AI Key Takeaways

• AI is a field of construction for a small, fluent minority, and an occasional service for most people.

• Only about one in five US adults relies on AI daily, and only about 3 percent of users pay for it.

• What separates the groups is mostly practice, along with the time, money and exposure that practice requires.

• Many people want relief, AI that removes friction without asking for attention, and embedded defaults such as Gemini deliver it while asking for no decision.

• Products built by builders tend to demand the effort most users hoped AI would remove, and the public asks for control as limits, transparency and the right to refuse.

AI has split into two realities: for a small, fluent minority, it has become a field of construction where almost anything can be imagined and built. For most people, it is a service consulted now and then: a question goes in, an answer comes out.

The products, the hype and much of the public debate are written from inside the first reality, while most users live in the second, and the history of successful products foretell us that the AI that succeeds with the general public will be the AI that serves the second without asking it to become the first.

The open field and the one-way exchange

The builder’s view has a recent manifesto. In April 2020, the investor Marc Andreessen published “It’s Time to Build,” arguing that Western institutions had failed through a reluctance to act and calling for a renewed commitment to building across infrastructure, manufacturing, housing and healthcare. The phrase became a rallying cry in Silicon Valley, and AI has carried it down to the individual. A fluent user can now assemble agents to sort email, compare contracts, prototype an app or automate a weekly report. People in this group are often inventive, and many build alone.

Most people meet a different AI. They type a request into a chatbot and receive a result, the way they would use a search engine. They have heard a great deal about AI, and they use it occasionally, and the open field the builders describe remains abstract to them.

Who gets to be a builder

The builders are a small group. In Menlo Ventures’ 2025 survey of 5,031 US adults, 61 percent had used AI in the previous six months, but only about one in five relied on it daily, and only about 3 percent of users paid for an AI service. Pew Research Center data shows how differently the two groups feel: 47 percent of AI experts said they were more excited than concerned about AI’s growing role in daily life, compared with 11 percent of the general public.

What separates them is mostly practice. A 2025 study by researchers from the University of Maryland, Microsoft and the University of Massachusetts Amherst found that people who frequently use AI for writing tasks could recognize AI-generated text almost perfectly without any training, while people who rarely or never use it were poor detectors and overestimated their ability. Fluency comes from repeated use.

Building also requires a form of conceptual imagination: the ability to see what a tool could do in one’s own work, and to translate that into a request the system can act on.

Awareness that AI exists is common. Understanding its opportunities, its risks and its consequences well enough to design with it is rare, and it depends on time, money, subscriptions and exposure that most people do not have to spare.

Relief as a legitimate demand

Many people also have no wish to become builders. Slavoj Žižek put a similar preference memorably in a 2014 Guardian webchat, speaking about participatory politics.

But for someone working long hours, a tool that requires setup and supervision is one more task.

What this group wants is relief:

AI that removes friction without asking for attention. Look at Google’s strategy. Gemini is built into Gmail, Docs and Android, and Google began winding down Google Assistant on September 4, 2026, moving eligible Android devices to Gemini as their default assistant. When people say “Hey Google,” Gemini now answers. Google announced that the Gemini app had reached a billion monthly active users, a figure critics note is boosted by Gemini’s status as a passive default on Android.

Builders design for builders

Products tend to reflect the habits of the people who build and fund them. Interfaces that assume users will write detailed prompts, chain tools together or monitor agents make sense to the 3 percent who already work that way.

Survey data suggests the public wants a different kind of control. In Pew’s June 2025 survey, 73 percent of Americans said they would let AI assist with day-to-day tasks, and 61 percent said they wanted more control over how AI is used in their lives. That control means limits, transparency and the ability to refuse. Building and maintaining one’s own system is a separate demand that most people have never made.

Two kinds of literacy

AI can work well for most people while the gap between the two realities remains, as long as the gap is acknowledged.

Builders benefit from remembering that their experience of AI is exceptional, and that the general public’s caution is a reasonable response to a technology it encounters mostly as noise, hype and occasional help.

The public benefits from enough literacy to judge the AI that is increasingly built into its phones and inboxes: what it does, what it sees and what it decides on their behalf. Relief without that understanding leaves people dependent on systems they never chose, and the builders are the only group currently in a position to make those systems legible.

The most vertically integrated company on earth looked at frontier AI, ran the math, and rented.

The build-vs-rent line runs through differentiation: Apple rented the model and kept the interface, the servers, and the customer relationship, because those are the layers that distinguish the product.

I wrote this in June; iOS 27 shipped in September and confirmed it, with one condition added on top of it: the rent looks like a bridge Apple built while developing its own model.

AI Key Takeaways

• The facts: Apple based its next foundation models and the rebuilt Siri on Google’s Gemini, for about $1 billion a year, after weighing Anthropic’s costlier terms. iOS 27 delivered the full integration on September 14, 2026.

• Rules one and three confirmed: frontier intelligence is now a procurement category, and the comparison-shopping that decided between Gemini and Claude is the proof at scale.

• Rule four gained a condition: Apple is reportedly developing its own trillion-parameter model to replace Gemini, which reframes the deal as a designed bridge, not a permanent dependency.

• Rule two is under live test: Apple’s privacy reassurances about Gemini query routing have not been backed by granular disclosure, and the gap is acknowledged as of iOS 27’s ship date.

In January 2026, a joint statement from Google and Apple announced a multi-year collaboration: the next generation of Apple Foundation Models would be based on Gemini, after Apple determined that Google’s technology offered “the most capable foundation.”

Bloomberg had reported that Apple would pay roughly $1 billion a year for a custom 1.2-trillion-parameter Gemini model, run on Apple’s own servers. At WWDC in June, Apple unveiled the rebuilt Siri. The final version shipped on September 14, with iOS 27, closing an eight-build beta cycle.

I recently had written about what this means for the model layer in FAANG had an A for Apple. MANGO doesn’t.

This teardown is for decision-makers, because the deal’s structure holds four rules that apply to every company agonizing over build versus rent. This article was drafted in June, now we had four months of evidence to stress it.

Rule one: vertical integration ends where utilities begin

Apple has always known exactly where to stop integrating.

It builds chips because chip decisions differentiate iPhones. It leaves power plants to others, though it consumes gigawatts, because electricity is a utility: uniform enough that owning production adds no distinguishable value to the product.

The transferable question for any company: for each capability on the build list, would owning production make the product distinguishable to a customer, or would it only make the cost structure heavier? Apple’s answer for intelligence was the second, and so far, few companies have answered that question better.

Four months later, the September rollout ran the test: iOS 27 has shipped on schedule. 1.5 billion daily users updated to a Siri running on a rival’s model, and the product held. Turn out, the utility classification was correct.

Rule two: the line runs through differentiation

Apple Intelligence continues to run on Apple devices and Private Cloud Compute, under Apple’s privacy architecture, behind Apple’s interface, inside Apple’s device and wrapped in Apple’s brand.

As of iOS 27’s ship date, the privacy side of this rule is under live test. Apple’s position is that Gemini queries route through its own Private Cloud Compute and are not used to train Google’s models.

Apple has not yet published granular disclosure about how personal data travels between those layers. For companies applying rule two, this is the stress point: owning the behavior is one thing, demonstrating that you own it is another.

Rule three: comparison-shopping is the commoditization tell

Apple tested Anthropic before choosing Google. Bloomberg’s Mark Gurman later said Apple had planned to rebuild Siri around Claude, and that Anthropic wanted “several billion dollars a year”, at a price that doubled every year for three years. A company that puts two frontier vendors side by side, scores them, and lets the terms decide is performing procurement, and procurement is how buyers treat commodities.

When a team evaluates model vendors, does the decision come down to capability gaps, or to price and terms? Once it comes down to price and terms, the strategic question is absolutely settled: the company is buying a utility, and every dollar of engineering spent duplicating it internally builds a private power plant to run the same lightbulbs.

With 1.5 billion daily users now running on the model Apple chose by procurement while the confirmation is at scale. No capability gap separated Google from Anthropic in Apple’s final decision.

Rule four: renting from a rival is survivable when the moat is elsewhere

But, Google historically finances Apple research department. It paid Apple $20 billion in 2022 to be the default search engine in Safari, according to court documents in the Justice Department’s antitrust case. Apple now returns a slice of that to rent Google’s model. Each company holds a hostage.

We see this pattern in older industries, for example Boeing sells to rival airlines, banks share payment rails with competitors, and everyone survives because competition moved long ago to layers the shared machinery leaves untouched.

Looking back now, Apple is reportedly developing its own trillion-parameter model that could replace Gemini as soon as late 2027. That reframes the deal’s reading. In June, renting from a rival read as a calculated acceptance of dependency, survivable because the moat sits elsewhere.

In October, it reads as a bridge: a way to ship a competitive Siri on a rival’s weights while Apple builds its own production capacity. Google collected rent on infrastructure Apple planned to own.

So far, so good

There are limits: Apple’s rules transfer cleanly to companies whose differentiation was never the model, aka nearly every enterprise, retailer, bank and hardware maker. They stop at companies whose product is the frontier (the labs themselves), and at domains where model behavior is the regulated substance and owning the weights is a compliance requirement.

The deal’s second act is already (partly) written now. A Siri arriving two years late on a rival’s model shipped on time and works. What remains open is the privacy architecture under pressure, and whether Apple’s own model, when it ships, performs well enough to make the bridge worth being crossed.

Apple characteristically will not answer these questions before the product does.

So, here it is: the most integration-minded company in business history looked at frontier intelligence and bought it the way it buys electricity.

Four months of evidence suggest it planned to use the time to build its own plant once it understood what it was really needed.

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 affair exposed an industry that could neither recognize AI writing nor discuss it: the Goncourt’s president enjoyed the novel, never suspected it, and says the Académie has no means to verify.

• · The Académie removed the book on the strength of outside analyses it never published, and most major French publishers use no detection tools at all.

• · Unable to argue about machine prose, much of the debate moved to race, a subject the literary world already knows how to discuss.

• · The novel kept moving readers: it reached number one in French bookstores at the height of the scandal, which raises the question of what a reader’s emotion depends on.

• · The case surfaces questions the debate has avoided: what prizes judge, what publishers vouch for, and whether authorship matters when no reader can tell.

The Orélien affair is usually told as a story about one author and one novel. Its more lasting content is a set of questions the French literary world found itself unable to ask.

A debut novel by a Haitian-Canadian writer was praised by critics, placed on the first lists of four major prizes and brought to market by two publishers. Then newsroom after newsroom tested it as overwhelmingly machine-written.

Whether Thélyson Orélien used AI is still unproven, and this analysis takes no side on it. What the affair made visible is an industry that could not recognize AI writing, could not talk about it once it was pointed out, and has no answer yet to a harder question: whether it matters, if readers cannot tell and are moved anyway.

The facts, briefly

Thélyson Orélien, 38, was born in Gonaïves, Haiti, and lives in Quebec, according to The Haitian Times. His first novel, C’était ça ou mourir, follows a Haitian man’s flight north toward Canada. It won the Prix du roman Fnac and appeared on the first lists for the Goncourt, the Renaudot, the Femina and the Médicis, as Presse-citron recounts. On September 21, an anonymous X account, “Balance ton Claude,” posted results from the AI detector Pangram flagging the novel as AI-written.

The tests that followed point the same way. Lucide.ai scored 34 of the novel’s 35 parts at 100% generated content. Radio-Canada ran the full text through Pangram and found 94% flagged, while the other Goncourt-listed novels and eight books by authors of Haitian origin all came back human. Orélien’s own texts from before ChatGPT also tested human.

The tests also have limits. When the AFP ran two passages through seven other detectors, the results diverged, and Pangram’s own chief executive says the tool should only start an inquiry. Boréal argued that its reliability had never been established on francophone novelistic prose.

A jury that could not see

The first question the affair raises concerns perception. The people who judge books for a living read this one closely, and none of them saw anything. Philippe Claudel, president of the Académie Goncourt, said he had greatly enjoyed the novel and had at no point suspected it could have been written by AI. He also acknowledged that the Académie has neither the technical nor the human means to verify.

Research suggests this blindness is common and learned. In a study presented at ACL 2025, people who frequently use AI for writing sorted 300 articles into human and machine-written almost perfectly, while people who rarely use it were poor detectors and overestimated their own accuracy. In a 2024 study in Scientific Reports, non-expert readers identified AI-written poems at 46.6% accuracy, below chance, and rated them higher than poems by Shakespeare and Dickinson on qualities such as rhythm and beauty.

What the readers who flagged the novel pointed to were features they associate with machine writing: runs of strange similes, the same idea stated three times in one sentence, stock formulas. Orélien described those same features as his own, rooted in Haitian and Caribbean oral tradition. The same page reads as a machine’s tics to one reader and as a living voice to another.

Which of them a literary jury should be able to tell apart, and whether reading skill now includes that, is a question nobody in the institution had prepared for.

An industry that could not talk about it

Once the question was raised, the institutions had no language for it. On September 25, the Académie Goncourt removed the novel from its first list, describing it as “selon toute vraisemblance très largement le produit d’une intelligence artificielle” on the strength of analyses by researchers and journalists. The decision was not unanimous, and the statement presented neither the passages in question nor the method behind those analyses.

Publishers were in the same position. The major French houses, from Actes Sud and Le Seuil to Gallimard and Robert Laffont, told France Inter they use no detection software at all. In Quebec, none of the six houses that answered Radio-Canada uses such a tool, although five have added AI clauses to their contracts. The responses that did appear were symbolic: Éditions de l’Observatoire launched a “100% humain” label for its literary titles.

On October 6, the Goncourt published its second list. Claudel said all eight remaining novels had been run through an AI detector and had passed “haut la main, si l’on excepte quelques scories ridicules,” and he proposed changing the prize’s rules to forbid the use of artificial intelligence.

What a “use of AI” ban would cover (a full draft, a translation, a spellcheck, a brainstorm) and who would check it are questions the rule has yet to answer.

The debate it reached for instead

Faced with an accusation it could not evaluate, much of the literary world turned to ground it knew. Grasset spoke of “une campagne de haine,” a hate campaign. Claudel noted that the author is Haitian and Canadian, Black, francophone, foreign, and writing about migrants. In Le Monde, Benjamin Hoffmann argued that a virulent campaign once again targeting a racialized person should prompt vigilance.

That reading drew on real experience and real history. Orélien said he had faced racist attacks, and Boréal suspended promotion of the book after hateful messages against him. In 1968, Yambo Ouologuem became the first African author to win the Prix Renaudot, was accused of plagiarism months later, and never published again; defenders have long argued that the attacks on his authorship were driven largely by racism.

The people behind the tests stood elsewhere. Samuel Fitoussi, a Figaro columnist, said he was one of the people behind the accusing account, driven by a fear that AI would make reading and writing disappear, and the newsrooms built their controls to test for bias directly.

The two conversations ran side by side on different ground, each answering a question the other was not asking.

If readers are moved, what was the prize for?

The hardest question sits with the readers. At the height of the scandal, C’était ça ou mourir reached number one in French bookstores, with 64,966 copies sold in six weeks. Some readers bought it “en soutien,” in support of the author. Others had been moved by it before anyone raised a doubt, Claudel among them. Their emotion was real, whoever or whatever produced the sentences.

French literature has met a version of this before. In 1975, the Goncourt went to Émile Ajar for La Vie devant soi. Ajar was a persona invented by Romain Gary, who had already won the prize in 1956, and the truth emerged only after Gary’s death. Readers had been moved by an author who did not exist, and the book survived the revelation.

The case then splits the question in several directions. If a reader is moved and cannot tell, what exactly would AI authorship take away from that reader? If prizes reward the writing alone, a flagged novel’s place on four first lists says something about what that writing now looks like, and if they also reward the author’s profile and the urgency of the subject, the prizes have never said so. A migration novel adds a further layer, because part of what it offers is lived testimony, and it is unclear whether testimony produced by a machine would still be testimony to the reader who wept over it.

But, should we care? We should at least discuss it openly

The Orélien case may end with drafts that prove his authorship, or with evidence that does the opposite. Either outcome answers the question about one book.

The questions about the literary world stay open either way: what a prize jury is judging when it cannot see how a text was made, what a publisher vouches for when it prints a name on a cover, and what a reader is owed when the emotion arrived intact and the origin is in doubt.

The industry has so far answered with detectors and labels, and the debate it still has to hold is whether a book’s value lives in the experience of reading it, in the person who wrote it, or in a promise between the two that nobody has yet put into words.

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

The static screen is dead. With AI, digital design is moving past the era of the fixed interface. We are structuring underlying systems instead of just building destinations.

You know who does that very well? Game designers.

AI Key Takeaways

• Digital products sit on two axes, static to dynamic and triggered to ambient, and the open quadrant is ambient dynamic: interfaces that reorganize themselves from accumulated behavioral signal without the user asking.

• In that quadrant the primary design artifact is the rule set, which signals accumulate, how they are weighted, and what the system produces when they cross a threshold.

• Game design has worked this way for decades, treating feedback as a product of system state, which makes it the discipline to study for this era of product design.

• Accessibility becomes an output the system produces from observed behavior instead of a setting the user has to know about and configure, which breaks WCAG-style certification because there is no single state to audit.

• Static design serves a primary audience and degrades outward; ambient dynamic design holds the primary experience intact and extends range by adding rules.

Static vs. Dynamic design / Triggered vs. Ambient AI

Most digital products operate over two axis:

• Static and triggered covers most of the web.

• Static and ambient includes recommendations and algorithmic feeds.

• Triggered and dynamic is generative UI.

• Ambient and dynamic is the space, the last quadrant, we are going to talk about.

The first is static and triggered. A user clicks, taps, or submits, and the interface responds. The structure remains identical for everyone. This has been the default model for websites and apps since the beginning.

In 2026, approximately 80 percent of content discovery on the platform is driven by these recommendations rather than manual search. By leveraging the fact that the human brain processes images 60,000 times faster than text, Netflix turns the interface into a proactive salesperson that identifies user intent before a single word is typed.

The second mode is static and ambient: personalization. The system continuously observes behavior and surfaces different content, like a recommendation, an algorithmically selected thumbnail, or a feed ordered by predicted relevance. The interface container stays fixed while the content changes. Take Netflix’s hero image and Amazon’s recommendations. The layout is identical for every user; the algorithm works beneath it to propose custom content that influences engagement.

Generative UI is a third, emerging mode: dynamic and triggered. A user asks a question, and the system builds an interface in response (a simulation, a custom tool, or a visual experience assembled on the fly). The output is genuinely dynamic, but it requires a prompt, with someone actually querying the model.

“Figure 1: We present Gradual Generation as a method for designing customizations in generative UI applications. Gradual Generation proposes UI/UX designers to organize UI customizations into multiple intermediate layers that AI progressively loads during interface generation, enabling users to discover and access customizations by rewinding to earlier stages.”

The fourth mode, dynamic and ambient, has been named Gradual Generation of UI, or Malleable Software / design. The interface reorganizes itself continuously and proactively based on accumulated behavioral signals, without user initiation. It is neither a selection from existing variants nor a response to a query. But instead, it is an ongoing, invisible calibration to who is using the product and how.

A-to-I

Consider a California two-spot octopus moving into colder water. Unnatural for it. Its DNA has not prepared it for this. Cold slows down the gates that reset nerve cells after they fire. When those gates lag, the nerves cannot fire again quickly enough, and the animal’s ability to move, hunt, and respond begins to fail.

Within hours, the octopus begins editing its genetic messengers, its RNA, at over 13,000 sites. First, the proteins that physically carry cargo along nerve fibers are reconfigured to move faster, compensating for the slowdown caused by the cold. Second, the proteins that trigger chemical signals between nerves are reconfigured to alter their sensitivity, restoring the exact timing of the nervous system. The system works again. When the water warms, the edits reverse.

The genome never changed and the same DNA remains in every cell. This demonstrates a system capable of reading the environment, identifying what was failing, and modifying its own components to compensate. That intelligence layer, rather than a simple biological feature, is the essential condition for survival and thriving in adverse environments.

What it means to design for ‘Ambient dynamic’

On a product detail page, a user lands, scrolls past the hero, opens the reviews, closes them, opens them again, zooms into the second image, compares two alternative products, and returns. In a static product, that behavior goes into a log. In an ambient dynamic product, it becomes a design instruction.

The information architecture reorganizes around demonstrated priority. A buyer who always checks materials and provenance before price receives a hierarchy reflecting that sequence. This happens because the system accumulated enough signal to act, not because the user set a preference. A spec-first buyer receives a technically rendered product. A buyer who responds to lifestyle context sees the product placed in one. It is the same page presenting a different argument.

In a dynamic experience, hero imagery adapts to inferred visual preference instead of selecting from a fixed set of existing assets. Voice and tone shift register based on observed behavior rather than assumed demographics. Onboarding dissolves as competence is demonstrated. Navigation reorganizes around workflow without requiring manual configuration. The shape and register of assistance changes with the user profile. A first-time earner on a financial dashboard receives proactive bubbles surfacing before confusion arrives, avoiding a chatbot waiting to be asked.

The system keeps score. None of this requires the user to signal intent.

Rules, not responses

A static product designer thinks about feedback, like hover states, click states, error messages, confirmations. Each one is a response to a discrete action, fully predictable, designed individually. This process relies on a collection of defined responses rather than a holistic system.

In comparison, a game designer thinks about the system first. The feedback is what the system produces at a given state: the same action produces different feedback depending on where the player is, what they have accumulated, what thresholds they have crossed. You cannot design the feedback without understanding the rules that generated it.

An ambient dynamic product works the same way: now what a user sees is a consequence of accumulated score across observed signals. You cannot design that state the way you would design a hover state. You can only design the rules and thresholds that produce it. A designer trained on static products who approaches this work at the level of responses is working at the wrong level of abstraction.

The rules define which signals accumulate, how they are weighted, and what the system produces when they cross a threshold across layout, information architecture, imagery, voice, and tone.

That rule set is the primary design artifact. It needs to be mapped, documented, and maintained. It can grow over time without breaking what exists, adding new signals, new surfaces, new thresholds as research surfaces, caring for new users and new behaviors.

Accessibility as a native output

The current accessibility model depends on a user recognizing they have a need, knowing a setting exists, finding it, and configuring it. For many people, one of those steps fails.

A system keeping score on behavioral signals doesn’t need self-identification. Slower navigation, repeated re-reading, friction at specific interaction types are signals. Larger text, simplified language, more confirmatory feedback are outputs the system produces when those signals accumulate past a threshold. The experience adjusts automatically for individuals who would never consider configuring settings manually.

This also breaks the current model of accessibility certification. Obviously, WCAG and similar frameworks were written for static products: a fixed set of requirements that your fixed interface either meets or doesn’t. A product that adapts differently for every user has no single state to audit. How do you certify something that is, by design, never the same twice?

To infinity, and beyond

The moment adaptation becomes proactive and dynamic, a product is in the fourth quadrant. What grows from there is depth: how many signals the system reads, how many surfaces it adapts, how rich the rule set is.

Static design serves a primary audience and extends to others in a degraded form. Dynamic, ambient design keeps the primary experience intact and builds range outward from it. Research shifts from validating what exists for a known audience to encountering more users and uncovering new behaviors. These findings translate into new rules for the system to act upon. Each round expands the product’s capabilities without disrupting what already works.

This requires a complete reversal of how we think about communication. In static design, we orchestrate signals moving outward from the screen to the user. In GenUI / Malleable design, we design for signals moving inward from the user to the machine. This demands both systematic intelligence and a design intelligence for systems. Leveraging this model means codifying the underlying rules that allow a machine to read behavioral signals and adapt every layer of the experience: from information architecture and layout to UI, text, visuals, and heuristics.

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 response to Allie Paschal’s “The hidden cost of AI prototypes that are made to die“. I argue that why she is auditing a thirty-minute artifact against a process built for a designer-week, and calling the artifact the problem. AI changed the pipeline. Asking whether a prototype can survive handoff assumes handoff is still a stage.

AI Key Takeaways

• AI prototypes are disposable because they moved upstream into ideation, the phase where ideas are supposed to die cheaply. Evaluating them for survival into production reads the direction of change backwards.

• Rework counted as waste when an artifact took a designer-week to produce. Once generation is nearly free, the costs that remain are choosing what to give the AI and judging what comes back.

• Cheap generation concentrated prototyping in expert hands: selecting the model’s inputs and recognizing a flawed output are both design judgment.

• A research job that ends in a prototype is now a design job with research as a prerequisite. That’s most jobs now.

• The failure mode worth worrying about is the polished prototype that refuses to die and gets mistaken for shippable product.

Allie Paschal’s “The hidden cost of AI prototypes that are made to die“ argues that product teams pay later for prototypes built to be thrown away, and that teams should favor tools whose output can survive into production. I read the same evidence and reached the opposite conclusion.

It is symptomatic of anxiety I am seeing more and more in research and product teams.

A response.

Validating ideas quickly is the ideation phase. The ideation phase is where nearly every AI-generated prototype is born, does its job, then dies. That single line covers almost everything these tools are used for, and the rest of the essay argues with what’s left over.

Her taxonomy of AI app-builders (full-stack generators, visual builders, design-native tools) is accurate and worth keeping. My disagreement is with the scorecard she builds on top of it:

Rework was only expensive because artifacts were

The old design-to-development pipeline was continuous for an economic reason: a high-fidelity mockup cost a designer-week, so abandoning one was irresponsible, so every artifact had to earn reuse downstream. The wireframe fed the mockup, the mockup fed the spec, the spec fed the build. Then, handoff existed as a ritual both designers and engineers endured because artifacts were too expensive to throw away.

Generation now costs minutes. So at that price, reuse stops being a virtue and there is no sunk cost left to protect. What still costs something is the judgment wrapped around the artifact: deciding what the AI should build, and recognizing what is wrong with what it returns. I’ve argued in a previous article that judgment is the asset the whole AI economy is repricing, and the prototype question is that thesis at desk scale.

Re-read her three survival questions with that inversion in mind:

“- Can the prototype be extended without starting over?

- Can it be handed off without reinterpretation?

- Can it live outside the tool that created it?”

>> All three price the artifact but none of them prices the decision it produced. A prototype that dies after thirty minutes, having ended a three-week internal debate, delivered more value than one that limps into a codebase carrying an unexamined assumption.

The tool puts the process on trial, bye bye handoff?

Ask whether a prototype can survive handoff and you have already assumed that handoff remains a stage.

AI entered the profession’s story as a faster way to produce the same artifacts, and it keeps being graded that way, while the pipeline those artifacts served was itself a workaround for their old price. I documented the same pattern in research methodology, where methods stay calibrated to conditions that stopped holding and the templates keep running on inertia.

AI moved the prototype upstream and handed the artifact to more people, earlier, cheaper, with less attachment. The polish that reads like ambition to graduate into the codebase is a side effect of price: fidelity got cheap, so everything looks finished. Mistaking that finish for intent is how a thirty-minute artifact ends up audited against production criteria.

Two people could make these prototypes, only one should

The first is a researcher with a recommendation. Before AI, that recommendation shipped as a slide deck with a lo-fi sketch, because anything richer required designer time no one would spend on a proposal that might die at the next prioritization. Now the recommendation can be experienced. The request designers keep hearing in rooms is some version of “I don’t want a recommendation deck, show me the thing.” Meeting that demand matters more than it sounds, because every gap between artifact and product is space where a non-expert bridges with imagination and lands on assumptions you will spend a quarter correcting. The closer the artifact sits to the final look, the less imagination gets invited. That’s a good thing for product teams.

The second is a designer with a proposal: research findings, stakeholder feedback, and their own knowledge, built into candidate directions. Ten mockups in an hour means no emotional stake in any single one. The sunk cost fallacy, the oldest distortion in design critique, starves.

The expertise did not travel with the tool

Anyone can technically generate the prototype now, including that researcher. Generating a good one is a different job. It requires knowing what to give the model, and it requires the eye that catches the flaw in what comes back: the interaction that can’t exist, the pattern that violates the platform, the technical dead end, the accessibility nightmare. An expert spots those at the prototype stage and corrects them before the stakeholder review, at a cost of minutes. Both acts, the selection and the catch, are design judgment.

The pushback these tools generate from designers starts to make sense here: they have never had more responsibility in the product pipeline, and are still held to old frameworks that don’t let them deliver on it. The public conversation covers what the machine generates, while the actual job now lives in what the designer chooses to give it and what she refuses to accept back. That choice is the design act. And choice requires taste, knowledge, expertise that are not, and shouldn’t, be expected from researchers.

Let the old UXR process die, too, as we’re at it

This whole reflection comes back to our collective reluctance to face the fundamental change AI-generation tools imply. They are accelerants that question the validity of UXR steps themselves. I have heard designers argue that lo-fi wireframes still have value in a world where we can generate final looking screens in seconds. They are clinging to old paradigms that make no sense from a client / business perspective. At this point, it’s thinking about the craft more than the needs and expectations. We are designing for outcomes, not for process. Process-first practice is what Art does.

Some things are easier to translate from before AI, the mental model is the same or close enough, so they encounter less pushback: a look-and-feel prototype from Figma Make or a Claude artifact plays the role the Figma click-through always plays, and a clean one carries research-reviewed content the way a linear-quality mock always has. Design at the service of Research, following Research’s script, the way Research has always used design’s material.

But it works only for static. It crumbles as soon as you are testing dynamic systems: embedded intelligence can’t be scripted.

From a process standpoint, UXR is fundamentally testing hi-fi products. To simplify, the useful outcome is a set of concrete instructions for the code (personality for a bot, dynamic decisions for an interface etc.). The model needs to be coded first. The UI is a prerequisite for testing. Technical expertise precedes research, limits research. The order of things is disturbed.

Read more about how to test for dynamic system using red-teaming here.

Even that artifact stays disposable. A live-model sandbox is an afternoon of work and an API playground. The new shape of UXR for dynamic systems is a different conversation that I won’t expand in this piece, but that, again, puts the designer (and engineer) at an earlier place in the product pipeline.

It’s hard to hear for most teams, because it feels uncomfortable for people who see that reorganization, as inevitable as it is, as a threat to their influence.

The prototype that refuses to die

Her essay closes with advice for product teams: evaluate AI tools by whether their prototypes can survive, prefer output that engineers can extend, code that can leave the tool that made it. The cost she wants teams to avoid is the rebuild, when engineering rewrites what the prototype already showed.

In working teams, the hazard is a vice president who sees a polished demo once and wants it on next quarter’s roadmap, because it already looks shipped. The demo was built in an afternoon to test whether an idea lands and now it’s a committed feature.

Following her advice would make that scenario more likely. A team that treats prototypes as future production code teaches its stakeholders that a finished-looking demo is progress toward shipping. The VP asking “why isn’t this live yet” has learned that lesson perfectly. The team’s one protection is the shared agreement that prototypes are disposable: the demo arrives labeled as an experiment, and “it looks done” has an answer, which is that it was never on the road to production.

Better code underneath makes it worse. The prototype answered an ideation question. It never asked whether the feature is accessible, whether it fits the technical environment, whether it survives the hundred constraints production exists to honor. Those questions got asked during the rebuild, the exact step her essay counts as waste. Remove the rebuild and the unexamined assumptions ship with the demo.

When a team runs a paper prototype, no one audits it for accessibility or compatibility with the tech stack, and no one asks why the pencil came in the wrong color. The AI prototype deserves the same courtesy. It was built to answer a question and die. Let it.

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

In a randomized trial published in JAMA Network Open in October 2024, fifty American physicians were tested on complex diagnostic cases: GPT-4 working alone scored a median 92%, physicians with GPT-4 available to them scored 76%, and physicians using conventional resources scored 74%.

AI Key Takeaways

• A randomized trial showed GPT-4 working alone outperforming both physicians with GPT-4 (92% vs 76%) and physicians without it — the model beat the doctor, and the model beat the model-plus-doctor.

• Non-maleficence, medicine’s oldest ethical commitment, has a second edge: a duty not to leave available accuracy on the table.

• The standard of care is not set by regulators; it is set retrospectively by what a reasonable practitioner would have done — and one case is enough to move it.

• A follow-up in Nature Medicine found improvement on management reasoning, which complicates the picture and does not change the core: the performance gap is widening, not closing.

The word for this is non-maleficence

Medical ethics rests on four principles, and one of them is non-maleficence, the duty not to inflict harm. It is the oldest commitment in the profession, older than consent, older than the codes, and it is what the phrase primum non nocere refers to. In law the same idea appears as the duty of care, and a breach of it is what negligence means.

Non-maleficence is normally invoked to restrain intervention. Do not operate unnecessarily. Do not prescribe what carries more risk than benefit. Do not experiment on a patient for your own curiosity.

A duty not to harm is also a duty not to leave available accuracy on the table. If a documented, cheap, widely accessible tool would have caught what you missed, the harm to your patient was avoidable, and you are the reason it was not avoided.

Looking at the numbers, and numbers are all that matters (to me) when they are not just numbers, but people, I believe it is not. And I think the profession will discover this through litigation rather than through debate. The sequence is easy to predict diagnosis is missed. The family’s lawyer runs the case through a model, which produces the correct answer in ninety seconds. He then produces the trial data showing that this was foreseeable at the population level, and asks the physician on the stand why he did not use it.

Once one case lands, the standard of care moves. Standards of care are not set by regulators; they are set by what a reasonable practitioner would have done, and what a reasonable practitioner would have done is established retrospectively, in court, by reference to what was available. Nothing needs to be legislated. Nothing needs to be adopted. The obligation arrives through liability, and it arrives whether or not anyone in the profession wants it.

The endpoint is a reversal of the current position. Today, AI in medicine is discussed as a tool a physician may choose to use, with the physician as the authority and the machine as the assistant. The reversal makes the machine the baseline and the physician the exception, required to document why he departed from it. Prescribing without that check becomes what prescribing while refusing to read the chart is now.

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.

The mechanism in the 2024 trial was not straightforward machine superiority. The physicians who had GPT-4 largely did not revise their view when it disagreed with them. They anchored on their own initial impression, which is why the assisted group barely outperformed the unassisted one. This is a finding about human deference, and it cuts in an uncomfortable direction: it suggests that giving doctors the tool is insufficient, and that capturing the accuracy would require making the machine primary and the human the reviewer. That is a larger change than adding software to a workflow.

A follow-up in Nature Medicine found the opposite result on a different task. Ninety-two physicians given model access alongside conventional resources did improve the quality of their reasoning on patient management. Diagnosis and management come apart here.

And this gap is not closing. When OpenAI’s o1-preview was run against the same six cases, it scored a median 97%, against GPT-4’s 92% (whatever the correct account of why doctors fail to use these systems well), the distance between unaided human performance and machine performance is widening, and the duty not to harm is indifferent to the explanation.

Soon it will be malpractice to diagnose or prescribe without AI oversight.

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

A delivery robot in Los Angeles finds its way using photographs that Pokémon GO players took of lampposts and storefronts. A bankruptcy judge priced fifteen million genomes at $305 million. A smart-ring CEO swore on stage that your sleep data will never be sold, and he was telling the truth. Three stories, one machine running underneath.

AI Key Takeaways

• Consumer products accumulate datasets as a side effect of use, and the dataset’s real value tends to surface after the product’s original purpose has ended.

• The lifecycle recurs in five stages: capture, accumulation, trigger, second life, denial. The cases differ; the sequence holds.

• The transfer uses a different door every time: a licensed model (Niantic), a court-supervised auction of the whole company (23andMe), a content license (Reddit).

• Consent attaches to the product, and the asset’s life attaches to the dataset, so every second life begins outside the consent that funded the capture.

• The public has learned the pattern faster than the industry has learned to talk about it, which is why sincere denials now read as evasions.

Coco Robotics runs delivery robots in Los Angeles and a handful of other cities. When one of them crosses a street, it checks its position against a model trained on 30 billion images of urban space, and it can place itself to within centimeters. The images were taken by people playing Pokémon GO and Ingress.

The scans were quest steps, rewarded in Poké Balls, disclosed in the terms, aimed at public landmarks. Then, in March 2025, Niantic sold its games division to Scopely for $3.5 billion and spun its technology out as Niantic Spatial, a company whose website leads with the phrase “real-world foundation models for physical AI.” In December 2025, Niantic Spatial announced a partnership with the defense firm Vantor to put its positioning capability into navigation for drones operating where GPS is jammed or denied. The route from a Snorlax hunt to a military drone took a few years and broke no promises along the way.

What happened to those scans is a lifecycle. It runs on ordinary business logic, no secrets required, and its stages are visible in public filings and press releases. Once you can name them, you will find the same machine running in a genetics company’s bankruptcy, in a smart ring’s public relations crisis, in a fitness app’s heatmap, in fifteen years of Reddit threads. Consumer products accumulate datasets as a side effect of being used. The value of the dataset tends to surface later, in another market, after the product’s original purpose has ended. This essay is about the machine.

The machine has five stages

The same sequence appears in every case that follows.

1. Capture. A product collects data as a side effect of use. The collection is disclosed and consented, and the consent is framed entirely by the product’s purpose: scan the landmark, spit in the tube, wear the ring to bed.

2. Accumulation. The dataset compounds while the product runs. It sits on the books at a value near zero, because nothing has forced anyone to price it.

3. Trigger. A business event forces a price: a pivot, a bankruptcy, a licensing deal, an acquisition. The trigger has nothing to do with the users, who usually learn about it from the news.

4. Second life. The value realizes in a market the original consent never imagined. Mapping. Drug discovery. Robotics. Model training. Defense.

5. Denial. The company states, accurately, that it has never sold anyone’s data. The transfer used another door.

The map outlived the game

Each stage of the machine is documented in Niantic’s case.

1. Capture: players scanned real locations as an opt-in game mechanic for most of a decade.

2. Accumulation: MIT Technology Review reports the resulting training set at 30 billion images of urban environments, thousands of overlapping views per location, taken at different angles, times of day, and weather conditions.

3. Trigger: the Scopely sale, which converted a games company holding a side-effect dataset into a spatial-AI company holding a core asset, funded with $250 million to develop it.

4. Second life: first delivery robots, then, through Vantor, drones for the defense market.

Stage five arrived on schedule. When the drone coverage landed in mid-2026, Niantic Spatial told Kotaku that player data was never shared with the defense contractor. The statement holds up. That is the shape of the modern transfer: the dataset stays in place, and the value extracted from it walks out the front door wearing a license agreement.

The genome bank outlived the company

23andMe entered bankruptcy in March 2025 holding DNA from about fifteen million customers, and its collapse produced something rare: a public price for a consumer dataset. Regeneron, a pharmaceutical company, won the first auction at $256 million. The court reopened the bidding, and in late June a judge approved the $305 million offer of TTAM Research Institute, a nonprofit led by 23andMe founder Anne Wojcicki. Between the two auctions the company remained dead. The asset gained $49 million.

Bankruptcy is the stage-three trigger in its purest form, because it removes all discretion: a court requires that assets be enumerated, marketed, and sold to the highest bidder. The kit business had stopped working years earlier; a genome only needs to be sequenced once, so the product model consumed its own market. The bank the kits filled was the durable thing, and both serious bidders wanted it for the same second life, research and drug development, a purpose customers had been invited to opt into under a company that, by the time the question mattered, existed only as an estate.

The revolt arrived before any event

Oura is the case where nothing happened, and that is what earns it a chapter.

In 2025 the company published a blog post about a new factory in Fort Worth and partnerships in support of military readiness. A rumor cycle concluded that Palantir had access to customer health data. Customers posted videos throwing away their rings. At Fortune’s Brainstorm Tech conference, CEO Tom Hale answered from the stage: “We will never sell your data to anyone, ever.” The verifiable reality was small: a company Oura had acquired held an ordinary software contract with Palantir, and no customer data flows through it.

Read through the lifecycle, the panic is pattern recognition. Oura’s customers have watched the machine complete its cycle elsewhere. A sleep-and-body dataset drawn from millions of people, held by a growth-stage company signing defense-adjacent partnerships, sits visibly at stage two, and the crowd priced the trajectory.

I have written about privacy cynicism, the settled belief that collection is pervasive and beyond individual influence Check my article about the Panopticon generation.

The ring-trashing videos are the other face of that literacy. The audience has learned the machine, and it no longer waits for stage four to react.

Hale’s promise deserves a close reading, because it is the strongest reassurance the industry knows how to give, delivered sincerely, on the record. It is a promise about the sale of data.

The pattern predates the panic

None of this arrived with the current AI wave. In January 2018, Strava published a global heatmap of user workouts, and an analyst noticed jogging loops glowing in the Syrian desert. Soldiers’ exercise logs, aggregated, had drawn the perimeters and internal layouts of military bases that appeared on no satellite map. A running app’s side effect turned out to be open-source intelligence. In February 2024, the same week it filed for its IPO, Reddit announced a deal reported at $60 million a year giving Google access to its archive for AI training. Fifteen years of people talking to each other became a priced input for someone else’s model, and the pricing event was, again, a corporate milestone with no user in the room.

Consent is versioned to the product

The research world solved a version of this problem long ago, which is how I know the consumer world hasn’t. When a participant consents to a study, the consent names a purpose, a duration, and a custodian, and reusing the material for a new purpose means going back to the participant. Product consent attaches to the product.

Every player who scanned a Pokéstop agreed to something real. The entity they agreed with no longer makes games. The question “may we use your scans to guide drones where GPS is jammed” was never asked, because at capture time the use did not exist, the partner did not exist, and the company as currently configured did not exist. The second life begins, structurally, outside the consent that funded the capture.

What the denial answers

There will be more Ouras, because the reassurance template is ready and every company can sign it in good faith: we have never sold your data, and we never will. All of the companies in this essay could put their names to that sentence today. Nobody sold your data. The asset moved anyway. Hold the next partnership announcement, the next pivot, the next bankruptcy docket against that sentence.

The product is the visible thing, and products will keep dying the way products die: pivoted, acquired, shut down, auctioned for parts. The dataset is what survives, and at some point the trust conversation will have to move to where the survival happens: custody terms that outlast acquisition, deletion that outlasts bankruptcy, consent that names the second life or forbids it. Until then, the most accurate privacy policy on the market is one no company has published: whatever you help us collect will outlive whatever you thought you were using.

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

Design leaders keep building investment cases for a function the company treats as optional. Flip the ledger: bad interfaces are a live expense, and in the AI era they can quietly burn the entire model budget.

AI Key Takeaways

• “Design ROI” arguments fail in boardrooms because they accept the wrong frame: design as an optional investment with speculative returns, argued with numbers too good to be believed.

• Interface costs are already on the books, filed under support, training, error remediation, abandoned funnels, and compliance exposure. They are simply never attributed to design.

• Safety-critical industries already think this way: aviation prices the cost of the failure a checklist prevents, and nobody computes the checklist’s return.

• AI raises the stakes. The model is rented plumbing available to everyone, and the interface is where adoption, trust, and the entire AI investment succeed or die.

• The practical move is a cost audit: pull the support logs, count the workarounds, measure the steering effort, and attach the existing spend to the screens that generate it.

Somewhere right now, a design director is assembling a deck to prove her team deserves to exist. She has the McKinsey study showing design-led companies outgrow their peers. She has the famous claim that every dollar invested in UX returns a hundred. She has case studies, before-and-afters, a slide titled “The Business Value of Design.”

She will lose, and the numbers won’t be the reason. The exercise concedes the argument before it begins.

The frame is the defeat

The ROI argument accepts three premises the moment it opens its mouth. That design is optional: a discretionary investment competing with everything else on the CFO’s list. That its value lives in the future: a return to be realized, someday, if the projections hold. And that the burden of proof sits with design, in a way nobody asks of legal, security, or accounting.

Accept those premises and the strongest evidence in the world won’t save you, because the evidence has a credibility problem of its own. A hundred-to-one return is a vendor’s number, and a CFO hears it as one. If design reliably returned a hundred dollars on one, capital would have flooded the function decades ago without a single deck being written. The ROI genre’s numbers are so good they refute themselves. Executives reject the design case because it arrives shaped like marketing, and for no deeper reason than that.

The frame that survives contact with a boardroom runs the other way. Your interface is not a pending investment, but a live expense. It is spending your money right now, today, whether or not anyone is watching the meter. So ask yourself what your current design already costs.

The ledger nobody reads

Interface costs are real, present, and sitting in your books under other names.

They’re filed under customer support: every ticket that exists because a flow was ambiguous is a design cost wearing a support badge. Under training: every hour spent teaching employees to operate internal software is the price of decisions someone made in a tool, years ago, and nobody has audited since. Under error remediation: rework, refunds, reconciliation. Under revenue operations: the abandoned carts, the stalled onboarding, the enterprise feature nobody adopts. Under shadow process: the spreadsheet an entire department secretly uses because the official system is unbearable. You paid for the system, and now you pay again in duplicated labor and unmanaged risk.

Occasionally the ledger surfaces where everyone can see it. Jared Spool’s famous “$300 Million Button” documented a retailer whose registration form (one form, one forced login) suppressed roughly three hundred million dollars a year in sales before anyone thought to question it. In 2020, Citibank wired $900 million to Revlon’s lenders by mistake, an error a federal court traced in part to a loan software interface whose checkbox logic three separate employees misread the same way. Citi eventually recovered the money, after two years of litigation it had no guarantee of winning. Nobody had ever asked the ROI of that screen. The screen billed them anyway.

These cases make headlines because of their size, but their structure is ordinary. Every organization runs interfaces that misprice themselves this way. The losses are distributed finely enough (a ticket here, an abandoned session there, a workaround nobody mentions) to stay below the threshold of attribution.

Industries where failure is priced already know this

I spent five years inside aviation manufacturing. Nobody in that world computes the return on investment of a checklist. Nobody builds a business case for making a warning label legible.

Safety-critical industries figured out long ago that prevention argued as “investment” loses to prevention argued as “exposure,” because exposure is already on the risk register, and investment is competing with next quarter’s priorities.

FDA medical device pathways

Healthcare knows it. Finance knows it, at least after each fine. These are also the industries where design is least discussed as a differentiator and most embedded as a control, and that is no coincidence. When the cost of interface failure is legible (a grounded aircraft, a mis-dosed patient, a nine-figure wire), nobody needs persuading that the interface is a serious object. The design conversation only degenerates into ROI theater in industries where the failure costs are just as real but nobody has done the accounting.

AI turns the leak into a flood

When every competitor can rent comparable intelligence from the same three vendors, the model is a shared cost line, and the layer where a human decides whether to trust the thing is the layer that isn’t shared. That layer is the interface. I’ve made this argument at length before. What matters for the cost math is this:

An enterprise AI deployment is a stack of sunk costs (licensing, integration, security review, data work) capped by a thin surface where an employee decides, in the first handful of sessions, whether this tool is worth steering. If the interface fails to signal what the system can and can’t do, fails to make uncertainty legible, fails to help the user recover the first time the model stumbles, adoption dies quietly. And when adoption dies, the failed interface bills you for the entire stack underneath it.

The widely cited MIT finding that some 95% of enterprise generative-AI pilots deliver no measurable P&L impact is usually read as a model problem or a strategy problem. Having watched these deployments up close, I’d put it differently: the pilot dies in the gap between the demo and the Tuesday afternoon workflow, and that gap is a designed surface. The most expensive interface in your company is now the one wrapped around your AI spend.

This is a cost argument, and I want to keep it one; promising that better design will make the AI investment soar would be the ROI trap again. The claim is that the current interface is where your AI investment is presently evaporating, at a measurable rate, and that the meter runs whether or not you look at it.

Run the audit

So retire the ROI deck. Replace it with cost accounting, presented the way a CFO already thinks.

• Pull twelve months of support tickets and tag the share caused by comprehension failures, as distinct from product defects. In most organizations that share is the majority, and it converts directly into dollars.

• Count the workarounds: every unofficial spreadsheet and side channel is a receipt for an interface that failed, so price the duplicated labor.

• Put a number on training that exists only to compensate for unintuitive internal tools.

• Measure abandonment where money leaks (onboarding, checkout, renewal) and attach the existing revenue loss.

• For AI products, measure steering effort: how many turns users need to get an acceptable output, and where they give up and go back to the old way.

• Then present one number: this is what the current interface costs per year. Fixing it stops an expense the company has already verified.

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

I laid out the methodology shift AI forces on design research: live models over scripted mockups, red-teaming over happy paths, longitudinal logs over lab sessions, rulesets over personas. This is the operational sequel: how to actually install those four shifts in a working team, in what order, and where the installation fails.

AI Key Takeaways

• The four shifts of dynamic systems research are a system, but teams shouldn’t adopt them as one. There is a correct order, and it starts with the cheapest change: the boundary-testing brief.

• Legacy methods are calibrated to a product that stays fixed after shipping.

• The most common adoption failure is hybrid theater: teams keep the scripted Figma prototype and add one “AI question” at the end, which produces the old data with new vocabulary.

• The second failure is tooling panic. Live-model sandboxes require an API playground and a written system prompt, and that’s all they require. Waiting for a platform purchase is a stall.

• Full adoption inverts the researcher’s role: from validating what the team built to mapping how the system behaves.

Every deliverable research produces has the same reader: a human in a room. The persona, the journey map, the readout with its recommendations slide, written to be read, nodded at, and remembered imperfectly.

AI products change the addressee. The most useful thing research can hand over now is something the system itself can consume: rules the model runs, failure cases the evals replay, constraints engineering can query later. Everything that follows: the four shifts, the order they install in, the ways the installation fails, is downstream of that one switch:

But, the argument for dynamic systems research hasn’t landed.

A year of conversations taught me that practitioners keep defaulting to their static-system audits and their fixed analysis frameworks. Researchers, designers, developers, they don’t yet see what a probabilistic product asks of research, or why methods built for fixed screens can’t answer it.

Part of this is comfort. These are people who are good at what they do, and expertise makes it hard to notice when the object of that expertise has changed underneath you. Part of it is awareness. Trust that forms over weeks, behavior that drifts at the boundaries: those questions never appear on a static audit, so nobody misses the answers.

This piece has two jobs. Make the case, then show how a working research team learns to evaluate behavior, in an order that survives a real roadmap.

One reassurance first, because it defuses the resistance that kills most rollouts. Your methods aren’t wrong. They’re calibrated to a product that no longer exists. Usability scripts, concept tests, personas: each was a correct answer to a world where you design a thing, ship the thing, and the thing stays.

Step one: change the brief

Start with the cheapest shift, the one that requires no new tools, no budget line, and no permission: replace the happy-path task script with a boundary-testing brief. Instead of “ask the assistant to help you find a flight,” the instruction becomes “try to confuse it, contradict yourself, change your mind halfway.” Same lab, same participants, same hour.

This goes first for a political reason as much as a methodological one. The first boundary-testing session produces findings the team has never seen: the model stalling, the interface hiding uncertainty, the user failing to notice a hallucination for four turns. Those findings sell the rest of the transition better than any argument.

Step two: put a live model in the prototype

The second shift replaces hardcoded responses with a live model driven by authored system prompts.

The fix costs an afternoon: write two or three system prompts encoding the tonal or behavioral variants you’d otherwise have scripted, put participants in front of an API playground, and let their questions vary the way real questions vary.

The failure mode here is tooling panic: teams stall for a quarter waiting for a research platform to add “AI testing features.” Nothing about this step requires procurement. A playground, a system prompt, and a note-taker constitute the entire stack. The team that runs its first live-model study on borrowed API credits is two quarters ahead of the team waiting for a vendor.

Step three: extend the study past the session

The third shift is where the calendar and the budget actually move: from single-session evaluation to multi-week prompt-stream analysis. Trust in a probabilistic system is cumulative. It forms and erodes over repeated, unmonitored use, and no 45-minute session can observe it. Participants use a live prototype inside their real workflows for two to three weeks, and the conversation logs become the primary data: how many turns to an acceptable output, where rephrasing cascades signal friction, the exact moment a user abandons the tool for their old workflow.

This is the step that requires stakeholder management, because it changes the shape of research deliverables from a readout after a study week to a trust curve after a month.

Step four: change what you hand engineering

The last shift converts the output. Static persona documents give a model nothing to operate on; behavioral rulesets give it constraints: when the user shows crisis signals, shorten outputs and confirm explicitly; when the user is exploring, tolerate ambiguity and offer branches. Written well, the ruleset doubles as an evaluation dataset, which means research stops delivering inspiration and starts delivering testable specification.

This shift goes last because it depends on the other three: you can’t write behavioral rules for contexts you’ve never observed, and you can’t observe them in a scripted session.

The two failure modes to watch

Adoption fails in two recognizable ways:

1. The first is hybrid theater: the team keeps the scripted prototype, keeps the happy-path script, and appends an “AI question” to the discussion guide. The deliverables look updated and the data is unchanged. The tell is a readout that still reports task completion rates for a system whose tasks are open-ended.

2. The second is skipping to the tooling. A team buys a logging platform before it has changed a single brief, and the new dashboard fills with data nobody has a question for. The sequence exists because each step generates the demand for the next: adversarial sessions surface behaviors worth testing live, live testing surfaces patterns worth tracking over weeks, and longitudinal patterns surface the rules worth handing to engineering.

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

• Usability testing measures the happy path, the one scenario the designer already knew would work. In a probabilistic system, the product’s fate is decided at the boundaries.

• UX red-teaming adapts adversarial testing from AI safety into a qualitative design method: participants are briefed to confuse, contradict, and push the system until it fails.

• The moderator’s role inverts, from guiding participants to task completion to silently cataloging a UX Vulnerability Matrix of breakdowns and recoveries.

• The working standard: if the interface doesn’t help a user recognize and correct a model error within two turns, the design has failed, whatever the satisfaction scores say.

• Red-team findings are the highest-value research artifact an AI team can hold, because every finding is a production incident discovered at lab prices.

Every usability test I’ve ever seen shares one quiet assumption: the session is designed to go well. The tasks are achievable, the script follows the intended flow, and the moderator gently steers drift back onto the path. For deterministic software this was defensible. The happy path was the product, and confirming that users could walk it answered the question that mattered.

An AI system doesn’t have a happy path in that sense. It has a distribution of behaviors, and the product’s fate is decided at the edges of the distribution: the moment it stalls, drifts, contradicts itself, or states a falsehood with perfect fluency. Those edges are exactly what a task script is designed to avoid.

Borrow the method from the people who test attacks

Red-teaming, inherited from security practice, assumes that a system’s advertised behavior and its actual behavior diverge, and that the divergence is found by adversarial probing. Model labs run standing red teams against their own systems before release.

Design research needs the same posture, aimed at a different layer: the safety red team asks what the model can be made to do. The UX red team asks what happens to the human when the model does it: whether the interface exposes the failure or hides it, how long the user takes to notice, and what it costs them to recover. That layer belongs to design, and nobody else is testing it.

The protocol

The brief

Give the participant a genuine goal, then license the adversarial posture explicitly: try to confuse it, ask something contradictory, change your mind halfway through, push until it gives you something wrong. Participants default to politeness with machines in labs, and politeness produces happy-path data. You are recruiting the participant’s natural chaos, the same chaos production will supply for free later.

The moderator’s silence

The moderator observes and does not rescue. Every intervention destroys the data point you came for, because in production nobody will be sitting next to the user. The discipline is harder than it sounds; moderators are trained helpers, and watching a participant argue with a confidently wrong model for three minutes violates every instinct the profession installs.

The Vulnerability Matrix

Findings get cataloged along two axes: what the system did (stall, drift, hallucination, context loss, refusal error) and what the human experienced (noticed immediately, noticed late, never noticed, recovered alone, recovered with effort, abandoned). The most dangerous cell is fluent failure the user never noticed. Every entry in that cell is a trust incident scheduled for production.

The two-turn standard.

A red team needs a pass/fail line, and this is mine: when the model errs, the design must help the user recognize the error and correct it within two conversational turns. Past two turns, frustration compounds, trust discharges, and abandonment follows. The standard is strict on purpose. It converts a vague aspiration (”graceful failure”) into something a team can test against and argue about.

What comes back from the first session

Teams running their first adversarial session reliably discover three things:

1. First, the model fails more interestingly than anyone predicted; the failure taxonomy in the backlog is a fraction of the real one.

2. Second, the interface almost never signals uncertainty where the model actually is uncertain; confidence styling is uniform across correct and incorrect outputs.

3. Third, users blame themselves first. They rephrase, apologize to the machine, and try again, sometimes for several turns, before concluding the system is wrong. That last finding changes roadmaps, because every turn a user spends absorbing blame for a system error is trust leaving the product, invisibly, while the analytics record an engaged session.

This is also why red-team findings are the cheapest incident response a team will ever buy. Each lab-discovered failure is one that will not first surface in a customer’s screenshot, a support escalation, or a regulator’s file.

Where it sits in the practice

Red-teaming precedes evaluative research. Run the adversarial pass first, map the failure modes, fix what’s fixable, and then measure the experience of the system you actually have. Run it again on every meaningful model update, because behavior you validated last quarter is not behavior you have this quarter; the distribution moved under your feet.

The resistance, when it comes, is usually aesthetic: adversarial testing feels hostile to a team proud of its product. The reframe that lands is that production is the adversarial test. Thousands of users will supply contradiction, confusion, and midstream pivots at scale, without a brief, and without a moderator taking notes. The only choice is whether the failure map gets drawn in a lab for the price of ten sessions, or in public for the price of the product’s reputation.

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

• AI creates generic, minimalist designs that can make digital interfaces feel forgettable and indistinguishable from one another.

• Minimalism no longer serves as a reliable marker of good taste because AI can now generate clean, professional-looking layouts instantly.

• Industry leaders are shifting toward bold, expressive design languages to ensure their products stand out as intentional human choices.

• Designers must now prioritize opinionated and unmistakably unique styles to differentiate their work from automated defaults.

Generate a website with AI and you already know what you’ll get. Lots of white space. A thin sans-serif. A soft gradient. Rounded cards in a tidy grid. It will be clean, balanced, and completely forgettable. It looks professional, which is exactly the problem, because so does everyone else’s.

For two decades, minimalism signaled taste. Stripping a thing down to its essentials took restraint and judgment, and the clean result told you a careful person had been there. That signal worked because not everyone could produce it. Now everyone can. The machine produces it by default, instantly, for free.

This is the trap I described before. When every output clears the same minimum bar of clean-and-professional, the bar stops telling anyone anything. A non-expert looking at your restrained, considered interface and a generated template sees two clean layouts. They can’t feel the difference, so to them there isn’t one. Your good taste has gone invisible, because it now looks like the floor.

The people who lead design at the top have noticed, and their answer is to get loud.

Mauro Porcini, now Samsung’s first Chief Design Officer, says it plainly: the tech industry should abandon minimalism for a more expressive design language. He points to fashion, cars, and architecture, where people expect personality and variety and are drawn toward complexity rather than away from it. Google made the same move with Material 3 Expressive, its most heavily researched design update ever, built around bold color, shape, and motion. Its own design lead told Dezeen that user testing found an appetite for interfaces that were “wild and way-too-playful.” Apple shipped Liquid Glass this year, a heavy, computationally expensive material that nobody could mistake for a default. None of these are safe choices. That is the point of them.

The strategy underneath is the one my whole argument keeps circling. Distinctiveness only counts when an untrained eye can register it as a decision. Expressive, opinionated, slightly divisive design reads as something a person chose on purpose. Clean and minimal no longer reads that way, because the machine got there first and does it for nothing. So you don’t escape the automated sameness by refining the default into a more tasteful version of itself. You escape it by making something loud enough that no model would have produced it.

This is uncomfortable for a generation of designers raised to revere restraint. Restraint was the whole aesthetic, the mark of someone who knew what to leave out. But restraint and the machine’s output now occupy the same visual space, and when you share a look with the free option, you lose. Being a little better than the template is worth nothing if the people paying can’t see the gap. You have to be unmistakable.

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 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.