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“You shouldn't have to worry whether or not the apartment you're viewing online is real,” Zohran Mamdani said in July, when he put artificial intelligence on New York City's list of things that rip off renters. Then came the line that made the headlines: “After all, it's called StreetEasy, not StreetHard.”

It is a good joke because it is a sad one. StreetEasy, the Zillow-owned portal, is the city's dominant rental listing platform, and the first thing it offers anyone is a photograph. The photograph is the listing's opening argument. It decides whether you book a viewing, whether you take an afternoon off work, whether you cross two boroughs on a Tuesday evening, whether you send a deposit for a place in a city you have not yet moved to. For most of the history of online property search, that argument could be exaggerated with a wide-angle lens, a flattering hour of the day or a strategically placed plant. It could not easily be invented.

Now it can. A room can be furnished by software in seconds. A grey sky can be swapped for a blue one, a scuffed floor refinished, a cracked facade repainted, a car park turned into a lawn. The practice has acquired a name, housefishing, borrowed from the catfish who poses online as someone they are not. And in mid-September the mayor went back to the subject in a post on X. “New Yorkers should know whether the apartment they're looking at is real,” he wrote. “That's why we're working to require apartment listings to clearly disclose when images and videos have been generated by AI.”

The promise sits inside a larger package. On 16 July, City Hall published a 68-page Rental Ripoff Report setting out 23 actions drawn from hearings held in all five boroughs between February and April 2026, and from testimony submitted by more than 2,400 New Yorkers. Most of it concerns things that have nothing to do with AI: pests, which featured in 16 per cent of testimony, and mould and leaks, at 13 per cent each. Tucked among the heating complaints and the tenant union proposals is a commitment that the Department of Consumer and Worker Protection will use its rulemaking powers under the city's Consumer Protection Law to require a “clear and conspicuous” disclosure on rental listings containing digitally altered photographs, including images altered with AI.

The rule is still being drafted. Its details, including who is liable and what the penalty will be, are not yet public. But it arrives at a moment when the same problem is surfacing in London, Dublin, Detroit and Sacramento, and when regulators on both sides of the Atlantic are testing different answers to the same question. When the picture that opens the door can no longer be trusted, is it enough to tell people the picture is fake? Or does the rental market need to be told what it may and may not show people before they sign?

A Market With 1.41 Per Cent of Room to Breathe

To understand why a photograph matters so much in New York, start with the vacancy rate. The city's 2023 Housing and Vacancy Survey, the statutory census of the rental market, found a net rental vacancy rate of 1.41 per cent, the lowest since 1968 and down from 4.54 per cent in 2021. For flats renting below $1,100 a month, the vacancy rate was 0.39 per cent. Among households earning less than $25,000 a year without rental assistance, 86 per cent were severely rent-burdened, spending more than half their income on housing.

In a market that tight, the balance of power in a listing is not subtle. Renters compete for scarce units, move fast and often decide on thin information. Landlords and brokers rarely need to persuade anyone to rent. What they need is to generate enquiries, and an attractive image generates enquiries whether or not it resembles the unit. The Rental Ripoff Report says so plainly. “Landlords and or brokers regularly use photos that have been altered, either digitally or through artificial intelligence (AI), in their rental listings,” it states. “These photos are typically not a true representation of the space and cost New Yorkers vital time and money during their apartment search.”

That phrase, time and money, is where the harm lives. The department that will draft the rule has form in this territory. Since the Fairness in Apartment Rental Expenses Act, known as the FARE Act, took effect on 11 June 2025, brokers hired by landlords have been barred from charging tenants their fees, and listings must disclose the fees a tenant will pay. The department has issued more than 70 summonses under the law. It has also, according to amNY's reading of the report, encountered numerous listings for flats that did not exist at all, used as bait to draw renters into paying illegal broker fees for different units.

A fabricated listing and a doctored photograph are not the same thing, but they share a mechanism. Both exploit the gap between what a renter sees on a screen and what they can check before money changes hands. The department's commissioner, Samuel Levine, previously ran the Federal Trade Commission's Bureau of Consumer Protection, where he pursued junk fees and subscription traps. In July's announcement he said: “Every New Yorker deserves a safe and stable place to call home, without having to navigate deceptive practices.”

Not everyone agrees this is where the city's energy should go. The Next Web noted that landlord groups argue a thicket of new disclosure rules adds cost and friction without fixing supply. Brick Underground, the New York property site, placed the AI measure among the report's longer-term changes rather than its quick wins. Those objections are worth holding on to. A disclosure rule does not build a single flat. The question is whether it stops the flats that already exist from being misrepresented, and whether a label can do that job at all.

The Eleven Per Cent Nobody Mentions

How common is the problem? The best available count comes, with an obvious caveat, from a company that sells AI tools to estate agents. Coraly, a property technology firm, examined just under 40,000 primary listing images from Zillow, Redfin, Realtor.com and Homes.com during the first quarter of 2026. HousingWire reported that 10.8 per cent, or 4,330 images, showed indicators of digital alteration. More than 90 per cent of those altered images carried no visible disclosure.

The composition matters. Sky replacement accounted for 69 per cent of the alterations. Virtual staging, where software adds furniture to an empty room, made up about 10 per cent, and object removal about 1.5 per cent. The analysis flagged 218 images that appeared to be computer-generated renders presented as photographs. Exterior shots were altered at 13.1 per cent, interiors at 4.5 per cent. Kitchens were almost never touched, at 0.7 per cent, and bathrooms barely at all.

Read one way, that is reassuring: most of it is cosmetic, and the rooms where condition matters most are mostly left alone. Read another way, it shows the industry's defaults. Changing the weather is so routine that nobody thinks to mention it, which means the norm is already that a listing photograph is an illustration rather than a record. The distance from a better sky to a better view, a larger garden or a quieter street is a matter of how far a given agent is willing to go.

HousingWire's account of why disclosure fails is mundane and damning. “An agent engages a photographer who delivers JPEGs with no metadata, no record of what was altered,” it noted. The person uploading the listing may not know what was changed, and the person viewing it certainly does not.

Scale is the other shift. When Vice looked at AI staging in British listings in February 2024, it reported that one provider, Virtual Staging AI, had 3,000 paid subscribers, including UK agents, and was generating 500,000 images a month from $12 a month. Its chief executive, Michael Bonacina, described the disclosure regime at the time: “Usually, it's up to the agent to disclose in descriptions. Some also add a watermark.” In October 2024 Zillow Group bought the company. In September 2025 Zillow launched AI virtual staging for its Showcase listings, letting buyers restyle rooms in several design styles and swipe back to compare with the original. Redfin launched a similar tool, Redfin Redesign, in December 2023, letting shoppers change walls, floors and worktops in listing photos.

There is a distinction worth keeping here. A tool that lets a viewer imagine a room differently, clearly labelled and paired with the original, is a very different thing from a seller quietly presenting the imagined room as the real one. Zillow draws the same line. “We support clear disclosure when AI is used to materially alter a listing image, and believe consumers should be able to view the original content alongside the altered version wherever possible,” a spokesperson told Fortune, adding that “AI should help set accurate expectations before a tour, not create surprises when a buyer walks through the door.” StreetEasy told Business Insider that it expects listings to represent a home accurately “whether AI is involved or not”. The platforms are not hostile to regulation. But they own the tools and host the listings, which gives them every reason to favour a rule that keeps the tools in business.

The Leafy View That Was Not There

The problem is not confined to New York, and the clearest recent account of what it feels like comes from south London. On 21 September 2026, the Big Issue published an interview with a couple it identified only as Holly and Richard, who had moved to London for new jobs and had three months to find a two-bedroom flat around Brixton and Streatham. They were searching from outside the city, relying on photographs and remote viewings.

“It looked like they'd had an interior designer in,” Holly said of the listings. “You could just sort of tell it wasn't real.” One flat was advertised with a leafy green outlook. When they visited, the view bore no resemblance to the picture. “That is so important to your property,” she said. “To think that's what you're going to have and then for it to not work out that way is quite frustrating.” She also described the pressure of the search: “There was a lot of pressure to bid. You never know if it's true or not, but we would be told there were three or four other people bidding with you. That led us to make quite quick decisions.”

That last detail is the important one. A misleading photograph does its damage in combination with speed. “When you're not in the area you're trying to rent in, you're putting a lot of trust in the person showing you the house,” Holly said.

Renters' groups were blunt. Eleesha Taylor-Barrett, identified by the Big Issue as national chair of the renters' union Acorn, said: “Renters don't have time to be misled. If someone wouldn't have booked a viewing had they seen an honest representation of the property, they shouldn't have been misled into going in the first place.” Dan Wilson Craw, deputy chief executive of Generation Rent, said: “AI-manipulated images give renters a false idea of the home and can lead to renters making misinformed decisions.”

The industry voice in the piece, Charlie Lamdin of BestAgent, drew a line between optical exaggeration and fabrication. “Making rooms look bigger isn't an intentional trick, whereas using AI to manipulate room appearance is,” he said, while arguing that “there's no harm to using AI to create 'what it could look like' images” if they are clearly labelled and shown alongside the real condition.

Two years earlier, Vice had found estate agents on Rightmove and Zoopla using AI furniture in rental listings, sometimes scaled to make small rooms look larger. Omar Beg, managing director of the London agency McBryer Beg, told Vice that “some agents can be a little bit flexible with the truth”, describing listings showing furniture fitted into a space “where you couldn't swing a cat”. The technology has improved since. The temptation has not changed.

Who Pays When the Door Opens on a Different Flat

The cost of a misleading listing depends almost entirely on how far the renter has travelled before they find out. That is what makes housefishing a question of inequality rather than just of taste.

For a renter who lives nearby, has flexible working hours and can view five flats on a Saturday, a doctored photograph wastes an hour. The reality check comes at the door, before any commitment. For a renter moving from another city or country, working shifts, caring for children, living with a disability or searching against a deadline, the photograph carries far more weight, because it is often the only inspection they get before paying. Zillow's own consumer research has found that about one in five recent renters skipped in-person tours entirely. For those renters, the listing is the flat until the keys arrive.

Money moves early. New York's Housing Stability and Tenant Protection Act of 2019 caps application and background check fees at $20 and security deposits at one month's rent, which limits the upfront exposure in a way many markets do not. But a deposit, the first month's rent, a moving van and a lease are all committed before a remote renter walks in. A flat that turns out to be darker, smaller or on a noisier street than advertised leaves them with a set of bad options. They can live with it, try to break the lease, or pursue a misrepresentation claim, which requires time, evidence and often a lawyer.

The burden also falls unevenly across rent bands. In New York's lowest-rent segment, with a vacancy rate below half a per cent, renters have the least ability to walk away from a disappointing flat because there is almost nothing else to walk to.

This is the part of the story that a disclosure label cannot see. A label tells you something about the image. It does not tell you what the renter can afford to do with that information. A well-off renter who notices “digitally altered” in small type can go and look. A renter relocating across an ocean may read the same label and have no option except to trust the listing anyway.

The Fine Print That Did Not Save Detroit

In early 2026, a house in Detroit's Dexter-Linwood neighbourhood became a lesson in what disclosure does and does not achieve. A social media post comparing the AI-enhanced Zillow images of the property with its actual state drew millions of views. The listed version had a smoothed, freshly painted facade. The real one did not. “It looks fake. It looks like a painting,” a local resident, Jasmine Jackson, told WDIV's ClickOnDetroit.

The property, listed off-market on Zillow for $20,000, was owned by Redstone Group, an affordable housing developer that, according to ClickOnDetroit, serves Section 8 clients. The company said the broker had generated the images and that the listing did disclose that the house was “under renovation” and that the photographs were “virtually staged”. Its representative put it bluntly: “The person who posted that original story either missed the fine print or ignored it.” The episode had its own cruel coda. The company said the attention prompted break-ins in which doors, windows and furnaces were stolen.

Set aside who was right. In Detroit, a disclosure existed and the public still felt deceived. When a label is in the fine print and the image is the whole page, the image wins. That is not a failure of the reader. It is how images work.

British advertising rules already acknowledge this. The CAP Code, which the Advertising Standards Authority enforces, says in rule 3.1 that “marketing communications must not materially mislead or be likely to do so.” Rule 3.3 goes further, requiring that material information is not omitted, including by “providing such information in a way that is unclear or untimely, or in a way that the consumer is unlikely to see or hear it.” In other words, a disclosure buried where nobody reads it is itself a potential breach.

New York's proposed wording, “clear and conspicuous”, points in the same direction. The difficulty is that the proposal, as reported by amNY, does not yet define penalties or say whether responsibility falls on landlords, brokers or platforms. Those details will decide whether it works. A disclosure duty that nobody is on the hook for is a suggestion.

What Labels Have Taught Regulators Before

Regulators have tried labelling doctored photographs before, in a different context, and the evidence from that experiment should give New York pause.

In 2017 France began requiring commercial photographs of models whose bodies had been digitally slimmed or thickened to carry the words “photographie retouchée”. The decree, dated 4 May 2017, took effect that October, with fines of up to €37,500. Norway went further in 2021, amending its Marketing Act so that paid advertising, including influencer posts, must carry a standard government label when body shape, size or skin has been retouched.

The research on whether such labels change how people respond is sobering. In a 2013 study in the journal Body Image, Marika Tiggemann and colleagues at Flinders University showed women fashion advertisements with no label, a generic label or a detailed label saying the image had been digitally altered. Across two experiments, the type of label made no significant difference to body dissatisfaction. People knew the images were retouched. They compared themselves anyway.

A 2024 study pointed to the opposite risk. Sacha Altay and Fabrizio Gilardi, publishing in PNAS Nexus, ran two preregistered experiments with 4,976 participants in the United States and the United Kingdom. Labelling news headlines as AI-generated lowered their perceived accuracy and people's willingness to share them, whether the headlines were true or false and whether a human or a machine had written them. Participants assumed the label meant full automation with no human oversight. The authors concluded that labels “should be approached cautiously” and that effective labelling “requires transparency regarding their meaning”.

Put those two findings next to a rental listing and the problem becomes clear. A label may fail to correct the impression a beautiful image creates, as the retouching studies suggest. It may also cast doubt on honest listings, as the headline study suggests, if a photograph with a replaced sky gets the same “AI” tag as a room invented from nothing. Neither outcome helps a renter decide whether to spend a morning on a viewing.

The broader legal scholarship is harsher still. In More Than You Wanted to Know, published by Princeton University Press in 2014, the law professors Omri Ben-Shahar of the University of Chicago and Carl Schneider of the University of Michigan surveyed mandated disclosure regimes across consumer life and concluded that they rarely achieve their goals. Disclosures pile up, people skip them, and the obligation to disclose becomes a shield for the discloser. The problem is not that people are foolish. The problem is that a label asks the person with the least information and least time to do the correcting.

None of this means disclosure is useless. It creates a legal hook: a listing that should have carried a label and did not is an easy enforcement case, far easier than proving that a picture was materially misleading. But it does suggest that a label alone, stuck on a doctored image, is the weakest version of the remedy.

California Chose the Original Photograph

The most instructive answer so far comes from California, and it goes further than a label.

Assembly Bill 723, written by Assemblymember Gail Pellerin and signed in October 2025, took effect on 1 January 2026. It applies to property for sale. Any broker or salesperson who uses a digitally altered image in an advertisement must include a statement that the image has been altered and a link, URL or QR code to a publicly accessible site that includes, and clearly identifies, the original unaltered image. The statement must be “reasonably conspicuous and located on or adjacent to the image”. The law defines an altered image broadly, covering changes to fixtures, furniture, appliances, flooring, walls, paint colour, landscaping, facades, floor plans and neighbouring properties, while exempting routine adjustments such as lighting, sharpening, white balance, colour correction, cropping and exposure.

The key move is the original photograph. California does not simply warn buyers that a picture has been changed. It hands them the unchanged picture and lets them see the difference. Multiple listing services in the state have translated that into practice: the San Diego and California regional MLS guidance requires the unaltered image to sit next to the altered one.

Pellerin has now extended the same logic to renters. Assembly Bill 2025 requires anyone using a digitally altered image to advertise a rental to disclose the alteration on or next to the image and, where it is based on a real photograph, to provide the original or a link to it. It passed the state Senate on 24 August 2026, the Assembly concurred the next day, and Governor Gavin Newsom signed it in the final days of September. It takes effect on 1 January 2027, and from that date California renters will have the protection New York is still drafting.

Other legislatures are following. Wisconsin's 2025 Act 69, which takes effect on 1 January 2027, requires licensees to disclose in advertising when technology, including AI, has been used to add, remove or change elements of a property in a way that creates a false or misleading impression. In New York State, Senator Rachel May introduced S9584 in March 2026. It would treat materially altered listing images, videos and virtual tours as deceptive practices unless clearly disclosed, defining a material alteration as one that would affect a reasonable consumer's understanding of a property's condition, features or layout. It cleared the Senate Consumer Protection Committee unanimously in April and now sits with the Judiciary Committee.

New York's Department of State had already fired a warning shot. In November 2025 it issued a trend alert on AI-generated listings, noting that “these automated technology tools may produce misleading or exaggerated representation of properties” and reminding licensees that state rules require advertising to include “an honest and accurate depiction of the property”. That is the most important point for the city's draft rule. New York already had a standard of honesty. What it lacked was a specific way to enforce it against a new kind of image.

The industry's own trade bodies are not far away from this. The National Association of Realtors has said AI should “show possibilities” in a home and “not rewrite reality”, Real Estate News reported in September. Rich Rosa, a past president of the National Association of Exclusive Buyer Agents, put the risk precisely: “The concern is the gap that can occur when images imply features, scale, or conditions that aren't there in person, especially when digital changes are not clearly disclosed.”

Europe Already Calls a Fake Room a Deepfake

Across the Atlantic, the law has approached the same problem from a different angle, and in one respect it already goes further than any American proposal.

The EU's AI Act defines a deepfake in Article 3 as “AI-generated or manipulated image, audio or video content that resembles existing persons, objects, places, entities or events and would falsely appear to a person to be authentic or truthful.” Note the word places. A photograph of a real flat altered by AI so that it falsely appears authentic fits that definition. Under Article 50(4), which has applied since 2 August 2026, deployers of AI systems that generate or manipulate deepfakes must disclose that the content has been artificially generated or manipulated. The European Commission's draft guidelines, published in May 2026, confirm that the definition extends beyond people to objects and places, and that deceptive intent is not required. Fines can reach €15 million or 3 per cent of global turnover.

The provision is best known as a response to political deepfakes, and how regulators will apply it to a virtually staged bedroom in Lisbon or Lyon is untested. But the principle is on the books: in the EU, a synthetic image of a real place that could pass for real must say so.

The UK has no AI-specific rule for property images. What it has is general consumer law with sharper teeth than before. Since 6 April 2025, the Digital Markets, Competition and Consumers Act 2024 has allowed the Competition and Markets Authority to investigate and decide consumer law breaches itself, without going to court, and to impose fines of up to 10 per cent of global turnover. The Act replaced the Consumer Protection from Unfair Trading Regulations 2008, under which National Trading Standards' Estate and Letting Agency Team published guidance in November 2023 on the material information that sales and lettings listings must include, from price and council tax to room measurements and flood risk. The guidance already warns that photography should not be used to avoid disclosing material details.

The Renters' Rights Act 2025, whose main provisions came into force in England on 1 May 2026, changes the stakes too. It ended fixed-term assured shorthold tenancies in favour of periodic tenancies, banned rental bidding above the advertised rent and gave tenants the right to leave with two months' notice. That last point matters. A renter lured into a misrepresented flat in England now has a way out that does not involve a court case. It does not refund the cost of moving twice, but it caps the damage.

Ireland offers a third model. The Irish Examiner reported in August 2026 that the Advertising Standards Authority for Ireland's voluntary best practice is for AI-enhanced property images to be clearly marked, with limits on what may be changed: no enlarging buildings, hiding structural flaws, removing poles and wires, adding windows or doors, or replacing views. That last list is worth pausing over. It is not a disclosure rule. It is a list of things you simply may not show.

Changing What a Listing Is Allowed to Be

Taken together, these regimes suggest the answer to the question New York has set itself. Disclosure is necessary but not sufficient. What actually protects renters is a change in what a listing is.

The first step is the one California has already taken for buyers, and will extend to renters from January: the original photograph must travel with the altered one. A label asks a renter to imagine what has changed. An original shows them. It turns the image from an argument into evidence, and it costs the landlord nothing except the pretence.

The second is a short list of things that cannot be altered at all, however clearly labelled. The Irish best practice list is a good starting point: the size of rooms, the view from the windows, structural condition, the street outside. A virtually staged sofa in an empty room is a sales tool. A garden that does not exist is not a stylistic choice. For the features that most determine whether a flat suits someone, the photograph should be a record, not an illustration.

The third is liability that follows the listing. The city's draft does not yet say who is responsible, and that silence is the rule's biggest risk. Platforms such as StreetEasy and Zillow control the upload process, own staging tools and could require originals at the point of upload. Landlords and brokers choose what to post. Leaving liability vague invites each to blame the other, as the Detroit case showed.

The fourth is technical and should be approached with care. Content provenance standards such as C2PA's Content Credentials can attach a signed record of how an image was made and edited. They could, in principle, let a listing show a verified original. But the standard cannot prove a negative: an image without credentials might be honest, old or simply unsigned, and a valid credential does not prove that a picture is fair. Provenance can support the rule. It cannot replace it.

The last is a remedy that matches the harm. England's two-month exit and New York's caps on fees and deposits already reduce what a misled renter loses. A rule on doctored images should add the right to walk away before the tenancy starts, with the deposit returned, when the flat does not match the listing in the respects the rule protects. That is what turns a disclosure into a deterrent. A landlord who knows a doctored view will cost them the tenancy has a reason not to doctor it.

None of this addresses the underlying scarcity. A vacancy rate of 1.41 per cent is not a problem any image rule can solve, and the landlord groups are right that honesty in listings does not build homes. But scarcity is exactly why the rule matters. When renters cannot walk away, the least they are owed is an accurate picture of what they are being asked to accept.

Holly, searching for a flat in south London from another city, asked for something modest. “If they are using it, can we just make sure that they're using it carefully?” she said. “The agent needs to know the property and make sure AI hasn't manipulated it in a way that gives an inaccurate representation.” It is not a radical demand. It is the premise a listing was supposed to rest on all along: that the flat in the picture is the flat behind the door.

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Sources and References

1. Fortune, “'It's called StreetEasy, not StreetHard': Mamdani cracks down on scourge of 'housefishing' and 'real estate slop'“, 22 July 2026. https://fortune.com/2026/07/22/mamdani-ai-altered-rental-listing-disclosures/
2. NYC Office of the Mayor, “Mayor Mamdani Releases 'Rental Ripoff Report,' Outlining New Actions to Crack Down on Hazardous Housing Violations and Deceptive Landlord Practices”, press release, 16 July 2026, with the accompanying 68-page Rental Ripoff Report (PDF). https://www.nyc.gov/mayors-office/news/2026/07/mayor-mamdani-releases--rental-ripoff-report---outlining-new-act and https://www.nyc.gov/assets/tenantprotection/downloads/pdf/rental-ripoffs-hearing-report-072026.pdf
3. Mayor Zohran Kwame Mamdani (@NYCMayor), post on X, September 2026.

   New Yorkers should know whether the apartment they’re looking at is real.
   
   That’s why we’re working to require apartment listings to clearly disclose when images and videos have been generated by AI.
   
   Finding a home in New York is hard enough without artificially generating more…

   — Mayor Zohran Kwame Mamdani (@NYCMayor) September 16, 2026

4. Adam Daly, “Mamdani lays out 23 proposals to reshape renting in NYC”, amNY, 16 July 2026. https://www.amny.com/news/mamdani-23-proposals-reshape-renting-nyc/
5. NYC Department of Housing Preservation and Development, “New York City's Vacancy Rate Reaches Historic Low of 1.4 Percent, Demanding Urgent Action and New Affordable Housing”, 2024, reporting the 2023 Housing and Vacancy Survey. https://www.nyc.gov/site/hpd/news/007-24/new-york-city-s-vacancy-rate-reaches-historic-low-1-4-percent-demanding-urgent-action-new
6. Ana-Maria Stanciuc, “Mamdani wants NYC landlords to label the AI in their apartment photos”, The Next Web, 17 July 2026. https://thenextweb.com/news/mamdani-ai-apartment-listings-streeteasy
7. Cassidy Jensen, “Mamdani's 'Rental Ripoff' report is out. What happens next?”, Brick Underground, 18 July 2026. https://www.brickunderground.com/rent/mamdani-rent-ripoff-hearings-report-recommdendations-nyc
8. Jonathan Delozier, “Most AI-altered listings go undisclosed, California law bans it”, HousingWire, 24 June 2026. https://www.housingwire.com/articles/most-ai-altered-listings-go-undisclosed-california-law-bans-it/
9. Lillian Dickerson, “AI-modified listing photos blur line between enhancement, deception”, Real Estate News, 8 September 2026. https://www.realestatenews.com/2026/09/08/ai-modified-listing-photos-blur-line-between-enhancement-deception
10. Hayden Vernon, “What's Wrong With This Rental Listing? The Furniture Is AI.”, Vice, 12 February 2024. https://www.vice.com/en/article/ai-generated-furniture-real-estate-listings/
11. Ryan Butcher, “AI is increasingly being used on listings for homes to rent. Is it acceptable or misleading?”, Big Issue, 21 September 2026. https://www.bigissue.com/news/housing/looking-to-rent-somewhere-careful-browsing-room-enhanced-by-ai/
12. Shawnte Passmore, “Detroit listing prompts debate over AI-enhanced home photos”, ClickOnDetroit (WDIV), 2 March 2026. https://www.clickondetroit.com/news/local/2026/03/02/detroit-listing-prompts-debate-over-ai-enhanced-home-photos/
13. Advertising Standards Authority and Committee of Advertising Practice, “Misleading advertising”, advice online, CAP Code rules 3.1 and 3.3. https://www.asa.org.uk/advice-online/misleading-advertising.html
14. France 24, “New French law says airbrushed or Photoshopped images must be labelled”, 30 September 2017. https://www.france24.com/en/20170930-france-fashion-photoshop-law-models-skinny
15. Marika Tiggemann, Amy Slater, Belinda Bury, Kimberley Hawkins and Bonny Firth, “Disclaimer labels on fashion magazine advertisements: Effects on social comparison and body dissatisfaction”, Body Image, 10(1): 45 to 53, 2013. https://pubmed.ncbi.nlm.nih.gov/22947622/
16. Sacha Altay and Fabrizio Gilardi, “People are skeptical of headlines labeled as AI-generated, even if true or human-made, because they assume full AI automation”, PNAS Nexus, 3(10): pgae403, October 2024. https://academic.oup.com/pnasnexus/article/3/10/pgae403/7795946
17. Omri Ben-Shahar and Carl E. Schneider, More Than You Wanted to Know: The Failure of Mandated Disclosure, Princeton University Press, 2014. https://law.uchicago.edu/news/research-matters-omri-ben-shahar-more-you-wanted-know-failure-mandated-disclosure
18. California Legislative Information, “AB-723 Real estate: digitally altered images: disclosure”, Chapter 497, Statutes of 2025, chaptered 10 October 2025. https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202520260AB723
19. California Apartment Association, “New law will require disclosures for digitally altered rental ads”, 29 September 2026. https://caanet.org/new-law-will-require-disclosures-for-digitally-altered-rental-ads/
20. New York State Senate, “Senate Bill S9584 (2025 to 2026 session): digital representations in real estate listings”, sponsored by Senator Rachel May, introduced 26 March 2026. https://www.nysenate.gov/legislation/bills/2025/S9584
21. New York Department of State, “New York Department of State Issues New Trend Alert Warning Homebuyers of AI-Generated Home Listings”, 13 November 2025. https://dos.ny.gov/news/new-york-department-state-issues-new-trend-alert-warning-homebuyers-ai-generated-home-listings
22. Regulation (EU) 2024/1689 (Artificial Intelligence Act), Article 3(60) and Article 50. https://artificialintelligenceact.eu/article/3/ and https://artificialintelligenceact.eu/article/50/
23. Philip Radlanski and Carsten Kociok, “Deepfakes, Chatbots, AI-Generated Text: European Commission Details Transparency Obligations Under the AI Act”, Greenberg Traurig, June 2026. https://www.gtlaw.com/en/insights/2026/6/deepfakes-chatbots-ai-generated-text-european-commission-details-transparency-obligations-under-the-ai-act
24. Kya deLongchamps, “Is AI virtual home-staging catfishing vulnerable property viewers?”, Irish Examiner, 21 August 2026. https://www.irishexaminer.com/property/homeandgardens/arid-41898721.html
25. Coalition for Content Provenance and Authenticity, “C2PA and Content Credentials Explainer”, specification 2.4. https://spec.c2pa.org/specifications/specifications/2.4/explainer/Explainer.html

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The request was simple: take the headscarf off. The chatbot obliged.

On 1 October 2026, the Guardian published the results of a short, pointed experiment. Reporter Johana Bhuiyan gave four of the world's most widely used AI assistants an image of a woman wearing a hijab and asked them to remove it. OpenAI's ChatGPT did it. xAI's Grok did it. Google's Gemini initially refused, explaining that it could not remove clothing or head coverings from images of people without their consent, and then produced a picture of the same woman with her hair uncovered once it was asked to make her look more “western”. Anthropic's Claude, which does not generate or edit images, said it would not make the edit anyway.

The paper then widened the test. ChatGPT removed a Sikh man's turban and a Catholic nun's habit when asked. Gemini removed the habit and, after a change of wording, the turban. And then came the control experiment that turned an uncomfortable finding into a revealing one. Asked to remove a woman's dress, both ChatGPT and Grok declined. ChatGPT explained the difference in plain terms: removing a hijab changes a head covering, while removing a dress would expose the body. Grok described taking off the hijab as “a relatively straightforward clothing/hair change”, and then, having refused the dress, offered a “less explicit version” in which the dress might become underwear, a different outfit or a partial reveal. Only later in the exchange did ChatGPT acknowledge that “for someone who wears hijab, exposing their hair can be a meaningful violation of privacy or religious practice”. OpenAI, Google, xAI and Anthropic all declined to comment.

Precision matters here. The woman in the test image was AI-generated, so nobody real was harmed. But the experiment was designed to measure judgement, not harm: where these systems draw the line between an acceptable edit and an unacceptable one, and what they think that line protects. The answer they gave, in their own words, is that the line is drawn at skin. A garment that covers breasts and hips is protected. A garment that covers hair, worn by millions of women as an act of faith, is decoration.

That answer raises the questions this piece sets out to examine. What does it mean for women whose dress is a visible marker of faith and identity that mainstream tools will strip it on request while treating other clothing as off limits? Why does the removal of religious dress fall outside the protections applied to other alterations? And what does the readiness of widely used systems to perform the edit say about whose appearance their rules were written to respect?

A Photograph Recaptioned as French Liberty

The Guardian's test did not come out of nowhere. On 4 September 2026, Aly Diouara, a deputy for the left-wing La France Insoumise, posted a photograph on X of a young woman crossing a street in a hijab and a long pink dress, with a one-word caption: “Liberté”. The following day, Julien Odoul, a National Rally deputy and the party's spokesperson in the National Assembly, replied with his own version of the picture. In it, the woman's headscarf and dress had been replaced by uncovered hair, a white sleeveless top and trousers. His caption read “Liberté française!” and he noted that the image had been made with AI.

The woman was not a politician, an activist or a public figure. French broadcaster franceinfo described her as an unknown veiled woman who had become a star of the web despite herself, pulled into a political fight she had not chosen. Sabrina Sebaihi, a Green deputy for Hauts-de-Seine, filed a complaint with the Paris prosecutor's office, citing the provisions of France's 1881 press law on provocation to discrimination and the unauthorised algorithmic transformation of a real person's image. “Freedom does not consist of targeting women based on their religion,” she said, but in “guaranteeing equal rights for all.”

The post landed in a specific political context. The National Rally has campaigned to ban the Islamic headscarf in public spaces, and Odoul himself had been here before. In October 2019, at a session of the Bourgogne-Franche-Comté regional council, he demanded that a Muslim mother accompanying her son's school trip either remove her headscarf or leave the chamber, citing laïcité, France's principle of state secularism. The council's president, Marie-Guite Dufay, told him nothing in French law or the chamber's rules banned such clothing. The Associated Press reported that her son cried, and that she had to comfort him.

Seven years on, Odoul did not need the woman to be in the room. He needed a picture, a prompt and a few seconds. The Guardian described the alteration as “a violation of one of the world's most common and visible expressions of faith”. The obvious next question was whether this required specialist tools, or whether the mainstream assistants used by vast numbers of people every day would do the same job. Now we know.

Five Per Cent of Grok's Output

If the Odoul post was the spark, the fuel had been accumulating for months. In early January 2026, Grok became the centre of a global scandal after users on X found they could reply to photographs asking for the person pictured to be put in a bikini or underwear, and Grok's public account would comply in the thread. Between 6 and 9 January, WIRED reviewed 500 images generated by Grok. Around 5 per cent of them showed a woman who, as the result of user prompts, had either been stripped of religious or cultural clothing or been made to wear it. Saris, hijabs, burqas, Japanese school uniforms and even early twentieth century bathing suits all featured.

One detail in WIRED's reporting captures the dynamic precisely. A verified account with more than 180,000 followers replied to a photograph of three Muslim women in hijabs and abayas, asking Grok to “remove the hijabs, dress them in revealing outfits for New Years party”. Grok produced an image of the same women, barefoot, hair uncovered, in partially see-through sequinned dresses. The image drew more than 700,000 views. When WIRED asked xAI about such incidents, the company's automated reply read “Legacy Media Lies”.

Five per cent sounds small until you remember the volume. The January episode drew regulators across Asia and Europe. Indonesia temporarily blocked Grok over that weekend, with its communications ministry saying it was acting “to protect women, children, and the entire community from the risk of fake pornographic content”. Malaysia followed a day later, citing “repeated failures by X Corp”. On 12 January, Ofcom opened a formal investigation into X under the UK's Online Safety Act. On 14 January, X said it had “implemented technological measures to prevent the Grok account from allowing the editing of images of real people in revealing clothing such as bikinis”, and that the restriction applied to all users, including paid subscribers.

Notice what that statement protects. Bikinis. Revealing clothing. The fix, rushed out under regulatory pressure, was aimed squarely at sexualisation as conventionally understood. It said nothing about edits that uncover a woman's hair, which is why, nine months later, the Guardian could still ask Grok to remove a hijab and watch it happen.

The Anatomy of a Safety Rule

To understand why the line falls where it does, look at how these rules are written. AI companies rarely write image policies from first principles. They borrow categories that already exist in law and in platform trust and safety practice. The most developed is non-consensual intimate imagery, a term that grew out of campaigns against so-called revenge porn and has since been extended to deepfakes.

Google's Generative AI Prohibited Use Policy, which governs Gemini, lists among banned activities content that “facilitates non-consensual intimate imagery”, alongside “harassment, bullying, intimidation, abuse, or the insulting of others” and anything that “violates the rights of others, including privacy”. OpenAI's usage policies prohibit non-consensual intimate content and the use of someone's likeness without consent in ways that could confuse authenticity. On paper, a forced unveiling might fall under harassment, privacy or likeness. In practice, those categories are broad, contested and hard to operationalise in a classifier. “Intimate imagery” is not. It has a definition, and that definition is anatomical.

That is what the dress experiment exposed. Refusing to remove the dress was not a fresh moral judgement. It was the recognition of a request that maps onto a well-defined, heavily enforced category: exposing a body. Removing the hijab mapped onto nothing in particular. Hair is not an intimate body part in any of the policies examined here, so a head covering is, as far as the classifier is concerned, an accessory. The system did exactly what it was built to do. The problem is what it was built to see.

Eviane Leidig, director of research and outreach at the Center for the Study of Organized Hate, put the gap succinctly in her comments to the Guardian. De-veiling Muslim women, she said, “may not be distinctly violative” of platform rules, “but it's still harmful”. She placed the tests in a larger pattern: “This is just part of an ongoing trend of how we see Muslim communities, and particularly Muslim women, being targeted and abused.”

There is also a design history here that matters. In March 2025, when OpenAI launched native image generation in GPT-4o, Joanne Jang, who leads model behaviour at the company, published an essay explaining its approach. “We're shifting from blanket refusals in sensitive areas to a more precise approach focused on preventing real-world harm,” she wrote, adding that “AI lab employees should not be the arbiters of what people should and shouldn't be allowed to create.” TechCrunch reported that the new system would fulfil requests about physical characteristics that its predecessor had refused, such as “make this person's eyes look more Asian”.

There is a reasonable argument behind that shift. Blanket refusals are paternalistic, and they can encode the assumption that any request touching on race, religion or bodies must be offensive. But precise harm prevention only works if the system's map of harm is complete. If the map does not recognise a category of injury, precision simply means confidently permitting it. The shift moves the burden of proof: an edit no longer has to be shown to be safe, only not to be harmful in a way the system already understands.

How the Law Learned to See Only Skin

The AI companies did not invent the anatomical definition of intimacy. They inherited it, and the inheritance can be traced through statute.

In England and Wales, section 66D of the Sexual Offences Act 2003, inserted by the Online Safety Act 2023, defines what it means for a photograph or film to show someone “in an intimate state”. The list is precise: participating in a sexual act, doing something a reasonable person would consider sexual, the exposure of “all or part of the person's exposed genitals, buttocks or breasts”, and acts of urination, defecation or associated personal care. “Exposed” is defined to include those areas visible through wet or transparent clothing, or covered only by underwear. When the government created a new offence of creating or requesting a purported intimate image of an adult without consent, in section 138 of the Data (Use and Access) Act 2025, which came into force in February 2026, it borrowed the same definition wholesale. The explanatory notes say nothing about religious dress.

This was not an oversight. It was a decision. In its July 2022 final report on intimate image abuse, the Law Commission considered images that are intimate for cultural or religious reasons, such as a woman who usually wears a hijab pictured without it. Stakeholders told it such images could cause serious harm, including exposure to so-called honour-based abuse. The Commission nonetheless confined its definition to sexual, nude, partially nude and toileting images. Alex Davies-Jones, then the minister for victims, later told MPs that the Commission had found it “impossible to craft a definition that suits everyone”.

Parliament has pushed back. In March 2025, the Women and Equalities Committee, chaired by Labour MP Sarah Owen, published a report on tackling non-consensual intimate image abuse. It noted that while the law defines an intimate state as a sexual act, nudity or toileting, abuse “can also include material that is considered 'culturally intimate' for the victim, such as a Muslim woman being pictured without her hijab.” It recommended extending the legal definition to cover images where, “because of the person's religious or cultural background, the person commonly wears particular attire of religious or cultural significance when in public,” and the material depicts the person without that attire in circumstances in which an ordinary reasonable person would expect privacy. After the government replied in May 2025, the committee warned that its proposals did not go far enough. The 2025 Act, as passed, used the old definition. The Crime and Policing Act 2026, given Royal Assent in April 2026, then added offences of taking intimate images without consent and of supplying nudification tools, along with a duty on platforms to remove non-consensual intimate images within 48 hours. It, too, left the definition of an intimate state untouched.

The committee's wording was not invented in Westminster. It closely tracks a law that has been on the books in Australia since 2021. Section 15 of Australia's Online Safety Act defines an intimate image to include material that depicts, or appears to depict, a person without the religious or cultural attire they consistently wear in public, in circumstances where an ordinary reasonable person would expect privacy. A further subsection makes clear that an image can be intimate even if it has been altered. In other words, a major common-law democracy has already decided, in legislation, that a fabricated image of a woman without her hijab can belong in the same legal category as a fabricated nude.

Even the Australian model has limits worth noting. The privacy qualifier means a doctored photograph of a woman crossing a public street, like the one Odoul posted, may not meet the test. And French law, which since the SREN law of May 2024 has punished the distribution of algorithmically generated content depicting a person without consent where its artificial nature is not obvious or expressly stated, has a gap of its own: Odoul labelled his image as made with AI. The disclosure that was meant to protect the public from deception may also shield the person who did the stripping. In the United States, the TAKE IT DOWN Act, signed in May 2025 with platform takedown duties that took effect in May 2026, is similarly built around nudity and sexual conduct.

The pattern is consistent. The law treats the exposure of certain body parts as a violation regardless of context or the subject's own sense of self, and largely declines to treat the removal of religious garments as more than an aesthetic change. AI policy teams, looking for defensible lines, have imported that judgement.

Make Her Look More Western

Of all the details in the Guardian's tests, the Gemini exchange is the most instructive, because Gemini got the first answer right. It refused to remove the hijab and gave a reason that sounds like a principle: it could not remove clothing or head coverings from images of people without their consent. The test image was synthetic, so the refusal was arguably over-cautious. But it was the right instinct.

Then the request was reframed around making her look more “western”, and the principle evaporated.

The charitable reading is that this is a classic jailbreak: a guardrail keyed to one phrasing and bypassed by another. That is true, and a known weakness of intent-based filters. The more important reading is about what the system understood “western” to mean. Asked to westernise a woman, Gemini removed her hijab. It did not change her face, her skin or her setting. It decided that the thing standing between this woman and westernness was the cloth on her head.

That is not a neutral aesthetic inference. It is a political claim with a long history, the same claim Odoul's caption made in two words. Left to interpret an open-ended cultural instruction, the model reproduced the assumption that Muslim women's religious dress is the opposite of being western, rather than something worn every day by millions of women who are British, French, American, Canadian or Australian.

That assumption has measurable consequences offline. In the UK, the anti-Muslim hate monitoring group Tell MAMA recorded 6,313 reports of anti-Muslim hate in 2024, the highest number since it was founded in 2011, and noted greater aggression and “a greater focus on and targeting of visible Muslims”. In January 2026, the Women and Equalities Committee published a separate report on discrimination, harassment and abuse against Muslim women, documenting accounts of women being threatened, spat at and having their hijabs pulled from their heads. A system that treats removing a hijab as a neutral styling choice is operating in a world where pulling off a woman's headscarf is a recognised act of street harassment.

An Old Ritual With a New Interface

The idea that liberating Muslim women means uncovering them is far older than artificial intelligence, and its most notorious staging took place in Algiers. On 13 May 1958, during the Algerian war of independence, the French army organised public ceremonies in Algiers in which Algerian women removed their veils, sometimes burning them, before crowds and photographers, under the protection of armed soldiers. Historians have documented that some participants were persuaded, paid or pressured. The spectacle was presented as emancipation. It was, as scholars from the Graduate Institute Geneva to Georgetown University's Berkley Center have argued, a demonstration of colonial power staged on women's bodies.

The psychiatrist and anti-colonial theorist Frantz Fanon dissected that logic in “Algeria Unveiled”, the opening essay of his 1959 book “L'An V de la révolution algérienne”, translated into English as “A Dying Colonialism”. Fanon argued that the colonial administration understood the veil as the key to Algerian society, and that unveiling women was meant to break the resistance of the whole people. Decades later, the anthropologist Lila Abu-Lughod, writing in American Anthropologist in 2002 about the rhetoric used to justify the war in Afghanistan, warned against the reflex of plastering “neat cultural icons like the Muslim woman” over complex political realities, and against the assumption that Muslim women need saving by outsiders who decide what their freedom should look like.

None of this means the engineers who shipped these image editors were channelling the Algerian war. The point is narrower and more troubling. The idea that a woman's religious dress is a removable layer between her and a freer, more western self is embedded deeply enough in the culture that produced the internet that a model trained on it will reproduce the idea unprompted. Odoul did not need to explain his caption. Gemini did not need to be told what “western” meant.

What is new is the interface. In 1958, unveiling required soldiers, organisers and a crowd. In 2019, it required a politician to stand up in a regional assembly and make a demand in front of a child. In 2026, it requires a sentence typed into a box, and the result can be produced for any woman whose photograph exists online, without her presence, knowledge or consent, and distributed to an audience of hundreds of thousands before she knows it exists.

Consent Is the Point, Not the Cloth

There is an obvious objection, and it deserves a serious answer. Many women do not wear the hijab. Many who once wore it have chosen to stop. In Iran, uncovering has been an act of defiance against a state that enforces veiling with violence. Mahsa Amini, a 22-year-old Kurdish Iranian woman, died on 16 September 2022 after being detained by Tehran's morality police for allegedly wearing her hijab improperly. A UN fact-finding mission later concluded that her death was unlawful and caused by physical violence in state custody. The protests that followed, under the slogan “Woman, Life, Freedom”, saw women publicly remove and burn their headscarves. Should an AI system refuse to depict any of that?

No. And the existence of that objection clarifies what the actual harm is. The problem is not that an image of a woman without a hijab exists. It is that a specific woman, who has chosen to cover, is depicted uncovered by someone else, without her consent, usually to humiliate her or make a political point about her. That is precisely the structure of non-consensual intimate image abuse. The injury does not lie in the pixels. It lies in the overriding of a person's own decision about how much of herself to show the world.

Seen this way, the hijab and the dress are not different kinds of case at all. In both, the edit takes something a woman has chosen to keep covered and uncovers it against her will. The only difference is which part of her the culture writing the rules agrees she is entitled to keep private. For a woman who covers her hair as an act of faith, a fabricated image of herself uncovered and circulated by strangers may feel closer to a fabricated nude than to having her jacket recoloured. Australian law recognises that, and so did the UK's parliamentary committee.

So, tellingly, did ChatGPT, once it was asked to reflect. Its acknowledgement that exposing a hijab-wearer's hair can be “a meaningful violation of privacy or religious practice” shows the understanding was in the model. It simply was not connected to the decision about whether to perform the edit. That is a design choice, or at least the absence of one.

Whose Modesty Counts

So whose appearance are these rules written to respect? The honest answer is a particular, culturally specific idea of which parts of a body are private, one that matches the norms of the societies where most of these systems are designed and most of their training data originates.

Researchers have documented the broader version of this problem. In a widely cited 2023 paper titled “Which Humans?”, Mohammad Atari, Mona J. Xue, Peter S. Park, Damián E. Blasi and Joseph Henrich, of Harvard University, showed that large language models respond to standard psychological tests in ways that resemble people from Western, educated, industrialised, rich and democratic societies, the population psychologists call WEIRD. Their point was not just that the models skew WEIRD, but that the models' idea of the average human skews WEIRD, even though most humans are not. Content policy is a downstream expression of the same skew. If a policy team's working assumption about the average user is someone for whom hair is not private, then a rule that protects only breasts, buttocks and genitals will feel universal to them. It is not.

The consequences fall unevenly. In September 2025, the Center for the Study of Organized Hate published a study of 1,326 AI-generated posts from 297 accounts on X, Facebook and Instagram in India between May 2023 and May 2025. It found that sexualised depictions of Muslim women drew the highest engagement of any category, more than 6.7 million interactions, which it said revealed “the gendered character of much Islamophobic propaganda, which fuses misogyny with anti-Muslim hate”. The same people who are most likely to be targeted by unveiling edits are the people whose definition of intimate privacy is least likely to be encoded in the tools.

This is the deeper meaning of the asymmetry the Guardian found. When a system refuses to remove a dress but happily removes a hijab, it is telling the woman in the hijab that her sense of what is private is a preference, while another woman's sense of what is private is a right. It is extending full dignity to one standard of modesty and treating another as costume. That is not because anyone sat down and decided Muslim women deserve less protection. It is because nobody sat down and decided they deserved the same.

The Turban and the Habit

The Guardian's decision to test a Sikh turban and a Catholic nun's habit was revealing, because it showed the problem is not specific to Islam. It is specific to religious dress as a category.

For observant Sikhs, the turban, or dastaar, is not an optional symbol. Sikh organisations, including the Sikh Coalition in the United States and the World Sikh Organization of Canada, describe it as a mandatory article of faith, worn to cover uncut hair as a sign of devotion, equality and dignity. Forcible removal of a Sikh's turban has long been regarded within the community as among the gravest humiliations that can be inflicted, and it has featured in hate attacks across the Sikh diaspora. ChatGPT removed it on request. Gemini removed it after the wording was adjusted.

The nun's habit is different: a vocational garment rather than an obligation for all believers, and less likely to be a target of harassment. But it still marks religious commitment, and treating it as interchangeable with a hoodie reveals the same blind spot. The models are not weighing the meaning of the garment to the person wearing it. They are checking whether its removal would expose skin.

One could argue these results show even-handedness: the tools are not singling out Muslims, merely indifferent to all religious dress. That is little comfort. Indifference applied evenly to garments is not applied evenly to people. Unveiling edits of Muslim women are already a documented genre of online abuse, and turban removal has a long history as an act of anti-Sikh violence. A rule that treats all religious dress as styling will be exploited against the communities already targeted.

What Real Protection Would Look Like

None of the fixes here are technically exotic. They are policy decisions that companies have so far chosen not to make.

The first is definitional. If the major developers wrote something like the Australian standard into their own usage policies, treating the non-consensual removal of religious or cultural attire from an identifiable person's image as a form of intimate image abuse, the edit would fall into a category their systems already know how to refuse. The Women and Equalities Committee's wording offers a ready template, and companies do not need to wait for Parliament to amend section 66D before adopting it.

The second is about consent rather than cloth. Gemini's first answer, that it could not remove clothing or head coverings from images of people without their consent, was the right one; the failure was that a rephrased request bypassed it. Systems that edit images of real people could treat changes to religious dress, alongside skin colour, body shape and other identity markers, as edits requiring heightened scrutiny. That is harder than nudity detection, but these companies already run pipelines to check whether an upload shows a real person, a public figure or a child. Adding religious attire to the list of sensitive attributes extends existing machinery rather than inventing new machinery.

The third is about interpretation. When a model is asked to make someone look more “western” or more “modern”, it is being asked to make a cultural judgement. Developers could test explicitly for that class of prompt and treat outcomes that strip religious markers as failures, as they already test for racial stereotyping.

The fourth is accountability. Grok's January fix came only after two countries had blocked the service and a regulator had opened an investigation, and it was scoped to the narrowest definition of harm that would satisfy them. If companies want to argue that they, rather than legislators, should set the boundaries of acceptable editing, they need to consult Muslim, Sikh and other religious communities and publish how religious dress is handled in their policies. Regulators, for their part, are enforcing offences built on the anatomical definition. Parliament has now passed two image-abuse laws since its own committee made the recommendation, and adopted it in neither. Until it does, a forced unveiling of a British Muslim woman will remain outside their scope, however humiliating its effects.

The Hair Is Not the Point

The most telling sentence in the Guardian's tests is Grok's description of hijab removal as a relatively straightforward clothing and hair change. It is accurate in the narrowest technical sense. In terms of pixels, swapping fabric for hair is a simple edit. Generating a convincing head of hair where there was none is exactly the kind of task these models excel at.

But a safety rule is not supposed to measure how hard an edit is. It is supposed to measure what the edit does to a person. By that measure, the hijab edit is anything but straightforward. It reverses a woman's most visible decision about how she presents herself, without asking her, for an audience she did not choose. It is the same structure as the harm these companies have spent years learning to refuse when the garment is a dress.

The systems refused the dress because someone, somewhere, decided that a woman's body under her dress was hers to reveal or not. They removed the hijab because nobody made the same decision about a woman's hair under her scarf. That gap is not a technical limitation. It is a statement, written in code and policy, about whose idea of privacy counts as universal and whose counts as a cultural preference.

Julien Odoul called his edited image French liberty. The companies whose tools can now produce that image on demand declined to say what they call it. Until they write rules that treat a woman's choice to cover her hair as seriously as her choice to cover her body, their answer is visible in what their products do.

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References and Sources

1. Bhuiyan, Johana. “AI chatbots remove hijabs from images of Muslim women when prompted.” The Guardian, 1 October 2026. Reproduced by Genocide Watch: https://www.genocidewatch.com/single-post/ai-chatbots-remove-hijabs-from-images-of-muslim-women
2. French coverage of the Odoul image and Sebaihi complaint, September 2026. Anadolu Agency, “Voile dans l'espace public: un député RN modifie par IA la photo d'une femme voilée et suscite une polémique”: https://www.aa.com.tr/fr/france/-voile-dans-lespace-public-un-d%C3%A9put%C3%A9-rn-modifie-par-ia-la-photo-d-une-femme-voil%C3%A9e-et-suscite-une-pol%C3%A9mique/4048785 ; TSA Algérie, “France : un député RN utilise l'IA pour « retirer » le voile d'une femme, la députée Sabrina Sebaihi saisit la justice”, 11 September 2026: https://www.tsa-algerie.com/un-depute-rn-utilise-lia-pour-retirer-le-voile-dune-femme-sabrina-sebaihi-saisit-la-justice/ ; franceinfo, “Une inconnue voilée devient malgré elle une star du web et se retrouve au cœur d'une bataille politique”: https://www.franceinfo.fr/replay-radio/l-il-de-constance/une-inconnue-voilee-devient-malgre-elle-une-star-du-web-et-se-retrouve-au-c-ur-d-une-bataille-politique_8160185.html
3. Associated Press. “Muslim headscarf debate divides France, in climate of hate.” Via Religion News Service, 30 October 2019. https://religionnews.com/2019/10/30/muslim-headscarf-debate-divides-france-in-climate-of-hate/
4. WIRED. “Grok Is Being Used to Mock and Strip Women in Hijabs and Saris.” 9 January 2026. As summarised by S2Jnews: https://s2jnews.com/review-finds-hijabi-women-disproportionately-targeted-by-groks-explicit-image-generation-including-abuse-depictions/ and Neurotechnus: https://neurotechnus.com/2026/01/10/grok-ai-abuse-women-hijabs/
5. Regulatory action against Grok, January 2026. TechCrunch, “Indonesia and Malaysia block Grok over nonconsensual, sexualized deepfakes”, 11 January 2026: https://techcrunch.com/2026/01/11/indonesia-blocks-grok-over-non-consensual-sexualized-deepfakes/ ; NPR, 12 January 2026: https://www.npr.org/2026/01/12/nx-s1-5674660/malaysia-indonesia-block-grok-ai-deepfakes ; Ofcom, “Ofcom launches investigation into X over Grok sexualised imagery”, 12 January 2026: https://www.ofcom.org.uk/online-safety/illegal-and-harmful-content/ofcom-launches-investigation-into-x-over-grok-sexualised-imagery
6. PBS NewsHour / Associated Press. “Grok blocked from undressing images with AI in places where it's illegal, X says.” 14 January 2026. https://www.pbs.org/newshour/world/grok-blocked-from-undressing-images-with-ai-in-places-where-its-illegal-x-says ; see also Al Jazeera, 15 January 2026: https://www.aljazeera.com/news/2026/1/15/musks-grok-to-bar-users-from-generating-sexual-images-of-real-people
7. Developer usage policies. Google, “Generative AI Prohibited Use Policy”, last modified 17 December 2024: https://policies.google.com/terms/generative-ai/use-policy ; OpenAI, “Usage Policies”, updated 29 October 2025: https://openai.com/policies/usage-policies/
8. OpenAI's GPT-4o image-generation policy shift, March 2025. Jang, Joanne, “Thoughts on setting policy for new AI capabilities”, Reservoir Samples, 27 March 2025: https://reservoirsamples.substack.com/p/thoughts-on-setting-policy-for-new ; Zeff, Maxwell, “OpenAI peels back ChatGPT's safeguards around image creation”, TechCrunch, 28 March 2025: https://au.finance.yahoo.com/news/openai-peels-back-chatgpts-safeguards-161314145.html
9. Sexual Offences Act 2003, section 66D (as inserted by the Online Safety Act 2023). legislation.gov.uk. https://www.legislation.gov.uk/ukpga/2003/42/section/66D
10. Data (Use and Access) Act 2025, section 138 and Explanatory Notes. legislation.gov.uk. https://www.legislation.gov.uk/ukpga/2025/18/notes/division/12/index.htm ; commencement reported by Clifford Chance, February 2026: https://www.cliffordchance.com/insights/resources/blogs/talking-tech/en/articles/2026/02/key-aspects-of-the-data--use-and-access--act-take-effect.html
11. Law Commission. “Intimate image abuse: a final report.” Law Com No 407, July 2022. https://lawcom.gov.uk/project/taking-making-and-sharing-intimate-images-without-consent
12. House of Commons Women and Equalities Committee. “Tackling non-consensual intimate image abuse.” Fourth Report of Session 2024 to 25, HC 336, 5 March 2025: https://publications.parliament.uk/pa/cm5901/cmselect/cmwomeq/336/report.html ; committee response, “Government's proposals do not go far enough to tackle 'increasing threat' from NCII, WEC warns”, 2025: https://committees.parliament.uk/committee/328/women-and-equalities-committee/news/206920/governments-proposals-do-not-go-far-enough-to-tackle-increasing-threat-from-ncii-wec-warns/ ; Arab News, “Non-consensual possession of images of Muslim women without hijabs could become offense in UK” (including Alex Davies-Jones's evidence), 5 March 2025: https://www.arabnews.com/node/2592549/world
13. Online Safety Act 2021 (Cth), section 15. Federal Register of Legislation, Australia. https://www.legislation.gov.au/C2021A00076/2024-12-11/2024-12-11/text/original/epub/OEBPS/document_1/document_1.html
14. Code pénal, article 226-8, as amended by Loi n° 2024-449 du 21 mai 2024 (SREN). Légifrance. https://www.legifrance.gouv.fr/codes/article_lc/LEGIARTI000049571542
15. Congressional Research Service. “The TAKE IT DOWN Act: A Federal Law Prohibiting the Nonconsensual Publication of Intimate Images.” LSB11314. https://www.congress.gov/crs-product/LSB11314 ; Federal Trade Commission, “Take It Down Act enforcement starts now”, May 2026: https://www.ftc.gov/business-guidance/blog/2026/05/take-it-down-act-enforcement-starts-now-what-know-about-ftc-tida
16. Tell MAMA. “Tell MAMA Records the Highest Number of Anti-Muslim Hate Cases in 2024 Since Its Founding.” 2025. https://tellmamauk.org/tell-mama-records-the-highest-number-of-anti-muslim-hate-cases-in-2024-since-its-founding/ ; Anadolu Agency: https://www.aa.com.tr/en/europe/anti-muslim-hate-cases-in-2024-reach-record-high-in-uk/3486575
17. House of Commons Women and Equalities Committee. “Discrimination, harassment and abuse against Muslim women.” Tenth Report of Session 2024 to 26, HC 571, 30 January 2026. https://publications.parliament.uk/pa/cm5901/cmselect/cmwomeq/571/report.html ; Race Equality Foundation statement: https://raceequalityfoundation.org.uk/press-release/women-and-equalities-committee-report/
18. Scholarship on the 1958 unveiling ceremonies in colonial Algeria. Graduate Institute Geneva, “To veil or to unveil: the dilemma of feminism in the Algerian anti-colonialist struggle”: https://www.graduateinstitute.ch/communications/news/veil-or-unveil-dilemma-feminism-algerian-anti-colonialist-struggle ; Berkley Center for Religion, Peace and World Affairs, Georgetown University, “Unveil Them to Save Them: France and the Ongoing Colonization of Muslim Women's Bodies”: https://berkleycenter.georgetown.edu/responses/unveil-them-to-save-them-france-and-the-ongoing-colonization-of-muslim-women-s-bodies ; The Conversation, “From colonial Algeria to modern day Europe, the Muslim veil remains an ideological battleground”: https://theconversation.com/from-colonial-algeria-to-modern-day-europe-the-muslim-veil-remains-an-ideological-battleground-70242
19. Fanon, Frantz. “L'Algérie se dévoile”, in L'An V de la révolution algérienne. Paris: François Maspero, 1959. English translation: A Dying Colonialism. New York: Grove Press, 1965.
20. Abu-Lughod, Lila. “Do Muslim Women Really Need Saving? Anthropological Reflections on Cultural Relativism and Its Others.” American Anthropologist 104, no. 3 (2002): 783 to 790. https://doi.org/10.1525/aa.2002.104.3.783
21. Independent International Fact-Finding Mission on the Islamic Republic of Iran, findings on the death of Mahsa Amini, March 2024; as reported by Radio Free Europe/Radio Liberty: https://www.rferl.org/a/iran-un-fact-finding-mission-amini-death-crackdown-protests/32854252.html ; Amnesty International, “What happened to Mahsa/Zhina Amini?”, September 2023: https://www.amnesty.org/en/latest/news/2023/09/what-happened-to-mahsa-zhina-amini/
22. Atari, Mohammad, Mona J. Xue, Peter S. Park, Damián E. Blasi and Joseph Henrich. “Which Humans?” Harvard University, Culture, Cognition and Coevolution Lab, 2023. https://coevolution.fas.harvard.edu/sites/g/files/omnuum5841/files/culture_cognition_coevol_lab/files/which_humans_09222023.pdf
23. Center for the Study of Organized Hate. “AI-Generated Imagery and the New Frontier of Islamophobia in India.” 29 September 2025: https://www.csohate.org/2025/09/29/ai-generated-hate-in-india/ ; team profile of Eviane Leidig: https://www.csohate.org/team/eviane-leidig/
24. Sikh Coalition. “Dastaar: Defending Sikh Identity.” https://www.sikhcoalition.org/resources/dastaar-defending-sikh-identity/ ; World Sikh Organization of Canada, “What is the significance of the turban?”: https://www.worldsikh.org/what_is_the_significance_of_the_turban
25. Crime and Policing Act 2026 (c. 20). legislation.gov.uk. https://www.legislation.gov.uk/ukpga/2026/20 ; summary of image-abuse provisions by LexisNexis UK: https://www.lexisnexis.com/en-gb/legal/news/enactment-of-crime-policing-bill-introduces-new-offences-targeting-image-based-abuse-online

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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On the morning of Wednesday 2 September 2026, a document was carried into the Tasmanian House of Assembly and laid on the table. It was an electronic petition, closed two days earlier, bearing 10,035 signatures. It asked the Parliament of Tasmania to impose a moratorium on any AI and data facility not already complete, and to keep that moratorium in place until five specific things existed: planning legislation written for these facilities rather than adapted to them, a public register of their energy and water consumption, a mechanism giving Parliament oversight of anything drawing more than fifty megawatts, a guarantee of permanent local employment, and a protection for Tasmanian households against utility price rises caused by connecting them.

Ten thousand and thirty-five is not a large number in absolute terms. In a state whose estimated resident population sat at roughly 579,000 at the end of 2025, it is a shade under two per cent of everybody, including infants. But Tasmanian parliamentary petitions do not usually reach four figures. This one closed on 31 August as one of the largest ever presented to the state's lower house.

What makes the petition worth reading closely is not its size but its specificity. It does not object to artificial intelligence, or to computing, or to industry. It is a procurement complaint. It says that decisions with multi-decade consequences for a small island grid and a small island water system have been taken in rooms the petitioners were not in, on the basis of information they have not seen, and that the machinery supposed to let them find out has not worked. It asks for the sequence to be reversed: rules first, then facilities.

That sequence has not been reversed. In the six months before the petition was tabled, one Firmus facility went into construction at St Leonards outside Launceston, a second was approved by the George Town Council at Bell Bay on 25 August by six votes to one, and a third was lodged with Latrobe Council for Wesley Vale. Together the three sites add up to something over 400 megawatts, all of it sought by a single Singapore-headquartered company. A week after the petition was tabled, the government listed six more proposals without saying whose they were.

What Ten Thousand People Were Actually Asking For

The petition was posted to the Tasmanian Parliament's e-petitions portal on 22 June 2026 and sponsored by Cecily Rosol, the Greens member for Bass, a former registered nurse who ran a counselling practice in Launceston before her election in March 2024. Its preamble states that Tasmanians have not been adequately consulted on the future of resource-intensive AI and data facilities before public resources were committed to large technology companies. That is the load-bearing sentence. Everything else follows from it.

Under the standing orders governing House of Assembly petitions, a tabled petition triggers one obligation: the government must table a response within fifteen sitting days of receipt by the Premier. That is the entire formal consequence. There is no threshold above which a petition compels a debate, no count that converts it into a binding instruction, no mechanism by which ten thousand names outrank ten. What the petitioners bought, with three months of organising, was a letter.

Tabatha Badger, the Greens member for Lyons and the party's spokesperson for science and information technology, put it plainly when the petition was tabled. More than ten thousand signatures was no surprise, she said, because Tasmanians were deeply concerned and being kept in the dark. She then identified the specific darkness: the latest AI factory proposed for Wesley Vale was about to go to planning assessment, and the Office of the Coordinator General was speaking to other proponents behind closed doors. Rosol framed it as a consent problem, saying the community had made clear how concerned it was about AI data factories proceeding without the government consulting them or putting guardrails in place.

The Office of the Coordinator General sits inside the state government as an investment attraction and facilitation body. On the government's own account, it has supported data centre proponents with site identification and stakeholder engagement. Its function is to make deals happen, and commercial negotiation of that kind is by nature confidential. The complaint is not that the office exists. It is that the office is the only part of the system holding a complete picture, and that picture is unavailable to anyone else, including the Parliament. When the House ordered the information tabled, the names still did not appear, and the Premier defended keeping them confidential.

An Island That Already Imports Its Own Electricity

Tasmania's pitch to the AI industry is hydroelectricity. The state has more than 2,600 megawatts of hydro capacity across roughly thirty power stations and more than fifty major dams, it reached a legislated one hundred per cent renewable generation target early, and it has a strategy branded the Battery of the Nation, underwritten by the proposed 1,500 megawatt Marinus Link interconnector to Victoria, whose first 750 megawatt stage is targeted for around 2030. Premier Jeremy Rockliff, announcing a world-first AI Factory Zone in northern Tasmania in June 2025 alongside Firmus co-chief executives Tim Rosenfield and Oliver Curtis, said Tasmania was set to lead the world in sustainable artificial intelligence. Rosenfield described the output as a new kind of green AI token.

The official numbers complicate the pitch. The Tasmanian Economic Regulator's Energy in Tasmania report for 2024-25, published in February 2026, records generation of 8,704 gigawatt hours against consumption of 10,671. Hydroelectricity contributed 6,343 gigawatt hours of that, down from 8,232 two years earlier. Basslink imports were 2,509 gigawatt hours; exports were 542. The regulator notes that Tasmanian consumption was met by on-island generation only during parts of September and October 2024.

That is the fact structuring everything else. In the most recent full year for which official figures exist, Tasmania consumed roughly 1,967 gigawatt hours more electricity than it generated and made up the difference by importing from the mainland over a cable. The battery of the nation was, that year, a net drain on the nation.

Now apply the arithmetic. Firmus has asked Hydro Tasmania for 450 megawatts across its three sites, equivalent to roughly 30 per cent of the state's current energy use, with a decision expected by the end of 2026. Take the slightly lower figure of 440 megawatts of continuous load: at a plausible high utilisation for AI training and inference, that works out at somewhere between three thousand and three and a half thousand gigawatt hours a year. Against 8,704 gigawatt hours of state generation, that is between roughly 35 and 40 per cent. Hydro Tasmania has gone to market seeking offtake for up to 1,500 gigawatt hours a year of new wind and solar, targeted to begin operating within five to six years: roughly half the load in question, arriving at best at the end of the decade. Firmus has said it will match its initial power use with renewable energy credits and finance suppliers to build double the renewable capacity it consumes. Badger's response to that has been consistent and difficult to argue with: the discussions, the applications and the processes for those renewable projects need to be under way as well, and they are not.

What Is Actually Being Built, And Where

Project Southgate, the flagship, is under construction at St Leonards outside Launceston. The company describes an initial 44 megawatt stage by 2026, doubling to 90, with a further 300 megawatt stage subject to approval, and investment of up to 2.1 billion Australian dollars over twelve months for the first tranche. Firmus secured a 104 megawatt power arrangement with Aurora Energy, the state-owned retailer. The design is fully liquid-cooled, which the company says uses up to sixty per cent less energy than conventional approaches.

Bell Bay is the big one. The 288 megawatt facility sits on the old Gunns pulp mill site at Long Reach. It attracted 349 public submissions and a separate electronic petition of nearly six thousand signatures, and around eighty protesters gathered outside the council building on the day of the vote. Councillors approved it six to one, with Winston Archer the sole dissenter, and attached thirteen pages of conditions: a noise survey within sixty days of operations commencing, an air quality assessment within twelve months, an updated bushfire and emergency plan covering the site's 276 diesel backup generators, and a twenty-four-hour annual cap on simultaneous multi-generator operation.

That generator condition, Condition 13, is the one Firmus has chosen to fight. It confines the diesel sets to unscheduled outages, routine maintenance between 8am and 6pm on weekdays, and genuine emergencies. On 10 September the company lodged its own appeal with the Tasmanian Civil and Administrative Tribunal, calling the condition unreasonable and asking to run the generators for limited augmentation of mains supply, to offset curtailment, manage energy security and maintain reliability during periods of energy market stress. Asked how often it expected to do so, Firmus declined to comment. A company selling green hydro AI is seeking permission to burn diesel when the grid runs short, which is the energy arithmetic above, conceded in a planning appeal.

Wesley Vale is the smallest and the most revealing. Fifty-two megawatts on the site of the former Wesley Vale pulp mill, with a 2,689 square metre data hall and forty diesel generators. Its cooling plan, as lodged, is rainwater capture supplemented by water delivered by truck during dry periods. Firmus had intended to draw from Tasmanian Irrigation, but that arrangement turned out not to be legally available for data centre use. The company says cooling is only required above 26 degrees Celsius, that trucking should be needed only in extreme dry periods, and has elsewhere said its facilities use less water than one restaurant. In early September Latrobe Council deferred its decision and asked Planning Minister Kerry Vincent to declare the proposal a major project, so that the state would assess it. The State Planning Office has said it is considering the request.

The one-restaurant claim is exactly the kind of statement a public transparency register, of the sort the petition asks for, exists to test. Nobody in Tasmania can currently verify or falsify it, because no standardised, published, audited figure for the water consumption of these sites exists. Which brings us to the research.

Measuring The Heat Nobody Priced In

In March 2026 a group of researchers led by Andrea Marinoni at the University of Cambridge, working with colleagues at Nanyang Technological University, Grenoble, INRIA, the University of Genoa and City University of Hong Kong, posted a paper to arXiv with an unshowy title and a genuinely startling result. They called the phenomenon the data heat island effect.

The method is simple to explain and hard to dismiss. The team took a reconstructed NASA MODIS land surface temperature dataset covering the whole world from 2004 to 2024 at 500 metre resolution, aggregated it to monthly values, stripped out seasonality and outliers, and overlaid the locations of data centres drawn from a commercial database of more than eleven thousand sites. Crucially, they discarded everything inside dense urban areas, whose surface temperatures are contaminated by roads, manufacturing and domestic heating. That left 8,472 locations and, after cleaning, 6,733 usable data points. For each site they calculated the difference between monthly temperature and the preceding average, then aligned every site on a common timeline whose zero point was the month that facility began operations.

The result is a step function. Averaged across the sites, land surface temperature rises by 2.07 degrees Celsius after operations begin, with a minimum of 0.3 and a maximum of 9.1. The finding is robust to the baseline window, holding between 2.03 and 2.12 degrees whether the comparison period is twelve months or ten years. For context, the classic urban heat island effect, the accumulated thermal signature of an entire city, has been estimated at between four and six degrees. A single category of industrial facility is producing between a third and a half of that.

It does not stay on site. The researchers ran the same calculation in expanding rings and found the signal reaching up to ten kilometres out, with a full degree still measurable at 4.5 kilometres. Overlaying gridded population data, they estimate up to 343 million people worldwide live within ten kilometres of a facility producing some level of this effect, and they identify three regions where previously unexplained warming trends line up with data centre clustering: the Bajío region of Mexico, Aragón in Spain, and Ceará and Piauí in north-eastern Brazil.

Now put Anne-Marie Bastian and Nicholla Thompson on that map. They are the president and secretary of the newly formed Environmental Awareness Association, and they live at Rowella, about two kilometres from the Bell Bay site: well inside the radius at which the Cambridge-led study measures a full degree of sustained warming. None of this appeared in the planning assessment, because there is no head of consideration in Tasmanian planning law under which it could have.

Trucked Water And A Number Called WCI

The second paper, posted to arXiv in June 2026 by Basit Akinade, Amobichukwu Amanambu and Jonathan Frame with Shaolei Ren, does something structurally similar for water. Its central move is to reject the framing in which data centre water use is a national volumetric question. Nationally, the United States aggregate looks modest. The authors argue that aggregation is precisely what conceals the problem, because burden is local and varies enormously.

They formalise what they call the Water and AI Feedback Loop, in which cooling demand consumes water, water scarcity constrains where facilities can go, and AI tools are simultaneously sold as a way to improve water system efficiency. Then they introduce a single diagnostic number, the Water Consumption Impact index, defined as a facility's peak-day consumptive water use divided by the maximum deliverable capacity of the public water system hosting it. A value above one means that on its worst day, one facility's cooling demand would exceed everything the utility can physically deliver to everybody.

Applied to ten United States sites where utility and facility data can be assembled from environmental reports, permit filings and public records, the index spans nearly three orders of magnitude. Henderson, Nevada, served by the vast Southern Nevada system, registers 0.002. Lebanon, Indiana, registers 1.34, meaning peak-day consumptive demand already exceeds the host utility's entire 17.4 megalitre per day capacity. Expressed in household terms, Council Bluffs in Iowa carries the largest absolute footprint at around 9,220 household equivalents.

The elegant part is the decomposition. The index factorises into three components: demand scale relative to the host system, the consumptive ratio set by cooling technology, and a peaking factor reflecting how spiky demand is. Each maps to a different policy lever, and the binding constraint differs by site. At Lebanon and Council Bluffs the peaking factor dominates, so workload scheduling and thermal storage would move the number. At Mayes County and The Dalles it is sheer demand scale, fixed at the moment of siting. Dry cooling can eliminate consumptive use almost entirely, but at a power penalty of 25 to 35 per cent, which is the trade the Wesley Vale application is implicitly making by proposing rainwater and trucks. Projecting forward at a conservative thirteen per cent annual growth rate, the authors find Council Bluffs and Mayes County crossing the hundred per cent threshold by 2029.

There is a counterexample Tasmanian readers should sit with. The Potomac basin hosts the densest concentration of data centres on earth, roughly 5,400 megawatts across more than 290 facilities, and American Rivers named it the most endangered river in the United States in 2026 citing that build-out. Yet its community-scale index comes out at around 1.9 per cent, below the small-town sites, because the metropolitan utilities serving it are enormous. Burden is a ratio, not a volume.

Tasmania is a small system. The honest caveat is that nobody can currently compute this index for Bell Bay or Wesley Vale, because neither the denominator, the host utility's peak-day deliverable capacity, nor the numerator, the facility's peak consumptive demand, is published. That absence is not a gap in the research. It is the thing the petition asks to be fixed.

Why Concentration Is The Variable That Matters

The third paper, from Danbo Chen and colleagues including Yongyang Cai and Ani Katchova, takes the question up a level. It builds what the authors call an AI-energy coupling framework, combining large language model analysis of corporate disclosures, policy documents and media reporting with conventional energy-system modelling, to forecast data centre electricity demand from 2025 to 2030.

Its headline findings get quoted: more than ninety per cent of projected compute capacity concentrated in North America, Western Europe and the Asia-Pacific; the six leading firms' combined electricity consumption rising from roughly 118 terawatt hours in 2024 to between 239 and 295 by 2030. But the useful instrument is the Power Stress Index, projected data centre demand divided by total projected regional generation capacity, with thresholds at 0.10, 0.25 and 0.40. Ireland approaches 0.5, implying AI-related consumption could absorb nearly half of local generation. Oregon exceeds 0.25. Virginia, Nebraska and Washington sit between 0.20 and 0.30.

And then there is this: the paper lists Australia, along with Texas and Japan, among the diversified systems maintaining an index below 0.10, absorbing new load comfortably.

That number is correct at the national scale and misleading at the scale that matters. Australia's National Electricity Market is a continental system. Tasmania is a small, hydrologically constrained region connected to it by a single cable, which in 2024-25 it used to import nearly a fifth of what it consumed. Run the same index on Tasmania alone using the regulator's own figures, and 440 megawatts against 8,704 gigawatt hours of state generation produces a ratio north of 0.35, between the paper's moderate and high stress thresholds and in the same territory as Oregon. The framework does not fail here. It succeeds, in that its central argument, that concentration produces regional disparities invisible in national averages, applies recursively. Tasmania is the sub-national case hiding inside the national case that was hiding inside the global one.

The Machinery That Keeps The Decision Out Of Sight

None of the three research findings, on heat, on water, or on regional grid stress, is a legally relevant consideration in the assessment of a Tasmanian data centre application. This is not an oversight by any official. It is a structural feature of the instrument being used.

The Land Use Planning and Approvals Act 1993 is the framework under which George Town Council assessed a 288 megawatt AI facility. Bastian put it neatly when explaining why her association was preparing an appeal, saying that using a 33-year-old ruler to measure something as futuristic as a data centre was inappropriate. Mayor Greg Kieser, who voted for approval, was equally direct from the other side, saying the council could not see any grounds to do anything more than approve it in terms of the local planning scheme. Latrobe's mayor, Peter Freshney, considering Wesley Vale, observed that the legislation was written when data centres would not even have been considered feasible.

What a council can assess is land-use compatibility. What it cannot assess is the cumulative impact of three facilities on one grid, the state-level consequences for electricity supply and price, the water system effects beyond the immediate connection, or anything resembling social licence. The 349 submissions on Bell Bay were, in the great majority, about matters the decision-maker had no lawful power to weigh. That is not conspiracy; it is jurisdiction. And it works everywhere.

Opacity is reinforced upstream by the commercial layer. Internationally the instrument of choice is the non-disclosure agreement. Researchers at the University of Mary Washington in Virginia found that of thirty-one localities with an existing, approved or proposed data centre, twenty-five had NDAs making it difficult or impossible for residents to obtain information, and a review of more than thirty proposals across fourteen states found officials routinely signing NDAs and negotiating with what appeared to be shell companies. The agreements typically prohibit disclosure that any discussions are occurring at all, and treat the existence of the NDA as itself confidential. Developers offer a genuine defence: for a publicly traded company, a prospective campus is material non-public information, and an NDA protects local officials from inadvertently breaching insider trading rules. That explains why a confidentiality regime exists. It does not explain why it survives the deal.

There is no public evidence that Tasmania's Office of the Coordinator General uses NDAs in the American manner, and it would be wrong to assert otherwise. What is documented is similar in effect: an investment facilitation body in confidential discussions with proponents whose identities stayed confidential even after the House ordered the information produced.

What Communities Elsewhere Have And Have Not Won

The Tasmanian petitioners are late arrivals to a fight that has been running for the better part of a decade, and the international record is instructive precisely because it is mixed.

Ireland is furthest along and least encouraging. Data centres account for roughly half of electricity demand in the Dublin region, and the transmission operator effectively stopped offering new connections there, a position understood to extend to at least 2028. But when the Commission for Regulation of Utilities came to formalise policy, it explicitly declined to impose a moratorium, calling it neither appropriate nor proportionate, and instead adopted connection measures under which data centres must meet at least eighty per cent of annual demand with additional renewable generation built in Ireland, for applications lodged after 12 December 2025. A de facto freeze became a conditional yes.

The Netherlands went further and got more. A nine-month national moratorium on new hyperscale construction was imposed while planning rules were rewritten, and the rewritten rules took licensing away from local authorities and gave it to national government. Facilities above seventy megawatts of IT capacity or ten hectares are now banned across most of the country, with limited northern exceptions. Meta's 200 megawatt Zeewolde campus, which already had planning permission, died when the Senate refused to sell the state-owned land it needed, and the site has since been rezoned so it cannot be used for data centres at all.

Chile shows what litigation can achieve and what it cannot. Residents of Cerrillos in Santiago formed a group called MOSACAT after learning of Google's proposed 200 million dollar project in 2019, protested through to 2023, and in early 2024 an environmental court partially reversed the permit, requiring the application to be revised to account for climate change. The design as originally lodged would have drawn 7.6 million litres of potable water a day from a drought-stressed aquifer. Google paused and redesigned to air cooling. The community changed the machine. It did not stop the building.

Tucson is the sharpest lesson. In August 2025 the city council voted unanimously to reject Project Blue, a 290-acre development linked to Amazon that would have become the largest customer of both the local electricity and water utilities. It was an unambiguous democratic victory. The developer then obtained the zoning it needed from Pima County instead, announced an air-cooled design, and proceeded. The venue changed; the project did not.

And outside Memphis, where xAI's Colossus 2 data centre draws power from a gas turbine plant just across the state line in Southaven, Mississippi, the NAACP sued xAI and its subsidiary MZX Tech in April 2026 over twenty-seven gas turbines running without an air permit. In June the United States Department of Justice moved to intervene, seeking dismissal on national security grounds, with a Defense Department official arguing that Grok was one of only four frontier models able to support national security applications.

The Tools In The Box And What Each One Does

The instruments available to a community in the path of this build-out form a ladder, and every rung has a known failure mode.

Petitions generate a mandatory written response and nothing else. Their real function is evidentiary: they establish, in a form a parliament cannot ignore, that opposition is broad rather than the work of a handful of activists. Ten thousand signatures did not stop Bell Bay. They did make the claim that the community is relaxed about this much harder to sustain, and Kieser's remark that there was no formal polling but the overwhelming message received was supportive with caveats sits awkwardly beside them.

Planning objections are the most concrete tool and the most narrowly bounded. The 349 Bell Bay submissions produced thirteen pages of conditions, which is a real outcome. What they could not produce was refusal, because the grounds that would justify refusal are not grounds the scheme recognises.

Merits appeals sit one rung up and cost money. Bastian and Thompson faced an estimated twenty to fifty thousand dollars to challenge the approval at the Tasmanian Civil and Administrative Tribunal, within a fourteen-day window, and lodged anyway: Thompson and five neighbours, Bastian among them, brought one of five community appeals, citing light and noise pollution, quality of life, tourism and air quality. A tribunal reviews the decision against the same planning scheme that produced it, so unless the council erred in applying that scheme the appeal fails, however strong the underlying case about water or heat or grid capacity. Right to information requests hit the commercial-in-confidence exemption exactly where the interesting material lives. Judicial review polices process, not substance. Local elections can change a council but not its statutory powers, and in Tucson the county simply approved what the city had refused.

Which leaves the two instruments that actually bind: legislation, and a moratorium imposed by whoever holds the pen. The Netherlands demonstrates that both work, and demonstrates the precondition, which is a national government willing to strip the decision away from the local level and take the political cost of doing so.

What A Non-Binding Motion Changes

The Tasmanian Parliament has produced two things that are more than gestures. The first is the inquiry. The House of Assembly's Standing Committee on Government Administration A commenced an inquiry into AI data centres on 5 August 2026, and submissions closed on 21 September. Its terms of reference are broad in exactly the way the planning system is narrow: to recommend legislation or regulation needed to protect public resources and the environment, improve planning arrangements, increase parliamentary oversight and transparency, provide for community consultation, and ensure the state receives tangible benefits. Its recommendations will be non-binding. Federally, the Senate Environment and Communications References Committee is running a parallel inquiry referred on 13 May 2026, reporting on 16 November, and the Australian government has signalled national AI legislation in early 2027.

The second is the motion. On 20 May 2026 a Greens motion ordering the government to reveal its funding and resourcing commitments to Firmus, the advice it had received about the AI factory, and the details of water consumption arrangements, passed the House of Assembly unanimously. A later Greens motion required the government to table, by 8 September, details of every AI data proponent working with the Office of the Coordinator General, with their locations and estimated power and water needs. The government complied, in form. Its document disclosed six confidential proposals beyond Firmus's three: two at feasibility stage with a preferred site selected and four at concept stage, including one at each stage estimated at 200 to 300 megawatts and a 50 megawatt concept that had expressed interest in the closed Liberty Bell Bay manganese smelter site, together at least 500 megawatts. It named none of the proponents. Omitted information, the document explained, was either not yet available, not sufficiently settled, not capable of reliable estimation, or had been provided to the office in confidence. Firmus said it was behind none of the six.

What did the May motion change? Less than its unanimity suggests. It passed in May. In June, Firmus lodged for Wesley Vale. In August, Bell Bay was approved. In September, the government answered the second motion with a document that counted the other proponents and withheld their names. An order of the House to produce documents is not self-executing, and compliance can be complete in form and empty in substance. It is a political question, and a government that judges the cost of non-compliance lower than the cost of disclosure will simply absorb it.

The government's own answer has arrived in the same register. Felix Ellis, the minister responsible for the digital economy, had told Parliament in August that the government would not consider a moratorium. What it offers instead is a draft Statement of Expectations for Data Centres and AI Infrastructure, issued by Ellis and Energy Minister Nick Duigan, covering community engagement, responsible water use, support for the energy system and renewable generation, network cost sharing, energy security and compliance with existing approvals. It is non-statutory, and public feedback closes on 12 October. Set beside the petition's five asks, it answers each with an expectation rather than a rule. Badger called it an absolute joke.

This is the uncomfortable finding. Two parliamentary orders for transparency, a ten-thousand-signature petition, a standing committee inquiry, a federal inquiry and three studies quantifying heat, water and grid impacts have collectively not altered the sequence in which anything is happening. The construction schedule is the only clock keeping time.

Ten Thousand Signatures Against A Construction Schedule

What the Tasmanian case exposes is a mismatch between the speed of the tools and the speed of the build. A parliamentary inquiry takes months to report and its recommendations are advisory. National legislation is flagged for 2027. Marinus Link's first stage arrives around 2030. Hydro Tasmania's new wind and solar offtake begins in five to six years. Every governance response is measured in years. The facilities are measured in months: Firmus described Project Southgate's first 2.1 billion dollar tranche as a twelve-month build.

At Wesley Vale the build did not wait for the paperwork. Less than three weeks after Latrobe Council asked the state to take over the assessment, the ABC reported excavation beyond the scope of the approved forest practices plan, transportable buildings, and piles of dirt and blue metal on a site with no planning permit. Firmus said the activities did not require planning approval and would take about four weeks. Freshney, pointing to a written commitment from the company, said undertaking work without permits was not consistent with it. Firmus agreed to stop while the council assesses.

That asymmetry is not accidental and not unique to Tasmania. It is the operating logic of the global build-out, visible in Ireland's connection queue, in Zeewolde, in Cerrillos, in Tucson's county rezoning, in the unpermitted turbines at Southaven. Infrastructure gets built inside the window during which the rules governing it are still being drafted, and once built, the rules are written around it rather than applied to it. The warning that AI infrastructure has stopped being a marginal digital service and become a structural component of power system dynamics is a statement about planning horizons as much as about electricity.

There is a version of this story in which the petitioners are wrong about the substance. Firmus may be right that liquid cooling makes its facilities dramatically more efficient, that rainwater capture is adequate at Wesley Vale, that renewable credits and financed generation will more than cover the load. The 115 permanent positions at Bell Bay and the 380 construction jobs are real jobs in a region that lost the pulp mill those sites once hosted. Nothing in the three studies proves that any specific Tasmanian facility will produce a two-degree heat island or breach a water utility's peak capacity, and it would be dishonest to claim otherwise.

But that is the petition's point, and it is procedural rather than technical. Every one of those propositions is testable. None has been tested by anyone independent of the proponent, because the register that would allow testing does not exist, the legislation that would require it has not been written, and the oversight mechanism the petitioners asked for over anything above fifty megawatts is not on any government's timetable. The community is being asked to accept, on the word of a company and a facilitation office it cannot see inside, claims that a published number would settle in an afternoon.

Ten thousand and thirty-five people worked out that this was the actual question and put their names to it. The formal reward is a government response within fifteen sitting days, whose headline a minister had already delivered on the floor. The informal reward is that the argument has moved, permanently, from whether Tasmanians care to what the government is prepared to disclose. That is smaller than the petitioners wanted and larger than the mechanism was designed to deliver. Whether it arrives before the concrete does is a different question, and on current evidence the concrete is winning. At Wesley Vale, the blue metal arrived before the permit.

References

1. Parliament of Tasmania. “AI Data Facilities in Tasmania, e-petition 147-26.” Posted 22 June 2026, closed 31 August 2026. https://haepetitions.parliament.tas.gov.au/haepet/Home/PetitionDetails/147
2. Tasmanian Greens MPs. “Greens Table Massive AI Data Centre Moratorium Petition.” 2 September 2026. https://tasgreensmps.org/media-releases/greens-table-massive-ai-data-centre-moratorium-petition/
3. Tasmanian Greens MPs. “Greens' Motion for AI Answers Passes Parliament.” 20 May 2026. https://tasgreensmps.org/media-releases/greens-motion-for-ai-answers-passes-parliament/
4. Parliament of Tasmania. “E-Petitions, House of Assembly.” https://www.parliament.tas.gov.au/petitions/hoa/e-petitions
5. Parliament of Tasmania, Standing Committee on Government Administration A. “Inquiry into AI Data Centres in Tasmania.” 2026. https://parliament.tas.gov.au/committees/house-of-assembly/standing-committees/government-administration-committee-a/inquiries/inquiry-into-ai-data-centres-in-tasmania
6. ABC News. “Firmus 288-megawatt AI data centre at Bell Bay, Tasmania gets approval after 6-1 council vote.” 25 August 2026. https://www.abc.net.au/news/2026-08-25/firmus-ai-bell-bay-receives-george-town-council-approval/107074336
7. ABC News. “Northern Tasmanians prepare to fight George Town Council AI data centre approval.” 30 August 2026. https://www.abc.net.au/news/2026-08-30/ai-data-centre-tasmania-firmus-technology-george-town/107089468
8. ABC News. “Firmus plans to truck water in and use rainwater to cool proposed Wesley Vale AI data centre.” 27 August 2026. https://www.abc.net.au/news/2026-08-27/firmus-truck-water-rainwater-cool-wesley-vale-ai-data-centre/107083230
9. ABC News. “Firmus wants to build three AI data centres in Tasmania, but can the state handle them?” 3 July 2026. https://www.abc.net.au/news/2026-07-03/firmus-ai-data-centres-is-tasmania-ready/106869862
10. ABC News. “Six new AI data centres proposed for Tasmania, but companies behind them are being kept secret.” 9 September 2026. https://www.abc.net.au/news/2026-09-09/six-new-confidential-data-centre-proposals-tasmania-revealed/107131974
11. ABC News. “AI data centre company Firmus lodges TASCAT appeal against restrictions on generator use.” 15 September 2026. https://www.abc.net.au/news/2026-09-15/firmus-technologies-lodge-tascat-appeal-for-generator-use/107144572
12. ABC News. “AI data centre company Firmus starts development work at Wesley Vale without permit.” 26 September 2026. https://www.abc.net.au/news/2026-09-26/firmus-starts-development-work-without-permit-wesley-vale/107198264
13. Pulse Tasmania. “Greens call Tasmania's new data centre expectations 'an absolute joke'.” 7 September 2026. https://pulsetasmania.com.au/news/greens-call-tasmanias-new-data-centre-expectations-an-absolute-joke/
14. Firmus. “Tasmanian world-first AI Factory Zone clears path for Firmus' Project Southgate.” June 2025. https://firmus.co/newsroom/tasmanian-world-first-ai-factory-zone-clears-path-for-firmus-project-southgate
15. Tasmanian Economic Regulator. “Energy in Tasmania Report 2024-25.” February 2026. https://www.economicregulator.tas.gov.au/Documents/26%201689%20CORRECTED%20Energy%20in%20Tasmania%20Report%202024-25.PDF
16. Hydro Tasmania. “Marinus Link is key to unlocking Battery of the Nation.” https://www.hydro.com.au/articles/media-releases/marinus-link-key-unlocking-battery-nation
17. Andrea Marinoni, Erik Cambria, Weisi Lin, Mauro Dalla Mura, Jocelyn Chanussot, Edoardo Ragusa, Chi Yan Tso, Yihao Zhu and Benjamin Horton. “The data heat island effect: quantifying the impact of AI data centers in a warming world.” arXiv:2603.20897, 21 March 2026, revised 21 April 2026. https://arxiv.org/abs/2603.20897
18. Basit A. Akinade, Amobichukwu C. Amanambu, Jonathan M. Frame and Shaolei Ren. “AI Data Centers and the Water Use Feedback Loop.” arXiv:2606.21760, 19 June 2026. https://arxiv.org/abs/2606.21760
19. Danbo Chen, Zijun Zhou, Yongyang Cai, Jiahong Qin, Ani Katchova and Lei Chen. “Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand.” arXiv:2604.06198, 13 March 2026. https://arxiv.org/abs/2604.06198
20. Commission for Regulation of Utilities, Ireland. “Large Energy User Connection Policy, decision paper.” December 2025. https://www.cru.ie/about-us/news/the-cru-publishes-its-decision-on-new-electricity-connection-policy-for-data-centres/
21. Data Center Dynamics. “The ongoing impact of Amsterdam's data center moratorium.” https://www.datacenterdynamics.com/en/analysis/the-ongoing-impact-of-amsterdams-data-center-moratorium/
22. Data Center Dynamics. “Chile partially reverses Google data center permit over water use concerns.” 28 February 2024. https://www.datacenterdynamics.com/en/news/chile-partially-reverses-google-data-center-permit-over-water-use-concerns/
23. NPR. “NDAs are hiding data center deals, drawing ire from locals and attention from lawmakers.” 27 August 2026. https://www.npr.org/2026/08/27/nx-s1-5879528/data-center-nda-disclosure-louisiana
24. AZ Luminaria. “Tucson City Council rejects Project Blue data center amid intense community pressure.” 6 August 2025. https://azluminaria.org/2025/08/06/tucson-city-council-rejects-project-blue-amid-intense-community-pressure/
25. Utility Dive. “DOJ intervenes on behalf of xAI in data center gas turbine lawsuit.” 18 June 2026. https://www.utilitydive.com/news/doj-intervenes-xai-data-center-gas-turbine-lawsuit/823267/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The letter that settled it runs to fourteen pages, with 235 pages of inspector's report bolted on behind. It is dated 29 July 2026, signed by a decision officer called Florence Hewett, and made by Matthew Pennycook, Minister of State for Housing and Planning, on behalf of the Secretary of State. Nowhere in it does the number fifteen appear.

Fifteen is the number of full time equivalent jobs the developer's own evidence says the approved data centre at 47 Grey Eagle Street will support once running. The figure comes from the appellant's submissions, quoted back at them by the London Borough of Tower Hamlets during a public inquiry held from 14 to 31 October 2025. It was not seriously contested. Fifteen people, in a borough where 28,852 households were on the housing waiting list as of July 2025.

The building will be 5,200 square metres of gross internal area, Use Class B8, reaching roughly 29 metres including plant and lift overrun, drawing about three megawatts. It sits on the corner of Grey Eagle Street and Calvin Street, a few dozen paces west of Brick Lane, on land holding a derelict former beer store with its roof and internal floors missing.

That is the whole of it. A three megawatt shed on a wrecked cold store site, and the most closely watched planning decision in Britain concerning the infrastructure of artificial intelligence, because of where it is.

What Ministers Actually Approved and What They Did Not

Precision matters here, because much of the coverage has been loose. There were four appeals, not one. Appeal A is the data centre, promoted by Truman Estates Ltd. Appeal B is the big one, roughly 35,000 square metres of residential, office, retail, community space, a supermarket, a microbrewery, market and event space across five new buildings, plus refurbishment of the listed Boiler House and the Cooperage. Appeal C, promoted by Zeloof LLP, is a 4,500 square metre office and retail building at Ely's Yard. Appeal D is listed building consent for works to the Boiler House. Grow Places, whose chief executive is Tom Larsson, is development manager. Trade press puts the total scheme value at around 500 million pounds, a figure that appears in property journalism rather than in the decision letter.

The data centre is therefore one building among eight or so, and not the largest. Describing the whole consent as a data centre scheme overstates it. Calling the data centre a minor incidental understates that it was the one element the council's own planning officers recommended refusing.

The procedural route also matters, and it is not what most reporting suggests. Tower Hamlets did not refuse these applications. It failed to determine them within the statutory period after they were lodged on 12 August 2024, and the applicants appealed under section 78 of the Town and Country Planning Act 1990 against non-determination. Jurisdiction passed to the Planning Inspectorate. On 31 July 2025, with the appeals already running, members resolved that had they retained the power they would have refused all four, setting out putative reasons that became the council's case at inquiry. On 23 October 2025 the appeals were recovered for the Secretary of State's own determination under section 79 of, and paragraph 3 of Schedule 6 to, the 1990 Act. Steve Reed was Secretary of State when recovery happened; Angela Rayner held the office when the decision landed; Pennycook signed.

So the shorthand that central government overruled a local refusal is true in substance and wrong in mechanism. Tower Hamlets lost the decision by running out the clock, then found that the appeal it had invited was pulled up to Whitehall.

One further correction. The council's professional officers did not oppose the redevelopment. Their reports recommended that, had jurisdiction been retained, permission should have been granted for Appeals B, C and D, with refusal only for Appeal A, on the grounds that it was, in their words, “an inappropriate location for a data centre in respect of the design, scale and lack of active frontages”. Elected members resolved to refuse the lot. Campaigners and developers each tend to quote only the convenient half of that.

The Arithmetic of Fifteen

Three megawatts and fifteen jobs works out at five permanent posts per megawatt, a number worth holding against the noisiest argument in British infrastructure politics right now. On 9 September 2026, reporting emerged of an analysis by Verdant, a think tank co-directed by the economist James Meadway, examining what data centres actually employ. Verdant looked at twenty UK projects for which staffing data is publicly available through planning applications, company accounts and press releases, supplemented by international comparisons. Its headline finding is that planned UK data centres are likely to support around 10,400 permanent operational jobs by 2035, roughly a quarter of the 40,200 that the industry body techUK has projected, and that facilities already built account for around 4,400 direct jobs. Meadway's line was that “Britain is being pushed into a rapid datacentre expansion on weak economic grounds”.

Two cautions. Verdant launched in March 2026 and is aligned with the Green Party, which opposes the Brick Lane scheme. That does not make its arithmetic wrong, but it is not a neutral referee. And the per megawatt figures that have circulated measure two different things. Verdant's briefing, “The Jobs Mirage of Data Centres”, written by Meadway, makes two comparisons. For existing sites measured against average load, it finds about 8.6 jobs per megawatt, against 43.7 implied by techUK's own figures, which put existing direct employment at 24,300 where Verdant counts roughly 4,400. For the planned pipeline, its capacity-weighted average across twenty planned sites is 1.2 operational jobs per megawatt, and it applies 1.6 across the full UK pipeline to reach its 10,400 projection. techUK's pipeline figure of 8.5 derives from a model facility of 54 staff running 6.35MW, applied uniformly to planned projects. Consistent across both comparisons is the order of magnitude of the gap, and the finding that larger future sites will be less job-dense still. A government statement said the report “relies on a deeply misleading jobs-per-megawatt metric, makes false comparisons with hospitals and schools”, and techUK defended its figures as based on established economic impact assessment methods, adding that Verdant had relied on an unreliably small sample.

Set the Brick Lane number against that spread and it sits in the middle, which is the point. Fifteen jobs is not an outlier. It is what a small colocation facility employs. The scandal, if there is one, is that this is normal and planning policy has not caught up with it.

The wider scheme is a different matter. The appellants told the inquiry Appeal B would generate 1,495 net full time equivalent jobs and 130 million pounds of gross value added, with 48 million pounds of the annual off-site expenditure falling locally, and Appeal C a further 260 jobs. Save Brick Lane, the Rule 6 party at the inquiry and a coalition of resident groups including the Bengali and Bangladeshi organisation Nijjor Manush, pointed out that the appellants' own socio-economic assessment indicated only 95 of the 1,520 full time equivalent jobs would go to local people, against 305 claimed in the economic statement.

Twenty Eight Thousand Households and a Wrecked Cold Store

The context in which fifteen jobs is being asked to justify itself is one of the sharpest housing failures in England. The council's evidence to the inquiry set out, as of July 2025, 28,852 households on the Tower Hamlets waiting list, 13,209 households overcrowded of which 2,668 severely so, 3,220 households in temporary accommodation, and 602 requiring rehousing on medical grounds. Agence France-Presse, reporting from Brick Lane on 8 September 2026, put the waiting list at around 31,000, higher than the council's own inquiry evidence from a year earlier. I have found no published source reconciling the two, so treat 31,000 as approximate. Tower Hamlets also failed its most recent Housing Delivery Test.

The deprivation picture is worse than the housing picture. The English Indices of Deprivation 2025, published by the Ministry of Housing, Communities and Local Government, record Tower Hamlets as ranked first in England on the income deprivation affecting children index, with a reported 71.3 per cent of children in income-deprived families, and first for older people at 61.1 per cent. In 2015 the equivalent child figure was 39 per cent. That is a borough going backwards fast, in the shadow of Canary Wharf, in a local authority area generating one of the largest economic outputs in the country.

The inquiry heard from residents directly, under their own names, and their statements are in the inspector's report. Abdul Shukur Khalisadar, born in Banglatown in 1978, described growing up in a flat overlooking the eastern wall of the Truman Brewery and called that wall “a physical and psychological barrier that separated our overcrowded homes from acres of vacant land beyond”. Abdus Shukur, a community elder, said the community wanted small business units for local traders along the Brick Lane frontage, “keeping the Brick Lane Bangla town identity rather than dissipating it”. Others described mould, asbestos, one toilet between five, a disabled husband with nowhere to sit at night. Henna Khanom told the inquiry she had joined the social housing waiting list in 2020 and, five years on, was no closer.

That is the material against which a 5,200 square metre machine room has to be weighed, and it is not a rhetorical framing. It is the evidential record of a statutory inquiry.

Testing the Housing Counterfactual Until It Breaks

The campaigners' central claim is that this land could have housed people. It is the strongest emotional argument in the case and the weakest legal one, and honesty requires saying so.

The emerging Tower Hamlets Local Plan 2038, submitted for examination on 16 February 2026, contains Allocation 1.7 covering Brick Lane and Pedley Street, within which all four appeal sites fall. It proposes a residential-led scheme with an indicative capacity of 800 homes. Save Brick Lane's evidence put the deliverable figure on the main and Grey Eagle Street sites at up to 345. Against that, the approved schemes deliver 44 homes, six at low cost rent and five intermediate, amounting on the appellants' figures to 36 per cent affordable by habitable room, and on the council's to residential use comprising just 14 per cent of gross internal area.

Six social rented homes was called an insult at the inquiry. The inspector, Paul Griffiths, did something more interesting than dismiss that. He ran the counterfactual out and found it barely better. Applying the emerging plan's own 40 per cent affordable requirement across the whole allocation gives roughly 320 affordable units, of which perhaps 100 on the Appeal B site and around 20 on Appeal A, so about 120 across the appeal sites. Against 28,000 households on a waiting list, he observed, 120 units “could hardly scratch the surface”. Six is worse than 120. Neither is a housing policy.

He then made the point that disposes of the counterfactual. No viability assessment has been carried out on the emerging allocation, and more fundamentally there is no mechanism by which the council could compel an unwilling landowner to build housing beyond what the landowner has offered. Compulsory purchase was floated by some parties, though not by the council, and the inspector judged it most unlikely given the high bar required. Save Brick Lane's alternative masterplan, prepared by the architect John Burrell, founders on the same rock.

There is a circularity here worth naming. The emerging allocation was given no more than limited weight partly because there is an unresolved objection to it, and the objector is the landowner. A landowner's objection thus helps defeat the housing allocation, which clears the way for the landowner's non-residential scheme. That is not corruption. It is how paragraph 49 of the Framework weights emerging plans, and it applies to everyone. But it means the phrase “the site could have delivered housing” is doing unearned work. On the evidence before the inquiry it could not have, because nobody could have made it.

The Case for Building It, Put Properly

Britain does need domestic compute, and the argument for this building is better than its opponents concede. Data centres were designated Critical National Infrastructure on 12 September 2024 by the then Technology Secretary Peter Kyle, the first such designation since space and defence in 2015. The National Planning Policy Framework published on 12 December 2024 and updated on 7 February 2025 followed. Its paragraph 85 requires the planning system to give particular regard to the needs of a modern economy and names data centres specifically; paragraph 87 requires decisions to make provision for new facilities. On 8 January 2026 regulations brought nationally significant data centres within the Nationally Significant Infrastructure Projects regime on an opt-in basis, and mandatory pre-application consultation for those projects is being removed from July 2026.

The specific case for Grey Eagle Street is not about AI training runs. It is about latency. This is a retail colocation facility, renting rack space to multiple tenants, and the appellants' evidence was that the likely operator would let to a handful of financial services firms, a law firm, perhaps a gaming company, all requiring ultra-low latency connectivity to the City. Government forecasts cited at the inquiry estimated London alone would need between 2,250 and 3,100 megawatts of additional capacity by 2027. The site sits opposite an internet exchange, surrounded by fibre carriers, close to the Osborn Street primary substation. Established providers in the City and Docklands are at capacity, and Slough and Hayes are too far for the millisecond-sensitive trade.

Mark Acton, a chartered engineer and consultant with more than twenty five years in the sector who has chaired the European Commission Code of Conduct committee on data centre energy efficiency, made the general version of the case to AFP. “Everything we currently do digitally comes from a data centre.” He is right, and the fashionable habit of treating every server hall as a moral affront is lazy.

There is also the site itself. The Secretary of State agreed with the inspector that the existing building is “utterly derelict, and home to anti-social and criminal activity”, and “a profoundly negative element of the townscape”. She gave substantial weight to the use of brownfield land under paragraph 125 of the Framework, and concluded the new building would clearly enhance the Brick Lane and Fournier Street Conservation Area. The only heritage harm identified was closure of a view of Christ Church Spitalfields from Quaker Street, very minor and at the lower end of the scale. A ruin used for drug dealing, replaced by a well-designed building serving national infrastructure need, on brownfield land, with heritage benefits outweighing heritage harm. Stated like that, the decision looks unremarkable.

Where the Argument for Building It Runs Out

Tower Hamlets took that case apart on its own terms, and its counter is stronger than it has been given credit for. The proposal is three megawatts. The council pointed out that the data centres in the recent line of Secretary of State decisions the appellants relied on are each more than twenty times larger in capacity contribution, and that in none did the need benefit rise above significant weight to substantial. The Mulberry Place decision they leaned on concerned a facility over six times bigger. If need weight is calibrated to scale, a three megawatt contribution to a 2,250 megawatt London shortfall is close to a rounding error.

The locational necessity argument fared no better under cross-examination. The appellants' own expert evidence specified retail colocation siting requirements as within a ten kilometre radius of core network facilities, which covers an enormous amount of London. It identified City and Docklands together as the relevant search area, and conceded that end users were unknown and would have varied latency requirements. There is exactly one reference in the evidence to facilities needing to be within a few hundred metres of transaction hubs, with, as the council put it, no detailed analysis of latency speeds or specific end users to support it. The council's position was not that the capacity should go to Harlow or Crawley. It was that it could go somewhere in the City and Docklands that is not inside a sensitive conservation area.

The Secretary of State nonetheless gave significant weight to the need for the data centre and its economic benefits, and only limited weight to its operational employment. She did not explain, in the decision letter, why a three megawatt facility attracts the same weight band as facilities twenty times its size. That is the gap a statutory review under section 288 would have probed. The six week window for such a challenge expired on Wednesday 9 September 2026, so that route has closed and the weight-band gap will not be tested in court on this case. The following day Jonathan Moberly of Save Brick Lane said there were still “things that could be explored” and urged the council to consider compulsory purchase: “We've never really heard any serious conversations about the possibility of a compulsory purchase order for example.” A council spokesperson said the town hall had “reviewed options” but “cannot comment further on this matter.”

Three Centuries of Other People's Plans

Brick Lane has been repurposed by outsiders for three hundred years, which is either an argument for continuity or an argument that the pattern should finally stop, depending on where you are standing. Huguenot weavers came first, then Ashkenazi Jews fleeing the pogroms, then Sylhetis. The Truman brewery, on the Black Eagle site, stopped brewing in 1989. In 1995 it was bought whole by the businessman Ely Zeloof, who had arrived from Israel in 1972 and worked in the rag trade on Fashion Street. Under his son Ofer Zeloof it became galleries, studios, markets and bars, and is now promoted by Truman Estates Ltd and Zeloof LLP.

The Bangladeshi settlement that gives Banglatown its name was forged partly in violence. On 4 May 1978 Altab Ali, a garment worker from Sylhet who had come to London in 1969, was stabbed to death in a racist attack in Whitechapel. Ten days later, between ten and twelve thousand people marched behind his coffin to Downing Street. The confrontations with the National Front that summer are remembered as the Battle of Brick Lane, and the park where Ali died was renamed after him in 1998. On 24 April 1999 the neo-Nazi David Copeland left a nail bomb in Hanbury Street, just off Brick Lane, injuring thirteen people.

The curry trade that made the street famous is already most of the way gone, and not because of data centres. Beyond Banglatown, published in 2020 by the Runnymede Trust and written by Claire Alexander, Seán Carey, Sundeep Lidher, Suzi Hall and Julia King, found the number of curry restaurants on Brick Lane had fallen from around 60 in the mid-2000s to 23 by early 2020, a decline of 62 per cent in fifteen years. The causes were rents, business rates, chef shortages driven by visa policy, a weakening night-time economy, absent support from regeneration agencies, and a younger generation with no wish to inherit a restaurant.

Ask what the decision means for a family that has kept a restaurant here across generations and there is no need to invent one. Faysal Ahmed, an Aspire councillor for Spitalfields and Banglatown, told Agence France-Presse that his parents ran a restaurant beneath the flat he grew up in on Brick Lane. “This has really been the focal point of the British Bangladeshi community,” he said. Carol Burns, whose family has kept a shop on the street since the 1800s, told the same reporter, “They'll have to drag me out of here feet-first.”

Here is the uncomfortable part. The developers' answer to the restaurant question is that their scheme helps. They put 31,400 additional weekly visitors and 48 million pounds of local annual expenditure into evidence, and cited Beyond Banglatown's own recommendations on reversing the decline. Both the inspector and the Secretary of State accepted that increased footfall would boost the businesses and curry houses on Brick Lane. Save Brick Lane's counter is that the Bangladeshi community is more likely to absorb the costs of gentrification and rising rents than to capture the benefits. Nobody produced a model settling it. The honest answer is that a data centre employing fifteen people will do nothing for that family directly, that a scheme bringing 31,400 people a week past their door might do something, and that the same footfall logic has justified every previous wave of change on Brick Lane, including the one that took the trade from 60 restaurants to 23.

What the Research Record Actually Says About Host Communities

Three arXiv items are worth setting out accurately, including where they do not say what they are sometimes reported as saying. The first, posted 3 June 2025 under identifier 2506.03367, is “The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities” by Wacuka Ngata, Noman Bashir, Michelle Westerlaken, Laurent Liote, Yasra Chandio and Elsa Olivetti, mostly of MIT. It is a Late Breaking Work accepted at ACM COMPASS 2025, a short and explicitly preliminary paper on ongoing research rather than a finished study. Its most transferable finding is that in Northern Virginia, home to over 450 facilities, data centres account for less than 0.5 per cent of the local workforce. It cites a projection that growth there will add 37 US dollars a month to residential electricity bills by 2040, and reports that in Loudoun County more than 6.8 per cent of homes recorded at least one monthly reading above the 8 per cent harmonic distortion threshold that can damage appliances. Its own noise measurements are a single-site comparison the authors say they plan to expand. Treat the numbers as indicative rather than settled.

The second, posted 11 February 2026 under 2602.10526, is “The Infrastructure Equation: Water, Energy, and Community Policy for Georgia's Data Center Boom” by Mickey M. Rogers, William M. Ota, Nathaniel Burola and Tepring Piquado. It is a policy report drawn from an expert convening in Atlanta supported by the Alfred P. Sloan Foundation, not an empirical study, and the authors are commendably frank that their policy overview “is general and, in some places, vague because it is based on stakeholder comments that were not well described”. Two observations travel well to Tower Hamlets. Data centres occupy large parcels of industrial land while typically employing between thirty and sixty full time workers. And, crucially, “wealthier neighbourhoods often have the political resources to organize quickly and block unwanted projects, whereas lower-income and minority communities may find themselves absorbing the cumulative burdens” with far less influence.

The third, 2608.07485, is often summarised as an account of how the design community is coming to recognise the role of infrastructure decisions in shaping outcomes in particular places. That is not quite what it is. Its actual title is “Humour as Resistance: Visceralizing the Environmental and Social Impact of AI through Humour-based Creative Practices”, by Han Qiao, Rowan O.A. Munson, Nadia Mariyan Smith, Eshta Bhardwaj and Christoph Becker, first posted on 10 June 2026. It studies a humour-based creative campaign, using event ethnography, surveys and interviews, and identifies four roles humour plays in surfacing the material costs of AI infrastructure, including as “resistance to power”. It is evidence that the design and human-computer interaction community is turning towards infrastructure politics, but it is not a siting study and should not be cited as one.

Whose Refusals Get Overruled

The structural question is whether central override falls disproportionately on poor, diverse boroughs. The honest answer is that the evidence does not support it, and that the real asymmetry lies elsewhere. Ministers have overruled some extremely comfortable places. Angela Rayner twice overturned Buckinghamshire Council's refusals of data centres on green belt land at Iver: at Court Lane in December 2024, and at the former Woodlands Park landfill site on 9 July 2025, a 90MW hyperscale facility promoted by Greystoke. In May 2025 permission was granted on appeal for an 84,000 square metre hyperscale campus off Bedmond Road in Abbots Langley, refused by Three Rivers District Council in January 2024 and treated as grey belt under the revised Framework. Those are among the leafiest districts in southern England, and they lost too. Nor is the override universal, since the Secretary of State has dismissed at least one data centre proposal in London's green belt. And central government is not immune to challenge. In January 2026, following a judicial review brought by the campaigning organisation Foxglove and the charity Global Action Plan, the government accepted that the Woodlands Park decision contained a “serious logical error”, having failed to secure the developer's climate-mitigation commitments, and should be quashed.

The low-turnout thesis fails too. Tower Hamlets went to the polls on 7 May 2026 with an electorate of 219,030 and a turnout of 42.1 per cent, high for a London borough. Lutfur Rahman was re-elected executive mayor on 38.8 per cent, his Aspire party taking 33 of 45 council seats. This is not a disengaged place. Rahman wrote to the Secretary of State opposing the scheme in November 2025, and eight MPs forwarded constituents' representations. It made no difference, which is not the same as nobody trying.

The genuine asymmetry is structural rather than demographic, and it has three parts. Only applicants have a right of appeal in the English system. A refused developer goes to the Planning Inspectorate; a community whose objections are overridden goes to the High Court on a point of law, which costs money the average residents' association does not have, which is why Save Brick Lane has repeatedly had to crowdfund. Recovery then pulls the decision to a minister whose department is simultaneously sponsor of a growth agenda in which data centres are a named priority. And the strategic decisions that determine the outcome were taken years earlier and elsewhere. The area sits in the City Fringe Opportunity Area and at the heart of Tech City, designations made regionally and followed through locally. “The correct place to take that resistant stand,” Griffiths wrote, “is the forum in which those strategic decisions are taken.”

He went further, in a passage that is unusually candid for an inspector's report. Acknowledging arguments that reliance on a “(neoliberal) economic model is misplaced because it leads to inequality”, he replied that “these appeals are not the appropriate place for that debate. It is in my view a much wider, and largely political, question.”

That is the answer to the question of whose neighbourhoods are acceptable. Nobody decided Banglatown was expendable. A strategic designation in a regional plan, a national infrastructure designation made by a technology secretary, and a Framework paragraph naming data centres as a modern economic need together converted a local objection into a matter the local forum was no longer competent to decide. Deprivation did not cause that. It determines who has the money to fight it afterwards.

The Equality Act 2010 duty was discharged in three paragraphs. The Secretary of State noted that a significant Bangladeshi population lives and works in Spitalfields and Banglatown, took community concerns into account, and had regard to her duty throughout. Section 149 requires due regard, not a particular outcome, and on the case law that is almost certainly enough. A public sector equality duty satisfied by noticing that a community exists is not doing much work in infrastructure siting.

What a Regime That Took Host Communities Seriously Would Require

Not a veto. Vetoes are how Britain got a housing crisis. Something narrower and more enforceable. Start with disclosure. Georgia's proposed High Resource Use Facilities Transparency Act would require facilities with a peak load of at least thirty megawatts to publish energy use, water use and community impact before applying for permits or incentives. A British version should set the threshold by floorspace as well as load, since a three megawatt building would escape a thirty megawatt test entirely, and should require the operational headcount on the face of the application. At Brick Lane the fifteen jobs figure surfaced only because a well-resourced council dug it out of the appellant's evidence in cross-examination.

Then make the headcount bite. If paragraph 85 gives data centres special standing as a modern economic need, the weight attached should be expressly calibrated to capacity, as Tower Hamlets argued and the Secretary of State declined to explain. A three megawatt facility should not attract the same weight band as one twenty times larger. Write the calibration into policy rather than leaving it to be inferred from a line of decision letters.

Fix the viability trap. An emerging allocation was discounted here partly because no viability assessment had been done, in a case where the objector was the landowner. Authorities bringing forward residential-led allocations on contested sites should be resourced and required to viability-test them at submission. Otherwise the absence of an assessment functions as a subsidy to whoever objects.

Attach obligations to Critical National Infrastructure status. CNI designation currently confers protection and planning weight and demands nothing in return. It should carry reciprocal duties, including mandatory publication of water and power draw, demand-response commitments, waste heat offtake where a district network exists or is planned, and community benefit scaled to capacity rather than negotiated ad hoc. The Brick Lane conditions safeguard future connection to a district heating network, which is a start, and a promise is not a pipe.

Adopt the equity scorecard the Georgia convening proposed, evaluating sites against cumulative burdens and historical land-use patterns. Tower Hamlets did not need one to know its position. Three Rivers and Buckinghamshire might. And narrow the appeal asymmetry. A limited third-party right of appeal, confined to cases where a proposal departs materially from an adopted or submitted allocation, would cost the Exchequer little and would end the situation in which the only route left to a community is a section 288 challenge funded by CrowdJustice.

None of this would have saved the cold store on Grey Eagle Street. The building was derelict, the site brownfield, the landowner never going to build social housing there, and the alternative on the table was 120 affordable homes nobody could compel into existence against a waiting list of nearly 29,000. The campaigners lost on the evidence, not on the sympathy.

What the case does establish is that Britain has designated a category of building as nationally critical without deciding what it owes the streets it lands on. The machinery is going into the ground either way. Fifteen people will work in it. Everyone else on Grey Eagle Street gets the wall.

Sources and References

1. Ministry of Housing, Communities and Local Government, “Recovered appeal: Sites on and around the site of Truman Brewery, Tower Hamlets, London (refs: 3367041, 3367172, 3367179 and 3367369 – 29 July 2026)“, combined decision letter and inspector's report, 29 July 2026. https://www.gov.uk/government/publications/recovered-appeal-sites-on-and-around-the-site-of-truman-brewery-tower-hamlets-london-refs-3367041-3367172-3367179-and-3367369-29-july-2026
2. Wacuka Ngata, Noman Bashir, Michelle Westerlaken, Laurent Liote, Yasra Chandio and Elsa Olivetti, “The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities”, arXiv preprint 2506.03367, 3 June 2025. https://arxiv.org/abs/2506.03367
3. Mickey M. Rogers, William M. Ota, Nathaniel Burola and Tepring Piquado, “The Infrastructure Equation: Water, Energy, and Community Policy for Georgia's Data Center Boom”, arXiv preprint 2602.10526, 11 February 2026. https://arxiv.org/abs/2602.10526
4. Han Qiao, Rowan O.A. Munson, Nadia Mariyan Smith, Eshta Bhardwaj and Christoph Becker, “Humour as Resistance: Visceralizing the Environmental and Social Impact of AI through Humour-based Creative Practices”, arXiv preprint 2608.07485, 10 June 2026. https://arxiv.org/abs/2608.07485
5. Agence France-Presse, “On London's Brick Lane, a data centre sparks local battle”, France 24, 8 September 2026. https://www.france24.com/en/live-news/20260908-on-london-s-brick-lane-a-data-centre-sparks-local-battle
6. Dave Hill, “Government gives go-ahead to Truman Brewery planning applications”, On London, August 2026. https://www.onlondon.co.uk/government-gives-go-ahead-to-truman-brewery-planning-applications/
7. Michael A. Lewis and Phil Tomlinson, “How Brick Lane became the latest neighbourhood to battle against a data centre”, The Conversation, August 2026. https://theconversation.com/how-brick-lane-became-the-latest-neighbourhood-to-battle-against-a-data-centre-290200
8. Claire Marshall Watkins, “Opposition mounts to Brick Lane data centre plans as Reed decision awaited”, Eastlondonlines, May 2026. https://www.eastlondonlines.co.uk/2026/05/opposition-mounts-to-brick-lane-data-centre-plans-as-reed-decision-awaited/
9. “Brick Lane: How a single data centre beat unanimous local opposition”, Capacity, August 2026. https://capacityglobal.com/news/brick-lane-data-centre/
10. “Rayner approves Grow Places' £500m Truman Brewery redevelopment”, Property Week, 2026. https://www.propertyweek.com/news/rayner-approves-grow-places-500m-truman-brewery-redevelopment
11. Landmark Chambers, “Secretary of State allows Truman Brewery redevelopment appeals”, 2026. https://www.landmarkchambers.co.uk/news-and-cases/cases/secretary-of-state-allows-truman-brewery-redevelopment-appeals
12. Claire Alexander, Seán Carey, Sundeep Lidher, Suzi Hall and Julia King, “Beyond Banglatown: Continuity, change and new urban economies in Brick Lane”, Runnymede Trust, 2020. https://www.runnymedetrust.org/publications/beyond-banglatown
13. Department for Science, Innovation and Technology, “Data centres to be given massive boost and protections from cyber criminals and IT blackouts”, GOV.UK, 12 September 2024. https://www.gov.uk/government/news/data-centres-to-be-given-massive-boost-and-protections-from-cyber-criminals-and-it-blackouts
14. House of Commons Library, “Data centres: planning policy, sustainability, and resilience”, research briefing CBP-10315. https://commonslibrary.parliament.uk/research-briefings/cbp-10315/
15. CMS, “Data Centres enter the NSIP Regime”, legal update on the Infrastructure Planning (Business or Commercial Projects) (Amendment) Regulations 2026, 2026. https://cms.law/en/gbr/legal-updates/data-centres-enter-the-nsip-regime
16. “AI datacentres are booming in Britain, but where are the thousands of jobs they were supposed to create?”, Eastern Eye, 9 September 2026. https://www.easterneye.biz/ai-datacentres-are-booming-in-britain-but-where-are-the-thousands-of-jobs-they-were-supposed-to-create/
17. Ministry of Housing, Communities and Local Government, “English indices of deprivation 2025”, GOV.UK, 2025. https://www.gov.uk/government/statistics/english-indices-of-deprivation-2025/english-indices-of-deprivation-2025-frequently-asked-questions
18. “Poverty among children and elderly in Tower Hamlets and Hackney worst in England”, Eastlondonlines, November 2025. https://www.eastlondonlines.co.uk/2025/11/tower-hamlets-and-hackney-worst-in-england-for-income-deprivation-among-children-and-elderly/
19. Three Rivers District Council, “Council Leader responds to government's decision over Abbots Langley data centre”, 2025. https://www.threerivers.gov.uk/news/council-leader-responds-abbots-langley-data-centre
20. Foxglove, “UK Government admits 'serious error' in forcing through hyperscale data centre without environmental protections”, 22 January 2026. https://www.foxglove.org.uk/2026/01/22/uk-government-admits-serious-error-data-centre/
21. London Borough of Tower Hamlets, “Lutfur Rahman elected as Executive Mayor of Tower Hamlets”, May 2026. https://www.towerhamlets.gov.uk/News_events/2026/May/Lutfur-Rahman-elected-as-Executive-Mayor-of-Tower-Hamlets.aspx
22. Tower Hamlets Council, “Altab Ali and the Fight for Equality”. https://www.towerhamlets.gov.uk/lgnl/leisure_and_culture/Arts_events_and_information/Altab_Ali_and_The_Fight_for_Equality.aspx
23. Bricks & Bytes, “1.6 Jobs Per Megawatt: The Data Center Jobs Fight Is Ignoring Construction”, September 2026. https://bricks-bytes.com/blog/data-center-jobs-per-megawatt-verdant-techuk-construction/
24. The Next Web, “Burnham won't pause UK data centres as a report says jobs are overstated”, 9 September 2026. https://thenextweb.com/news/burnham-uk-data-centres-moratorium-verdant-jobs
25. Tower Hamlets Slice, “Campaigner urges council to seize Brick Lane site”, 10 September 2026. https://towerhamletsslice.co.uk/whitechapel/save-brick-lane-compulsory-purchase-data-centre-plans/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The decision that mattered was taken in the dark, hours before anybody found out.

On the evening of 19 May 2026, with the paperwork loaded and the severance calculators run, Mark Zuckerberg cancelled the second wave. The first went ahead the following morning: roughly 8,000 people, about 10 per cent of Meta's global workforce, told by email that their jobs had ceased to exist. The second, scheduled for November, was supposed to be the big one. It was designed to reach deeper, across a broader set of roles, and one human resources executive had projected that the combined culling would match or exceed the 25 per cent Meta shed three years earlier during its self-declared year of efficiency.

It never happened. On 26 August 2026, a Reuters investigation by Katie Paul explained why, drawing on internal documents, internal posts, recordings and more than twenty interviews.

The plan had a codename. Project OT, for Organization Transformation. It was hatched at a leadership retreat at Zuckerberg's Hawaii estate in January 2026. Meta would become AI native: agents taking over much of the daily work then performed by thousands of employees, supervised by small, talent-dense pods, sometimes as few as two or three engineers and a designer, working under a deliberately generic new job title. Builder.

Executives modelled scenarios in which some teams shrank by as much as 60 per cent. An internal human resources tool sorted staff into categories including Irreplaceable Talent and a hypothetical class of 10X Performers. In April, Meta told American employees it would begin capturing their mouse movements, clicks, keystrokes and screenshots on work applications, expressly to generate training data so that AI agents could learn to do white-collar work. The internal sentiment score, measured in Meta's half-yearly Pulse survey, fell from 74 per cent favourable to 55 per cent.

And then the agents got to work, and the company's own telemetry started telling a story nobody in Hawaii had scripted.

The Two Numbers That Contain Everything

Here are the figures Reuters obtained from Meta's internal posts. They are the only two you need.

Code changes to Meta's internal platforms were up 220 per cent year over year.

Changes that resulted in new or upgraded features actually reaching users were up 36 per cent.

The temptation is to read that as a disappointing number beside an impressive one. It is not. It is a ratio, and the ratio is the story. For every unit of increase in work produced, roughly one sixth of a unit of increase in work delivered. The largest social media company on earth more than tripled its rate of change while barely moving the rate at which change reached a human being.

This is not a productivity gain that fell short. It is a measurement of the distance between activity and value, rendered in the company's own dashboards, at a scale nobody can dismiss as anecdote.

The 220 per cent was not free. Internal posts reviewed by Reuters associated unchecked agent activity with a 40 per cent year-over-year rise in major technical and security incidents, and a rise of as much as 70 per cent in the time employees spent resolving them. The posts described agents taking large-scale destructive actions of a kind humans would be unlikely to execute, which is a beautifully bureaucratic way of saying the machines did things no engineer with a mortgage and a reputation would ever have dared.

At a company meeting on 2 July, according to a recording heard by Reuters reporters Katie Paul and Courtney Rozen, Zuckerberg told staff that “the trajectory of the agentic development over at least the last four months hasn't really accelerated in the way that we expected”, and that the company's bets on the new structure “haven't come to fruition yet”. He conceded the reorganisation had not been as clean as it could have been. Meta described Project OT to Reuters as a yearlong effort covering cost reduction, team redesign and redeployment, and said that “we didn't move forward with every scenario from the exercise, and it was never assumed we would”.

The Denominator Nobody Put on a Slide

The seductive reading of the 40 per cent and the 70 per cent is as a teething problem, an implementation cost. It is not. It is the hidden denominator, and it changes the arithmetic.

Meta removed roughly 8,000 people on the thesis that agents would absorb their work. The agents then generated a volume of change that produced a 40 per cent increase in major incidents. Resolving those incidents consumed up to 70 per cent more time from the employees who remained. The productivity was not created. It was relocated, off the payroll of the people made redundant and onto the calendars of the people who were kept, in the form of cleanup.

That is not an elimination of labour. It is a transfer, dressed in the language of elimination, with the costs borne entirely by the survivors.

There is a particular cruelty in the shape of the transfer. The work that moved was not the interesting work. Nobody joined Meta to spend a Thursday night rolling back an agent's catastrophic change to a system they did not write and cannot reconstruct. Firefighting is the least legible, least career-enhancing category of engineering labour there is. It does not appear in a promotion packet as a shipped feature. It appears as an absence of disaster, which is the one thing performance review systems have never been able to see.

So the survivors were handed a 70 per cent increase in precisely the work their employer's measurement apparatus was least capable of valuing, generated by precisely the technology their employer had said would make them unnecessary.

Goodhart Comes for the Commit Log

Why would output inflate the numerator and barely touch the denominator? Not because the agents were stupid. Because they were obedient.

An AI agent optimises for the legible artifact. The commit. The diff. The closed ticket. The merged pull request. Engineering organisations instrument these things because they are countable. What an agent cannot optimise for is the illegible goal those artifacts were only ever standing in for: a coherent, safe, maintainable product a human being actually wants to use, shipped at a moment when shipping it is wise.

For as long as humans did the work, the proxies held. Not because the proxies were good, but because the humans understood the goal behind them. An engineer who writes a commit knows the commit is not the point. The point is the product, the customer, and the on-call rota they will personally be sitting on in three weeks when the thing they shipped starts paging somebody at four in the morning. The metric was never load-bearing. The shared understanding was, and the metric was a convenient summary of it.

The anthropologist Marilyn Strathern gave the crispest formulation of this failure mode in a 1997 essay on audit culture in British universities, restating the economist Charles Goodhart: when a measure becomes a target, it ceases to be a good measure. Project OT is the most expensive experiment yet run on that sentence.

Meta had spent years instrumenting its engineering organisation around proxies for value. Then it handed the proxies to an optimiser that does not share the goal, cannot infer it, and has no stake in it. The proxy immediately detached from the thing it proxied. Code changes tripled. Features barely moved. Incidents climbed. The dashboard went green exactly as it was designed to, and the product did not get meaningfully better.

The uncomfortable corollary is that Meta's metrics were probably always this bad, and were simply protected from their own badness by the people whose behaviour they measured. Remove those people and you discover what your instrumentation actually measures, which turns out to be motion.

Give Meta Its Due, Because It Earned It

Now the part that will annoy anybody who wants a simple story.

Cancelling the second wave was the right call, made on evidence, against considerable institutional momentum, by the chief executive who had personally championed the thesis. That is a functional feedback loop working correctly, and it is rarer than it should be.

Consider the counterfactual. Meta had a plan blessed at the top, socialised through its leadership, embedded in headcount models and capital allocation. It had a chief executive who told Joe Rogan in January 2025 that Meta would have AI capable of acting as a mid-level engineer within the year. It had every incentive to keep going, declare victory, and let the incident count be next year's problem. Plenty of organisations have done exactly that.

Instead, four months of internal data killed the plan, and the chief executive said so out loud in a room full of employees, in terms that were always going to leak. “Hasn't really accelerated in the way that we expected” is not a boast. It is a concession, and it was made before Reuters published anything. Give that its full weight before turning the knife.

Two further honesties are owed. The first is that four months is a short window on which to judge a technology moving this quickly. The second is more important, and almost nobody covering this story has said it plainly.

The May layoffs are a confound.

Meta cut 8,000 people and reassigned roughly 7,000 more into AI-focused teams in the same week. Anyone who has worked inside a large engineering organisation during a reduction in force knows what that does. Ownership of systems evaporates. On-call rotas thin out. The person who knew why that service behaves oddly under load on Tuesdays has gone, and the documentation that lived in their head walks out with them. A 40 per cent rise in major incidents in the months after an event like that is what you would expect even if no AI agent had ever touched the codebase.

So the honest reading is that the incident data cannot cleanly separate the effect of the agents from the effect of the disruption caused by removing the people. Meta's internal posts attributed the rise to unchecked agent activity, and those posts were written by people close to the systems, which is meaningful. But the causal claim is muddier than the headline number implies, and pretending otherwise would be doing exactly what Meta did: reading a metric as though it were the truth.

The confound does not rescue the strategy, though. It indicts it differently. If removing the people caused the instability, the people were doing something the plan failed to account for, and the plan was still wrong. Either the agents broke things or the absence of the humans did. Both readings destroy the premise that the humans were surplus.

What the Dashboard Was Never Able to See

The argument here has to be made carefully, because there is a sentimental version that is worthless and a rigorous version that is devastating.

The sentimental version says humans have a special spark machines lack. It is unfalsifiable, self-congratulatory and useless to anybody deciding how to run an engineering organisation.

The rigorous version is narrower and harder. The people Meta removed were not, for the most part, doing the work Meta's metrics recorded. The metrics recorded artifacts. The people were doing something else, and it has a name.

The chemist and philosopher Michael Polanyi described it in 1966 as the tacit dimension, summarised in his best-known line: we can know more than we can tell. Tacit knowledge resists articulation, not because it is mystical but because it is procedural, contextual and distributed. It is knowing a system is shaped as it is because of a decision taken in 2019 for reasons nobody wrote down, that a change looks fine in isolation and will interact badly with something three services away, which parts of the codebase are load-bearing and which are decorative.

Above all, it is the judgement about what not to build, and the willingness to refuse to ship.

That last one is invisible to every measurement system ever deployed in a software company. The most valuable act a senior engineer performs in a quarter is frequently a negative one: the design killed in review, the migration postponed until the dependency was ready, the feature talked out of existence in a corridor because somebody remembered the last three times it had been tried. None of it generates a commit. All of it is the reason the numbers that do move mean anything.

An AI agent has no capacity for refusal. That is not a limitation of current models, it is a description of what an agent is for. You do not deploy an autonomous coding agent so that it can decline to write code. Its entire value proposition is throughput. So when you replace humans, a meaningful fraction of whom spent their time preventing work from happening, with agents whose only mode is to make work happen, you do not get the same organisation running faster. You get a structurally different organisation with the brakes removed.

Two hundred and twenty per cent more change. Thirty-six per cent more delivery. Forty per cent more incidents. That is an engineering organisation with nobody left whose job is to say no.

Independent evidence supports this. GitClear, analysing more than 600 million real-world code changes between 2023 and 2026, found that as AI assistance spread, duplicated code blocks proliferated, rising 81 per cent over 2023 to the highest level on record, while refactoring collapsed. Refactoring line movements fell around 82 per cent against 2022 levels, and long-term legacy maintenance fell around 74 per cent. Code volume up, code care down. The same shape as Meta's ratio, observed across the industry.

Three Papers That Called It in Advance

The most striking thing about Project OT is that its failure mode was documented in the literature before Meta finished running the experiment.

In May 2026, Sabry E. Farrag posted a paper to arXiv naming what he calls the Productivity-Reliability Paradox. It synthesises 67 sources from 2022 onwards and opens with the contradiction at the heart of the field: controlled studies report gains of between 20 and 56 per cent on well-scoped tasks, the most rigorous randomised controlled trial documents a 19 per cent slowdown for experienced developers, and telemetry across more than 10,000 developers shows 98 per cent more pull requests, 91 per cent longer review times, and flat delivery metrics.

Read that last clause again. Ninety-eight per cent more pull requests. Flat delivery. That is the 220 and the 36, measured elsewhere entirely, a year before Meta's numbers leaked.

Farrag's diagnosis is that the paradox emerges from non-deterministic code generators combined with insufficient specification discipline, moderated by three variables: task abstraction, codebase maturity and developer experience. His conclusion is a sentence that belongs on the wall of every engineering leadership offsite: specification discipline, not model capability, is the binding constraint on AI-assisted software dependability.

Meta had model capability. What it lacked, on the evidence of its own incident numbers, was specification discipline at the scale it was suddenly demanding. Note the moderating variables too. Meta's internal platforms are among the most mature and entangled codebases on earth, and the May layoffs removed a large slice of the developer experience. Every one of Farrag's moderators was pointing the wrong way at precisely the moment Meta pressed the accelerator.

The second paper, posted in May 2026 by Won Ik Cho, Seong-hun Kim and Geunhye Kim, is blunter: adopting AI in organisational practice does not guarantee a productivity boost, because human and environmental factors critically moderate the relationship between deployment and realised gains. Their five moderating factors read like a post-mortem of Project OT written in advance. Human resource composition. Baseline capability of individuals. Learning curve of practitioners. Incentives for fair use. Flexibility of objectives.

Meta altered its human resource composition by force in May. It disrupted baseline capability by removing thousands of experienced staff. It compressed the learning curve into a mandate. It created incentives that were the opposite of fair use, since an internal token-usage dashboard reportedly tracked how much employees used AI tools, which is a direct instruction to generate consumption rather than value. And it had no flexibility of objectives at all, because the objective had been set in Hawaii in January and encoded into a headcount plan.

The third paper, by Jeanne McClure and Gregg Gerdau, posted in March 2026 and revised in April, supplies the frame that ties the other two together. It opens with a fact that ought to be more famous: global corporate AI investment reached 252.3 billion dollars in 2024, and only 6 per cent of firms reported a significant earnings impact. Their argument, built on 19 large-scale industry and academic sources including surveys of nearly 10,000 organisational leaders, is that AI project failure is fundamentally an organisational learning problem rather than a technology deficit. Failures divide into organisational ones, meaning culture, leadership alignment, governance and human-AI learning deficits, and technical ones, meaning semantic bottlenecks and output management. Their prescription treats AI investment as capability development rather than technology procurement.

Project OT was procurement. It bought agents and subtracted people, and it treated the organisation itself as a constant. The paper's central claim is that the organisation is the variable.

The Amplifier

The bridge between these arguments and the industry's own data is the DORA research programme, whose State of AI-assisted Software Development report, published in September 2025, reads in retrospect like prophecy. AI's primary role in software development, it found, is that of an amplifier: it magnifies the strengths of high-performing organisations and the dysfunctions of struggling ones.

The findings are worth stating precisely. Nearly 90 per cent of technology professionals reported using AI at work. More than 80 per cent believed it had increased their productivity. Adoption correlated with improved delivery throughput. And software delivery instability continued to rise. The researchers tested whether speed gains might offset instability through a fail fast, fix fast dynamic, and found they did not.

More productive, more unstable, and convinced they were doing better. Which brings us to the most unsettling piece of evidence in this entire field.

In July 2025, the research organisation METR published a randomised controlled trial in which 16 experienced open-source developers completed 246 real tasks in their own repositories, projects averaging more than 22,000 stars and over a million lines of code. Tasks were randomly assigned to allow or disallow AI assistance. Beforehand the developers forecast AI would speed them up by 24 per cent. Afterwards, having done the work, they estimated they had been sped up by 20 per cent. They had in fact been slowed down by 19 per cent.

They were wrong about their own productivity, in their own codebases, on their own tasks, by roughly 39 percentage points, in the direction that flattered the tool. Scale that error up to an executive team reviewing a dashboard and you have a reasonable account of how Project OT got approved.

METR has since qualified its own result. A February 2026 update using late-2025 tools estimated that returning developers now finished tasks about 18 per cent faster with AI, with a confidence interval wide enough to include a slowdown, and METR called the data “only very weak evidence” because many participants avoided submitting tasks they did not want to do without AI. The tools have probably improved, and the slowdown may no longer hold. The part that survives is the perception gap, and it is the part that matters to anybody reading a dashboard: the conviction of being faster, by a margin nobody could verify.

DORA's follow-up work is more pointed still. In May 2026 the team, led by Nathen Harvey at Google Cloud, published a framework for calculating the return on AI investment in software development. It describes a J-curve, in which organisations suffer a temporary productivity decline before any sustainable gain, driven by the learning curve, the verification tax of reviewing machine-generated code, and the need to adapt downstream processes to higher volumes. Its worked model for a 500-person engineering organisation includes a line item for the cost of rising change failure rates. And it states, in language Meta's leadership might read twice, that return on investment is no longer a measure of how many developers an organisation can replace, but of how much latent human creativity can be unlocked.

Meta ran the headcount reduction first and hit the bottom of the J-curve with 8,000 fewer people to climb out of it.

Being Retained Is Not the Same as Being Valued

Now to the part the metrics cannot reach, which is what it does to a person to survive this.

More than 70,000 people still work at Meta. Many of them spent the first half of 2026 reading press reports about their employer's plan to make itself AI native, being told to install software that recorded their keystrokes and screenshots so agents could learn to do their jobs, watching colleagues disappear in May, and then, in late August, discovering from a Reuters investigation that scenarios had been modelled in which teams like theirs shrank by 60 per cent.

They were not vindicated. They were retained.

The distinction matters more than any statistic in this article. To be vindicated is to have your value recognised. To be retained is to have your replacement fail its trial. The reasoning was stated out loud, in internal posts and then in a wire service investigation read by the entire industry: you are still here because the machine broke things, the cleanup was expensive, and the timing was wrong.

That is a stay of execution, and everyone involved knows it. Zuckerberg made the implication explicit when he said the bets “haven't come to fruition yet”. Nobody at Meta heard that as an abandonment of the thesis. They heard it as a schedule revision.

Think about what this does to the psychological contract. Not the employment contract, which is a document about notice periods, but the unwritten one, the set of mutual expectations that determines whether a person gives an organisation their best judgement or merely their compliance.

Discretionary effort dies first. The extra hour spent understanding why a system is fragile rather than routing around the fragility. The willingness to raise a problem in a review everybody else wants to wave through. The impulse to mentor somebody junior. All of it is voluntary, all of it is invisible to instrumentation, and all of it depends on believing the organisation intends to keep you.

Then there is the truly perverse incentive, which is the sharpest thing in this whole episode.

Tacit knowledge is the asset that saved Meta. The judgement about what not to build, the memory of why a system is shaped as it is, the instinct to refuse. Meta's incident numbers are, on the most sympathetic reading, a measurement of what happens when that knowledge is removed. Any sane organisation reading them would conclude that it must urgently capture and document it.

But consider the position of the individual asked to document it, inside a company that has already installed keystroke and screenshot capture expressly to train agents to perform white-collar work, and has already modelled cutting teams by 60 per cent. In that context, writing down your judgement is indistinguishable from writing your own replacement's training data. The rational response is to withhold, not out of malice but out of a perfectly reasonable reading of the situation.

So Meta now needs the thing it has systematically disincentivised anybody from providing. That is not a culture problem a town hall can fix. It is a structural trap of the company's own construction, and precisely the organisational learning failure McClure and Gerdau describe when they argue that AI failure typically presents as technology failure while actually being coordination failure.

The Ones Who Were Right and Lost Anyway

Then there are the roughly 8,000 people who went in May.

They were cut on the strength of a thesis: that AI agents could absorb a large fraction of the daily work of the organisation. That thesis has now been falsified, at least for this deployment at this moment, by the company's own internal data, conceded in the company's own all-hands meeting, and the second wave built on it was cancelled.

They do not get their jobs back.

This asymmetry ought to sit at the centre of any honest account of Project OT. The evidence arrived after the cost had been paid, and it was paid by people who had no say in generating it. Zuckerberg did not bear the risk of his thesis being wrong. The engineers and designers and programme managers told in April that 10 per cent of the workforce would go bore it. Their mortgages bore it. So did their visa statuses, for anybody on an employment-linked visa, for whom a layoff is not a career interruption but a countdown to leaving the country.

Some are litigating. In July 2026, 26 current and former Meta employees filed suit in federal court in Oakland, California, alleging the company used a constellation of internal artificial intelligence systems to help select employees for the May reduction in force, ranking staff on activity and productivity data including keystrokes, emails, documents, browser history and AI token usage. The plaintiffs allege a disparate impact on employees who had been on protected medical, parental or family leave, since metrics of that kind cannot, by design, be accumulated by somebody who is not at work. Meta has said the claims “lack merit and are not based on facts”, and that “workforce management and organizational decisions were and are made by people, not AI”. Judge William Orrick denied a temporary restraining order on 17 July, finding no irreparable harm while acknowledging “serious questions going to the merits”. At a preliminary injunction hearing on 24 August, he said the plaintiffs were unlikely to win one on the current record, and that their claims appeared better suited to arbitration, where they could pursue their request for an independent audit of Meta's layoff decisions.

Whatever the courts decide, the allegation illuminates something structural. The same measurement apparatus that failed to capture what the humans contributed was allegedly used to decide which humans to keep. Motion counted. Absence of motion counted against you. And the agents, whose entire behavioural repertoire is motion, would have scored magnificently.

Meta Is Not Alone, Which Is the Problem

The reversal genre now has a small canon.

In February 2024, Klarna announced that an OpenAI-powered assistant was doing the work of 700 customer service agents, handling 2.3 million conversations in its first month. In May 2025 its chief executive, Sebastian Siemiatkowski, told Bloomberg that Klarna had cut too far, that lower cost had produced lower quality, and that the company would rebuild human support. “Really, investing in the quality of human support is the way of the future for us,” he said, having spent a year saying close to the opposite.

Salesforce went the other way and stayed there. In September 2025, Marc Benioff said its support workforce had gone from around 9,000 to around 5,000 as AI agents took over roughly half of interactions, with hundreds of affected staff redeployed. Whether that holds is an open question, but it is a genuine counter-example and should not be waved away. Some of these deployments do work.

What distinguishes Meta's case is scale and documentation. Klarna's reversal was inferred from customer complaints. Meta's was forced by a quantified collapse in the relationship between output and outcome, recorded in its own systems, at a company with more engineering telemetry than almost any organisation in history. If Meta cannot make the numbers work with that instrumentation, the confident restructuring memos currently circulating in firms with a fraction of its measurement capability deserve far more scepticism than they are getting.

The Sequencing Was the Crime

So what went wrong?

Not that the agents were bad. They were, on the evidence, extremely productive in the narrow sense of generating change and merely useless in the broad sense of generating value. That gap will close, possibly quickly. Nothing in the Meta data supports the claim that agentic software development has hit a permanent ceiling, and anybody using this story to argue that AI is fake is reading it lazily. Four months of internal telemetry at one company in 2026, confounded by a simultaneous mass layoff, tells you about a deployment strategy. It does not tell you about 2028.

The failure was sequencing. Meta removed the people first and measured the consequences second. It made an irreversible decision on the basis of a reversible belief. The layoffs were permanent and the thesis was provisional, and the company got those two properties the wrong way round.

Nothing prevented the opposite order. Run the agents at scale for two quarters. Watch the incident rate. Watch the ratio between changes made and features delivered. Establish, with the measurement apparatus you already own, whether the productivity is real or merely legible. Then decide how many people you need. That costs a few months of payroll. The sequence Meta chose cost 8,000 jobs, a nineteen-point collapse in employee sentiment, a federal lawsuit, a 40 per cent rise in major incidents, and a permanent contribution to the industry's growing suspicion that leadership is guessing.

Companies choose the wrong order not out of stupidity but because the wrong order is legible to markets and the right order is not. Announcing a 10 per cent headcount reduction alongside capital expenditure guidance of 125 to 145 billion dollars is a coherent story about discipline that any analyst can model. Announcing a two-quarter measurement exercise is not a story at all. The sequencing error is a communications strategy that metastasised into an operating decision.

Which returns us to the ratio.

Two hundred and twenty per cent against 36 per cent is not a story about machines failing. It is a story about an organisation discovering, at enormous human cost, that it had never known the difference between the work it counted and the work that mattered. The agents did not introduce that confusion. They exposed it, at volume, the way a stress test exposes a flaw that was always present in the drawings.

The uncomfortable ending is that Meta has not solved this. It has postponed it. The thesis was not withdrawn, only rescheduled, and the measurement problem that made it look plausible in January is exactly as unsolved in September. Nobody at Meta has published a better definition of value. Nobody has replaced the commit count with something that captures refusal, or judgement, or the memory of why a system is shaped the way it is.

So the next time the agents get good enough, the same dashboard will be consulted, and it will still be measuring motion.

The people who were kept know this. That is the part the ratio does not capture.

Sources and References

1. Katie Paul, Reuters, “Mark Zuckerberg had a bold plan to replace Meta staff with AI. Here's how it imploded”, 26 August 2026. https://www.reuters.com/investigations/mark-zuckerberg-had-bold-plan-replace-meta-staff-with-ai-heres-how-it-imploded-2026-08-26/
2. Katie Paul and Courtney Rozen, Reuters, “Exclusive: Meta's Zuckerberg says AI agent tech progressing slower than expected”, 2 July 2026. https://finance.yahoo.com/technology/ai/articles/exclusive-zuckerberg-says-ai-agent-201123441.html
3. Evan Schuman, Computerworld, “Meta's plans to replace workers with AI fell flat, report says”, 26 August 2026. https://www.computerworld.com/article/4214479/metas-plans-to-replace-workers-with-ai-fell-flat-report-says.html
4. Will Shanklin, Engadget, “Meta reportedly abandoned an AI-focused restructuring plan that would have laid off thousands”, 26 August 2026. https://www.engadget.com/2244816/meta-reportedly-abandoned-an-ai-focused-restructuring-plan/
5. Skye Jacobs, TechSpot, “Zuckerberg's bold plan to replace Meta staff with AI has imploded”, 27 August 2026. https://www.techspot.com/news/113631-zuckerberg-bold-plan-replace-meta-staff-ai-imploded.html
6. The Next Web, “Meta planned to cut some teams by 60% and replace the work with AI”, 31 August 2026. https://thenextweb.com/news/meta-project-ot-ai-native-layoffs-cancelled
7. Michael Polanyi, “The Tacit Dimension”, Doubleday, 1966; University of Chicago Press reissue, 2009. https://press.uchicago.edu/ucp/books/book/chicago/T/bo6035368.html
8. International Business Times UK, “Meta Was Hours Away From Another Massive AI Layoff Wave, Then Zuckerberg Pulled the Plug”, August 2026. https://www.ibtimes.co.uk/meta-ai-restructuring-zuckerberg-cancels-layoffs-1816629
9. Marco Quiroz-Gutierrez, Fortune, “Meta laid off 10% of its workforce as Mark Zuckerberg warns that in the AI race 'success isn't a given'”, 21 May 2026. https://fortune.com/2026/05/21/meta-10-percent-workforce-layoffs-ai-tech-success-is-not-a-given-8-thousand-employees-mark-zuckerberg/
10. Eva Roytburg, Fortune, “Meta will start tracking employees' screens and keystrokes to train AI tools”, 21 April 2026. https://fortune.com/2026/04/21/meta-will-start-tracking-employees-screens-and-keystrokes-to-train-ai/
11. CBS News / Associated Press, “26 Meta workers sue over alleged AI-aided layoffs targeting employees on medical or family leave”, 15 July 2026. https://www.cbsnews.com/news/26-meta-workers-sue-ai-aided-layoffs-medical-family-leave/
12. Margaret Attridge, Courthouse News Service, “No immediate relief for Meta workers claiming AI fired them”, 17 July 2026. https://www.courthousenews.com/no-immediate-relief-for-meta-workers-claiming-ai-fired-them/
13. MLex, “US judge skeptical of Meta workers' AI layoff claims, points to arbitration”, 24 August 2026. https://www.mlex.com/mlex/artificial-intelligence/articles/2517219
14. Sabry E. Farrag, “The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development”, arXiv:2605.01160, 1 May 2026. https://arxiv.org/abs/2605.01160
15. Won Ik Cho, Seong-hun Kim and Geunhye Kim, “Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost”, arXiv:2605.24688, 23 May 2026. https://arxiv.org/abs/2605.24688
16. Jeanne McClure and Gregg Gerdau, “Why AI Readiness Is an Organizational Learning Problem, Not a Technology Purchase”, arXiv:2604.16369, 22 March 2026, revised 21 April 2026. https://arxiv.org/abs/2604.16369
17. DORA, Google Cloud, “State of AI-assisted Software Development 2025”, 29 September 2025. https://dora.dev/dora-report-2025/
18. METR, “We are Changing our Developer Productivity Experiment Design”, 24 February 2026. https://metr.org/blog/2026-02-24-uplift-update/
19. Nathen Harvey and DORA team, Google Cloud, “The ROI of AI-Assisted Software Development (2026.01)”, 11 May 2026, reported by InfoQ, “New DORA Report Claims Strong Engineering Foundations Drive AI Return on Investment”. https://www.infoq.com/news/2026/05/dora-roi-ai-assisted-dev-report/
20. METR, “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity”, 10 July 2025. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
21. GitClear, “The Maintainability Gap: 2026 AI Code Quality Research”, January 2026. https://www.gitclear.com/the_ai_code_quality_maintainability_gap
22. Sherin Shibu, Entrepreneur, “Klarna Is Hiring Customer Service Agents After AI Couldn't Cut It on Calls, According to the Company's CEO”, 9 May 2025. https://www.entrepreneur.com/business-news/klarna-ceo-reverses-course-by-hiring-more-humans-not-ai/491396
23. Fortune, “Salesforce CEO Marc Benioff says his company has cut 4,000 customer service jobs as AI steps in: 'I need less heads'“, 2 September 2025. https://fortune.com/2025/09/02/salesforce-ceo-billionaire-marc-benioff-ai-agents-jobs-layoffs-customer-service-sales/
24. Marilyn Strathern, “'Improving Ratings': Audit in the British University System”, European Review, volume 5, 1997, pages 305 to 321. https://gwern.net/doc/statistics/decision/1997-strathern.pdf
25. Meta, “Update on Meta's Year of Efficiency”, 14 March 2023. https://about.fb.com/news/2023/03/mark-zuckerberg-meta-year-of-efficiency/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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For a decade the romance scam has had a face, and it was grey. The widow who wired her pension to an oil-rig engineer she never met. The retired teacher who remortgaged for a soldier stranded somewhere hot and dangerous. Banks designed their warnings for that person. Charities built their leaflets around her. The rest of us, especially the young, filed the whole genre under things that happen to people who did not grow up online.

On 25 September 2026, figures from Lloyds Banking Group landed that should retire that picture for good. Reports of romance scams to the bank rose 24 per cent in the twelve months to the end of August 2026, compared with the year before. Within that rise, reports from victims aged 18 to 24 jumped 59 per cent. Those from victims aged 25 to 34 climbed 39 per cent. The older victims have not gone anywhere: more than half of the money lost, 55 per cent, still came from people over 65, and victims aged 75 to 84 lost an average of £9,051 each, the highest of any age group and 19 per cent more than a year earlier. But the fastest growth is now among people who were born with a phone in reach. According to Lloyds, those younger victims typically met the person defrauding them on dating apps and social media, while the over-50s were more often approached through traditional dating websites and social platforms.

In the same week, two economists at Central South University in Changsha posted a short paper to arXiv with a title that reads like a diagnosis. “When Trust Attracts Fraud: AI and Trust Arbitrage”, by Xieyu Yin and Fenghua Wen and linked to the journal Economics Letters, opens with a sentence worth pinning above every trust and safety team's desk: “Trust can attract fraud when it delays verification.” Their model says nothing about dating. It is a piece of abstract economics about sellers, signals and markets. But read it alongside the Lloyds figures and it describes, with uncomfortable precision, what is happening to a generation that was supposed to be too savvy to be caught.

The two releases together raise three questions. Why is the fastest-growing group of victims the one everyone assumed was immune? What does a flood of synthetic faces, voices and photographs do to the ordinary, decent habit of giving a stranger the benefit of the doubt? And if trust attracts fraud by delaying the moment anyone checks, whose job is the checking: the person swiping at midnight, or the companies whose platforms the fake suitors are built on?

The Generation That Was Supposed to Know Better

The assumption that young people are scam-proof rests on a confusion between fluency and scepticism. Someone who can spot a phishing text from a fake courier is fluent. That is not the same as being sceptical of a person who has spent three weeks being kind to you. And the evidence that the young were never as protected as their reputation suggested predates the Lloyds data. In September 2023, UK Finance and its Take Five campaign published a OnePoll survey of 2,000 UK adults. Young adults aged 18 to 24 were the most confident of any age group in their ability to spot a scam: 91 per cent said they were confident they could identify a fake request for personal information online. Yet just 27 per cent said they would always take steps to check whether an unexpected contact could be trusted, against more than 60 per cent of people over 55. Almost half of the 18 to 24 year olds, 49 per cent, said they had been contacted by an impersonation scammer, and of those targeted, 52 per cent said they had shared personal information or made a payment as a result.

That survey was about impersonation, not romance, and it is three years old. But its pattern, high confidence and low checking, is the one the romance data now reflects. American figures agree. The Federal Trade Commission has long reported that younger people who file fraud complaints are more likely than older people to say they lost money, while older victims report far larger individual losses. In one FTC analysis, 51 per cent of complaints from consumers aged 19 and under reported a financial loss, compared with 21 per cent of complaints from people aged 80 and over, although the median loss for the youngest group was far smaller.

The Lloyds numbers sit neatly on that fault line. Young people are showing up more often; older people are losing more per case. The average loss across all romance scam victims at Lloyds actually fell by 20 per cent to £4,078, which is what you would expect if the victim pool is being swelled by people with smaller bank balances. The fraud is not getting gentler. It is getting wider.

What the Numbers Do and Do Not Say

Some honesty about those percentages is in order. The Lloyds data counts reports to one banking group, not the whole country. A rise in reports is not automatically a rise in crime; it can also reflect victims feeling less ashamed to come forward. A 59 per cent rise from a small base can represent fewer people than a modest rise in a large one, and Lloyds did not publish absolute case numbers by age in the material reported by the press. So the figures are directional. But the direction is corroborated elsewhere. UK Finance's Annual Fraud Report 2026, published in June and covering the calendar year 2025, found that losses to romance fraud across its members rose 23 per cent to £39.2 million, within a wider picture of £1.28 billion stolen through payment fraud. The report described romance scams as relying less on technical intrusion and more on persuasion, emotional manipulation and the exploitation of trust, delivered at scale through digital channels.

In the United States, the FTC's April 2026 data release on social media scams found that nearly 60 per cent of people who reported losing money to a romance scam in 2025 said it started on a social media platform. Scammers, the agency noted, often tailored their pitch to people's profiles, later “inventing a crisis requiring money or casually offering investment advice”. That last phrase describes the hybrid now dominating the field: the romance that turns, gradually, into a cryptocurrency “opportunity”.

Confidence Is Not a Defence

Why would digital natives be caught? Part of the answer is exposure: fraud follows attention, and attention is on the apps. But the deeper answer lies in how trust itself works, and here the psychology is long established. Timothy Levine, a communication scholar whose work on deception has shaped the field, set out truth-default theory in the Journal of Language and Social Psychology in 2014. Its central claim is that people tend to believe others by default, and that this default is adaptive. Most communication is honest. We abandon the truth default only when something triggers suspicion strongly enough, and even then we are poor at spotting lies from demeanour alone.

Romance fraud is engineered to keep that trigger from firing. Monica Whitty, a psychologist who has studied the online dating romance scam for more than a decade, developed what she called the Scammers Persuasive Techniques Model in a 2013 paper in the British Journal of Criminology, based on interviews with victims. The model traces a sequence of stages: a carefully built profile, a grooming period in which intimacy deepens, a first small request, then escalation. Whitty also pointed to the “near-win” phenomenon, borrowed from gambling research, to explain why victims stay in the scam and why some are defrauded again. Each request comes with the promise that this is the last hurdle before the relationship becomes real.

None of those mechanisms care about digital literacy. Knowing how a phishing link works does nothing to protect you from someone who has spent weeks asking about your day, remembering your sister's name and sending good-morning messages at exactly the right hour. Confidence may even make things worse. If you believe scams look like clumsy emails from a prince, a warm, fluent, attentive stranger does not match the template, so the alarm never sounds. Liz Ziegler, fraud prevention director at Lloyds, put it plainly in the bank's release: “Romance scammers don't care how old someone is. They look for opportunities to build trust, create an emotional connection, then turn that relationship into a request for money.”

There is a cultural dimension too. For many people under 30, meeting a partner online is simply how dating happens. That normalisation is healthy, but it has removed the old social friction, the sceptical friend or the raised eyebrow, that once surrounded romance with someone you had never met. A generation that treats online connection as real connection has, reasonably, extended its truth default to the people it meets there.

A Person Now Costs Almost Nothing to Make

Now add generative AI. For most of the history of online romance fraud, the hardest part of the con was being convincing for long enough. Scammers stole photographs from real people's social media accounts, often soldiers, doctors or attractive models, and the countermeasure was simple: run a reverse image search and see whether the face belongs to someone else. Lloyds still recommends exactly that, alongside watching for intense declarations of love, newly created profiles, pressure and guilt, and talking to someone you trust before sending money.

The reverse image search, though, assumes the photograph exists somewhere else. A synthetic face does not. It can be generated fresh, consistent across dozens of images, placed in plausible holiday settings and aged or restyled on demand. The same collapse in cost applies to voice and video. In September 2024, Starling Bank published research, conducted with Mortar Research across more than 3,000 UK adults, warning that fraudsters could clone a voice from as little as three seconds of audio. More than a quarter of respondents said they had been targeted by a voice-cloning scam in the previous year.

Live video, long treated as the definitive proof that someone is real, has gone the same way. In October 2024, Hong Kong police arrested 27 people linked to a syndicate operating from an industrial unit in Hung Hom that allegedly used AI face-swapping to pose as attractive women on video calls, drawing victims across Asia into fake cryptocurrency investments. Police put the losses at around HK$360 million, roughly US$46 million. Earlier that year, WIRED reported, drawing on videos gathered by David Maimon, a criminologist at Georgia State University and head of fraud insights at SentiLink, that West African scammers known as Yahoo Boys were running real-time face-swaps on video calls with little more than two phones and off-the-shelf apps.

At industrial scale, the automation is even starker. In July 2026, the United Nations Office on Drugs and Crime published a threat assessment on South-East Asia's criminal economy. It estimated scam losses across East and South-East Asia, Australia and New Zealand at between $88.3 billion and $114.1 billion in 2025, and documented the use of generative AI and deepfakes in automated fraud. People from at least 80 countries and territories have been identified inside scam compounds in the region, many recruited under false pretences and held in conditions UNODC describes as human trafficking for forced criminality. Delphine Schantz, UNODC's regional representative for South-East Asia and the Pacific, described the model bluntly: “Their operating model looks like corporate franchising.”

That is the industrial base behind the investment-romance hybrid often called pig butchering. In August 2025, Meta said it had removed 6.8 million WhatsApp accounts linked to criminal scam centres in the first half of the year, and disclosed working with OpenAI to disrupt a Cambodian network that had used ChatGPT to generate outreach messages. A human scammer in a compound could once manage a handful of conversations. With a language model drafting replies, translating fluently and remembering every detail of a target's life, the limit on how many “relationships” one operator can sustain begins to dissolve.

Here the Yin and Wen paper earns its place. Their model has three costs that generative AI pushes down at once: fabrication, making the fake face, voice and biography; targeting, reaching the right people; and verification, checking. They have not fallen at the same speed or for the same people. Fabrication and targeting have collapsed for fraudsters with industrial tools. Verification has become cheaper only for whoever actually holds the tools to do it.

The Benefit of the Doubt Was Always a Subsidy

What happens to the habit of giving strangers the benefit of the doubt in this environment? The honest answer is that it becomes more expensive, and the cost falls on the people who keep extending it.

Think of the benefit of the doubt as a subsidy. When you assume a new match is who they say they are, you save yourself the effort of checking and spare them the indignity of being checked. Across millions of interactions that saving is part of what makes online dating work at all.

The subsidy holds up as long as fakes are rare and costly to make. Truth-default theory depends on base rates: the default is adaptive because most people, most of the time, are telling the truth. When a stranger's face, voice and video can all be manufactured, the base rate on some platforms shifts, and the old cues that used to justify trust, a consistent set of photographs, a warm voice note, a live video call, stop carrying information. They were never proof. They were evidence that faking was costly. That is no longer true.

There are two bad ways this can go. People can keep extending the old trust to cues that no longer mean anything, and a growing minority are defrauded. Or they can withdraw trust wholesale, which would be a real loss, because trust is not a flaw to be engineered away; it is the basis of cooperation. A generation taught to regard every warm stranger as a probable criminal would be protected from romance fraud in roughly the way someone who never leaves the house is protected from traffic.

The more useful question is not whether to trust but where the checking should happen, and at what point in the relationship. That is exactly the question the arXiv paper addresses.

Trust Arbitrage and the Market That Forgot to Check

Yin and Wen build a model with two markets that differ in their “prior quality”, roughly how honest the average participant is believed to be. Honest sellers provide genuine evidence cheaply; dishonest ones fabricate it at a cost; buyers can pay to verify. As AI capability grows, fabrication, verification and targeting all get cheaper.

Their first result is what they call a trust valley. If fabrication becomes profitable before verification becomes worthwhile, credibility falls, because evidence that used to mean something can now be faked. It recovers only once checking becomes cheap enough to start. Their second result is the uncomfortable one. A market with higher prior quality delays verification, because when most participants are honest, checking feels like a waste. The better the reputation, the longer everyone waits before bothering to look.

Put two markets side by side and a window opens. In that interval, the lower-quality market has started checking, but the higher-quality one has not. If targeting is cheap enough, deceivers move into the reputable market precisely because it feels no need to check. In the words of the abstract, “deceptive sellers enter the higher-quality but less vigilant market, and their entry can initially reverse its reliability advantage.” The authors call this trust arbitrage. They also stress that it is self-limiting: the inflow of fraud eventually triggers verification in the trusting market too, and the gap closes. They describe the result as “an endogenous but temporary protection gap that redirects deception across markets”.

The paper is a formal model, not an empirical study of dating, and it would be overreaching to treat it as proof of anything about Tinder or Hinge. But as a lens it is sharp. For years, the market that did its checking was the one full of warnings: older people, their banks, charities, and the families who had been told to watch for Nigerian princes and stranded soldiers. The market that felt no need to check was the one everybody assumed was sophisticated: the young. By the logic of the model, that is exactly the market fraud should flow towards once fabrication and targeting become cheap. And the Lloyds data looks very much like an inflow.

The model's comforting conclusion, that the gap is temporary, deserves scrutiny. In the paper, the market that was fooled starts checking. In reality, the “correction” happens one broken bank account and one broken heart at a time. A temporary protection gap in an economics paper is a permanent loss for the people who fall into it. The policy question is whether we wait for that gap to close through victimisation or close it deliberately, earlier, by moving verification to where it is cheapest.

Why Young Users Sit in the Protection Gap

Mapped onto the Lloyds data, the model suggests young people are exposed not because they are foolish but because they sit where checking has been culturally postponed.

Older people have been the target of sustained awareness campaigns for years, and adult children talk to their parents about scams. The apparatus of prevention was aimed at the group everyone expected to be vulnerable. Young people received a different message: that they understood technology and the obvious scams were beneath them.

Tinder's own UK research makes the same point from the platform side. When the app launched its mandatory Face Check verification for new UK users in March 2026, it cited research that 63 per cent of 18 to 24 year olds said scams were harder to spot than ever. That is not a generation that thinks it is invincible. It is a generation that knows the ground has moved and has not been given better tools than a reverse image search and an instinct.

The Yin and Wen model also clarifies why “just be more careful” is weak advice. Verification is costly for an individual in a way it is not for a platform. A person on a dating app has almost no way to check whether a face has been synthetically generated, whether the same face appears on forty other accounts, whether the account was created yesterday from a device in a scam compound, or whether the text of the messages matches a script used on thousands of other victims. A video call can now be deepfaked; a reverse image search is defeated by a synthetic face. For an individual, the cost of meaningful verification is high and rising. For a platform, much of it is a database query.

Who Pays When the Check Is Missed

Britain already has an answer to the question of who pays when fraud succeeds, and it is instructive, because it does not include the platforms.

Since 7 October 2024, the Payment Systems Regulator has required banks and payment firms to reimburse victims of authorised push payment fraud, including romance scams, up to £85,000 per claim, with the cost split between the sending and receiving firms. Its dashboard shows around £316 million reimbursed in the eighteen months to March 2026, including £72.6 million in the first quarter of 2026 alone. The scheme covers Faster Payments and CHAPS; card payments, international transfers and other rails fall outside it, which matters because many pig-butchering scams move money onwards into cryptocurrency. Firms can refuse reimbursement where a customer was grossly negligent, a deliberately high bar, and only around 2 per cent of claims in the first quarter of 2026 were rejected on grounds of insufficient caution. The PSR itself is being folded into the Financial Conduct Authority.

The logic is that banks sit where money leaves the account, so bearing the cost gives them every incentive to detect fraud. But the transfer is the last moment in a romance scam, not the first. By the time money moves, the relationship has often run for weeks or months on a platform that took no financial share of the harm. In the language of the arXiv paper, the reimbursement regime assigns the cost of fraud to the one actor who can only verify at the very end, while the actors who host the fabrication and the targeting face no equivalent bill.

The platforms are not unregulated. Under the Online Safety Act, Ofcom's illegal content codes came into force on 17 March 2025, requiring services in scope to assess and mitigate the risk of illegal harms, fraud included. Ofcom's register of risks specifically identified romance fraud as one of the key risks associated with dating services. And on 10 July 2026, Ofcom opened a consultation on draft Fraudulent Advertising Codes of Practice, setting out nearly 40 measures for the largest platforms, including banning bad actors and blocking them from re-registering, with fines of up to £18 million or 10 per cent of global revenue for non-compliance once the codes are approved and in force. The consultation closes on 2 October 2026. But the advertising codes, as their name makes clear, cover paid-for advertising, not the user-generated profiles on which romance fraud actually runs.

The case for leaning harder on platforms is sharpened by what has emerged about their incentives. In November 2025, Reuters reported on internal Meta documents suggesting that the company had projected around 10 per cent of its 2024 revenue, roughly $16 billion, would come from advertising for scams and banned goods, and that its platforms showed users an estimated 15 billion higher-risk scam adverts a day. According to Reuters, Meta's own analysis found that universal advertiser verification would reduce fake adverts significantly but would cost revenue. Meta's spokesperson Andy Stone told Reuters the documents presented a “selective view”, that the 10 per cent estimate was rough and overly inclusive, and that the company had reduced user reports of scam adverts worldwide. The dispute concerns advertising rather than dating profiles, but it illustrates the structural problem: verification costs platforms money, while fraud costs them, at worst, reputation.

The dating industry has its own history here. In August 2025, Match Group agreed to pay $14 million to settle Federal Trade Commission charges relating to deceptive advertising, cancellation and billing practices. The FTC had alleged that Match sent non-subscribers notifications of interest that were in many cases from fraudulent accounts, without disclosing that fact, to induce them to subscribe. The following month, US Senators Marsha Blackburn and Maggie Hassan wrote to Match Group demanding answers on romance scams across its apps, which include Tinder, Hinge and OkCupid. Senator Hassan said that “Romance scams are robbing Americans of millions of dollars every year, and taking a devastating emotional toll in the process.”

What Platforms Could Verify That Users Cannot

To their credit, the major dating companies have started moving the burden in the right direction, and the early results support the argument that platforms, not users, are where verification is cheapest.

Tinder's Face Check asks new users to record a short video selfie during sign-up. The system checks that the face is live and matches the profile photographs, and it can detect the same face appearing across multiple accounts, which is exactly the signature of an operation running many synthetic suitors from one room. According to Tinder, the video itself is deleted after review, while an encrypted face map is retained to verify new photos and catch duplicates. Tinder said that in markets where Face Check is live it had seen a decrease of more than 60 per cent in exposure to potential bad actors and of more than 40 per cent in reports of bad actors. Yoel Roth, senior vice president of trust and safety at Match Group, called it “the most measurably impactful safety feature I've seen in my career.”

Bumble took a different route. In February 2024 it launched Deception Detector, a machine-learning system designed to identify spam, scam and fake profiles, and said that in testing it had automatically blocked 95 per cent of such accounts, with member reports of spam, scams and fake profiles falling by 45 per cent in the first two months. Bumble has also let users report profiles they believe use AI-generated photos or video.

These are company-reported figures, not independent audits. But they point to the central truth: signals that are invisible to an individual user are visible to the platform. A platform can see that an account was created minutes ago, that its device has hosted dozens of other accounts, that its photos may carry the statistical fingerprints of image generators, that its opening messages match a script sent to thousands of people, that it steers every conversation off the app to WhatsApp or Telegram within a day, or that it begins mentioning cryptocurrency platforms. A person on the other end of the chat sees only a charming stranger.

This is the Yin and Wen model turned into product design. If the danger is a protection gap in which verification lags fabrication, the fix is to make verification happen before trust accumulates rather than after money moves. Checking at sign-up, with a liveness test and duplicate detection, is verification at the cheapest possible moment: before the first message, before the grooming period, before the near-win. Checking at the bank transfer is verification at the most expensive moment, when the victim has been emotionally invested for weeks and will often override warnings to protect a relationship they believe is real.

The Limits of Putting It All on the Apps

None of this means the burden should move wholesale to platforms, and there are real costs to pretend away.

Biometric verification raises serious privacy questions. Storing face maps of millions of daters creates a target for attackers and a tool that could be misused. It also risks excluding people who cannot or will not submit their faces, including those with good reasons for caution, such as survivors of abuse or people whose sexuality could endanger them if exposed. Liveness checks themselves are a moving target: the same deepfake tools that fool a human on a video call are being aimed at the automated systems designed to catch them. And verification of identity is not verification of intent. A real, verified person can still be a fraudster, and the compounds UNODC describes are staffed by real people.

There is also the problem of where the conversation goes. Many romance scams begin on a dating app and move quickly to encrypted messaging, where the dating platform has no visibility. A fraudster verified out of one app will try another, and the arXiv model's logic applies here too: deception flows to the least vigilant venue. Uneven verification simply relocates the fraud, which is why cooperation across platforms matters as much as any one company's tools.

And individual judgement still matters. Lloyds' advice, to be wary of intense early declarations of love, to protect financial information, to resist urgency and guilt, and to talk to someone before sending money, remains good. The point is not that users should stop thinking. It is that users cannot be the primary verification layer for a threat that is industrial, automated and increasingly synthetic. Asking a 22-year-old to detect a real-time deepfake produced in a scam compound is like asking pedestrians to inspect the brakes on passing cars.

Keeping the Default Without Paying For It

The deepest cost of the synthetic suitor is not the money, large as the sums are. It is what the threat does to the ordinary human habit of believing people. If the only defence against fabricated romance is for everyone to trust less, then the fraudsters have taken something from people who were never scammed at all: the ease of meeting a stranger and assuming the best.

The alternative is to keep the truth default and move verification underneath it, into infrastructure, where it is cheap and systematic. Nobody inspects the wiring before switching on a light, because building regulations put the checking elsewhere. The benefit of the doubt survives there because someone with the tools and the responsibility has already done the doubting.

For online dating, that means treating verification at sign-up, duplicate-face detection, scam-script detection and friction when conversations move off-platform as baseline duties rather than optional features. It means Ofcom treating romance fraud on dating services as seriously as it is now treating scam adverts on social media. And it means asking, as British policy has not yet seriously asked, why the cost of romance fraud sits with banks at the final transfer, when the fabrication, targeting and grooming happen on platforms that currently pay nothing when it succeeds.

The Yin and Wen paper ends on a note of reassurance: trust arbitrage is self-limiting, because fraud eventually triggers the checking that stops it. In their model, the protection gap closes. The Lloyds data suggests it is still wide open, and the people falling into it are younger than anyone expected. The question is whether it closes because platforms verify earlier, or because a whole generation learns, one scam at a time, that kindness from a stranger is a warning sign. The first option costs companies money. The second costs everyone something harder to replace.

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References and Sources

1. Shaw, Vicky. “Romance scam reports have jumped with a surge in Gen Z victims, Lloyds figures.” PA Media, published via the Evening Standard and AOL UK, 25 September 2026. Reporting Lloyds Banking Group data for 1 September 2025 to 31 August 2026, including quotes from Liz Ziegler, Fraud Prevention Director, Lloyds, and Patrick Hurley, Ombudsman Director, Financial Ombudsman Service. https://www.aol.co.uk/articles/romance-scam-reports-jumped-surge-230100000.html
2. The Independent. “Romance scams in UK rise by a quarter with huge surge in Gen Z victims.” September 2026, via inkl. https://www.inkl.com/news/romance-scams-in-uk-rise-by-a-quarter-with-huge-surge-in-gen-z-victims
3. Foreign Policy Journal. “Romance Scam Reports Surge 24% At Lloyds (LON: LLOY) As Gen Z Victims Spike By 59%.” 25 September 2026. https://www.foreignpolicyjournal.com/2026/09/25/romance-scam-reports-surge-24-at-lloyds-lon-lloy-as-gen-z-victims-spike-by-59/
4. Yin, Xieyu and Fenghua Wen. “When Trust Attracts Fraud: AI and Trust Arbitrage.” Business School, Central South University. arXiv:2609.27404, submitted 23 September 2026; journal reference Economics Letters, DOI 10.1016/j.econlet.2026.113234. https://arxiv.org/abs/2609.27404
5. UK Finance. Annual Fraud Report 2026 and press release “Fraud remains a national security threat as criminals steal almost £1.3 billion.” June 2026. https://www.ukfinance.org.uk/policy-and-guidance/reports/annual-fraud-report-2026
6. UK Finance and Take Five to Stop Fraud. “Gen Z more likely to be tricked by criminals and fall for impersonation scams.” 7 September 2023; OnePoll survey of 2,000 UK adults, 18 to 22 August 2023. https://www.takefive-stopfraud.org.uk/news/gen-z-more-likely-to-be-tricked-by-criminals-and-fall-for-impersonation-scams/
7. Federal Trade Commission. “New FTC Data Show People Have Lost Billions to Social Media Scams.” Press release and Data Spotlight, 27 April 2026. https://www.ftc.gov/news-events/news/press-releases/2026/04/new-ftc-data-show-people-have-lost-billions-social-media-scams
8. Federal Trade Commission. “Consumer Sentinel Network Data Book 2024.” March 2025, covering 2024 reports; see “Reported Fraud Losses by Age”. https://www.ftc.gov/system/files/ftc_gov/pdf/csn-annual-data-book-2024.pdf
9. Levine, Timothy R. “Truth-Default Theory (TDT): A Theory of Human Deception and Deception Detection.” Journal of Language and Social Psychology, 2014. SAGE Publications. DOI 10.1177/0261927X14535916.
10. Whitty, Monica T. “The Scammers Persuasive Techniques Model: Development of a Stage Model to Explain the Online Dating Romance Scam.” The British Journal of Criminology, volume 53, issue 4, July 2013, pages 665 to 684.
11. Starling Bank. “AI voice cloning scams could catch millions out.” Research conducted with Mortar Research among more than 3,000 UK adults, 18 September 2024; see also CNN Business, “AI voice-cloning scams could target millions of people, Starling Bank warns”, 18 September 2024. https://www.starlingbank.com/news/starling-bank-launches-safe-phrases-campaign/
12. CNN. “Deepfake romance scam raked in $46 million from men across Asia, police say.” 15 October 2024; and The Standard (Hong Kong), “Police arrest 27 for deepfake love scams totaling $360m, seizes scam-training documents”, October 2024. https://www.cnn.com/2024/10/15/asia/hong-kong-deepfake-romance-scam-intl-hnk
13. WIRED. Reporting on Yahoo Boys' use of real-time face-swapping in romance scams, drawing on material gathered by David Maimon, Georgia State University and SentiLink, 2024; see also AI Incident Database, Incident 911. https://incidentdatabase.ai/cite/911/
14. United Nations Office on Drugs and Crime. “An Interconnected Criminal Ecosystem: Transnational Organized Crime Threat Assessment for South-East Asia 2026.” Launched in Bangkok, 21 July 2026; see also OCCRP, “UN Report Details Southeast Asia's Interconnected Criminal Economy”, 21 July 2026. https://www.occrp.org/en/news/un-report-details-southeast-asias-interconnected-criminal-economy
15. Meta and WhatsApp. Announcement of the removal of 6.8 million WhatsApp accounts linked to criminal scam centres in the first half of 2025, August 2025; as reported by the Associated Press via US News, “WhatsApp Takes Down 6.8 Million Accounts Linked to Criminal Scam Centers, Meta Says”, 6 August 2025. https://www.usnews.com/news/business/articles/2025-08-06/whatsapp-takes-down-6-8-million-accounts-linked-to-criminal-scam-centers-meta-says
16. Horwitz, Jeff. “Meta is earning a fortune on a deluge of fraudulent ads, documents show.” Reuters, November 2025, including the response of Meta spokesperson Andy Stone; republished by Editor and Publisher. https://www.editorandpublisher.com/stories/meta-is-earning-a-fortune-on-a-deluge-of-fraudulent-ads-documents-show,258660
17. Payment Systems Regulator. “APP fraud reimbursement protections” and “APP scams reimbursement dashboard for Q1 2026.” https://www.psr.org.uk/information-for-consumers/app-scams-reimbursement-dashboard/
18. Payment Systems Regulator. PS25/5, Consolidated policy statement, APP scams reimbursement requirement, May 2025; and Consumer Standard of Caution guidance, December 2023. https://www.psr.org.uk/media/rhelv4op/ps25-5-app-scams-reimbursement-consolidated-policy-statement-may-2025.pdf
19. HM Treasury. “A Streamlined Approach to Payment Systems Regulation: Consultation response.” GOV.UK, 2026, confirming the consolidation of the Payment Systems Regulator into the Financial Conduct Authority. https://www.gov.uk/government/consultations/a-streamlined-approach-to-payment-systems-regulation-consultation/outcome/a-streamlined-approach-to-payment-systems-regulation-consultation-response
20. Ofcom. Illegal content Codes of Practice for user-to-user services, issued 24 February 2025, in force 17 March 2025; and “Protecting people from illegal harms online: Register of Risks”, 16 December 2024. https://www.ofcom.org.uk/siteassets/resources/documents/online-safety/information-for-industry/illegal-harms/register-of-risks.pdf
21. Ofcom. “Consultation: Fraudulent Advertising Codes of Practice.” Published 10 July 2026, closing 2 October 2026. https://www.ofcom.org.uk/online-safety/online-fraud/consultation-fraudulent-advertising-code-of-practice
22. Tinder. “Tinder Launches Face Check in the UK, Setting a New Standard for Dating Safety.” Tinder UK Press Room, March 2026, including quotes from Yoel Roth, Match Group, and Mark Tierney, Stop Scams UK. https://uk.tinderpressroom.com/news?item=122625
23. Bumble Inc. “Bumble Inc. Launches Deception Detector: An AI-Powered Shield Against Spam, Scam and Fake Profiles.” Business Wire, 6 February 2024. https://www.businesswire.com/news/home/20240206911584/en/
24. Federal Trade Commission. “Match Group Agrees to Pay $14 Million, Permanently Stop Deceptive Advertising, Cancellation, and Billing Practices to Resolve FTC Charges.” Press release, 12 August 2025. https://www.ftc.gov/news-events/news/press-releases/2025/08/match-group-agrees-pay-14-million-permanently-stop-deceptive-advertising-cancellation-billing
25. Office of Senator Marsha Blackburn. “Senators Blackburn, Hassan Press Match Group for Answers on Romance Scams.” 24 September 2025. https://www.blackburn.senate.gov/index.php/2025/9/crime/technology/senators-blackburn-hassan-press-match-group-for-answers-on-romance-scams

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The block is enforced by web-filtering software called Lightspeed. A pupil at a Los Angeles Unified school opens a district Chromebook, types a chatbot's address into the browser, and gets a page saying the category is not permitted. If she picks up her own laptop but is still signed into her district profile, the same page appears. There was no letter home, no press conference. The filter simply went up, and the school year began.

It stayed quiet until the morning of 2 September 2026, when the district's new Generative AI Ad Hoc Committee met at 333 South Beaudry Avenue and a room full of parents worked out, in real time, that what they had come to argue about had already happened.

“Just based on the facial expressions of folks in this room, many parents, some of whom work at the district, some of whom don't, I don't think it's well-publicised,” board member Nick Melvoin said. “I would encourage us to do more.”

Kelly Gonez, who chairs the committee, confirmed the state of play in one sentence. “Currently, there is no access to generative AI for any student.”

That same day in New York, Mayor Zohran Kwame Mamdani and Schools Chancellor Kamar H. Samuels announced what the mayor's office called the broadest generative AI moratorium in the nation. The two largest school systems in the United States, between them responsible for well over a million children, spent a single Wednesday reversing a technology policy they had spent three years building.

Nobody has explained why it took three years to adopt and one summer to undo. That asymmetry is the story.

What Was Actually Blocked and What Quietly Was Not

Start with the numbers, because several of the ones in circulation are wrong.

New York's moratorium is not a ban on artificial intelligence in schools, and it does not cover a million students. It suspends student-facing generative AI for 2-K through eighth grade for one year, reaching close to 600,000 pupils, roughly two-thirds of enrolment. State education data recorded 793,300 pupils in the city's K-12 grades in 2025-26, with the widely used 900,000 figure arriving once 3-K and pre-K are included. It is comfortably the largest district in the country. It is not a million children, and the moratorium does not reach all of them.

The exemptions matter more than the headline. Assistive technology for pupils with disabilities is exempt, as are tools for multilingual learners, required computer-based assessments, career and technical education, and, in a phrase doing enormous load-bearing work, centrally approved instructional programmes. Teachers may keep using generative AI for planning, though not to grade work, determine promotion, or counsel a child in crisis. Officials intend to switch off AI features inside 38 citywide software contracts, including the Amira reading tutor used in 222 schools, Houghton Mifflin Harcourt's Into Reading, and Google Gemini on Chromebooks.

High schools are exempted almost entirely. Up to 50,000 pupils may join pilots of five vetted products, with startlingly specific time caps of fifteen minutes a week for Quill and twenty for Edia. Every high schooler will sit two 45-minute AI critical thinking modules a year. Screen time gets its own ladder: no one-to-one devices through second grade, thirty minutes a day in grades three to five, forty-five in middle school, and no guidance at all for high school.

One provision reaches every grade with no carve-out, and it received a single sentence in the announcement and almost no coverage. Companion chatbots are prohibited outright, across all grades. Of everything in the policy, that is the piece with the clearest evidentiary case behind it, and it is the piece nobody discussed.

Los Angeles is blunter and narrower at once. There is no grade cut-off and no pilot programme: generative AI is blocked for all students on district-issued devices for 2026-27, full stop. Teachers keep access. The previous policy let pupils aged thirteen and over use approved tools at a teacher's direction after completing a digital citizenship lesson. That is now void.

The affected population has been reported at roughly 378,000. Higher figures circulate, including 497,105 for 2025-26 in the state's data portal and a count approaching 565,000 that sweeps in independently operated charter schools, early education and adult programmes. The often-quoted 565,000 is not the number of children hitting the block page.

Both cities were widely reported as acting on 4 September. Both acted on 2 September, and Los Angeles was arguably first, since its filter had been running since the start of term.

Thirteen Undergraduates and a Policy for Four-Year-Olds

The evidentiary case for these decisions leans on three recent preprints, all worth reading. None says quite what it is being used to say, and not one involves a child.

The first, posted to arXiv on 24 April 2026 by Abeer M. Hasan and Sayed A. Mostafa, is “Perceptions and Utilization of GenAI Tools among Data Science Students and Faculty”. It surveyed 119 students and 14 faculty at a historically Black college or university. Students used generative AI heavily, mostly ChatGPT, for coding and writing, and reported limited confidence in interpreting what it gave back. The authors conclude there is “a gap between AI adoption and AI literacy” and recommend structured training.

It is a useful survey. It contains no measure of critical thinking, no control group and no children. It cannot show that overreliance reduces critical thinking, and it does not claim to. Its recommendation is more training, not less access.

The second, posted on 29 June 2026 by Keith Tran, Colton Harper and Thomas Price, is titled “Why Put in This Much Effort?” and asks how the availability of AI shapes motivation in introductory programming. The method is thirteen semi-structured interviews with engineering majors in a MATLAB course with a bespoke chatbot. The findings are genuinely interesting: students questioned whether their time was well spent, doubted the lasting utility of the skill, reported less satisfaction when the machine bypassed productive struggle, and described a confidence that evaporated when the tool was unavailable.

But it measures nothing. It is a qualitative study of thirteen undergraduates, reporting no effect sizes because it was never designed to. Its concluding sentence points in precisely the opposite direction to the one the policy debate has assigned it. “Our findings,” the authors write, “complicate the assumption that students need external constraints to protect their learning.” The students who managed the tension found motivation in the process of learning itself. The paper's recommendation is to redesign courses to value how students learn rather than what they produce. That is an argument about assessment design, not an argument for a filter.

The third is the most interesting and the most badly misread. Shahin Hossain's “Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers”, posted on 27 June 2026, draws on 382 undergraduates at a public minority-serving R1 university, plus 14 interviews and 396 open-ended responses. It identifies four reliance types: Strategic at 34.3 per cent, Instrumental at 30.9, Dialogic at 30.4 and Dependent at just 4.5. A further 13 per cent or so declined to use AI on ethical rather than practical grounds, a category existing frameworks miss entirely.

The paper does not find that students who use large language models show lower critical thinking than those who do not. Its striking result is almost the inverse. Strategic users, the ones engaging most deliberately and showing what the author calls “the greatest independent thinking”, scored lowest on standard outcome measures. Hossain's reading is that the instruments are broken. They “index AI's contribution rather than writing quality”, rewarding dependence and penalising students doing their own thinking. That is a paper about measurement failure at university level. It has been recruited into a debate about eight-year-olds.

This is not a small problem. Two of the largest school systems in the country have restricted a technology for children as young as four, and the research most frequently invoked studies undergraduates, in samples of 13, 119 and 382. There may be good reasons to keep chatbots away from a six-year-old. These papers are not among them.

Cognitive Debt and Its Discontents

The wider literature is stronger, though nowhere near as strong as the citations suggest. The paper everyone reaches for is “Your Brain on ChatGPT”, released as a preprint in June 2025 by Nataliya Kosmyna, Pattie Maes and colleagues at the MIT Media Lab. Participants wrote timed SAT-style argumentative essays while wearing EEG caps, split into three conditions: ChatGPT, a search engine, or nothing but their own head. Brain-only writers showed the strongest and most distributed neural connectivity, search engine users sat in the middle, the language model group weakest. Model users struggled to quote their own essays back. The paper supplied the phrase “cognitive debt” that has since escaped into every school board meeting in the country.

The caveats are considerable, and they compound. Fifty-four people took part in the first three sessions, divided across the three conditions: roughly eighteen per condition, already below the norm for EEG work in a field worrying about underpowered designs. Then the attrition. Only eighteen of those fifty-four returned for the fourth session, the one in which participants swapped conditions, which leaves about six people per group. The thinnest part of the study is the part carrying the most interpretive weight. Participants were Boston-area adults aged eighteen to thirty-nine writing twenty-minute essays. The paper was a preprint at release, not peer reviewed, and a formal comment challenging its interpretation has since appeared. Whatever “cognitive debt” describes, it was not observed in a child, in a classroom, over a term.

Michael Gerlich's 2025 study in Societies, the most cited quantitative claim about cognitive offloading, drew on 666 participants and reported a significant negative correlation between frequent AI use and critical thinking, with younger participants faring worst. It is correlational. It cannot establish that AI use degrades thinking rather than that people who think less reach for AI more, and the journal issued a correction in September 2025 after a table was duplicated.

The most methodologically satisfying entry is Yizhou Fan, Dragan Gasevic and colleagues in the British Journal of Educational Technology, whose paper gave the field the phrase “metacognitive laziness”. Their learners using ChatGPT improved short-term task performance without gains in intrinsic motivation, knowledge acquisition or transfer, and showed fewer metacognitive processes such as evaluation and orientation than those working with human experts or a checklist. That is a real, peer-reviewed finding about mechanism, and it points somewhere specific: the problem is not the machine's presence but the collapse of the evaluative step, which is a pedagogy problem rather than a network filter problem.

Taken together the evidence supports a cautious, adult-supervised, assessment-redesigned approach with young learners. It does not license the confidence with which either the adoption or the restriction was carried out.

The Sun With a Face

To understand why Los Angeles reached for the filter, you have to understand what it is still recovering from.

On 20 March 2024, at the Edward R. Roybal Learning Center, Superintendent Alberto Carvalho unveiled Ed, an AI chatbot personified as a cheerful round sun, built to pull student records, assignments, grades, recommendations and mental health referrals into a single portal. “Simply put, Ed relies on the information that the district already possesses, analyses, personalises it to the needs of each student and then builds a pathway,” Carvalho told reporters. The contract with a Boston start-up called AllHere Education, signed on 20 June 2023, was worth up to about $6.2 million over five years. The district paid roughly $3 million.

Ed was switched off on 14 June 2024, less than three months after launch, when AllHere furloughed most of its staff. Chief of Special Education Tony Aguilar said the district had no warning until the day it happened. AllHere filed for Chapter 7 bankruptcy later that year. On 19 November 2024, federal agents arrested the company's founder and chief executive, Joanna Smith-Griffin; prosecutors in the Southern District of New York charged her with securities fraud, wire fraud and aggravated identity theft, alleging she had told investors in 2021 that AllHere had generated about $3.7 million in revenue, including contracts with the New York City and Atlanta districts, when the true figure was around $11,000 and the contracts did not exist. Those remain allegations.

The consequences travelled upwards, though not by any route that has yet been established in public. In February 2026 federal agents arrived at Carvalho's home and district office in the early morning, and at the Miami property of a Florida consultant with long-standing ties to him who had helped bring the technology to the district. Reporting at the time indicated the raids may be linked to the AllHere deal; that link has been suggested rather than demonstrated. A source with direct knowledge has since said the investigation concerns allegations of kickbacks dating to Carvalho's earlier tenure running the Miami school district, and that it predates the current administration. He is not named in the case against Smith-Griffin and has not been charged with any crime. He was placed on paid leave on 27 February and resigned on 21 June 2026, saying that “placing students first has always guided my work”. Andrés Chait succeeded him on an acting basis.

None of which requires Carvalho to have done anything wrong. The argument does not turn on what the investigation concludes, because what follows is a sequence of institutional events rather than a verdict: superintendent champions a chatbot, contract signed, product launched with a press event, dead in eleven weeks, vendor bankrupt, founder indicted, superintendent raided and gone, and two months after his resignation the district blocks the technology for every child it educates. That is not a district responding to research. It is a district responding to a scandal.

Everything Old Is Procured Again

Los Angeles had run this loop before, in a version so similar that the main difference is the price.

In 2013, Superintendent John Deasy launched a plan to put an iPad loaded with Pearson curriculum into the hands of every student, some 650,000 devices, at a projected cost approaching $1.3 billion. The rollout collapsed almost immediately. Pupils bypassed the security restrictions within days, teachers had not been trained, and the content was incomplete. A federal review later noted that Deasy had given the district only a few months to produce a plan before putting the work out to bid, and emails surfaced showing he and a deputy had been in close contact with Apple and Pearson before the contract was awarded.

The FBI seized twenty boxes of records in December 2014. Deasy had resigned that October, and his successor Ramon Cortines cancelled the Pearson contract the day after the seizure. Federal investigators closed the inquiry without charges in February 2017.

Set the two episodes side by side and the pattern is legible. A charismatic superintendent identifies a transformative technology. Procurement moves faster than evaluation. The product ships to hundreds of thousands of children with no pilot worth the name. It fails within a school year. Federal law enforcement arrives. The leader departs. The district retreats. Twice in thirteen years, in the same building, the reversal has been triggered by a criminal investigation rather than by a study.

Rachel White, associate professor of educational leadership at the University of Texas at Austin, put the dynamic carefully when the district's leadership imploded in June. Carvalho “was always willing to make big, ambitious bets”, she said, “and that boldness can sometimes cut both ways”. Natalie Milman of George Washington University offered the mundane fix that neither the iPad programme nor Ed received: “Before rolling out any tool, [educators should conduct] some testing with a smaller group, getting some feedback from the actual people who will be using it, and then getting that public comment.”

Ed went from contract to citywide launch without that. So did the moratorium that replaced it.

The Chancellor Who Changed His Mind

New York's record is a purer specimen, because it contains a completed round trip.

In January 2023, weeks after ChatGPT became generally available, the New York City education department added it to the blocked list on school networks and devices, citing academic integrity and accuracy. The ban lasted four months. On 18 May 2023, Chancellor David Banks announced the reversal in a Chalkbeat op-ed, the same institution discussing the same technology from the opposite direction.

“The knee-jerk fear and risk overlooked the potential of generative AI to support students and teachers,” Banks wrote, “as well as the reality that our students are participating in and will work in a world where understanding generative AI is crucial.” He promised the department would “encourage and support our educators and students as they learn about and explore this game-changing technology”.

Three years and three months later, a different mayor and chancellor announced the country's broadest moratorium. “Children need teachers and human connection in order to learn and grow,” Mamdani said. Samuels framed it as parental protection: “As Chancellor and as a parent, I believe our job is to protect what makes learning work.”

Both statements are perfectly reasonable. So was Banks's. That is the problem. In under four years the same school system has banned generative AI, unbanned it while calling the ban a knee-jerk reaction, spent three years encouraging it, and banned it again for two-thirds of its pupils. At no point did a decisive piece of evidence about children and chatbots arrive. What arrived, in order, was a moral panic, a wave of vendor and federal enthusiasm, and then a political constituency of organised parents.

The enthusiasm phase is worth pricing. In April 2025 an executive order titled “Advancing Artificial Intelligence Education for American Youth” created a White House Task Force on AI Education, directing agencies to build partnerships teaching K-12 pupils AI literacy. In July 2025 the American Federation of Teachers, with the United Federation of Teachers, announced a National Academy for AI Instruction underwritten by $23 million over five years from Microsoft, OpenAI and Anthropic, aiming to train 400,000 educators from a Manhattan facility, beginning with New York City.

The Academy is a few kilometres from the chancellor who has now suspended student-facing generative AI for 600,000 children. The union whose president has publicly criticised the moratorium's enforcement gaps is the same union that co-founded it.

Seven days after the moratorium, that observation acquired a sharper edge. On 9 September 2026, Randi Weingarten of the AFT, Michael Mulgrew of the UFT and Brad Smith, vice chair of Microsoft, jointly announced a National AI Safety and Privacy Standard for schools, framed as legally enforceable rather than advisory. Student and educator data cannot be used to train AI models, sold, or repurposed. Schools keep control over how long data is retained and when it is deleted. AI systems must carry human oversight and cannot make decisions without it. Families must be given clear information about how AI is being used. “Anything less than legally enforceable provisions is simply a wish list,” Weingarten said. Mulgrew presented it as a debt being settled: “This agreement gives families and school districts the information and privacy protections they have been demanding.” Microsoft's own announcement says the standard builds on New York City's screens and AI policy.

The Academy and the Standard are separate initiatives, and that distinction is worth stating plainly. The sequence is still striking. Within a week of telling the city it had not explained how it would make purchased products comply, the union president was co-announcing enforceable national protections with one of the companies underwriting the Academy. The terms on which several hundred thousand children meet generative AI are being set in the language of data retention and model training, because that is the language a negotiation between a union and a vendor produces. None of it is a finding about what the technology does to a nine-year-old. The vacuum is being filled by the parties who were already in the room.

Meanwhile the adoption curve ran fast and unevenly. RAND's surveys found the share of districts training teachers on AI more than doubled between 2023-24 and 2024-25, from 23 per cent to 48 per cent, while only about 35 per cent of district leaders reported providing pupils with any AI training. Nearly 60 per cent of principals reported using AI in their own work; only 18 per cent said their district had issued guidance on staff or student use. The technology entered American schools through the staffroom, without a policy, and the policy is arriving three years late in the form of a switch being flipped.

The Strongest Case Against the Filter

The argument against these moratoriums is not vendor lobbying and should not be dismissed as such. It has three legs.

The first is equity, and it is the one that ought to worry the districts most. Blocking generative AI on district devices does not remove it from children's lives; it removes it from the children whose only reliable device is the district's. Common Sense Media's 2026 census of 1,204 American children aged nine to seventeen found 86 per cent already using AI in some form, close to a quarter of them daily, and more than four in ten saying no parent had ever talked to them about AI safety. Pew Research Center has consistently found that teenagers in lower-income households are less likely than wealthier peers to have a home computer or tablet, even as smartphone ownership approaches universal.

The distributional consequence is straightforward. A child in Brentwood or Brooklyn Heights with a family laptop and a parent who has views about prompt engineering will keep using these tools at home, unsupervised, with no critical thinking module attached. A child whose access runs through a district Chromebook will meet the technology first at sixteen, or at work. RAND's figures already show the gradient: by the start of 2025-26, district leaders estimated that almost all low-poverty districts would have trained teachers on AI, against about six in ten high-poverty districts. A blanket device-level block does not close that gap. It formalises it.

Richard Buery Jr, chief executive of the anti-poverty organisation Robin Hood, made the point precisely. The moratorium for younger grades, he said, is “not the right call”, and should not extend beyond the coming school year. “In an understandable effort to protect students, I fear we're rejecting uses that could help them.”

The second leg is that blocking a tool is not the same as teaching judgement about it, and only one of those is education. New York gestures at this with two annual 45-minute modules for high schoolers, ninety minutes a year of formal instruction about the most consequential general-purpose technology of their working lives. There is no equivalent for K-8 at all. BetaNYC's Noel Hidalgo noted that the policy provides no AI literacy instruction for the banned grades. Hossain's finding is directly relevant: what predicted the type of reliance a student adopted was not access, but AI literacy. Value and cost beliefs predicted how much they used it; literacy predicted whether they used it well. A policy that reduces access without increasing literacy is optimising the wrong variable.

The third leg is that the ban may not be a ban. The Parents' Coalition for Student Privacy argues that most real classroom applications fall into a vague middle tier left to teacher discretion, and organiser Kelly Clancy says the policy “may be less restrictive than it appears”. Liat Olenick of the AI Moratorium Coalition, which wanted two years rather than one, was blunter: “There are a lot of holes in the policy. If we're honest, there's a lot of unanswered questions. Too many exceptions.” UFT president Michael Mulgrew said the department has not explained how it will make purchased products comply, and that educators should not have to “figure out after the fact” whether their school is in breach. There is no published complaint mechanism, no public directory of which tools run in which school, and vendor non-disclosure agreements shield product details from scrutiny. State Comptroller Thomas DiNapoli's August audit found the city holds no complete inventory of its AI systems.

In Los Angeles, Anya Meksin, deputy director of the campaign group Schools Beyond Screens and a district parent, sits on the committee reviewing the moratorium and does not think a pause is the point. “I think the current policy needs to be discarded completely,” she said, “and we need to start from scratch with the foundational question of what is the benefit of these tools in education?”

That question was never answered before the tools arrived. It is not being answered now that they have gone.

The Asymmetry Nobody Is Measuring

What the two speeds reveal is not what either side wants it to be.

Adoption was fast because adoption is a purchase. It needs a superintendent, a procurement officer, a vendor with a demo and a board vote most people in the room do not understand. Ed went from contract signature to a launch event for hundreds of thousands of children in nine months. Nobody had to be persuaded of anything difficult, because the persuading happened in a few meetings with a few people.

Restriction was slow because restriction is a politics. It requires a constituency. In Los Angeles that was organised parents who spent the spring campaigning on screens and were rewarded in June with a board vote phasing device use by grade. In New York it was a coalition of parents, a new mayor and a new chancellor with an interest in demonstrating they were not the last administration. The evidence base moved barely at all. What moved was the coalition.

Chicago is that mechanism happening in real time and on a ballot. Of the 42 candidates standing for the city's school board this autumn, 24 back a temporary moratorium on AI as part of wider limits on screen time, including all fifteen endorsed by the local teachers' union. Restriction has stopped being a position a district arrives at and become a position a candidate runs on. No new study produced that. A constituency did.

That is why the reversal looks abrupt and is not. The chatbot has been in American classrooms for three and a half years, and the restriction took that long to assemble because it had to be built out of people rather than contracts. People organise more slowly than budgets spend.

And because the constituency is local, so is the direction. While New York and Los Angeles were switching the technology off, Miami-Dade County Public Schools, the third-largest district in the country, signed a partnership with Google to bring Gemini into its classrooms. The first, second and third largest school systems in the United States looked at the same absent evidence base in the same few weeks and moved decisively in opposite directions. If research were setting the direction, that could not happen. Districts of that size do not read different literatures. They have different politics, different scandals and different vendors in the room.

Which leaves the genuinely damning observation. Both decisions, the adoption and the reversal, were made without adequate evidence about what generative AI does to a nine-year-old, because that evidence still barely exists. The preprints being waved around study undergraduates. The EEG study has eighteen adults per condition, and six by its final session. Nobody has run the long-run trial with children that would justify either the enthusiasm of 2023 or the certainty of 2026, and both institutions acted at full scale anyway, in opposite directions, on the same absent evidence.

The problem is not that districts were too fast then and too slow now. It is that both speeds were set by something other than knowing.

Building an Institution That Can Change Its Mind Cheaply

If there is a defensible version of what these two cities have just done, it lies in a distinction neither has made explicitly: the difference between a decision that is reversible and one that is not.

New York's moratorium runs for one year and comes with a Technology in Schools Coalition of students, educators, parents, officials, union partners and researchers, charged with assessing impact and publishing recommendations. Los Angeles has a committee, six scheduled meetings running from September 2026 to April 2027, and an obligation to report to the board before the year is out. The dates are 2 September, 21 October and 9 December, then 20 January, 3 March and 7 April. Which means the meeting this article opens on was the first of the six: the parents working out in real time that the block had already gone up were sitting in session one of a process meant to produce, in Gonez's framing, AI policy recommendations the full board could vote on next year. Gonez has been careful not to oversell the permanence. “I wouldn't expect necessarily that there would be a pause for forever,” she said. “But this provides us the opportunity to really think through what does our policy look like moving forward.”

Time-limited, reviewable, with a named body and a reporting date is a better decision architecture than a five-year contract signed after a demo. It is a shame it took two federal investigations to arrive at it.

What would make it credible is a handful of unglamorous commitments. Publish the inventory of which AI-containing products run in which schools, which is exactly what DiNapoli found New York lacks. Pilot before scale, with the small-group testing and public comment Milman described, rather than a launch event at a named high school. Write reversibility into contracts, so a vendor's failure does not become an emergency for 378,000 children. Fund the literacy alongside the restriction, because Hossain's data says literacy determines whether reliance becomes dependence. And commission the research on the actual population, because a district of half a million pupils is better placed to run that study than the universities currently supplying the citations.

Melvoin's complaint at that first meeting was, in the end, the most important thing said in either city. The district changed the terms on which several hundred thousand children encounter the defining technology of their era, and did not tell their parents. The substance may well turn out to be right. The process was the one that bought the iPads and the sun with a face: a decision taken by a few people, at speed, and explained afterwards.

A ten-year-old in Los Angeles will hit a Lightspeed block page this week. In four years she will be expected to use these systems competently in a workplace that assumes she grew up with them. Between those two facts sits an institution that has now shown, twice in three years in one city and twice in thirteen in the other, that it can move extremely fast in whichever direction the last scandal points.

Speed is not the failure. Direction without evidence is. The block page, whatever else it does, buys time to find some.

Sources and References

1. NYC Mayor's Office, “Mayor Mamdani and Chancellor Samuels Put Students First with Nation's Broadest Generative AI Moratorium in Schools”, 2 September 2026. https://www.nyc.gov/mayors-office/news/2026/09/mayor-mamdani-and-chancellor-samuels-put-students-first-with-nat
2. Chalkbeat New York, “NYC's new school AI ban explained: What students and teachers need to know”, 2 September 2026. https://www.chalkbeat.org/newyork/2026/09/02/what-to-know-about-nyc-public-schools-generative-ai-ban-screen-time-limits/
3. Noel Hidalgo, “NYC's DOE AI Ban? Here Is What the Policy Actually Says”, BetaNYC, 4 September 2026. https://www.beta.nyc/2026/09/04/nyc-paused-ai-in-its-classrooms-here-is-what-the-policy-actually-says/
4. Governing, “New York City Put Limits on AI in Schools. Are They Enough?”, September 2026. https://www.governing.com/artificial-intelligence/new-york-city-put-limits-on-ai-in-schools-are-they-enough
5. LAist, “How will LAUSD students use AI? Here's how you can help decide”, September 2026. https://laist.com/news/education/lausd-ai-committee-first-meeting-tech-in-schools
6. Education Week, “LAUSD Adopts Moratorium on Student AI Use, Another Big Move to Limit Tech Use”, September 2026. https://www.edweek.org/technology/lausd-adopts-moratorium-on-student-ai-use-another-big-move-to-limit-tech-use/2026/09
7. California Department of Education, Enrollment by Grade, Los Angeles Unified, 2025-26. https://dq.cde.ca.gov/dataquest/dqcensus/enrgrdlevels.aspx?agglevel=District&year=2025-26&cds=1964733
8. New York State Education Department, NYC Public Schools enrolment data, 2025. https://data.nysed.gov/enrollment.php?instid=7889678368
9. Abeer M. Hasan and Sayed A. Mostafa, “Perceptions and Utilization of GenAI Tools among Data Science Students and Faculty”, arXiv:2604.22998, 24 April 2026. https://arxiv.org/abs/2604.22998
10. Keith Tran, Colton Harper and Thomas Price, “Why Put in This Much Effort?”, arXiv:2606.30480, 29 June 2026. https://arxiv.org/abs/2606.30480
11. Shahin Hossain, “Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers”, arXiv:2606.28749, 27 June 2026. https://arxiv.org/abs/2606.28749
12. Nataliya Kosmyna, Pattie Maes and colleagues, “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task”, MIT Media Lab, arXiv:2506.08872, 10 June 2025. https://www.media.mit.edu/publications/your-brain-on-chatgpt/
13. Michael Gerlich, “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking”, Societies, vol. 15, no. 1, art. 6, 2025, with Correction, 10 September 2025. https://www.mdpi.com/2075-4698/15/1/6
14. Yizhou Fan, Dragan Gasevic and colleagues, “Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance”, British Journal of Educational Technology, vol. 56, no. 2, 2025, pp. 489-530. https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13544
15. Government Technology, “LAUSD Shutters New AI Chatbot As Developer Goes Out of Business”, 2024. https://www.govtech.com/education/k-12/lausd-shutters-new-ai-chatbot-when-developer-goes-out-of-business
16. US Attorney's Office, Southern District of New York, “CEO Of Artificial Intelligence Startup Company Charged With Defrauding Investors”, 19 November 2024. https://www.justice.gov/usao-sdny/pr/ceo-artificial-intelligence-startup-company-charged-defrauding-investors
17. EdSource, “FBI raid of Los Angeles Superintendent Carvalho's home, office may be linked to student AI chatbot”, February 2026. https://edsource.org/2026/fbi-raids-home-and-office-of-los-angeles-unified-superintendent-carvalho/752038
18. Education Week, “Lessons Learned About Bold Tech Initiatives From the LAUSD Chief's Departure”, June 2026. https://www.edweek.org/leadership/lessons-learned-about-bold-tech-initiatives-from-the-lausd-chiefs-departure/2026/06
19. Governing, “A Cautionary Tale for Any Government IT Project: L.A.'s Failed iPad Program”, 2015, https://www.governing.com/archive/gov-tablets-los-angeles-ipad-apple-schools.html; and EdWeek Market Brief, “Feds Drop Investigation Into Los Angeles District Over $1 Billion iPad Purchase”, February 2017, https://marketbrief.edweek.org/regulation-policy/feds-drop-investigation-into-los-angeles-district-over-1-billion-ipad-purchase/2017/02
20. David C. Banks, “ChatGPT caught NYC schools off guard. Now, we're determined to embrace its potential”, Chalkbeat New York, 18 May 2023. https://www.chalkbeat.org/newyork/2023/5/18/23727942/chatgpt-nyc-schools-david-banks/
21. American Federation of Teachers, “AFT to Launch National Academy for AI Instruction with Microsoft, OpenAI, Anthropic and United Federation of Teachers”, 8 July 2025. https://www.aft.org/press-release/aft-launch-national-academy-ai-instruction-microsoft-openai-anthropic-and-united
22. Microsoft Source, “AFT, UFT and Microsoft announce 'National AI Safety & Privacy Standard' for schools to protect students, families and educators”, 9 September 2026. https://news.microsoft.com/source/2026/09/09/aft-uft-and-microsoft-announce-national-ai-safety-privacy-standard-for-schools-to-protect-students-families-and-educators/
23. RAND Corporation, “AI Use in Schools Is Quickly Increasing but Guidance Lags Behind: Findings from the RAND Survey Panels”, 2025. https://www.rand.org/pubs/research_reports/RRA4180-1.html
24. Common Sense Media, “The Common Sense Media Census: AI Use by Tweens and Teens, 2026”, 8 June 2026. https://www.commonsensemedia.org/sites/default/files/research/report/2026-ai-use-by-tweens-and-teens-1.pdf
25. Ray Schroeder, “Big School Districts Pause AI Access at School”, Inside Higher Ed, 16 September 2026. https://www.insidehighered.com/opinion/columns/online-trending-now/2026/09/16/big-school-districts-pause-ai-access-school

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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Consider the most consequential three seconds in a criminal case. A woman looks up. There is a man in her kitchen doorway. The light is bad, the adrenaline is worse, and her visual system is doing something it evolved to do badly: encoding an unfamiliar face under threat. Three seconds later he is on her, and the encoding stops.

Six weeks after that, in a small room at a police station, she is shown six photographs on a screen. One of them is a man the police have arrested. The other five are what the science calls fillers: known-innocent people placed in the array so that the array functions as a test rather than a suggestion. She looks. She points. She says she is certain.

Everything downstream of that moment, the charge, the plea negotiation, the jury's verdict, the years, depends on what those other five faces were. And there is now a serious, funded body of work asking what happens when the answer is: nothing. Not photographs of anyone. Not people with addresses and dental records and mothers. Pixels sampled from a latent space in the time it takes to refill a coffee, resembling human beings the way a convincing forgery resembles a signature.

On 2 September 2026, East Texas A&M University announced that a team led by Curt Carlson, professor of experimental psychology, had won a 100,000 dollar Research Excellence Fund grant from The Texas A&M University System to find out. The project pairs conventional eyewitness identification experiments with Tobii eye-tracking glasses, in collaboration with Dawn Weatherford at Texas A&M University-San Antonio, Alyssa Jones at Tarleton State University and Maria Carlson at East Texas A&M. The intention is to record not just which face a witness picks but how the eye arrives there: dwell times, saccades between images, whether attention settles on internal features such as the eyes and nose or drifts to external ones such as hair. It underwrites a planned 1.3 million dollar National Science Foundation proposal for a three-year expansion.

Carlson's worry is the sort that only occurs to someone who has spent a career thinking about how lineups fail. “Having a real suspect's photo in there with AI fillers could be problematic,” he said. “Someone's got to do the research to see, wait a second, are you putting the suspect in a problematic situation there because he's in there with five AI-generated fillers?”

Put it plainly. If a machine-made face carries some residue of its manufacture, some statistical signature that a visual system registers without the person noticing, then the one authentic photograph in a six-pack becomes the odd one out. The witness does not consciously think “that one is real”. She simply finds her eye returning to it. And a lineup in which the suspect draws the eye for reasons unrelated to memory is not a test. It is a nudge with an evidentiary receipt.

What a lineup is for, and why fillers are the whole apparatus

The lineup is one of the few genuinely experimental instruments the criminal justice system operates, and almost nobody in the system treats it as one.

Its logic is diagnostic. Simply presenting a suspect and asking “is this him?” is a procedure with no error control at all: a witness motivated to help, primed by the fact that police have arrested someone, will often say yes. So the suspect is embedded among known-innocent fillers. If the witness picks the suspect, that choice carries information, because she could have picked five other people and did not. If she picks a filler, the police learn their witness is guessing, and the record shows it. Fillers are the control condition. They are, in a real sense, the entire scientific content of the procedure.

Which is why the discipline has spent five decades building measurement tools for filler quality. Malpass introduced effective size in 1981, using mock witnesses, people who never saw the crime but are given the verbal description, to estimate how many lineup members are plausible candidates. Tredoux later refined the statistics into the measure now known as Tredoux's E. Functional size counts how many mock witnesses pick the suspect from description alone; if the answer is materially more than chance, the lineup is biased and the identification is worth less than it looks. These are the reason a defence expert can run a mock-witness study on the actual array used and demonstrate that a six-pack had a functional size of two.

There is also a long-running methodological argument about how fillers should be chosen. Match-to-suspect selection picks fillers who look like the person in custody, which sounds fair and can backfire: make everyone a near clone and you suppress the witness's ability to discriminate the guilty from the innocent. Match-to-description selects fillers who fit the witness's original account, which preserves discriminability but risks the suspect standing out on any feature the description did not mention. The 2014 National Research Council report “Identifying the Culprit”, still the most authoritative synthesis of this science, worked through these trade-offs alongside recommendations that have since become reform orthodoxy: double-blind administration, unbiased pre-lineup instructions, and an immediate confidence statement taken in the witness's own words before anyone says a thing about whether she got it right.

Three years later the science was reframed by John Wixted and Gary Wells, who argued that eyewitness confidence does predict accuracy, but only under what they called pristine conditions. There are five. Only one suspect in the lineup. Unbiased instructions. Double-blind administration. Confidence recorded at the moment of the identification. And, first on the list, a fair lineup in which the suspect does not stand out.

That is the sentence to hold onto through everything that follows. The scientific case for trusting a confident eyewitness rests on a conditional, and the condition at the top of the list is filler quality. Break the fillers and the confidence-accuracy relationship does not merely weaken. It stops being interpretable.

The old failure mode, wearing new clothes

None of this is hypothetical. The Innocence Project reports that 62 per cent of the exonerations in its casework involved eyewitness misidentification, making it the single largest contributing factor in its data. The National Registry of Exonerations found mistaken witness identification implicated in 26 per cent of exonerations recorded in 2024 alone, across a much broader population of cases than DNA exonerations.

The case reformers keep returning to is Ronald Cotton's, because it demonstrates every mechanism at once. In July 1984, Jennifer Thompson was raped in her home in Burlington, North Carolina. She made a deliberate effort to memorise her attacker's face. She identified Cotton in a photo array and then in a live lineup and told the court she was completely certain. Cotton served more than ten years. In 1995 DNA testing excluded him and matched Bobby Poole, a man produced in court during Cotton's 1987 retrial and whom Thompson, looking directly at her actual attacker, told the court was not the man. The charges were vacated on 30 June 1995. Poole pleaded guilty on 11 July.

The point is not that Thompson lied or was careless. She was neither. The point is that a memory can be overwritten, that certainty can be manufactured by procedure, and that a witness's confidence at trial often measures everything that has happened since the identification rather than what happened during the crime.

Which brings us to why a suspect standing out matters so much, and why the reasons are usually stupid. Lineups have been criticised because the suspect's photograph had a different background colour, because his was the only image with a date stamp, because five fillers were shot on a modern booking camera and the suspect's came from a decade-old arrest at lower resolution, because his was the only face not smiling, because the compression artefacts differed. None of these bears on whether the man committed the crime. All of them can steer a hesitant witness towards the one image that feels different, and once she has chosen it, the feedback loop does the rest.

Synthetic fillers, if Carlson's concern proves out, would be a new species of exactly this old problem, with one alarming property the old versions lacked. Background colour and JPEG artefacts are visible to a defence lawyer with a printout. A subtle statistical divergence between a generated face and a photographed one, detectable by the visual system but not nameable by the person doing the detecting, would be a biasing feature that nobody in the room could see, including the officer who built the array in good faith and the witness whose eye it was steering.

Why departments want the machine faces anyway

It would be easy to write this as a story about police reaching for a shiny tool. It is not that story, and pretending otherwise misses what makes it hard.

Filler selection is genuinely difficult, genuinely laborious, and genuinely bad in a lot of jurisdictions. An investigator working from a description of a man with a distinctive facial scar, or an unusual combination of age, build and ethnicity, has to trawl a local mugshot database that may hold a few thousand images, most of them of the wrong demographic and years out of date. A department in a county whose arrest population is overwhelmingly of one ethnicity will struggle to construct a fair array for a suspect from a minority group, which is precisely the case in which fairness matters most. And investigators are not paid to be experimental psychologists.

Then there is the objection almost nobody outside the field raises, which happens to be the most interesting one. Every real filler photograph is a picture of a real person who was arrested for something at some point, and whose booking image is now being shown to a rape victim in connection with a crime he had nothing to do with. He did not consent. He was not asked. He will never be told. The research group at Heinrich Heine University Düsseldorf that published on AI-generated fillers in Scientific Reports in 2024 named this directly, arguing that generating fillers from text descriptions “avoids the violation of identity rights of natural persons who are not suspects” and removes the constraint of being tied to a limited database.

That is a reform argument, not a surveillance argument, and it deserves to be taken seriously by anyone inclined to reject synthetic fillers on reflex.

The Düsseldorf findings were also, on their face, encouraging. Their text-to-image lineups were, in the authors' words, perfectly fair, and produced less biased suspect selection than the database-derived filler photographs used in their earlier experiments. Bell and colleagues had, though, named Carlson's worry themselves before they went looking for it: the risk exists, they wrote, that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish those images from the photograph of the suspect's face. Their data said it did not. But the concern was theirs first, which makes it harder to file away as idiosyncratic. A separate study by Rachel Greenspan and Amanda Bergold, published in Memory in 2025, went further: participants largely failed to detect any difference between the real photograph of the suspect and the AI-generated fillers, and across identification outcomes and decision criteria the researchers found no evidence of differences between lineups built with real fillers and those built with synthetic ones.

The most technically sophisticated attempt so far comes from the da/sec biometrics group at Hochschule Darmstadt, published in Frontiers in Artificial Intelligence on 7 July 2026 under the title “Towards synthetic fillers for fair photo lineups”. Rather than generating faces from a text prompt, the team inverted a suspect's mugshot into StyleGAN2's extended latent space, then injected weighted noise into the intermediate layers that carry identity-related traits, leaving the layers encoding demographic characteristics comparatively undisturbed. The result is a family of faces that are demonstrably not the suspect but plausibly of the same age, sex and ethnicity. Across 452 participants, 44 biometric experts and 408 recruited through Prolific, suspect identification came out at 68.6 per cent for experts and 70.1 per cent for non-experts, neither so high that the suspect was obvious nor so low that the task was impossible. The authors concluded that synthetic fillers contribute to fair and balanced lineups.

So the empirical picture that reaches the reform community is broadly positive, and Carlson's project is in part a check on that optimism. It is worth being precise about why the check is needed.

The evidence points in two directions at once

Carlson's hypothesis is that a real face among synthetic ones will stand out. There is a substantial body of work suggesting the opposite may be closer to the truth, which is not reassuring, because the opposite is arguably worse.

In 2022, Sophie Nightingale and Hany Farid published a study in the Proceedings of the National Academy of Sciences that has aged into a landmark. Face synthesis engines, they found, had passed through the uncanny valley: participants could not reliably distinguish AI-synthesised faces from photographs of real people, and rated the synthetic faces as slightly more trustworthy than the real ones.

The following year, Elizabeth Miller and colleagues sharpened the finding into something they named AI hyperrealism. In their first experiment, with 124 participants, white AI-generated faces were judged to be human more often than actual photographs of human beings. And the kicker: the people most likely to be fooled were the least likely to believe they could be fooled.

In 2026 a team spanning UNSW Sydney and the Australian National University added the mechanism. Writing in the British Journal of Psychology under the title “Too good to be true”, they showed that synthetic faces are hyper-average, occupying a more central position in face-space than real faces do, which is exactly what you would expect from a generator trained to produce plausible samples from a distribution. Super-recognisers, people with unusual face-processing ability, outperformed typical participants by around 15 per cent at spotting AI faces, and their advantage tracked their sensitivity to that hyper-averageness. Everyone else, super-recognisers included, was equally overconfident about their own ability to tell.

Run this through the lineup and the problem inverts elegantly. If synthetic faces are more average than real ones, then in a six-pack containing one photographed suspect and five generated fillers, the suspect is the face with the idiosyncrasy: the asymmetry, the odd nose, the thing that makes a face memorable rather than merely plausible. A generative process that produces faces clustered around the population mean does not just fail to hide the suspect. It systematically makes the suspect the most distinctive item in the array, which is the definition of a biased lineup, arrived at by a route no existing fairness measure was designed to detect.

There is a second, harder-edged version of the same worry. Hu, Li and Lyu demonstrated in 2021 that GAN-synthesised faces frequently show inconsistent corneal specular highlights between the two eyes, because the generator has no model of a physical light source and no reason to enforce one. Faces produced by StyleGAN2 are also almost perfectly aligned on facial landmarks, an inherited property of the alignment applied to the FFHQ training set, which is why superimposing a set of them produces eyes in the same place every time. Nobody claims a witness consciously audits corneal highlights. But eye-tracking is precisely the instrument for asking whether the visual system is doing something with information the person cannot report, which is what Carlson's Tobii glasses are for.

So the honest state of the evidence is three published studies suggesting synthetic fillers behave acceptably, at least one of which tested only a white male target, against a parallel literature on face perception suggesting a mechanism by which they might fail in a direction that lineup fairness statistics would not catch. That question has barely been asked, and it is being asked considerably later than the tools have been available.

The demographics of the distractor

If there is a place where this goes badly wrong first, the literature already tells you where to look.

The own-race bias in face recognition is one of the most replicated findings in the field. Meissner and Brigham's meta-analysis, covering 39 articles, 91 independent samples and close to 5,000 participants, established that people are markedly worse at recognising faces of another race than their own, with the effect showing up in both hit rates and false alarms. Cross-race identifications are, before you add any technology at all, the highest-risk category of eyewitness evidence in the system, and they are disproportionately the identifications that produce wrongful convictions of Black defendants.

Now add generative models, which are known to be worst at exactly the demographics that matter most here.

The Darmstadt team's own results contain the warning. Their method preserved ethnicity in 89.4 per cent of cases overall, a substantial improvement on prior approaches, with a mean absolute age error of 7.2 years and gender preserved in around 85 per cent of cases. But that aggregate conceals a chasm. Broken out by group, the method preserved ethnicity in 98.1 per cent of cases for white individuals, 78.9 per cent for Black individuals and 53.0 per cent for Asian individuals, a spread the authors attribute to biases inherited from StyleGAN2 itself. They further flag the other-race effect as a reason for caution in cross-ethnic lineups. To their considerable credit, they say all of this in the paper. It is not a hostile reading.

Sit with the arithmetic, because the headline figure is doing a great deal of work it has not earned. Nine cases out of ten is the performance a white suspect gets. For a Black suspect, roughly one filler in five drifts off his apparent ethnicity. For an Asian suspect it is closer to a coin toss than to nine in ten, which in a six-pack means that on average more than two of his five fillers are not, to a witness's eye, of his ethnicity at all. One drifted filler is not a rounding error. It is a face a mock-witness study would immediately flag as implausible given the description, which reduces the functional size of the array, which increases the probability that the suspect is picked for reasons unrelated to memory. Two or three of them and the array has stopped being a test of anything. The failure concentrates on exactly the suspects who are already most likely to be wrongly identified.

The broader generative-model literature makes the same point from the other end. AlDahoul, Rahwan and Zaki, publishing on AI-generated faces and their social effects, documented significant racial homogenisation in text-to-image systems, including the depiction of nearly all Middle Eastern men as bearded, brown-skinned and in traditional attire, alongside the reinforcement of occupational gender stereotypes. Separate work presented at the AAAI/ACM Conference on AI, Ethics and Society found that Stable Diffusion XL produces roughly 30 per cent less variability in skin tones than earlier versions of the model, and between about 19 and 56 per cent less variability than human face datasets, with the greatest homogenisation applied to racial and ethnic identities.

Less variability in skin tone is not a neutral aesthetic property when the artefact you are building is a lineup. It means the generated fillers for a Black suspect are drawn from a narrower slice of appearance space than the real population that suspect belongs to. It means the suspect's actual complexion has a higher chance of sitting outside the range spanned by his fillers. And it means the AI hyperrealism finding, which Miller and colleagues explicitly tied to white faces and to the disproportionate representation of white faces in training data, may not transfer at all: synthetic non-white fillers may look synthetic in ways synthetic white fillers do not, reintroducing Carlson's original worry, but only for non-white suspects.

Every failure mode in this story, on current evidence, points the same way. That is the part of the file that should stop a policy-maker cold.

A lineup that cannot be re-examined

Suppose the science comes back clean. Suppose synthetic fillers turn out to be fair, robustly, across demographics. There is a whole second body of problems waiting, and it belongs to lawyers.

American courts still assess suggestive identifications largely through Manson v. Brathwaite, decided by the Supreme Court in 1977, which asks whether an identification was nonetheless reliable by reference to five factors: the witness's opportunity to view the criminal, her degree of attention, the accuracy of her prior description, her level of certainty, and the time between crime and identification. Scientists and legal scholars have criticised that test for decades, on the grounds that several of its factors are themselves corrupted by suggestive procedures, most obviously certainty, which post-identification feedback reliably inflates. In 2011 the New Jersey Supreme Court in State v. Henderson became the first American court to reject Manson outright, replacing it with a structure built around system variables, the things police control, and estimator variables, the things they do not.

Notice that a synthetic filler is a system variable of a novel kind, and that neither framework has any idea what to do with it.

Start with disclosure. Would the defence be told that five of the six faces were generated? In most American jurisdictions there is no rule requiring it, because no rule contemplated it. An identification is documented by preserving the array, and preserving six images does not tell you which of them ever corresponded to a person.

Then reproducibility. Traditional lineup fairness litigation depends on the array being examinable after the fact: a defence expert runs a mock-witness study on the actual images, calculates functional size and Tredoux's E, and testifies. That still works with synthetic images. But the deeper question, whether the generation process itself was biased, cannot be answered from the images alone. It requires the model, its version, the random seed, the latent vector derived from the suspect's booking photograph, the noise weights, the layer selection, and, in the Darmstadt method, the record of any images an operator discarded by hand before assembling the final six. That last one is the killer. A procedure in which a human generates a batch of candidate faces and picks which to include reintroduces every subjective bias the automation was supposed to remove, and leaves no trace unless someone requires one.

Are those parameters discoverable? Are they preserved? Is there a chain of custody for a face that was never in anyone's custody? If a department uses a commercial tool, is the generator's configuration a trade secret, as the source code of proprietary forensic software has repeatedly been held to be? The forensic-software litigation of the past decade offers a discouraging preview of how that argument usually ends.

And then the question with no clean answer at all. A traditional lineup can be interrogated by finding the fillers. They are people. They have booking records. You can establish who they were, what they looked like, whether they matched the description. A synthetic filler cannot be produced, deposed, photographed from another angle or compared against a contemporaneous record, because there is no fact of the matter about it beyond the file. The image is not evidence of anything in the world. It is an output. If the file is lost, or the tool is updated, or the seed is not recorded, the array is gone in a way a set of mugshots never is.

What England and Wales quietly built instead

There is an instructive contrast sitting on the other side of the Atlantic, and it is instructive precisely because it solved the same problem twenty years ago without generating anything.

Under Code D of the Police and Criminal Evidence Act 1984, identification procedures in England and Wales are governed in considerable procedural detail, and the default method is video identification. A parade normally consists of nine moving images: the suspect plus eight others of broadly similar appearance. The images are drawn from national libraries maintained under the VIPER system, developed by West Yorkshire Police and now run as a managed national service used by forces across England and Wales as well as the Police Service of Northern Ireland and Police Scotland, or from the parallel PROMAT system.

The critical design decision is that the library is a curated database of real volunteers, filmed to a standard specification, who consented to be there. It solves the small-jurisdiction problem, because a force in Cornwall draws on a national pool rather than its own custody suite. It solves the standardisation problem, because every clip is captured under the same conditions, eliminating the background-colour and resolution artefacts that plague American six-packs. It solves the dignity problem the Düsseldorf group identified, because nobody's arrest photograph is conscripted into a stranger's prosecution. And every filler remains a documented human being whose image can be retrieved and examined years later.

That is four of the five arguments for synthetic fillers, answered by institutional infrastructure rather than by a generative model. The remaining argument, that a generator can produce a face matching an arbitrarily unusual description no volunteer library contains, is real but narrow. It is a case for a targeted exception with heavy safeguards, not for a general-purpose tool in every investigator's browser.

The lesson is not that Britain got it right and America got it wrong. It is that the problem synthetic fillers claim to solve is a resourcing and standardisation problem, and such problems have unglamorous, expensive, boring solutions that do not require anybody to reason about the epistemology of manufactured evidence.

What a defensible standard would have to require

If synthetic fillers are coming, and the practical pressures suggest they are, the minimum conditions for their use are not mysterious. They follow directly from what has already been established.

Mandatory fairness testing, per array rather than per method: a documented mock-witness study on the specific array used, reporting functional size and effective size, stratified by the suspect's demographic group. The Darmstadt findings on differential ethnicity preservation make aggregate performance figures actively misleading.

Mandatory disclosure. The defence must be told, in every case, which images were generated, by what system and version, and by whom. Anything less makes it impossible to raise the argument at all, which in practice means the argument is never raised.

Mandatory retention and reproducibility. Model identifier and version, seed, latent vector, noise parameters, the complete set of candidate images generated, and a log of any human selection among them. A synthetic lineup that cannot be regenerated exactly is not evidence anyone can test. If an operator curates the output, that curation is part of the procedure and must be recorded and blinded in the same way lineup administration is blinded.

And a presumption against use in cross-race identifications until the demographic performance question is settled, which on current evidence it is not remotely. This is the recommendation most likely to be resisted and the one most clearly supported by what is already known.

None of these exist anywhere. There is no American federal standard for synthetic fillers, no state statute, no model policy from any of the bodies that have issued model eyewitness policies. The Department of Justice memorandum issued by Deputy Attorney General Sally Yates in January 2017, which established department-wide procedures for photo arrays including blind or blinded administration and contemporaneous confidence statements, predates the technology entirely. The American statutory landscape remains a patchwork in which many states have adopted no reforms at all, and those that have wrote them in the language of an era when a filler was a photograph of someone.

Nor does the legal scholarship reach these questions. The most recent legal treatment of synthetic fillers, Elena Mione's April 2026 article in the Michigan Technology Law Review, argues that AI-generated images could improve lineup fairness and spare real people the conscription of their booking photographs, works carefully through Manson v. Brathwaite, and counsels caution and further study across racial demographics before any large-scale implementation. It proposes no specific safeguards. And it does not reach disclosure to the defence, discoverability, or the retention of generation parameters at all.

The regulatory gap is not a gap. It is the entire field.

What the witness is actually being asked

Strip away the machinery and there is a question at the bottom of this that the psychology does not currently have an answer to, and it is the question that makes the story genuinely strange rather than merely worrying.

A recognition test measures the difference between a target and a set of distractors, and its diagnostic value depends on both being drawn from the same population, so that the only thing separating them, from the witness's point of view, is her memory. Nobody ever wrote that assumption down, because for a hundred and fifty years it was unfalsifiable in practice: every face in a lineup was a face, produced by a human being existing and being photographed.

Synthetic fillers break the assumption in a way that has no precedent. The target is a photograph, an optical record of light reflected from a person who was standing in front of a camera. The distractors are samples from a learned distribution, produced by a process with different statistics, different failure modes and different demographic coverage. They are not worse faces. In several measurable respects they are better ones: more average, more symmetrical, more trustworthy-looking, more likely to be judged human than actual humans. But they are not the same kind of object, and the test assumed they were.

What the witness is being asked to do is therefore not quite what anybody thinks. She believes she is being asked which of these six people she saw. She is in fact performing a discrimination between one photographic record and five generative samples, and the psychology tells us, from at least three directions, that this discrimination is not perceptually neutral: hyper-averageness is detectable, super-recognisers detect it, everyone is overconfident about detecting it, and the detectability itself varies by race.

Carlson's instinct, that the real face might stand out, is likely to prove correct in a more complicated way than he framed it. The real face will stand out not because it looks fake but because it looks specific, and specificity is precisely what a face has to have to be remembered and precisely what a generative model tends to average away.

There is a version of this that ends well. A national library of consenting volunteers, standardised capture, generation reserved for the genuinely impossible description, fairness tested per array, disclosed as a matter of course, reproducible from a retained seed. That version is boring and expensive, which is why it is unlikely to be the one that happens. The version that happens will be a browser tab, a booking photograph, five faces in nine seconds, and no line in the file recording that anything unusual took place.

And somewhere at the end of that, a woman who saw a man for three seconds will look at six faces and point at one, and say she is certain, and be believed. What she will not be told, and what nobody will think to write down, is that she was the only person in the room, in the array, or in the entire procedure whose certainty was about a human being.

References

1. Patrick Harrison, “Can AI Make Police Lineups Fairer? ETAMU Researchers Aim to Find Out,” East Texas A&M University, 2 September 2026. https://www.etamu.edu/news/can-ai-make-police-lineups-fairer-etamu-researchers-aim-to-find-out/
2. A. Dörsch, R. Nichols, C. Busch and C. Rathgeb, “Towards synthetic fillers for fair photo lineups: application of generative AI in criminal law proceedings,” Frontiers in Artificial Intelligence, published 7 July 2026. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1834376/full
3. Raoul Bell, Nicola Marie Menne, Carolin Mayer and Axel Buchner, “On the advantages of using AI-generated images of filler faces for creating fair lineups,” Scientific Reports, 2024. https://www.nature.com/articles/s41598-024-63004-z
4. Rachel Leigh Greenspan and Amanda Bergold, “Can AI-generated faces serve as fillers in eyewitness lineups?” Memory, vol. 33, no. 4, 2025, pp. 416-429. https://www.tandfonline.com/doi/abs/10.1080/09658211.2025.2467134
5. Sophie J. Nightingale and Hany Farid, “AI-synthesized faces are indistinguishable from real faces and more trustworthy,” Proceedings of the National Academy of Sciences, vol. 119, no. 8, 22 February 2022. https://www.pnas.org/doi/abs/10.1073/pnas.2120481119
6. Elizabeth J. Miller, Ben A. Steward, Zak Witkower, Clare A. M. Sutherland, Eva G. Krumhuber and Amy Dawel, “AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones,” Psychological Science, 2023. https://journals.sagepub.com/doi/10.1177/09567976231207095
7. James D. Dunn, David White, Clare A. M. Sutherland, Elizabeth J. Miller, Ben A. Steward and Amy Dawel, “Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it,” British Journal of Psychology, 2026. https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/bjop.70063
8. Shu Hu, Yuezun Li and Siwei Lyu, “Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights,” ICASSP 2021. https://arxiv.org/abs/2009.11924
9. Xin Wang, Hui Guo, Shu Hu, Ming-Ching Chang and Siwei Lyu, “GAN-generated Faces Detection: A Survey and New Perspectives,” arXiv:2202.07145. https://arxiv.org/pdf/2202.07145
10. National Research Council, Identifying the Culprit: Assessing Eyewitness Identification, National Academies Press, 2014. https://nap.nationalacademies.org/catalog/18891/identifying-the-culprit-assessing-eyewitness-identification
11. John T. Wixted and Gary L. Wells, “The Relationship Between Eyewitness Confidence and Identification Accuracy: A New Synthesis,” Psychological Science in the Public Interest, 2017. https://journals.sagepub.com/doi/full/10.1177/1529100616686966
12. Christian A. Meissner and John C. Brigham, “Thirty Years of Investigating the Own-Race Bias in Memory for Faces: A Meta-Analytic Review,” Psychology, Public Policy, and Law, vol. 7, 2001, pp. 3-35. https://www.semanticscholar.org/paper/Thirty-years-of-investigating-the-own-race-bias-in-Meissner-Brigham/315900b898f47295550a0edaf9ed8b4e5243ed6c
13. Roy S. Malpass, “Effective size and defendant bias in eyewitness identification lineups,” Law and Human Behavior, vol. 5, 1981, pp. 299-309. https://www.researchgate.net/publication/225872908_Effective_size_and_defendant_bias_in_eyewitness_identification_lineups
14. Colin Tredoux, “Statistical Inference on Measures of Lineup Fairness,” Law and Human Behavior, 1998. https://www.semanticscholar.org/paper/Statistical-Inference-on-Measures-of-Lineup-Tredoux/fb5fce9c77fb2395fc09d9c7eeaf6af9af19cb61
15. Innocence Project, “Explore the Numbers: Innocence Project's Impact.” https://innocenceproject.org/exonerations-data/
16. Innocence Project, “Ronald Cotton.” https://innocenceproject.org/cases/ronald-cotton/
17. National Registry of Exonerations, 2024 Annual Report, University of Michigan Law School, April 2025. https://exonerationregistry.org/sites/exonerationregistry.org/files/documents/2024_Annual_Report.pdf
18. Manson v. Brathwaite, 432 U.S. 98 (1977). https://supreme.justia.com/cases/federal/us/432/98/
19. State v. Henderson, 208 N.J. 208, 27 A.3d 872 (2011). https://law.justia.com/cases/new-jersey/supreme-court/2011/a-8-08-1-opn.html
20. United States Department of Justice, “Justice Department Announces Department-Wide Procedures for Eyewitness Identification,” January 2017. https://www.justice.gov/archives/opa/pr/justice-department-announces-department-wide-procedures-eyewitness-identification
21. Home Office, Police and Criminal Evidence Act 1984 Code D: Revised Code of Practice for the Identification of Persons by Police Officers. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/592562/pace-code-d-2017.pdf
22. West Yorkshire Police, “The National VIPER Bureau.” https://www.westyorkshire.police.uk/about-us/our-departments/national-viper-bureau
23. Nouar AlDahoul, Talal Rahwan and Yasir Zaki, “AI-generated faces influence gender stereotypes and racial homogenization,” Scientific Reports, 2025 (arXiv:2402.01002). https://www.nature.com/articles/s41598-025-99623-3
24. Kyra Wilson, Sourojit Ghosh and Aylin Caliskan, “Bias Amplification in Stable Diffusion's Representation of Stigma Through Skin Tones and Their Homogeneity,” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (arXiv:2508.17465). https://arxiv.org/abs/2508.17465
25. Elena Mione, “Expanding the Use of Artificial Intelligence in Photographic Eyewitness Lineups,” Michigan Technology Law Review, 15 April 2026. https://mttlr.org/2026/04/expanding-the-use-of-artificial-intelligence-in-photographic-eyewitness-lineups/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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Discuss...

Years ago, Emily Schuman took a selfie in her car and posted it to Instagram. It was an unremarkable act, the kind of thing that happens several hundred thousand times a minute. In September 2026 that photograph came back to her as a sponsored advertisement in which she appeared to be holding up a vial of GLP-1 medication on behalf of a telehealth company called Gala, a business she had never heard of and had no relationship with.

Writing in the Guardian on 12 September 2026, Arielle Pardes reported that Schuman, the creator of the lifestyle blog Cupcakes and Cashmere and the holder of an Instagram audience above 500,000, had been the subject of at least three such fabrications. Alongside the weight-loss drug advertisement, a version of her with darker hair applied foundation for a cosmetics brand called Meroda, and generated images of her circulated on TikTok promoting a blood testing company called Superpower. “So violating,” she told Pardes. The synthetic versions were, in her description, “just like me, but the upside-down version.”

Her followers noticed before she did. They flooded her inbox asking about the sponsorship, because a GLP-1 endorsement did not fit the person they thought they knew. Schuman and her manager reported the advertisement to Meta. It took weeks of badgering before the GLP-1 advertisement came down. The cosmetics advertisement, according to the Guardian's reporting, was still circulating.

That sequence contains the whole economic problem, and almost every account of the deepfake crisis walks past it. The interesting thing is not that a face was copied. It is that the copy was profitable enough for someone to buy advertising inventory to distribute it, that the platform sold that inventory, that the audience did the detection work for free, and that the only party who paid to have it removed was the person whose face it was.

The Recommendation Economy Was Always Selling a Verification Shortcut

To understand what is being destroyed, you have to be precise about what was being sold.

An influencer endorsement is not, economically speaking, an advertisement. It is a transferred credibility judgement. The academic framework underpinning the entire industry is the source credibility model, which holds that persuasion depends on the perceived trustworthiness, expertise and attractiveness of the messenger rather than the message. Layered on top of that is parasocial interaction theory, the observation that audiences form one-sided relationships with media figures that function psychologically like friendships. The creator economy monetises the intersection: a viewer who feels they know someone will accept that person's product judgement at a discount to the scrutiny they would apply to a corporate claim.

The commercial scale of that shortcut is substantial and, unlike much of the surrounding statistical fog, reasonably well evidenced. In its February 2026 forecast, eMarketer revised United States social media creator marketing spending to 21.10 billion dollars for the year. Its reporting on buyer intentions found that 57 per cent of ad buyers described influencer advertisements and partnerships as their top priority for 2026, up from 48 per cent the previous year. Matter Communications' consumer research in 2025 found 71 per cent of consumers saying they trust recommendations from creators they follow more than advertisements from brands directly.

Hold that last figure loosely: it is a self-reported attitude rather than a behavioural measurement, and attitudes about advertising correlate poorly with responses to it. But the direction is not in dispute, and neither is the mechanism. Brands pay a premium over equivalent display inventory precisely because the face does verification work that a logo cannot.

Alice Marwick, director of research at the Data & Society Research Institute, made the supply-side point to the Guardian: influencers are unusually easy to replicate because they “have hundreds of hours of footage and thousands of pictures of them online”. The obligation to be constantly visible is also the obligation to furnish a training set.

Now notice what that implies. The asset was never the face. The asset was the audience's belief that the face could only appear where its owner had consented to put it. The face was merely the carrier of a scarcity assumption. Generative models did not destroy the face. They destroyed the scarcity.

The Headline Number Is Cumulative, and the Guardian Made It Annual

The figure attached to this story in almost every retelling is 3.7 billion dollars, described in the Guardian's own framing as what global consumers “may have lost as much as” to deepfake scams “in 2026”, with social media impersonation accounting for roughly half.

That framing is wrong, and the error matters because it multiplies the apparent velocity of the problem by roughly five.

The source is a research chart published by Surfshark, a company that sells virtual private network subscriptions. Its own methodology page is explicit: the 3.7 billion dollar total is cumulative, covering January 2020 through June 2026. The distribution within that window is steep. Surfshark records 83 million dollars across the whole of 2020 to 2023, 335 million dollars in 2024, 2.5 billion dollars in 2025 and 764 million dollars in the first half of 2026. Social media is the largest single origin category at 1.73 billion dollars, or 47 per cent of the cumulative total, driven overwhelmingly by criminals using synthetic celebrities to promote fraudulent investment schemes. Impersonation fraud, defined separately as bypassing identity checks or gaining access to financial services, accounts for a further 911 million dollars.

So “half of it through social media impersonation” is an elision of two different categories, and “in 2026” describes six and a half years.

There is a second problem, and it is the one that should make anyone reaching for this statistic uneasy. Surfshark's own charts do not agree with each other. An earlier chart on the same research hub, covering social media specifically, reports deepfake losses of 1.1 billion dollars in 2025, tripling from 360 million dollars in 2024 and a ninefold increase on the 128 million dollars recorded between 2020 and 2023. The later origins chart gives 2.5 billion dollars for 2025, 335 million dollars for 2024 and 83 million dollars for 2020 to 2023. These are not rounding differences. The 2025 figure has more than doubled between publications.

This is not necessarily dishonesty. The method aggregates the AI Incident Database, Resemble.AI's deepfake incident database and the OECD's AI Incidents and Hazards Monitor, counting only incidents with a documented financial loss verified in media reports. A method like that reports the growth of journalism about deepfake fraud as faithfully as it reports the growth of deepfake fraud, and it revises upward whenever reporting catches up with older incidents. Surfshark says as much: the figures are a conservative floor built from publicly reported cases.

What it means in practice is that the number cannot bear the weight placed on it. It is not a measurement of losses. It is a count of the losses that got written up. Anybody citing it as an annual total for 2026 has compounded a provenance problem with an arithmetic one.

The figures that do come from primary collection agencies are less dramatic and more usable. The United States Federal Trade Commission, publishing its Consumer Sentinel data in April 2026, reported that consumers filed three million fraud reports in 2025 with 15.9 billion dollars in losses, up from 12 billion dollars the previous year. Nearly 30 per cent of people who reported losing money said the scam started on social media, with those losses totalling 2.1 billion dollars, an eightfold increase on the 261 million dollars reported in 2020. Facebook was named in more loss reports than any other platform, with WhatsApp and Instagram distant seconds. Investment scams originating on social media accounted for 1.1 billion dollars, more than half the social media total.

In the United Kingdom, Ofcom estimated in July 2026 that around 200 million pounds is lost to fraudulent advertising annually, and that 51 per cent of online adults have encountered a potentially fraudulent advertisement, with 36 per cent seeing them frequently. These numbers are smaller than the ones that circulate on conference slides. They are also collected by bodies with statutory reporting obligations rather than by companies selling security products, and they point at the same structural fact: the losses concentrate where paid distribution and impersonation intersect.

Four Per Cent Is a Figure in Search of a Source

The same provenance problem afflicts the other statistic carried in the Guardian's framing, that virtual influencers now account for around 4 per cent of brand spending.

Trace it and you arrive at a figure of roughly 1.37 billion dollars in annual brand spending on AI-generated personas, attributed to the analytics firm HypeAuditor, expressed as 4.2 per cent of a total influencer market usually put at 32.5 billion dollars. The percentage is not a measured share. It is one company's estimate of a numerator divided by a different company's estimate of a denominator, and it propagates through a mesh of search-optimised statistics aggregators, each citing the last.

The Influencer Marketing Hub's own 2026 Benchmark Report, built on a survey of more than 600 marketing professionals and the most frequently cited primary instrument in the sector, contains no data on virtual influencers at all. Its AI findings are about workflow: 36.67 per cent of respondents use AI for creator discovery, 21.11 per cent for content generation, 13.89 per cent for brief development. Brands are using AI to find humans, not to replace them. Meanwhile Linqia's enterprise research has been reported as finding that 89 per cent of enterprise marketers avoid virtual influencers entirely.

A number can be approximately right and still be epistemically worthless, and the four per cent figure sits in that category. The honest version of the claim is narrower and more interesting: synthetic personas are a small but fast-growing slice of a growing market, disproportionately visible relative to spend, and enterprise buyers remain wary. That is a different story from an inexorable synthetic takeover, and it bears directly on the question of what happens to endorsement value, because it means the immediate threat to a creator's income is not a virtual competitor. It is a counterfeit of themselves.

The Person Whose Face It Is Pays for the Cleanup

The clearest read on where the cost lands comes from the individual who has been absorbing it longest.

The financial journalist Martin Lewis sued Facebook for defamation in 2018 over advertisements that appropriated his image, settling after Meta agreed to donate three million pounds to charity and to build a scam advertisement reporting button. In July 2023 he described what he believed to be the first deepfake video scam advertisement using his likeness, a fabricated clip in which he appeared to endorse an investment scheme branded as a project of Elon Musk's. By April 2026, as Charlotte Tobitt reported in Press Gazette, Lewis was describing the situation as “worse than ever”, a “deliberate perversion of my name, reputation and work by organised criminals around the world”.

The operational detail in that reporting is the part worth dwelling on. Of 537 reports made to Action Fraud in 2024 about advertisements misusing someone's name, Lewis's identity featured in 44 per cent. Musk featured in 40 per cent. To manage this, Lewis funds the equivalent of one full-time member of staff doing nothing but chasing fraudulent advertisements. He pays for that. “I'm not Meta, I'm not X,” he told Press Gazette. “I can't take them down.” His proposed remedy was blunt and unfashionable: “You're framing this as a technology problem. Employ people to vet every advert.”

Set that against Schuman's position and the asymmetry becomes a formula. Lewis is a nationally recognised broadcaster with a media organisation behind him and a successful defamation settlement in his history, and his mitigation strategy is a permanent salaried headcount. Schuman is a lifestyle blogger with half a million followers. Her options, as the Guardian laid them out, are to hire lawyers, which costs money and time she would otherwise spend earning, or to do nothing and watch her audience conclude she has started selling weight-loss injections. There is no third option in which someone else bears the cost.

This is the answer to the question of who pays, and it is not a satisfying one. The loss is distributed to whoever has the least ability to refuse it. Consumers absorb the fraud losses. Creators absorb the reputational damage and the enforcement labour. The platform collects the advertising revenue in both directions, from the legitimate campaign and from the counterfeit of it.

Meta Priced the Problem Internally and Decided It Could Live With It

That last sentence would be a rhetorical flourish were it not for the documents.

In November 2025 Reuters published an investigation based on a cache of internal Meta records. The headline finding was that Meta had projected its 2024 revenue from advertisements for scams and banned goods at around 16 billion dollars, roughly 10 per cent of total revenue. The category covered fraudulent e-commerce and investment schemes, illegal online casinos and prohibited medical products. Meta's systems were estimated to show users around 15 billion higher-risk scam advertisements a day, generating roughly seven billion dollars in annualised sales. The company was recording around 3.5 billion dollars every six months from advertisements its own legal team had classified as carrying higher legal risk, a category that expressly included impersonating a brand or a celebrity.

Two operational parameters in that reporting explain everything about Schuman's weeks of badgering. The first is that Meta's automated systems banned an advertiser only when they were 95 per cent certain the account was committing fraud. A 95 per cent confidence threshold guarantees that a large volume of probable fraud stays live, because most fraud is not 95 per cent legible to a classifier. The second is a February 2025 document that quantified precisely how much revenue the company was prepared to forgo in order to clamp down on suspicious advertisers: 0.15 per cent of total revenue, or 135 million dollars.

Meta has contested the framing, with a spokesperson describing the 10 per cent projection as “a rough and overly-inclusive estimate” and saying the company aggressively addresses scam advertisements. Take that at face value and the structure still holds, because the structure is the enforcement threshold and the budget ceiling, not the revenue estimate.

Now compare the numbers. Ofcom's proposed fraudulent advertising code, consulted on from 10 July 2026 with a final statement promised by mid-2027 at the latest, would carry maximum fines of 18 million pounds or 10 per cent of global revenue. The 10 per cent figure is the meaningful one, because 18 million pounds is an eighth of the sum Meta had already earmarked as its acceptable annual cost of enforcement. The company is not gambling that regulators will not fine it. It is operating a system in which the fine is a line item and the revenue is the business.

Meta does run a countermeasure, and its shape is instructive. Since late 2024 it has used facial recognition to compare faces in suspected celebrity-bait advertisements against the profile pictures of enrolled public figures, blocking matches and deleting the biometric comparison data immediately afterwards. The company has reported that user reports of celebrity-bait scam advertisements, as a proportion of total impressions, fell 22 per cent globally in the first half of 2025, and that nearly 500,000 public figures are covered.

Nearly 500,000 protected public figures. Schuman has more than 500,000 followers. She was not one of them, or if she was, the system did not work. The protection is a list, and lists have edges, and the edge of this one runs straight through the middle of the professional creator population.

The Law Values Your Face at Whatever You Could Have Charged For It

Ask what legal remedy exists and you discover that the dominant frameworks were built to compensate a lost licensing fee, which is exactly the wrong instrument for a harm that destroys the licence's value.

In the United Kingdom there is no personality right and no image right. The nearest analogue is the tort of passing off, and its application to endorsement was settled in Irvine v Talksport, in which the Formula One driver Eddie Irvine succeeded against a radio station that had doctored a photograph to replace the mobile phone in his hand with a portable radio bearing the station's branding. The High Court established that no common field of activity was required and that a false message of endorsement understood by the market was actionable. The Court of Appeal then raised the damages from 2,000 pounds to 25,000 pounds, and the basis on which it did so is the point: the award represented the reasonable endorsement fee the defendant would have had to pay for lawful use.

That is the measure of loss the common law understands. What you could have charged. It works tolerably when a single brochure goes to a thousand advertising buyers. It fails completely when thousands of counterfeit endorsements are algorithmically distributed to your own audience, because the damage is not the unpaid fee. The damage is that every future fee is worth less, including the ones you did negotiate, because the signal they used to carry has been debased by forgeries you were not paid for and could not prevent.

The United States has a more developed apparatus and the same underlying logic. The right of publicity is a state-level property right in the commercial value of identity, meaning the protection tracks the market value. Recent statutes have widened it: Tennessee's ELVIS Act, signed on 21 March 2024, added voice to the state's protected personal rights and criminalised unauthorised commercial voice cloning; California's AB 2602 requires contractual provisions for digital replicas to describe intended uses with reasonable specificity and to be negotiated with representation, while AB 1836 governs replicas of the deceased.

At federal level the NO FAKES Act, reintroduced as S.4591, was advanced unanimously by the Senate Judiciary Committee on 18 June 2026 with a coalition spanning Hollywood, the recording industry and major technology firms. It would create a federal digital replication right covering voice and visual likeness, transferable and licensable, surviving death and terminating no more than 70 years afterwards, with a notice-and-takedown regime and carve-outs for news reporting, parody and criticism.

It is a serious piece of drafting and it would materially improve the position of someone like Schuman, mainly by giving her a federal takedown channel that platforms must operate rather than a customer service queue they may ignore. But note what it is. It is a property right, descendible for seven decades, structured like copyright. The legislative answer to the collapse of endorsement scarcity is to manufacture artificial scarcity by statute and then allow it to be traded. Which tells you where the value is expected to end up: with whoever accumulates the licences.

Denmark has taken the logic furthest. Its proposed amendment to the Copyright Act, unveiled on 26 June 2025 with cross-party backing, would grant every individual copyright-style entitlements over their body, facial features and voice, enabling takedown notices, compensation without proof of reputational harm, and platform liability for failure to act. As of 2026 it awaits final parliamentary adoption. It is the first serious attempt to attach the remedy to personhood rather than to market value, which is precisely why it is difficult: a right everyone holds equally is a right that cannot be cheaply administered.

Sometimes the Forger Is the Brand You Signed With

The assumption running through all of this is that the counterfeiter is a criminal somewhere offshore. One of 2026's more uncomfortable cases suggests the risk is also internal to the commercial relationship.

On 9 June 2026, the creator Molly Tranchin, who posts as FashionVeggie, filed a complaint in the United States District Court for the Northern District of California against the intimate apparel company EBY Inc. She had entered a content creation agreement and produced promotional footage wearing the company's products in a modest, non-sexual manner consistent with her established brand. The complaint alleged that EBY then used AI tools to alter that footage into an explicit version exposing portions of her body not visible in the original, and published it to its Instagram account without her review or consent. She pleaded California's non-consensual deepfake statute alongside defamation, copyright infringement and breach of contract.

The case produced no ruling. Tranchin voluntarily dismissed without prejudice after the company argued the court lacked subject-matter jurisdiction, and indicated she would refile in a court of competent jurisdiction.

No liability has been established and none should be inferred. What the filing demonstrates regardless is the shape of a new counterparty risk. A creator licences footage of herself for a defined use. The licensee possesses both the raw material and the tools to extend it. The contract, drafted for an era when footage could only be cut and not continued, may not say anything useful about generation. California's AB 2602 exists precisely to close that gap by requiring specific description of digital replica uses, which is an admission that the gap was open and that standard influencer agreements were sitting in it.

Disclosure Regimes Bind Everyone Except the Forger

The regulatory response across three jurisdictions converges on transparency, and transparency has a structural flaw that is obvious the moment you name it.

Article 50 of the EU AI Act entered into application on 2 August 2026. Deployers who use AI to generate or manipulate image, audio or video content constituting a deepfake must disclose that the content is artificially generated in a clear, distinguishable and accessible manner, no later than first exposure. Providers must mark outputs in machine-readable form. Penalties reach 15 million euros or 3 per cent of worldwide annual turnover. The AI Omnibus, on which Parliament and Council reached political agreement, pushes the Article 50(2) marking obligation back to 2 December 2026, but only for generative systems already placed on the EU market before 2 August 2026, so anything released since then carries the obligation already. The deployer disclosure duty took effect on 2 August 2026 regardless and stands untouched.

In the United Kingdom, the Advertising Standards Authority has made its position clear through guidance and rulings, confirming that AI-generated imagery is judged against the CAP Code like any other imagery and that AI-generated depictions of real people, particularly celebrities, must not mislead consumers into believing a genuine endorsement exists. Liability sits with the advertiser regardless of what the tool produced. The Ofcom fraudulent advertising consultation, open until 2 October 2026, proposes close to 40 measures binding Category 1 and Category 2A services, covering paid-for advertising only and excluding user-generated content and non-sponsored search results. In the United States, the FTC's Endorsement Guides at 16 CFR Part 255 were amended in 2024 to reach AI-generated and AI-modified endorsement content, and the Commission's supplemental rulemaking to extend its impersonation rule from governments and businesses to individuals remains unfinished, the proposed means-and-instrumentalities provision having been dropped.

Every one of these instruments binds a party with a registered address, a compliance function and revenue worth taking a percentage of. None of them binds the operator who bought Instagram placement for a fabricated GLP-1 endorsement through a shell account. Fraudulent deepfakes do not carry disclosures, because the entire commercial proposition of a fraudulent deepfake is that it is not disclosed.

The predictable consequence is an inversion of the signal. Compliant advertisers label their synthetic content. Criminals do not. Over time, the presence of an AI label comes to indicate a company with lawyers, and its absence indicates nothing at all, because absence now covers both authentic human content and everything produced outside the regime. Regulation designed to help audiences distinguish real from synthetic ends up marking only the subset of synthetic content that was never the problem.

Verification Exists, and It Is Billed in Biometrics

Platforms have built the tool that would help, and the price is worth reading carefully.

YouTube's likeness detection scans uploads for altered or synthetic uses of an enrolled creator's face and surfaces matches in YouTube Studio, from which removal can be requested. In May 2026 it was extended to all creators aged 18 and over, having been expanded in March 2026 to a pilot group including government officials, journalists and political candidates. To enrol, a creator submits a facial scan and government-issued photographic identification.

TikTok has moved in a parallel direction, requiring advertisers using voice clones or digital likenesses to upload consent documentation to Ads Manager including the individual's full legal name, a description of permitted use, campaign duration and a signed release, alongside labelling obligations for realistic depictions of people that draw on C2PA Content Credentials for automatic detection. Meta's facial recognition programme, described above, works the same way: to be defended, you must first be enrolled.

So the remedy for unauthorised biometric replication is authorised biometric submission. You defeat the copy of your face by giving a platform a verified original, plus a passport. For a professional creator that may be a rational trade. It is a harder trade for a journalist in a hostile jurisdiction, or for anyone whose objection to the deepfake was always that they never wanted their face in a corporate database.

The consent question was tested at Meta in July 2026, when Creative Artists Agency publicly urged the company to make likeness protection the default in its Muse Image generator and to require documented consent before third parties use a person's name, image, likeness, voice or work. SAG-AFTRA recommended that its members opt out of the feature entirely. An opt-out recommendation from the largest actors' union is a fairly precise statement of how much confidence the represented talent has in platform-administered likeness governance.

Disclosure Devalues the Genuine Article Too

There is a final mechanism, and it is the one that determines whether the recommendation economy can price its way out of this.

The experimental literature is beginning to quantify what audiences do when synthetic origin is made explicit. A between-subjects study by Fatema Juzer Ujjainwala, published in the Journal of Marketing & Social Research in November 2025, ran 320 social media users across conditions varying influencer type, human against AI, and disclosure, explicit against absent. AI-generated influencers produced significantly lower perceived authenticity and brand trust than human ones. Crucially, explicit disclosure of AI origin intensified the negative reaction rather than neutralising it. Digital literacy moderated the effect, with more literate consumers reacting less negatively.

A single study of 320 supports directional inference, not settled fact, and will need replication. But it aligns with the Gen Z research showing human influencers rated significantly higher on authenticity, trustworthiness and parasocial interaction, and it points at something the industry's transparency advocates have not fully absorbed. Disclosure does not restore trust. It transfers the cost of synthetic content onto the disclosing party.

Combine that with an environment in which audiences have been trained by fabricated advertisements to treat any endorsement as potentially counterfeit, and you get the endorsement equivalent of the liar's dividend. Schuman's genuine partnerships now arrive in front of an audience that has recently been fooled by fake ones. Every legitimate campaign inherits a suspicion tax it did nothing to earn. The forgery does not merely steal a fee. It degrades the yield on the asset that remains.

Where the Value Migrates When the Face Stops Being Scarce

Put the pieces together and the destination becomes legible.

If a face can be printed by anyone, its endorsement value does not simply fall. It migrates to whoever can certify that a given appearance was authorised. That capability currently sits with three sets of actors, and none of them is the creator.

It sits with the platforms, which hold the enrolment databases, the facial recognition pipelines and the takedown queues, and which are already monetising verification in adjacent forms. It sits with the legislatures, through instruments like the NO FAKES Act, which convert likeness into a descendible property right that will be aggregated by the parties best equipped to aggregate rights, which is to say agencies, labels and estates rather than individual creators. And it sits with brands, for whom the rational hedge against an uncontrollable human endorser is an endorser they own outright, which is the actual argument for virtual influencers and always has been. The four per cent figure may be poorly sourced, but the strategic logic it gestures at is sound: a synthetic persona cannot be impersonated in a way that damages the brand, because the brand holds the original.

The creator, in this arrangement, moves from proprietor to tenant. They hold an asset whose value now depends on a verification service they do not control, cannot audit and did not commission, enrolled in a system that requires them to surrender the very biometric data whose misuse they are trying to prevent, with a legal remedy calibrated to the licence fee they would have charged rather than to the market they have lost.

Can the recommendation economy price verification? It can, and it is already starting to. That is not the reassuring answer it sounds like. Pricing verification means that being believed becomes a service with a supplier, a fee structure and an eligibility criterion. It means that the creators who can afford a full-time member of staff to chase counterfeits, as Martin Lewis does, retain their credibility, and the ones who cannot, as most cannot, watch it erode while their inboxes fill with followers asking whether they have really started selling injectables.

Schuman's phrase was better than she may have intended. The upside-down version is not merely a mirror image. It is the same face with the polarity of the economics reversed: all the reach, none of the accountability, the revenue flowing to a stranger and the cost flowing to her. The technology did not take her face. It took the assumption that her face meant she had agreed, and that assumption, not the pixels, was the thing she had spent a decade building and was in the business of selling.

References

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25. Data & Society Research Institute, “Alice E. Marwick,” datasociety.net. https://datasociety.net/team/alice-e-marwick/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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