead children actually change laws?

The lesson isn’t that tragedy moves the system: gun violence proves it doesn’t, has been proving it for decades, and will likely continue to prove it for a long time.

Which means the path forward isn’t better regulation. It’s democratic framing. Dr. Alondra Nelson, who spearheaded the White House Blueprint for an AI Bill of Rights, has been arguing exactly this:

If citizens establish a right to human truth, a right to algorithmic due process, a right to cognitive liberty, they create a democratic anchor stronger than Silicon Valley’s capital. The US law does not care about the moral argument.

In American democracy, a right is the only thing that cannot be bargained away.