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In the first week of June 2026, with more than fifty-six million cases choking its courts and a population long accustomed to waiting years, sometimes decades, for a hearing, the Supreme Court of India did something that runs directly against the grain of the moment. It did not reach for the machine that promised to clear the backlog. It drew a line around what the machine would never be allowed to do.

The document that contains that line is a dry, thirty-five-page draft titled Regulations for Use of Artificial Intelligence in Courts, 2026, published for public consultation under the aegis of the Court's Artificial Intelligence Committee and dated the third of that month. It reads, in the manner of all such instruments, like an exercise in administrative housekeeping: definitions, committees, secretariats, audit schedules. But buried in its general principles is a sentence that amounts to a constitutional statement about the nature of judgement itself. “The use of Artificial Intelligence in Court processes,” it declares, “shall at all times remain strictly subservient to human judgment and judicial authority.” Every AI system, it continues, “shall function solely in an assistive capacity and shall not supplant or compromise the independent exercise of judicial authority by a duly appointed judicial officer.”

This is not a hedge or an aspiration. The regulations translate the principle into a list of absolute, non-derogable prohibitions, uses of AI that “shall not be subject to relaxation or modification by any authority” under any circumstances. No machine may adjudicate or sentence. No machine may be used for risk scoring of any kind, including the prediction of recidivism, the assessment of bail eligibility, or the evaluation of a witness's credibility. No machine may profile a defendant, predict a litigant's future conduct, or surveil the people moving through a courthouse. The draft permits AI for the unglamorous work of running a court, scheduling, transcription, translation, legal research, document verification, and forbids it, categorically, from touching the act of deciding.

What makes this striking is not the prohibition in the abstract. Plenty of jurisdictions issue cautious guidance about emerging technology. What makes it striking is the contrast it draws, by the sheer fact of its timing, with the direction the rest of the world has quietly been travelling. Because at the very moment India was writing the words “AI may assist judicial processes but cannot replace human judicial authority” into its draft principles, courtrooms in the United States and probation offices in England were already feeding human beings into exactly the kind of predictive machinery India had just declared off-limits, and had been doing so, in some cases, for the better part of a decade.

The Country That Had Every Reason to Automate

To understand the weight of India's choice, you have to understand the pressure it was under to choose differently. The numbers are almost incomprehensible. According to the National Judicial Data Grid, the public dashboard that tracks pendency across the entire system, the total number of cases pending before Indian courts has now passed fifty-seven million, with just over fifty million of those, close to ninety per cent, sitting in the district courts at the bottom of the pyramid. The High Courts hold a further six and a half million. More than a hundred and eighty thousand cases have been pending for over thirty years, some eighty-one thousand of them in the district courts alone. Even the Supreme Court, the smallest tier of the system and the one at its apex, carries a backlog approaching ninety-six thousand matters. For an ordinary litigant, a property dispute or a wrongful-dismissal claim can outlast the person who filed it.

A backlog of that magnitude is not an abstraction. It is a daily denial of justice, the thing the legal cliché about delay actually means in practice: witnesses die, evidence decays, the accused sit in pre-trial detention for longer than any sentence they might eventually receive. If there were ever a justice system with a rational, humane case for throwing automation at the problem, for letting an algorithm triage bail applications or predict which cases will settle or score defendants by risk so that scarce judicial attention can be rationed, it is India's. The efficiency argument is not a straw man here. It is a genuine moral claim, and it has powerful advocates inside and outside the judiciary.

India had also already begun, tentatively, to build the tools. In 2021 the Supreme Court launched SUPACE, the Supreme Court Portal for Assistance in Court Efficiency, an AI system intended to help judges sift and extract relevant material from mountains of case data. A year earlier it had introduced SUVAS, the Supreme Court Vidhik Anuvaad Software, a machine-translation engine that now renders judgments and orders between English and nineteen Indian languages, having processed tens of thousands of documents and counting. These were not decision-making systems. They were, deliberately, assistive ones, research and translation aids that left the judge firmly in charge. But they established a direction of travel, and they seeded the institutional appetite, and the technical capacity, for more.

So the 2026 regulations are not the nervous flinch of a system that does not understand the technology. They are the considered position of a judiciary that has used AI, knows what it can do, feels the full weight of the backlog that might tempt it to use AI for more, and has nonetheless decided where the boundary lies. That is what gives the document its force. It is a renunciation made from a position of need, not comfort.

What the Machine Is Forbidden to Touch

The architecture of the prohibition is worth reading closely, because its precision tells you that the drafters were not gesturing vaguely at “ethical AI” but responding to specific, documented failures elsewhere in the world.

The core of it sits in the regulation governing prohibited uses, which opens by stating that the prohibitions that follow “are absolute and non-derogable.” The first forbids training, testing, or refining any AI system on a person's data without prior approval and compliance with data-protection law. The second establishes that no judicial outcome, “including any judgment, order, or finding of fact or law,” may be reached “through Algorithmic Decision-Making alone or solely on the basis of AI-generated information,” and that “the human judicial authority shall be the determinative authority in all adjudicative decisions.” The third permits AI to surface advisory material on adjudicative or sentencing questions only with a mandatory human in the loop, and insists any such output “shall be treated as advisory only and shall be subject to independent judicial evaluation.”

Then comes the clause that reads like a direct rebuttal of a specific foreign technology. “No AI System shall be used for Risk Scoring for any purpose in Court processes, including the assessment of flight risk, prediction of recidivism, evaluation of bail eligibility, or determination of the credibility of parties or witnesses.” The regulations even define the term with forensic care: Risk Scoring means “the use of an AI System to assign a numerical or categorical score to an individual that purports to estimate the probability of that person engaging in a specified future behaviour, including the commission of an offence, recidivism, or failure to appear before a Court.” If you wanted to draft a one-sentence ban on the entire category of tools that American and British justice systems have adopted, you could hardly do better.

The remaining prohibitions close the gaps. No opaque or unexplainable system may be used where it materially affects liberty or legal rights. No system may predict, profile, or infer the future conduct of parties, accused persons, or witnesses. No system may be used for surveillance or continuous monitoring of judges, advocates, or litigants on court premises. No AI-generated output may be submitted as evidence without disclosure of its synthetic character. And no system may compromise the confidentiality of judicial deliberations or the independence of the decision-making process.

Around this hard core the draft builds an apparatus of accountability that is, in its own way, as significant as the bans. Every AI tool must clear a Technical and Ethical Impact Assessment, a “structured pre-and-post-deployment evaluation” against the regulations' general principles, before an AI Committee may approve it. Courts must maintain an AI Register documenting every system in use, an AI Incident Database tracking failures, and must publish an Annual Transparency Report. A new Centre of Research and Excellence on Artificial Intelligence, CoRE-AI, staffed by judges, lawyers, technologists, and academics, will conduct ongoing research, evaluate tools, and maintain a centralised record of evaluations. And crucially, the draft pins responsibility squarely on the human: accountability for any decision taken with AI assistance “shall rest exclusively upon” the officer who took it, and it shall not be permissible to invoke “the outputs of an AI System, the opaqueness of a Black Box system, or the occurrence of hallucination” as an excuse for a wrong decision. The machine, in other words, can never be the thing that is blamed. A person always is.

It would be wrong to paint the document as reflexively anti-technology. It contains a “presumption in favour of responsible AI adoption” and a principle, baldly titled “Innovation over Restraint,” directing courts to “actively seek opportunities” to deploy AI that improves access to justice. The drafters want the efficiency. They simply refuse to buy it at the price of the judgement. The whole design is an attempt to have the assistance without the abdication.

The Honesty Requirement

There is a second, quieter innovation in the draft that has drawn less attention than the prohibitions but may prove just as consequential, because it reaches into the daily conduct of every lawyer and litigant rather than the rare drama of a contested sentence. The regulations impose a sweeping duty of disclosure. Where an AI tool “materially assists in any aspect of case management, document analysis, or judicial administration that may affect the conduct of their proceedings,” the court must inform the parties in a timely and accessible way. And where an AI tool is used “by any party or his legal representative in the preparation or submission of any document, pleading, or evidence, the AI-assisted character of such material shall be disclosed to the Court at the time of submission” by way of a formal declaration in a prescribed format.

The teeth are in the clause that follows. If any document turns out to be “fabricated, false, misleading, or inaccurate by reason of its AI-generated character,” the person who submitted it “shall bear full responsibility” and “shall not be entitled to rely upon the character of the AI output as a defence.” There is no hiding behind the machine here either. A lawyer who files a brief stuffed with confabulated citations cannot plead that the chatbot did it; responsibility runs to the human who signed and submitted the work.

This is not an abstract precaution. Across multiple jurisdictions, courts have already disciplined lawyers who submitted filings citing judicial decisions that an AI tool had simply invented, complete with plausible-sounding case names, docket numbers, and quotations, none of which corresponded to any real ruling. The phenomenon has a clinical name, hallucination, and it is a structural feature of how large language models work rather than a bug that a software update will quietly resolve. By demanding disclosure and verification, and by stripping away the AI excuse, India's regulations treat the technology's tendency to fabricate as a permanent hazard to be managed through human responsibility, not a temporary glitch to be waited out. The same instinct runs through the whole document: trust the human, verify the machine, and never let the second stand in for the first.

The draft pairs this with a dedicated AI Content Verification Authority, charged with maintaining the standards and tools for checking AI-generated content placed before a court, and with a requirement that anyone using synthetic data or synthetic information in a proceeding disclose that fact. Taken together, the disclosure architecture treats the courtroom as a space where the provenance of every claim matters, and where the difference between something a person attests to and something a machine generated must never be allowed to blur. It is a recognition that the threat AI poses to justice is not only the dramatic one of a robot judge, but the mundane, pervasive one of fabricated material seeping into the record, dressed in the confident prose that makes a falsehood hard to catch.

The Tool India Was Built to Refuse

To see why that refusal matters, look at what risk scoring has actually done in the jurisdictions that embraced it. The cautionary tale here is not hypothetical. It has a name, COMPAS, and a paper trail.

COMPAS, the Correctional Offender Management Profiling for Alternative Sanctions, is a proprietary risk-assessment instrument developed by the company then known as Northpointe and used across multiple American states to score defendants on their likelihood of reoffending. The tool produces its scores from the answers to a long questionnaire, and those scores have been placed in front of judges making decisions about bail, sentencing, and parole. It is precisely the kind of “numerical or categorical score” estimating “the probability of that person engaging in a specified future behaviour” that India's regulations now define and forbid.

In May 2016 the investigative newsroom ProPublica published an analysis, written by Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner, that became the defining indictment of the technology. Examining the COMPAS scores of more than seven thousand people arrested in Broward County, Florida, and following them over two years to see who actually reoffended, the reporters found a stark racial asymmetry in the tool's errors. Black defendants who did not go on to reoffend were nonetheless labelled high-risk at nearly twice the rate of comparable white defendants, 44.9 per cent against 23.5 per cent. White defendants who did reoffend were disproportionately mislabelled as low-risk. Controlling for prior crimes, age, and gender, the analysis found Black defendants were 77 per cent more likely to be flagged as a higher risk of future violent crime. The company disputed the methodology and the conclusions, and a genuine statistical debate followed about which definition of “fairness” the tool satisfied and which it failed. But the central finding, that the burden of the system's errors fell unequally along racial lines, has shadowed algorithmic risk assessment ever since.

The human face of the problem appeared in ProPublica's reporting in the form of two real people. Brisha Borden, a Black eighteen-year-old who had taken a child's bicycle and scooter and then put them down when confronted, was rated high-risk, an 8 out of 10. Vernon Prater, a white man with prior armed-robbery convictions who was picked up for shoplifting, was rated low-risk, a 3. Over the following years Borden was not charged with any new crime. Prater was sentenced to eight years in prison for a subsequent burglary. The scores had got it exactly backwards, and the pattern was not an accident of two cases but a structural tendency the data laid bare.

The legal system's response to these concerns is, if anything, more troubling than the concerns themselves. In State v. Loomis, decided by the Wisconsin Supreme Court in 2016, a defendant named Eric Loomis challenged his six-year sentence on the ground that the judge's reliance on his COMPAS score violated his right to due process. He argued, among other things, that he could not test the accuracy of a score generated by a proprietary algorithm whose inner workings Northpointe refused to disclose as a trade secret, and that the tool improperly used gender as a factor. The court upheld the use of the score. It acknowledged the opacity, acknowledged the documented risk of racial bias, and required that judges in future be given a written warning about these limitations, and then permitted the practice to continue. The United States Supreme Court declined to hear the appeal, leaving the warning-label compromise in place. A defendant's liberty could turn, in part, on a number produced by a black box that neither he nor the judge was permitted to inspect.

This is the precise scenario India's draft regulations make impossible. Where Wisconsin asked judges to use a proprietary risk score carefully, India forbids the score outright. Where Loomis tolerated opacity with a warning, India's principle on explainability demands that any system materially affecting liberty be capable of an account “that can be understood without requiring specialist technical knowledge,” and bans the opaque ones from that domain altogether. The two systems were looking at the same technology and the same risks. They reached opposite conclusions about whether the risks could be managed or had to be excluded.

Thirteen Hundred Lives a Day

If COMPAS shows the American path, the United Kingdom shows how thoroughly such systems can become invisible furniture, scoring people at industrial scale long after the controversy that should have stopped them has faded into routine.

The instrument here is OASys, the Offender Assessment System, which the Ministry of Justice has used across the prison and probation services of England and Wales since 2001. OASys assesses people for the risk of harm they pose and the likelihood they will reoffend, and feeds the resulting scores into sentence planning, categorisation, and decisions about release and supervision. According to reporting by the civil-liberties organisation Statewatch in April 2025, the system is used to profile more than 1,300 people every single day, and its database held over seven million such risk scores. The system incorporates machine learning, the same family of techniques at issue everywhere else, and Statewatch documented persistent concerns, raised over years, about racial disparities and data inaccuracies in its outputs, concerns that had not prevented its continued daily operation.

The numbers are worth sitting with. Thirteen hundred people a day, every working day, scored by a system whose accuracy and fairness have been repeatedly questioned and whose outputs help determine how long they stay in prison and under what conditions they live afterwards. Seven million scores held in a database. This is not a pilot or a controversial experiment. It is the settled administrative reality of how a major Western democracy assesses the people in its criminal-justice system, and it is precisely the kind of routinised, large-scale, consequence-laden risk scoring that India's regulations place beyond the reach of any court.

It is also, as of this year, being replaced rather than reconsidered. The Ministry of Justice has confirmed that a project called Assess Risks, Needs and Strengths, ARNS, is developing a new digital tool to take over from OASys. An early prototype has been in pilot since December 2024, with a view to a national roll-out during 2026, and the work is being done in-house by a team from Ministry of Justice Digital liaising with Capita, the contractor that currently provides technical support for the older system. Read one way, this is unremarkable, a quarter-century-old piece of government software reaching the end of its life and being succeeded by something better engineered. Read another way, it is the whole argument in miniature. Nowhere in that succession does the prior question get asked: whether the scoring of a person's future by a machine belongs in the determination of their liberty at all. It was not asked when OASys was rolled out and it is not being asked now. What is being procured is a better-built version of the same answer, arriving by the same route the original arrived by, through a project plan and a delivery timetable, as an upgrade rather than a decision.

The contrast is not that one country uses AI in justice and the other does not. Both do. India runs translation and research engines; Britain runs an enormous risk-assessment apparatus. The contrast is about where each has drawn the line between assistance and judgement. Britain has allowed the machine to score the human and let that score shape the human's liberty. India has said the machine may carry the files into the courtroom but may never weigh what is inside them.

The Argument That India's Drafters Did Not Need

There is a tidy version of this story, circulating in the way that tidy versions do, in which India's regulations arrive hand-in-hand with a scholarly consensus that AI should be “categorically barred from domains requiring irreducibly human judgement,” with criminal sentencing offered as the obvious example. The claim is often attached to a specific February 2026 paper deposited on the arXiv preprint server. It is worth pausing on, because it is not accurate, and the inaccuracy is itself instructive.

The paper in question, arXiv:2602.20080, is real. It is titled “The Digital Gorilla: Rebalancing Power in the Age of AI,” written by researchers affiliated with Harvard, and it is a serious piece of work. But it makes no argument about barring AI from sentencing. Its actual thesis is something else entirely: that existing AI governance suffers from an “analogy trap,” mistakenly treating advanced AI systems as conventional products or platforms, when they should instead be understood as a kind of fourth societal actor alongside people, states, and enterprises, requiring a “federalized, polycentric governance architecture” of checks and balances. It is a paper about the distribution of power, not about the limits of machine judgement in the dock. The neat citation that supposedly underwrites India's choice does not say what it is claimed to say.

This matters for two reasons. The first is simply that a good argument does not need a fabricated authority to prop it up, and attaching one to it weakens rather than strengthens the case. The second is more pointed. The way a confident, specific, and false claim about a scholarly source can attach itself to a real policy debate and travel as fact is a small, exact illustration of the very problem the Indian regulations are trying to legislate against. AI systems generate plausible citations that do not hold up; they assert with fluency things that turn out, on inspection, to be confabulated. The courts of several countries have already sanctioned lawyers who filed submissions citing cases that an AI invented and that never existed. A regulation that insists every AI output be “treated as advisory” and “subject to independent” human “evaluation,” and that no AI-generated material be relied upon without verification, is, among other things, a defence against exactly this failure mode. The misattributed paper is not evidence for India's position. It is a live demonstration of why India took it.

Strip away the borrowed authority and the underlying intellectual case stands on its own, and it is an old one. There are domains, the argument runs, in which the thing being asked for is not a prediction but a judgement, and the two are not the same. To decide what sentence a person deserves is not to forecast their future behaviour; it is to weigh their culpability, their circumstances, the gravity of what they did, and the demands of mercy and proportionality, and to take responsibility, as a human holding public authority, for the result. A risk score can tell you, imperfectly and with documented bias, how statistically similar people have behaved. It cannot tell you what this person deserves, because desert is not a fact about the world that can be measured. It is a determination a society entrusts to a person it has authorised to make it. India's regulations encode that distinction in law. They permit the machine to inform and forbid it to decide.

What a Society Reveals by What It Will Not Automate

It is tempting to read all this as a contest between efficiency and caution, with India choosing caution. That framing is too thin. The deeper thing the regulations reveal is a claim about what justice is for, and about who must be answerable when it goes wrong.

Consider the accountability provision again, the one that says a judge may never hide behind the algorithm. In a system where a risk score shapes a sentence, accountability dissolves into a fog. The judge can say the tool flagged the defendant as high-risk; the company can say the judge exercised independent discretion; the tool's logic is a trade secret no one is permitted to examine. Everyone is responsible and therefore no one is, and the defendant is left with a deprivation of liberty that cannot be traced to a decision any identifiable human being is willing to own. The Loomis compromise, use the score but heed the warning, institutionalises exactly this diffusion. India's regulations refuse it. By insisting that the human officer is the “determinative authority” and bears responsibility “exclusively,” they preserve the thing that makes a judgement a judgement rather than an output: a person who must stand behind it and can be held to account for it.

There is a constitutional logic at work here too, one the draft signals by anchoring itself in the Bangalore Principles of Judicial Conduct and in fairness guarantees against discrimination on the grounds of “race, religion, caste, sex, gender, disability, language, economic status.” India is a country whose social fabric is shot through with precisely the categories along which algorithmic risk scoring has been shown to discriminate. A COMPAS-style tool trained on Indian arrest and conviction data would inherit and launder the biases of Indian policing and Indian society as surely as COMPAS inherited America's. The drafters appear to have understood that automating risk assessment in a deeply unequal society does not remove human prejudice from justice; it encodes it, scales it, and wraps it in the false objectivity of a number. Banning the category is a way of refusing that laundering.

There is also a subtler point about the psychology of decision-making that the regulations seem to grasp. When a judge is handed a number, even one she is formally free to disregard, the number exerts a gravitational pull. It anchors. To depart from it requires the judge to second-guess a system marketed as objective and statistically validated, and to do so on the record, exposing herself to the charge that she ignored the data. The “human-in-the-loop” safeguard that other jurisdictions lean on as a sufficient protection often turns out, in practice, to be a human rubber-stamping the loop, because the cost of overriding the machine is borne by the human alone while the machine bears nothing. India's approach sidesteps this trap not by trusting judges to resist the anchor but by removing the anchor from the high-stakes domains altogether. There is no score to defer to, so there is nothing to rubber-stamp.

None of this means the Indian framework is flawless or that its principles will survive contact with implementation. A draft is not a law. Comments were first invited by the twentieth of June, then extended to the fifteenth of July at the request of stakeholders who wanted longer to answer; that window has now closed, and the text sits with its drafters awaiting finalisation, notified nowhere and binding no one. Even once it is settled, it will bite only as and when the Chief Justice of India appoints a commencement date for the Supreme Court and the Chief Justice of each High Court appoints one for the courts beneath it, provision by provision if they choose. There is a great deal of room between a published principle and a working rule, and the text may still change on the way through. The same backlog that gives the renunciation its moral weight will keep generating pressure to relax it, and a regulation that forbids efficient injustice does nothing, by itself, to deliver slow justice faster. The permissible uses, scheduling, triage, research, will have to actually work, and at scale, or the prohibition on the rest will come to feel like a luxury the system cannot afford. The history of high-minded judicial reform in India and elsewhere is littered with principles that were honoured in the gazette and ignored in the courtroom. There is no guarantee this will be different.

But the choice has been made, in writing, at the level of principle, by the highest court of the most populous country on earth, and it points the other way from the prevailing current. The world's wealthier justice systems drifted into algorithmic risk assessment incrementally, tool by tool, procurement by procurement, until thirteen hundred people a day were being scored before anyone had decided, as a matter of principle, that this was the kind of thing a justice system ought to do. India, facing a far greater temptation, stopped to decide first.

The Path India Declined to Take

What can other judicial systems actually learn from this? Not, primarily, the specific prohibitions, though those are instructive. The deeper lesson is procedural and almost philosophical: that the question of whether to let a machine judge a human is too important to be answered by accretion, by a thousand small operational decisions made by procurement officers and pilot programmes, none of which ever quite amounts to a decision at all. The American and British risk-scoring regimes were never the product of a deliberate, public, foundational choice that algorithmic risk assessment belonged in the determination of human liberty. They emerged. India's regulations are the opposite: an attempt to make the foundational choice explicitly, in public, before the tools become load-bearing, and to write the answer down where everyone can read it.

The values embedded in that answer are not hard to name. They are the conviction that justice is an irreducibly human act of judgement rather than a prediction problem to be optimised; that the person deprived of liberty is owed a human being who will take responsibility for the decision; that the appearance of objectivity a number provides is more dangerous, in a system riddled with inequality, than the visible fallibility of a judge who can be questioned, appealed, and held to account; and that efficiency, however urgently needed, is not the supreme value against which all others must yield. These are contestable values. Reasonable people, including those drowning in the backlog India must clear, can disagree about whether the cost is worth it. But India has at least done the thing the rest of us mostly have not: it has stated the values, accepted the cost, and accepted it precisely where the temptation to compromise was strongest.

The line in the draft, “AI may assist judicial processes but cannot replace human judicial authority,” will strike some as obvious and others as naive. It is neither. It is a deliberate act of restraint by an institution that had every incentive to do otherwise, and its significance lies less in the technology it governs than in the question it forces every other judiciary to answer out loud. Sooner or later, every justice system will have to decide what it will not let a machine do. India has decided to decide on purpose. The countries already scoring thirteen hundred lives a day, or feeding proprietary risk numbers to judges behind a warning label, have, so far, mostly decided by not deciding. The most valuable thing about the path India has chosen may simply be that it is a choice, made in daylight, that the rest of the world has been quietly avoiding.

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References

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23. data.gov.uk, “Offender Assessment System (OASys)”. https://www.data.gov.uk/dataset/911acd3c-495f-48ca-88b6-024210868b06/offender-assessment-system-oasys
24. M. Alejandra Parra-Orlandoni, Roxanne A. Schnyder and Christopher J. Mallet, “The Digital Gorilla: Rebalancing Power in the Age of AI”, arXiv:2602.20080, February 2026. https://arxiv.org/abs/2602.20080
25. Harvard Journal of Law & Technology, “Algorithmic Due Process: Mistaken Accountability and Attribution in State v. Loomis”. https://jolt.law.harvard.edu/digest/algorithmic-due-process-mistaken-accountability-and-attribution-in-state-v-loomis-1

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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 clip lasts fifteen seconds. A woman pushes a pram along a sunlit street, then turns, and a man in military uniform steps into the frame to fold her into his arms. The light is warm. The faces are familiar. For the length of a held breath, a family is whole again. Then the man releases the embrace, turns, and begins to climb. A staircase resolves out of the air beneath his boots, rising into a blue sky thick with cloud, and as he ascends he is met by figures with wings. He pauses near the top, looks back once, and is gone into the light.

None of it happened. The man, in reality, vanished at the front in Ukraine, one of tens of thousands of Russian soldiers whose families have never received a body, a grave, or an official word confirming what they already suspect. The street, the embrace, the staircase, the angels: all of it was assembled by a generative artificial intelligence model, prompted by a paying customer and produced, in many cases, in a matter of minutes by a creator who may never have met the dead man or the grieving woman holding the pram.

This is one of the strangest and most quietly devastating cultural phenomena of the fourth year of Russia's full-scale invasion of Ukraine. It is a cottage industry of digital resurrection, and it has grown up in the gap between a state that will not acknowledge its dead and a population that cannot stop grieving them. A BBC investigation published on 14 June 2026 documented the practice in detail: the AI-generated farewell videos, the deepfake photographs, the visual grammar of religious ascent, the creators earning real money from manufactured reunions. The investigation gave the phenomenon a name that captures its peculiar emotional logic. It called it the unfinished goodbye.

What follows is an attempt to take that phenomenon seriously, not as a curiosity or a parade of the grotesque, but as a live experiment in how human beings might grieve in an age when the dead can be made to move and smile and ascend to heaven on demand. Because that is precisely what one of the few credentialed experts on the subject has called it. “We are all in the midst of a technological and cultural experiment,” Katarzyna Nowaczyk-Basińska, a researcher at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, told the BBC. The ethical assessment, she added, is “extremely complex.” She is right on both counts, and the second point is the harder one.

A Death Toll the State Will Not Name

To understand why so many Russian families have turned to a fifteen-second fantasy, you have to understand the scale of the loss and the silence that surrounds it.

Russia does not publish meaningful casualty figures. It has not done so in any honest or sustained way since the invasion began in February 2022. Into that vacuum stepped two independent newsrooms, the exiled Russian outlet Mediazona and the BBC Russian service, which have spent years assembling a verified count of the dead from open sources: obituaries, social media posts by relatives, regional newspaper reports, photographs of fresh graves, statements from local officials who let slip what the Kremlin will not. That painstaking work had confirmed, by early June 2026, more than 225,000 Russian military personnel killed in Ukraine; by the end of that month the tally had passed 227,000. On 3 August 2026 the two newsrooms put it at 236,066 named dead, 2,867 of them added since the previous update a fortnight earlier: more than 86,600 volunteers, 26,200 men recruited from penal colonies, 19,600 mobilised soldiers, 7,465 officers. The count does not pause while it is being read.

These are not estimates. They are named, documented, individually corroborated deaths, and the journalists who compile them are emphatic that the true figure is considerably higher. A separate modelling exercise by Mediazona and the outlet Meduza, drawing on inheritance records, put the probable number of Russian military fatalities at around 352,000 over four years. The confirmed count is a floor, not a ceiling. Behind every verified name there are families, and behind an unknown but vast number of the missing there are families who have been given nothing at all: no confirmation, no remains, no closure, only an absence where a son or a husband or a father used to be.

This is the soil in which the AI resurrection industry has grown. A state that conscripts and then disappears its young men, that recruits from prisons and remote regions and then declines to count the cost, leaves an enormous population of mourners with no officially sanctioned object for their grief. There is no telegram from the front. Often there is no funeral. The bureaucratic machinery that, in most societies, exists to convert a death into a recognised social fact has been deliberately switched off. And grief, denied its ordinary channels, will find others.

The Grammar of Ascent

The videos and images follow a remarkably consistent visual liturgy, and the consistency is itself revealing.

The typical clip, as documented across multiple accounts of the BBC investigation and earlier reporting by Meduza, runs to a few seconds. A man in uniform appears, often reconstructed from a single photograph supplied by the family. He embraces a wife, a mother, a child. Sometimes he speaks a few prewritten words of farewell. Then he turns and walks away from the camera, and as he walks he begins to climb, mounting an invisible staircase that materialises into a sky of blue and gold. Angels gather around him. He becomes one of them, or is received by them, and disappears upward into the light.

It is worth pausing on how legible this iconography is. Nothing about it is accidental. The ascending staircase, the cloud, the winged figures, the backward glance: these are the inherited images of Christian and folk-Orthodox eschatology, the visual vocabulary of icons and funerary art, repurposed and animated by a diffusion model. The technology is brand new. The symbolism is a thousand years old. What the AI does is collapse the distance between them, taking the abstract consolations of religious imagery (your loved one is at peace, he has gone to a better place, he is watching over you) and rendering them as a literal, moving, photorealistic event starring the actual face of the actual dead man.

The images do not stay on screens. The BBC documented their migration into the physical infrastructure of mourning. AI-generated portraits have been displayed at funerals. They have been etched onto headstones, fixing a synthetic image of the deceased as the permanent public marker of a real grave. One woman, the investigation reported, hung two AI-generated images of her late husband in her bedroom, turning the most intimate space in her home into a small private shrine to a man rendered by software. The dead, in this telling, are not merely remembered. They are reissued, in a flattering and consoling edition, and installed in the places where the living sleep and pray and say goodbye.

There is one more feature of these videos that cannot go unmentioned, because it changes their meaning entirely. They are not neutral. The man is always a hero. The war that killed him is never shown. There are no ruined Ukrainian cities in these clips, no civilian dead, no acknowledgement of the cause for which the soldier was sent to die. The angelic ascent sanctifies not only the man but the manner of his death. To Ukrainians who encounter this content online, and many do, the effect is grotesque: the aggressor's foot soldiers, beatified by algorithm, floating heavenward while the country they invaded burns out of frame. Grief and propaganda are braided together here so tightly that it is not always possible to say where one ends and the other begins.

The People Who Make the Dead Walk

The most uncomfortable fact about the unfinished goodbye is that it is a business, and a reasonably good one.

The BBC investigation identified real creators earning real money. One, who works under the name Ulyana Lebed and is herself married to a serviceman, was reported to earn between 150,000 and 200,000 roubles a month, roughly the equivalent of one and a half to two thousand pounds, by producing this content at volume; she told the BBC she works through between forty and fifty photographs a day. That is approximately double the average monthly wage in Russia. She charges, like many in the field, somewhere between a few hundred and ten thousand roubles per piece. The margins are extraordinary, because the marginal cost of generating an image with a commercial AI tool is close to nothing; the BBC's Russian service estimated that the production cost of some of these videos can be thirty roubles or less. Other creators in the reporting, including a young woman known online as Katya Jin (whose own husband is among the missing) and another named Anna Korableva, have built substantial audiences around the work.

It would be easy, and lazy, to cast these creators as simple ghouls cashing in on a nation's bereavement. The reality the reporting describes is more tangled. Korableva, who frames her work explicitly around helping people address “unfinished farewells,” told reporters that “in the first months of working on these videos, I cried almost every day.” Lebed is herself the wife of a soldier and so sits inside the very community she sells to. These are not detached operators in some distant content farm. Many are themselves embedded in the grief economy of wartime Russia, simultaneously its merchants and its members.

But the commercial frame is exactly what makes the phenomenon so difficult, and it is where the absence of any regulatory or clinical scaffolding becomes most glaring. Consider what is missing from every one of these transactions. The deceased person's consent was never sought; he cannot give it, and in the chaos of a disappearance at the front he was never asked how he might wish to be remembered, let alone whether he would consent to being animated into an angel for a fee. No grief counsellor reviewed the purchase. No clinician assessed whether the bereaved customer was in a psychological state where a photorealistic simulation of her dead son would help or harm her. There is no aftercare. The creator delivers the file and the relationship ends. What the buyer does with a moving image of a loved one ascending to heaven, how she integrates it into a grieving process that may already be going badly, is nobody's professional responsibility. It is a pure market transaction conducted over one of the most psychologically fraught events a human being can experience.

This is not a uniquely Russian problem, though the Russian case is uniquely stark. The phenomenon sits at the leading edge of what researchers have begun to call the digital afterlife industry, a global market for products that simulate, preserve, or reanimate the dead. Analysts project that the broader digital legacy and afterlife sector, valued in the low tens of billions of dollars in the mid-2020s, could grow several-fold within a decade. Western firms have already begun probing the commercial logic of grief. The founder of one American service that builds simulations of deceased relatives has openly discussed integrating advertising into the conversations users have with the dead, including the prospect of making non-paying users watch an advert before speaking to a simulation of a lost loved one, or having the simulation itself deliver targeted marketing mid-conversation. The Russian farewell videos are a wartime, downmarket, hyper-accelerated version of a temptation that the entire industry faces: once you can manufacture the presence of the dead, you can sell it, and once you can sell it, the incentives of the marketplace begin to shape how an entire culture mourns.

What We Actually Know About Grieving With Machines, Which Is Very Little

Here is the central, vertiginous problem. We are deploying these tools at scale, in a population traumatised by war, with essentially no evidence about what they do to the people who use them.

The clinical literature on grief is not silent, and it is worth bringing to bear because it cuts in two directions at once. For most of the twentieth century, the dominant Western model of healthy mourning was one of detachment: the bereaved person was meant to “let go,” to sever the bond, to relocate their emotional energy onto the living and thereby “move on.” That model was overturned in the 1990s by the continuing bonds theory, set out in the influential 1996 volume edited by the psychologists Dennis Klass, Phyllis Silverman, and Steven Nickman. Their argument, drawn from close study of how people actually grieve, was that maintaining an ongoing relationship with the dead is not pathological at all. It is normal, often healthy, and a feature of grieving across most human cultures. The widow who talks to her husband's photograph, the parent who keeps a child's room intact, the family that sets a place at the table: these are not failures of mourning but a natural reconfiguration of a bond that does not so much end as change form.

On the face of it, continuing bonds theory might seem to vindicate the AI farewell video. If maintaining a connection to the dead is healthy, what could be wrong with a tool that vividly sustains that connection? But the theory contains a crucial proviso that the technology threatens to violate. A healthy continuing bond is built on the truth of the loss. The widow knows her husband is dead even as she talks to his photograph. The bond continues, but it continues as a relationship with someone who is gone. The danger of a photorealistic, animated, speaking simulation is that it can blur exactly the boundary that healthy grieving depends on, the boundary between presence and absence, between memory and hallucination.

This is the boundary that one bereaved mother put her finger on with brutal clarity. Asked whether the technology had helped her, she told the BBC: “Could technology help me accept that I will never hug my son again? No. It's an illusion.” It is hard to imagine a more precise lay diagnosis. The work of grief, in almost every clinical model, requires the acceptance of an irreversible reality. An illusion of reunion does not perform that work. At best it postpones it. At worst it substitutes for it, offering the feeling of contact without the fact of it, and in doing so risks holding the griever in a permanent present tense where the loved one is always about to walk back through the door.

The risk has a clinical name. Since the publication of the DSM-5-TR in March 2022, the American Psychiatric Association has formally recognised prolonged grief disorder, a condition in which acute grief remains intensely distressing and disabling beyond twelve months after a death, marked by persistent yearning, preoccupation with the deceased, and an inability to reintegrate into ordinary life. Studies suggest it affects somewhere in the region of five to seven per cent of bereaved people. No one yet knows whether immersive AI simulations of the dead increase that risk, but the mechanism by which they might is not hard to sketch. A technology whose entire emotional appeal is the suspension of the fact of death is, almost by definition, in tension with a recovery process whose first requirement is the acceptance of that fact.

And then there is the opposite possibility, which the honest commentator has to hold in mind at the same time. For some users, the videos may genuinely help. One Russian widow described how AI gave her “an opportunity to feel close to her husband again, nearly two years after his death,” a way of carrying a continuing bond rather than dissolving in unresolved loss. For a mother who has been denied a body, a grave, and any official word that her son is dead, an image of him ascending peacefully to heaven may supply something the state has cruelly withheld: a ritual, a marker, a place to put the grief. The point is not that the technology is uniformly harmful. The point is that we do not know, that it almost certainly helps some and harms others, and that at present there is no way to tell which is which, and no one whose job it is to find out.

The Academic Frontier, Such As It Is

The research community has begun, belatedly, to map this territory, and what is striking is how candidly its leading voices admit how much remains dark.

A paper published in Frontiers in Psychology on 10 December 2025 offers one of the more rigorous recent attempts. Authored by a team of researchers (Mengxi Fu, Hanyu Xiao, Haoyi Ruan, Yuran Lin, and Xiangyu Dong), it examined grief in virtual environments by staging avatar-based memorial ceremonies on the social platform VRChat and interviewing sixteen participants aged between eighteen and sixty-five. The study is not about war videos, but its findings are directly relevant. The researchers proposed what they call a Hybrid Grief Model of Virtual Mourning, describing digital grief not as a tidy linear progression through stages but as a restless oscillation between emotional states, mediated throughout by technology and shot through with the impulse to maintain continuing bonds.

Two of the paper's conclusions matter especially here. The first is a clear generational fault line. Younger participants tended to approach loss in virtual settings with humour, narrative play, and a flexible, experimental attitude toward identity, while older participants engaged with more solemnity, more ritual structure, and a stronger pull toward symbolic continuity. If that pattern generalises, it implies that the same AI memorial artefact may land in radically different ways depending on who is grieving, which makes any one-size-fits-all judgement about the technology's effects almost meaningless. The second conclusion is an admission. The authors state plainly that avatar loss “remains an underexplored form of grief,” that “little is known” about how people experience these new modes of mourning, and that the emotional processes involved are not well characterised. This is the considered verdict of specialists actively working in the field: we are flying nearly blind.

The same humility runs through the work of Nowaczyk-Basińska, the Cambridge researcher whose phrase about a “technological and cultural experiment” has become the most quoted line in the coverage. Together with her colleague Tomasz Hollanek at the Leverhulme Centre for the Future of Intelligence, she published a paper in the journal Philosophy and Technology in 2024 with the title “Griefbots, Deadbots, Postmortem Avatars: on Responsible Applications of Generative AI in the Digital Afterlife Industry.” It is among the most serious attempts yet to think through the ethics of simulating the dead, and its central image is haunting in both senses of the word. The researchers warn of the possibility of unwanted digital “hauntings,” in which a simulation of a dead loved one keeps reappearing, sending notifications, or delivering marketing messages dressed up as words from beyond the grave, in ways the bereaved never asked for and cannot easily switch off.

Their recommendations read, in light of the Russian farewell videos, like a catalogue of everything that industry lacks. They argue that re-creation services should secure the mutual consent of both the data donor, the dead person whose likeness is used, and the recipient who interacts with the result. They call for meaningful transparency, so that no one is misled about what they are dealing with. They argue these services should be restricted to adult users. And they insist on the need for dignified ways to retire a simulation, even gesturing toward the idea of “digital funerals” or rituals that would allow a griever to bring the relationship with a deadbot to a close that offers emotional resolution rather than abandonment. Almost none of this exists in the Russian market. The consent of the dead soldier is structurally impossible to obtain. There is no transparency requirement, no age gate, no retirement protocol, no aftercare. The artefact is generated, sold, and abandoned to whatever role the buyer assigns it.

When the Simulated Dead Enter the Public Square

The Russian phenomenon may feel remote, a product of a specific and terrible war. It is not. The capacity to animate the dead is becoming a general feature of the technological landscape, and it is already crossing from the private grief of the bedroom into the shared institutions that depend on a clear distinction between the living and the dead.

The most arresting Western example to date unfolded in an Arizona courtroom in May 2025. Christopher Pelkey, a thirty-seven-year-old army veteran, had been shot and killed in a road rage incident in Chandler, Arizona, in 2021. At the sentencing of the man convicted of killing him, the court permitted something unprecedented in an American courtroom: a victim impact statement delivered by Pelkey himself, four years after his death, in the form of an AI-generated video. His sister, Stacey Wales, and her husband, who work in technology, had trained the simulation on photographs and old footage. On screen, the reconstructed Pelkey addressed his killer directly. “To Gabriel Horcasitas, the man who shot me,” the simulation said, “it is a shame we encountered each other that day in those circumstances.” The presiding judge, Todd Lang of Maricopa County Superior Court, sentenced the defendant to ten and a half years and remarked from the bench that he “loved that AI.” The defence appealed, its attorney Jason Lamm arguing that the trial court erred in allowing the AI-generated video to be played at sentencing at all. The case has now been briefed and argued before the Arizona Court of Appeals, which has yet to rule.

Set the courtroom beside the Russian bedroom and a common thread emerges. In both cases, a grieving family used generative AI to make a dead person say and do things he never said or did, and in both cases the synthetic artefact was granted a kind of authority: emotional authority on the headstone, quasi-evidentiary authority in the court. The technology does not merely console the bereaved in private. It is beginning to insert simulations of the dead into the public processes by which societies establish facts, mark deaths, and render judgements. Each instance is sympathetic on its own terms. The aggregate is a slow erosion of the line between what a dead person actually said and what software can be made to have them say, at precisely the moment when that line matters most.

Who Is Responsible?

This brings us to the question the brief poses most sharply, and the one for which there is currently no satisfactory answer. When the commercial production of AI simulations of the dead becomes the primary available response to a loss the state will not acknowledge, who, if anyone, is responsible for the long-term psychological wellbeing of the people who use these tools?

Run through the candidates and watch each one fall away. The state that sent the soldiers to die and then refused to count them is, in the Russian case, the least plausible guardian imaginable; the entire phenomenon exists because that state abdicated its most basic duties to the dead and the bereaved. The AI creators are unregulated, often untrained, frequently grieving themselves, and bound to their customers by nothing more durable than a single transaction. The platforms on which the videos are made and shared have shown little appetite for policing content that, after all, drives engagement and breaks no obvious rule. The clinical profession that might offer aftercare is nowhere near the point of sale and, in many cases, would not know the artefact existed. And the bereaved themselves, the people least equipped in the rawest hours of loss to make a cool judgement about the psychological wisdom of buying an animated angel bearing their child's face, are left to carry the entire burden of caveat emptor.

The legal landscape offers no rescue either, though it is no longer quite the blank it recently was. On 18 June 2026, four days after the BBC published its investigation, the US Senate Judiciary Committee advanced the NO FAKES Act by unanimous voice vote, sending to the full Senate the first federal intellectual property right over AI-generated replicas of a person's voice and visual likeness: rights inheritable by heirs, executors and estates for at least seventy years after death, with platforms liable for up to $750,000 per offending work. Senators Mike Lee, Ted Cruz and Eric Schmitt registered First Amendment misgivings even so. But set that bill beside the Russian bedroom and it passes clean over the case. It is built to stop the commercial exploitation of a likeness, usually a famous one, and says little about a private recreation commissioned by the very heirs in whom the new right would vest. It is American law besides, of no consequence to a market operating in Russian for Russian families. The vacuum is filling at its commercial edges while its intimate centre stays open, governed by nothing better than a thin and inconsistent patchwork of publicity and personality laws, written long before the technology, that vary enormously in whether they survive a person's death at all, for how long, and with what exceptions. A simulation of the dead falls into the cracks between intellectual property, data protection, and the dignity of the deceased, claiming the protection of none of them with any reliability. In practice this means that the most intimate possible use of a human likeness, the reanimation of someone's dead child, is among the least regulated, governed by less than the law would demand of a company wanting to use that same face to sell soap.

The result is a closed loop of unaccountability. A bereaved person experiences a loss the state denies. The market offers, for a modest fee, a consoling illusion of reunion. The illusion may soothe or may entrench the very inability to accept the loss that grief recovery requires. And there is no one in the chain whose responsibility it is to know the difference or to act on it. The researchers who have studied the field most closely are reduced to calling it an experiment, which is an accurate description and also an indictment, because an experiment conducted on traumatised human subjects at population scale, without consent, without oversight, without aftercare, and without any mechanism for learning from its results, would not pass the ethics board of any university in the world. Yet that is precisely what is being conducted, in the open, on the families of a country's war dead.

The Goodbye That Cannot Be Finished

The deepest cruelty of the unfinished goodbye may be hidden in the phrase itself. The promise of the technology is completion: the farewell you never got to say, the embrace you were denied, the ascent to peace you can now witness. The reality, by the testimony of the very people who buy it, is the opposite. “It's an illusion,” the mother said, and an illusion is by definition something that cannot finish anything, because it never began. The goodbye remains unfinished precisely because the thing performing it is not real, and some part of the griever, the part that hangs two images in the bedroom and visits the headstone, knows this even as another part of her longs to believe.

What the AI farewell videos expose, more than anything, is how much of the human apparatus of mourning was load-bearing all along. The body to be buried, the grave to be visited, the rite performed by a community that gathers and witnesses, the official word that converts a private suspicion into a shared fact: these were never mere formalities. They were the technology, in the oldest sense, by which a society helped its members metabolise loss and rejoin the living. When a state switches off that apparatus, and a market rushes in to replace it with a thirty-rouble fantasy of angels and staircases, something is lost that is harder to name than a soldier. It is the cultural scaffolding of grief itself, the accumulated wisdom of how to say goodbye, dismantled and sold back to the bereaved one heart-rending clip at a time.

We are, as the Cambridge researcher said, all in the midst of a technological and cultural experiment. The Russian families of the missing are simply its first mass cohort, the involuntary subjects of a trial whose results no one is gathering and whose ethics no one cleared. Whether the machines that animate the dead can help us complete our grief or whether they merely sell us the illusion of a goodbye that, by their very nature, can never be finished, is a question the available evidence cannot yet answer. What is clear is that the question is being settled in practice, at scale, in real time, in the bedrooms and graveyards of a country at war, and that no one has been left in charge of the answer.

Sources and References

1. BBC News, “Russian families use AI to 'resurrect' loved ones killed in Ukraine,” 14 June 2026 (republished by The Star, Kenya): https://www.the-star.co.ke/news/world/2026-06-14-russian-families-use-ai-to-resurrect-loved-ones
2. Kyiv Independent, “Over 236,000 Russian soldiers killed in Ukraine identified by media investigation,” 3 August 2026: https://kyivindependent.com/over-236-000-russian-soldiers-killed-in-ukraine-identified-by-media-investigation/
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13. Tomasz Hollanek and Katarzyna Nowaczyk-Basińska, “Griefbots, Deadbots, Postmortem Avatars: on Responsible Applications of Generative AI in the Digital Afterlife Industry,” Philosophy and Technology, 2024: https://link.springer.com/article/10.1007/s13347-024-00744-w
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23. CNN Business, “He was killed in a road rage incident. His family used AI to bring him to the courtroom to address his killer”: https://www.cnn.com/2025/05/09/tech/ai-courtroom-victim-impact-statement-arizona
24. Holland & Knight, “Senate Judiciary Committee Advances Legislation to Protect Name, Image, Likeness and Voice Against Unauthorized AI Use,” June 2026: https://www.hklaw.com/en/insights/publications/2026/06/senate-judiciary-committee-advances-legislation-to-protect-name
25. NPR, “AI 'deadbots' are persuasive — and researchers say they're primed for monetization,” 26 August 2025: https://www.npr.org/2025/08/26/nx-s1-5508355/ai-dead-people-chatbots-videos-parkland-court

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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 last Friday of July 2025, something strange happened to the shape of British internet traffic. It did not announce itself with a press release or a ministerial statement. It showed up instead in the dry telemetry of app stores and sign-up dashboards, in the kind of numbers that usually only matter to growth marketers and venture capitalists. Proton, the Swiss privacy company, watched its hourly UK sign-up rate climb by fourteen hundred per cent above a normal Friday baseline. Across the weekend that followed, the increase settled at around eighteen hundred per cent. Five of the ten most downloaded free apps on Apple's UK store were suddenly virtual private networks. By some estimates, roughly two million VPN downloads landed on British phones in a matter of days.

The trigger was not a scandal, a hack, or a viral panic. It was a law. Specifically, it was the moment the United Kingdom's Online Safety Act stopped being a thing that lawyers discussed at conferences and became a thing that ordinary people bumped into when they tried to open a website. From 25 July 2025, any service that might serve pornography or other content judged harmful to children had to deploy what the regulator calls highly effective age assurance. In practice that meant a face scanned by an algorithm, or a passport photographed and uploaded, or a credit card checked, before a user could proceed. The state had decided to put a bouncer on the door of large parts of the internet. And within hours, millions of adults went looking for a way to climb through the window.

This is a story about that window, and about the people climbing through it, and about what they found on the other side. Because here is the uncomfortable thing that a peer-reviewed study published in June 2026 has now documented in careful detail: a law written to protect children from the worst of the web appears to have nudged a large number of adults, and an unknown number of teenagers, towards a category of software whose own privacy practices range from genuinely excellent to actively predatory. The British government set out to make the internet safer. The measurable, near-term consequence was a surge in demand for tools that route your entire digital life through a third party you have never met, governed by a privacy policy you will never read, based in a jurisdiction you cannot name. Whether that is a tragedy, a farce, or simply the predictable physics of regulation depends a great deal on where you are standing.

A bouncer for the whole internet

To understand how Britain arrived here, you have to take the Online Safety Act seriously on its own terms, because its authors certainly did. The Act received Royal Assent in October 2023 after years of parliamentary wrangling, and it was sold, repeatedly and earnestly, as the legislation that would make the United Kingdom the safest place in the world to be online. It is a genuinely sweeping piece of law. It imposes duties on user-to-user services and search engines to assess and mitigate the risk of illegal content, and a further set of duties to protect children from material that is legal for adults but harmful to minors, a category that includes pornography, content promoting suicide or self-harm, and material glorifying eating disorders.

The Act did not arrive all at once. It came in waves, and the waves matter, because they are the rhythm to which everything else in this story moves. The first major milestone fell on 17 March 2025, when the illegal content duties took effect and platforms were obliged to have completed their risk assessments and begun removing material relating to terrorism, child sexual abuse, fraud, and a long list of other offences. The second came on 25 July 2025, when the Protection of Children Codes came into force and, with them, the requirement that pornography services and other risky platforms verify that their users were adults. The regulator, Ofcom, had published those codes in April, giving platforms a deadline of 24 July to complete their children's risk assessments. The third wave is ongoing, a slow accretion of enforcement actions, investigations, and the looming possibility of the most onerous Category 1 duties being applied to the largest services.

What turned an abstract legal duty into a daily friction for ordinary people was the phrase highly effective age assurance, which Ofcom shortened to the slightly clinical acronym HEAA. The regulator was careful not to prescribe a single method. Instead it set out criteria. An age check has to be technically accurate, reliable, robust, and fair, and it has to be hard to circumvent. Self-declaration, the old tick-box that asked whether you were over eighteen and trusted your answer, was explicitly ruled out as inadequate, as were payment methods that did not actually confirm an adult was present. What remained were methods with real teeth: photographic identity matching, where you upload a government document and a selfie; facial age estimation, where an algorithm studies your face and guesses your age; checks routed through mobile network operators; and verification handled by trusted third parties whose entire business is deciding whether you are old enough to proceed.

It is that last category, the trusted third party, that quietly became the fulcrum of the whole arrangement. Most websites do not want to build a biometric age-estimation pipeline from scratch, and most would rather not store a database of their users' passports, which is the digital equivalent of keeping a vault full of dynamite in the basement. So they outsourced it. A handful of specialist firms now sit between British internet users and the content they are trying to reach, scanning faces and reading documents on behalf of platforms that would rather not touch the data themselves. The state mandated the check. The market supplied the intermediary. And the British public, asked to trust an unfamiliar company with their face or their passport in order to read a Reddit thread or watch a video, did the thing that publics have always done when confronted with a demand they find unreasonable. They looked for a way around it.

The data point that frames everything

The most rigorous account of what happened next comes from a study uploaded to the arXiv preprint server in June 2026, the work of researchers spread across the universities of Edinburgh, Newcastle, and Durham. Its title declines to hedge: Online Safety Regulation Increases Privacy Risk: Evidence from the UK Online Safety Act. The team, which included Dhyey Mehta, Marc Juarez, and Tuğrulcan Elmas at Edinburgh, Eldar Jalilzade, Maksim Kalameyets, and Lei Shi at Newcastle, and Rebecca Owens and Stergios Aidinlis at Durham, set out to measure something that intuition suggested but data had not yet pinned down: did each phase of the Online Safety Act actually change how British people behaved towards privacy tools, and if so, how much, and why?

Their method was a combination of the social and the technical. They mined discussion from VPN-focused and politics-focused communities on Reddit, tracking how often and in what terms British users talked about virtual private networks in a regulatory or privacy context. They cross-referenced this against Google Trends data, and they conducted a structured risk assessment of sixty-nine separate VPN services, grading their logging, tracking, and data-sharing practices. The result was a portrait not just of how much VPN interest grew, but of what kind of VPNs people were reaching for, and what story they were telling themselves as they did it.

The headline numbers are stark, and they line up almost perfectly with the legislative calendar. At the first milestone, the illegal content duties of March 2025, VPN discussion in a regulatory or privacy framing rose by one hundred per cent. At the second, the age verification deadline of July 2025, it rose by two hundred and seventeen per cent. By the third phase, the figure had climbed to four hundred and fifteen per cent. In the more politically engaged corners of Reddit, the increases were steeper still, with rises of two hundred and thirteen per cent, five hundred and forty-five per cent, and four hundred and sixty-four per cent at the successive milestones. Google Trends recorded an eighty-nine per cent spike in VPN searches around the age-verification deadline alone. This was not noise. It was a stepwise, escalating response, each turn of the regulatory ratchet answered by a larger turn of the public towards circumvention. What gives the figures their weight is the sheer regularity of them. A one-off spike could be dismissed as a news cycle, a flurry of curiosity that fades within a week. A response that climbs at every successive milestone, in two separate communities, corroborated by an independent search-trends dataset, is something else. It is a population learning a behaviour and repeating it, with the lesson reinforced each time the law tightened. The researchers were not capturing a moment of panic. They were capturing the formation of a habit.

The most revealing finding, though, was not the magnitude but the motive. The researchers paid close attention to how users framed their own behaviour, and the framing was overwhelmingly defensive rather than acquisitive. People were not, for the most part, talking about VPNs as a way to reach pornography. They were talking about them in the language of privacy, surveillance, and distrust. The recurring villain in these discussions was not the content restriction itself but the age-verification intermediary, the unfamiliar company being handed a face or a passport. Users framed the VPN as a shield against being catalogued, not as a key to a locked door. That distinction is the entire argument of this piece compressed into a single observation. A law aimed at the supply of harmful content provoked a response aimed at the demand for personal privacy. The two were never quite talking about the same thing.

The window everyone climbed through

There is a tidy assumption buried in a lot of online safety policy, which is that the people inconvenienced by a content restriction must be the people who wanted the content. It is a comforting assumption because it lets you dismiss the inconvenienced as a rogue's gallery of would-be transgressors. The Reddit data punctures it. The privacy framing means that a substantial share of the VPN surge came from people who had no particular interest in the restricted material and every interest in not surrendering their identity documents to a company they had never heard of.

This is not paranoia, and it is worth dwelling on why. By the time the age-verification deadline arrived, British users had been handed a steady drip of reasons to distrust the machinery of identity verification. In July 2025, the same month the law took effect, a dating-advice app called Tea suffered a breach that exposed roughly seventy-two thousand user images, including selfies and government identification documents that users had uploaded to prove who they were, after the files were left sitting in a misconfigured cloud storage bucket. In October 2025, Discord disclosed that a breach at one of its third-party support vendors had exposed something in the order of seventy thousand images of government-issued IDs that had been submitted for age verification. The Electronic Frontier Foundation, which had spent years warning that age-verification mandates would turn identity databases into honeypots, marked the Discord episode with a pointedly named award for breaches that vindicated earlier warnings. An earlier breach at the identity-verification vendor AU10TIX had already established the pattern.

So the average British adult, watching the news and then watching a website demand their passport, was not being irrational when they reached for a VPN. They had been handed a working theory of the world, and the theory was that handing your identity documents to an intermediary is a gamble whose downside is permanent and whose upside is the privilege of carrying on as before. A VPN, which makes your traffic appear to originate somewhere outside the United Kingdom and therefore outside the reach of the age check, looked like a rational hedge. The trouble is that the hedge carries its own, less visible, downside.

A shield that may also be a sieve

Here is where the story curdles. A virtual private network does one straightforward thing: it routes all of your internet traffic through a server operated by the VPN provider before sending it on to its destination. To the websites you visit, you appear to be coming from wherever that server sits. To your internet service provider, and to the age-verification systems keyed to UK addresses, your traffic becomes opaque. This is precisely why a VPN defeats a geographically scoped age check. But the same mechanism that hides your traffic from everyone else also exposes it, completely and intimately, to the VPN provider. You have not eliminated the trusted intermediary. You have swapped a regulated one, operating under the gaze of Ofcom and the Information Commissioner's Office, for one of your own choosing, operating under nobody's gaze in particular.

For a paid service with an independently audited no-logs policy, run by a company whose entire reputation depends on not betraying its users, that swap can be a genuine improvement in privacy. For a free VPN downloaded in a hurry from an app store, the swap can be catastrophic. The research literature on free VPNs is bleak and consistent. One widely cited investigation tested the hundred most popular free Android VPN apps, a set accounting for more than two and a half billion installs worldwide, and found that seventy-one per cent shared personal user data with third parties while eighty-eight per cent leaked data outright. The academic work is no kinder. In February 2026, researchers from the University of New Mexico, the University of Michigan, and IIT Delhi presented a study at the Network and Distributed System Security Symposium built on an automated auditing tool they called MVPNalyzer, which they turned on two hundred and eighty-one free Android VPN apps drawn from the Google Play Store. Sixty-one transmitted data without encryption at all. Five sent sensitive VPN configuration files in cleartext, leaving the tunnel itself open to hijacking. Twenty-nine leaked browser or DNS traffic outside the encrypted tunnel, which is to say they leaked precisely the record the user had installed them to conceal. And seventy-six sent device identifiers, Android's Advertising ID among them, to third parties, handing over exactly the handle needed to track and fingerprint a person across the web. The business model is the problem. Running a global network of servers is expensive, and a service that charges you nothing has to make its money somewhere. Very often, the product being sold is you: your browsing history, your connection timestamps, your location, packaged and passed to advertisers or data brokers, sometimes in flat contradiction of the marketing copy that lured you in.

This is the cruel symmetry at the heart of the whole episode. The Online Safety Act tried to remove one set of intermediaries from a position of trust, the platforms that had been allowing children to stumble into harmful content, and it inadvertently created demand for a different set of intermediaries who are subject to far less scrutiny. The age-verification firms, whatever their flaws, operate inside a regulatory perimeter. They can be audited, fined, and hauled in front of a select committee. A free VPN headquartered in an opaque jurisdiction with a privacy policy carved full of exceptions answers to no one a British user can name.

The arXiv study is careful here, and its care is to its credit. The researchers did not find evidence that the Act was disproportionately funnelling people towards the very worst providers. When they mapped the surge in demand across their sixty-nine graded services, the increase spread across low-risk, medium-risk, and high-risk VPNs in a relatively stable distribution. In other words, the regulation did not act as a malicious filter steering the public towards the most dangerous tools. But that nuance cuts both ways. It means a substantial number of people did end up at higher-risk providers, simply because higher-risk providers got their share of a vastly enlarged pie. When you grow the total demand for a category of software by hundreds of per cent, and a known fraction of that category is dangerous, you have manufactured a great deal of new exposure even if the proportions never shifted. The researchers framed their conclusion precisely: online safety regulation can create secondary privacy costs even without disproportionately pushing users towards higher-risk providers. The cost is real. It is just distributed quietly, across millions of individual decisions, rather than announced in a headline.

The teenagers nobody planned for

The group whose position is most exposed, and least examined, is the one the law was built to protect. Consider the logic from a fifteen-year-old's point of view. The entire point of the age check is to stop them reaching certain content. A VPN, by relocating their apparent position outside the United Kingdom, makes the check vanish. Teenagers are, as a class, more comfortable with this kind of technical workaround than the adults legislating over their heads, and they have every incentive to deploy it. The age gate, in other words, does not merely fail to stop a determined minor. It actively teaches the minor that the solution to an online obstacle is to install a piece of intermediary software and route everything through it.

That lesson has consequences that outlast any single blocked website. A teenager who installs a free VPN to slip past an age check is now funnelling their entire mobile internet life, their messages, their searches, their logins, their location, through a third party whose data practices they cannot assess and whose jurisdiction they cannot identify. The state set out to reduce the harms a child encounters online and may, at the margin, have introduced a new and durable one: a generation conditioned to treat unvetted privacy intermediaries as the normal cost of getting online, and to make that trade reflexively, at speed, under irritation, with no capacity to tell the audited Swiss service from the data-harvesting clone with a near-identical icon.

This is the part of the story that the original policy debate never really priced in. The Act's defenders tend to argue, reasonably, that no measure is perfect and that raising friction still reduces casual exposure, that a gate a determined teenager can climb over still keeps out the child who wanders past. There is truth in that. Friction is a real lever and not every minor will reach for a VPN. But the arXiv data suggests the friction is doing something more complicated than simply deterring. It is reshaping behaviour, and the reshaped behaviour has its own harms, and those harms land on the same population the law named as its beneficiary. A safety measure that creates a new safety problem for the people it is meant to safeguard is not obviously a net gain. The honest answer is that nobody modelled it, and the country is now running the experiment on its own children in real time. There is a further wrinkle that the design of the law all but guarantees. A child who routes their traffic through a VPN to defeat the age gate does not only defeat the age gate. They also defeat every other protection that the same geographic signalling underpins, from regional content controls to the platform's own attempts to tailor a safer experience to a UK minor. The VPN does not surgically remove the one restriction the teenager objected to. It dissolves the platform's ability to know where the user is at all, and a great deal of online child safety, for better or worse, is built on exactly that knowledge. In trying to wall off a single category of content, the law may have handed minors a tool that switches off the lights in the entire building.

The politics of a law that will not sit still

None of this has played out in a vacuum. By the time the VPN numbers were spiking, the Online Safety Act had already become one of the most contested pieces of legislation in recent British political life. A parliamentary petition demanding its repeal, launched in July 2025 as the age checks bit, gathered more than five hundred and fifty thousand signatures, comfortably clearing the threshold that forces a Commons debate. That debate took place in Westminster Hall in December 2025. Members of Parliament declined to repeal the Act, but the very fact of the debate was a measure of how much public temperature the law had generated in under half a year.

The criticism arrived from directions that rarely agree on anything. Civil liberties organisations including the Open Rights Group, Big Brother Watch, and Index on Censorship, alongside the Electronic Frontier Foundation, called publicly for the Act to be reformed or repealed, warning about its implications for privacy and free expression. From the populist right, Reform UK denounced it as censorship. Critics catalogued a sprawl of seemingly absurd over-blocking, with users reporting that the better-safe-than-sorry instinct of platforms terrified of eighteen-million-pound fines had led to the age-gating of material that no reasonable person would call harmful, from particular Spotify playlists to, in one widely circulated example, the recording of a Member of Parliament's own speech about child sexual abuse. When the machinery built to protect children blocks a speech about protecting children, the gap between intention and execution has become hard to ignore.

The Wikimedia Foundation, which operates Wikipedia, took its objection to the High Court, arguing that if the encyclopaedia were swept into the most stringent Category 1 duties it might have to verify the identities of its contributors, something it said would force it to cut off large numbers of British users rather than compromise the privacy of the volunteers who build it. The court rejected the challenge in 2025, though the judge left the door open for a fresh case should Ofcom actually try to classify Wikipedia as a Category 1 service. In July 2026 that question was answered, provisionally. Ofcom determined on the tenth of the month that Wikipedia did not currently meet the criteria for Category 1 status, sparing it the strictest duties, contributor identity verification among them. But the reprieve arrived with a caveat more revealing than the decision itself. Ofcom placed the encyclopaedia on a watch list and retains the discretion to revisit the classification whenever it chooses, which is why the Wikimedia Foundation continues to treat future Category 1 designation as a live threat rather than a closed chapter. It is a telling vignette. One of the most benign and valuable resources on the internet found itself contemplating whether it could continue to serve the British public at all, not because of anything it hosts, but because of the identity-verification architecture the law could oblige it to adopt.

Ofcom, for its part, has not been idle, and it would be unfair to paint the regulator as either toothless or reckless. By early 2026 it had opened investigations into more than ninety online services and issued a series of fines. In December 2025 it penalised AVS Group Ltd, which operates eighteen adult websites, a million pounds for failing to put robust age checks in place, and added a further fifty thousand for failing to respond to its information requests. In February 2026 it fined Kick Online Entertainment SA, an adult website operator whose properties include motherless.com, eight hundred thousand pounds on the same grounds, with thirty thousand more for ignoring information requests. The regulator is plainly enforcing the law it was handed. The question this story raises is not whether Ofcom is doing its job. It is whether the job, as defined, produces the safety it promises or merely relocates the risk somewhere harder to see.

Part of that answer arrived in July 2026, and it deserves stating plainly because it cuts against the grain of this article. On the fifteenth of that month Ofcom published its statutory report on the use of age assurance, and the findings were not the damp squib the law's critics had forecast. Across a sample of thirty-two online services, more than sixty-nine million age checks were completed in the second half of 2025, a twenty-threefold increase on the previous six months. The proportion of children who met a highly effective age check when asked to verify their age rose from twenty-five per cent to forty-three per cent between July 2025 and January 2026. Ofcom's conclusion was that when they are implemented properly, highly effective age checks work, though it warned that tougher action was still needed and singled out social media platforms as the laggards, the services that had proved most adept at avoiding implementation altogether. That evidence has to be taken seriously, and taking it seriously does not dissolve the argument of this piece. It sharpens it. The gates are being built, and where they are built they do what gates do. The claim here was never that the Act achieves nothing. It is that its ledger has a second column nobody totals, and the column has been filling at roughly the same rate as the first. Both things are happening. Only one of them appears in the regulator's report.

Who pays when the model is wrong

Step back from the detail and a pattern emerges that should be familiar to anyone who has watched regulators try to govern the internet. The Online Safety Act treats online harm as a problem of supply. There is bad content; children reach it; therefore the law should make platforms build gates so children cannot reach it. This is intuitive, it polls well, and it is not entirely wrong. But it models human beings as static targets of content rather than as adaptive agents who respond to incentives, and that omission is where the trouble breeds. Put a gate in front of a determined population and the population does not evaporate. It routes around the gate, and the route it chooses has consequences the gate-builder did not intend and frequently cannot see.

The arXiv researchers gave this dynamic a precise name when they described the secondary privacy costs of online safety regulation. The phrase is dry, but the thing it describes is not. The cost is borne, first, by the privacy-conscious adult who only ever wanted to read a forum without photographing their passport and who now routes their traffic through an intermediary they cannot vet. It is borne, second, by the teenager the law was built to protect, who absorbs, young, the habit of trading data for access without understanding the terms. It is borne, third, by everyone whose identity documents now sit in the databases of age-verification vendors that have already demonstrated their capacity to leak. And it is borne, finally and most diffusely, by the public conversation itself, handed a law so sprawling and so prone to over-blocking that it manufactures distrust faster than it manufactures safety.

The impulse behind the Act was never cynical, and a government that did nothing would also bear responsibility for the harms that nothing allowed. The case for action is strong. The difficulty is narrower and more technical, and for that reason easier to miss in a debate conducted in the language of moral urgency. It is that the specific mechanism chosen, geographically scoped age gates enforced through identity-revealing intermediaries, has a predictable failure mode, and the failure mode transfers risk from a regulated sphere to an unregulated one while presenting itself as a reduction in risk overall.

The cleanest summary of the gap between intention and consequence is this. The Online Safety Act intended to reduce the harm British internet users encounter. Its measurable near-term effect was a stepwise surge in the adoption of privacy tools whose risk profile the public is poorly equipped to judge, undertaken largely by people who were never the target, framed not as a hunger for forbidden content but as a flight from surveillance. It is the law working and producing a second-order harm at the same time, the way a dam that stops a flood can also drown the valley behind it.

The state has now noticed the window. Ofcom has confirmed that it is monitoring UK VPN usage through a third-party monitoring tool, prompted by concern that VPNs are being used to circumvent the Act, and it has said it will keep gathering information to guide its thinking on whether there is a need for further action in this area. It is widening the evidence base too, putting the question to youth panels and surveying families on how familiar they are with the software. The limits are real. VPNs cannot themselves be blocked under the Act, and a ban on apps promoting their use is not being considered. But the shape of the thing is unmistakable. A law written to close one door has produced a regulator studying the window, and the next move in a supply-side model of harm is always to look for a way to close that too. Which would, in turn, produce a new window. This is how ratchets work.

What would a better version look like? The study's authors do not prescribe one, and neither, honestly, can a single article. But the shape of the problem points at the shape of the answer. Age assurance that does not require revealing identity, the privacy-preserving cryptographic approaches that prove you are over eighteen without disclosing who you are or storing what you showed, would remove much of the rational basis for the flight to VPNs in the first place. So would a regulatory regime that treated the privacy of the verifying adult as a first-order design constraint rather than an afterthought, and that recognised the data risks of the circumvention it provokes as part of its own ledger rather than someone else's problem. The technology to verify age without harvesting identity exists. The current implementation, for the most part, does not use it, and the gap between what is possible and what was built is the gap into which two million VPN downloads disappeared in a single July weekend.

The lesson is not that Britain should not try to protect children online. It is that protection designed without a model of how people actually behave will keep generating these unhappy surprises, where the intended beneficiaries learn new bad habits and the unintended bystanders pay the bill. The internet is not a passive surface onto which a regulator can paint a rule and watch it set. It is a system full of agents who respond, adapt, and route around obstruction, and the only regulation that survives contact with that reality is the kind written by people who expected it. The window through which Britain climbed in July 2025 is still open. The question that should haunt the next iteration of online safety law is not how to board it up, but why so many ordinary, law-abiding people felt they had no choice but to use it, and what it cost them on the other side.

References

1. ITV News. “VPN downloads spike as UK introduces age checks for adult online content.” 28 July 2025. https://www.itv.com/news/2025-07-28/vpn-downloads-spike-as-uk-introduces-age-checks-for-adult-online-content
2. TechRadar. “VPN demand skyrockets in the UK as age verification checks are enforced.” July 2025. https://www.techradar.com/vpn/vpn-privacy-security/vpn-demand-skyrockets-in-the-uk-as-age-verification-checks-are-enforced
3. Mehta, Dhyey; Jalilzade, Eldar; Kalameyets, Maksim; Owens, Rebecca; Juarez, Marc; Aidinlis, Stergios; Shi, Lei; Elmas, Tuğrulcan. “Online Safety Regulation Increases Privacy Risk: Evidence from the UK Online Safety Act.” arXiv:2606.05273, 3 June 2026. https://arxiv.org/abs/2606.05273
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23. CMS Law. “2025 UK Online Safety Act: Key Milestones and Future Steps.” December 2025. https://cms-lawnow.com/en/ealerts/2025/12/2025-uk-online-safety-act-round-up
24. Ofcom. “Use of Age Assurance Report 2026.” 15 July 2026. https://www.ofcom.org.uk/online-safety/protecting-children/use-of-age-assurance-report-2026
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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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There is a particular kind of betrayal in being charged more for staying. It runs against every instinct a customer brings to a long relationship with a shop, a bank, an insurer. You assume, reasonably, that years of repeat business buy you something: goodwill, a better deal, the warmth of being a known quantity. The opposite is closer to the truth. In a growing number of markets, the longer you remain a customer, the more a pricing system has learned about you, and the higher the figure it concludes you will tolerate. Loyalty is not rewarded. It is metered, modelled and monetised. The system reads your steadfastness as a signal of your inability to leave, and prices the difference.

This is the strange logic at the heart of what regulators have started calling surveillance pricing, and it is worth pausing on how counter-intuitive it is. Surge pricing, the kind that lifts a taxi fare when it rains, at least responds to something visible and shared. Everyone standing on the same wet pavement faces the same multiplier. Surveillance pricing does something else entirely. It responds to you: to your browsing history, your device, your postcode, your past purchases, the financial circumstances it has inferred about you and, crucially, to how trapped you appear to be. By 2026 this is no longer a thought experiment kept warm in economics seminars. It is law in three American states, the subject of formal orders from the United States Federal Trade Commission to eight companies, and the documented practice of firms from grocery delivery platforms to airlines. The question it forces is not whether the technology works. It plainly does. The question is what happens to the idea of a fair public price, the same number for anyone who wants the same thing, when that idea quietly stops being true.

The Difference Between a Crowd and a Person

The vocabulary matters here, because the industry has a strong interest in blurring it. Dynamic pricing is old, familiar and broadly understood. Airlines have flexed fares with demand since deregulation. Hotels lift rates around conferences. Energy tariffs move with wholesale markets. What unites these practices is that the price, however much it jumps, is a fact about the market at a given moment. It is aimed at a condition, not a customer. Two strangers booking the same seat at the same second see the same number.

Surveillance pricing breaks that symmetry. It is the calculation turned inward, away from the conditions of supply and demand and towards the individual standing in front of the screen. The Federal Trade Commission, in the issue spotlight it published in January 2025, drew the line precisely: instead of a price being a static feature of a product, the same product could carry a different price for different people based on consumer data, behaviours, location, time and purchase channel. The agency described an opaque market of third-party intermediaries that use advanced algorithms, artificial intelligence and personal information to categorise individuals and set a targeted price for each. The signals it documented were granular to the point of intimacy: not merely what you bought, but the movement of your cursor across a page, the products you placed in a basket and then abandoned. One example named in the FTC's findings was a cosmetics company tailoring promotions to a customer's skin type and skin tone.

The distinction is not pedantry. It is the whole moral architecture of the thing. A dynamic price is a message the market sends to everyone. A surveillance price is a private message addressed to you alone, written from a profile you cannot read, by a system that knows things about you that you have not agreed to share and may not even know about yourself. The crowd has become a person, and the person has become a target.

The Loyalty Penalty, and Why Britain Saw It First

If you want to understand surveillance pricing before the algorithms arrived, look at British insurance. For years the home and motor insurance markets ran on a practice the industry euphemistically called price walking, and which everyone else called the loyalty penalty. The mechanism was simple and cynical. Insurers offered cheap, often loss-making premiums to new customers to win them, then ratcheted the price upward at each annual renewal, year after year, betting that inertia, busyness or simple trust would stop the customer from shopping around. The longer you stayed, the more you paid, not because your risk had risen but because your loyalty had been identified as exploitable.

The scale of it was extraordinary. In September 2018, Citizens Advice lodged a super-complaint with the Competition and Markets Authority arguing that across five essential markets, mobile, broadband, home insurance, mortgages and savings, British consumers were losing around 4.1 billion pounds a year to the loyalty penalty. That worked out at roughly 877 pounds per affected household, equal to about three per cent of the average household's annual spending. Eight in ten people, the charity found, were paying a significantly higher price in at least one of those markets simply for staying with their existing supplier. The CMA accepted the substance of the complaint when it responded that December.

The Financial Conduct Authority went furthest in the insurance sector. Its market study concluded that six million existing customers would have saved 1.2 billion pounds in a single year had they paid the average price for their actual risk rather than the inflated renewal figure their loyalty had earned them. From January 2022 the FCA banned the practice outright, requiring insurers to offer renewing customers a price no higher than they would quote a new customer for the equivalent policy. It was, in effect, a legal insistence that loyalty must not be priced as a weakness.

What makes this history so relevant is that it was the analogue rehearsal for the digital performance now underway. Price walking was the loyalty penalty done crudely, by hand, with renewal letters and call-centre scripts. It worked because insurers could make a rough guess about who was unlikely to switch. Surveillance pricing is the same instinct supercharged. Where the old insurer guessed at your inertia from your renewal record, the modern pricing engine infers it from hundreds of signals in real time, and applies the penalty not once a year but at the moment of every transaction. The loyalty penalty was never really about insurance. It was about the seller's ability to read a customer's captivity and charge for it, and that ability has grown beyond all recognition. Even the FCA's ban proved porous. Research by the consumer group Which? in 2024, two years after the rules took effect, found that only around one in five car insurance customers had thought to test their renewal quote against what their own insurer would offer them as a new customer, and that just over half of those who did were quoted less for identical cover. Nor was the leakage always accidental: Direct Line Group, having misapplied the new rules, agreed to hand back an estimated 30 million pounds to renewing customers it had overcharged. The penalty, banned in one form, leaks back in others.

The Oldest Dream, Finally Buildable

Economists have a clinical name for the destination all of this is travelling towards. They call it first-degree, or perfect, price discrimination: the seller's fantasy of charging every buyer the exact maximum they are willing to pay, capturing for the company every last scrap of value that, under a single posted price, would have stayed in the customer's pocket. The theory has sat in textbooks for a century. What it always lacked was a mechanism, because individual human beings are historically rather good at concealing their personal ceiling. The single posted price, the same number on the same shelf for everyone, emerged in part because sellers simply could not do better. It was a technological limit that hardened, over time, into something we mistook for a moral norm.

That limit has now dissolved. Lina Khan, who chaired the FTC from 2021 to 2025, put the shift plainly. The idea of charging every individual person according to their individual willingness to pay, she said, had for the most part been a thought experiment. Through the enormous quantity of behavioural and individualised data that brokers and other firms now collect, she went on, the environment had changed: technologically it had become much more possible to serve every individual person an individual price based on everything they knew about you. Her framing of the consequence is the one that has stuck to the debate. We are moving, she warned, from a transparent market with public prices to an opaque world in which we are alone against secret algorithms.

The first public glimpse of the dream came in September 2000, when an Amazon shopper noticed that deleting the cookies from his browser dropped the price of a DVD. The company had been varying prices according to what it could infer about each customer, and in some cases loyal returning buyers were quoted more than newcomers. The backlash was fierce, Amazon retreated, calling it a random test, and the industry drew a careful lesson. Not that personalised pricing was wrong, but that it must never again be visible. For two decades the dream advanced quietly, dressed in the respectable language of demand and yield, until the data and the machine learning matured enough to make the old fantasy operational.

A TV That Costs More in the Car Park

The clearest way to feel surveillance pricing is to watch a price move not because the market shifted, but because you did. In 2019 investigative journalists at the Minneapolis television station KARE 11 documented exactly this on the Target retailer's app. A Samsung 55-inch smart television was listed at 499.99 dollars when a shopper browsed it from home. When the same shopper drove into the car park of a Target store, the app's price for the identical set leapt to 599.99 dollars. The variable was not supply, demand or time of day. It was geolocation: the app could tell the customer was now physically present, less likely to walk away empty-handed, and it priced that captivity at a hundred dollars. The television was not a fluke. Reporters picked ten products at random, from toys to bottled water to vacuum cleaners, and four of them rose in price once they were inside the shop: an Apple Watch band by two dollars, a Shark vacuum cleaner by forty, a Graco child car seat by seventy-two and a Dyson vacuum cleaner by a hundred and forty-eight. The spread is the instructive part. A system that moves a watch strap by two dollars and a vacuum cleaner by a hundred and forty-eight is not applying a blunt in-store surcharge; it is judging, item by item, what the fact of your presence is worth. Target adjusted the app after the investigation, but the episode remains a near-perfect demonstration of the mechanism, because the only thing that changed about the customer was how committed they appeared.

The principle scales far beyond a single retailer's app. The same season the FTC published its findings, the practice was visibly spreading into one of the most price-sensitive markets there is: air travel. In July 2025, the airline Delta confirmed it was expanding the use of artificial intelligence to set fares, working with an Israeli startup called Fetcherr, with the stated ambition of using AI to price a fifth of its domestic network by the end of the year. Three United States senators, Mark Warner, Ruben Gallego and Richard Blumenthal, wrote to the airline demanding answers. Their letter, dated 22 July 2025, warned of prices tailored to an individual consumer's willingness to pay, and cited Fetcherr's chief executive describing the company's use of all the data it could get its hands on. The senators borrowed Khan's most vivid illustration of the danger: a system that charges a higher fare because it knows the traveller just had a death in the family and needs to fly across the country at any cost. Pricing, in other words, calibrated not to the seat but to the customer's pain point, the precise moment of maximum desperation. Delta denied that it used personal data to set individual fares, insisting the AI forecast demand and market conditions rather than identifying any one passenger's willingness to pay. The denial is instructive, and we will return to it, because the more telling fact is not whether Delta is doing it today but that the infrastructure to do it now exists and is being marketed as a route to extraordinary profit.

Who Actually Said Yes

Strip the practice to its foundation and the question that will not go away is one of consent. Somewhere in the architecture of surveillance pricing there is supposed to be a moment at which the customer agreed to be priced as an individual, to have their financial circumstances inferred and held against them, to be charged according to a secret model of who they are. Find that moment. It does not exist.

What exists instead is a chain of consents to other things entirely. You agreed, in some buried clause, to let an app use your location, ostensibly to find your nearest store. You agreed to cookies so the site would remember your basket. You agreed to a loyalty card so you could collect points. You agreed to terms of service no one reads, in exchange for a service you actually wanted. At no point in any of this did a screen ask whether you would consent to those signals being fused into an estimate of your maximum tolerable price, and at no point would a rational person have said yes if it had. The Federal Trade Commission, examining the third-party intermediaries that build these systems, found a market drawing on data both volunteered by consumers and, more troublingly, inferred about them from first and third-party sources. Inference is the crucial word. You cannot meaningfully consent to a conclusion drawn about you that you never disclosed and may not even know is true.

This is what consumer advocates and privacy lawyers mean when they describe surveillance pricing as a black box. The customer cannot see what data the company holds, cannot see how the price was reached, and cannot see what other shoppers are paying for the same item at the same moment. The ordinary apparatus of fairness, the ability to know the reason for a decision and to contest it, simply never engages, because the reason is locked inside a proprietary model and the decision arrives disguised as a fact of nature. A price, to the person looking at it, appears to be something the world has handed down. It does not look like a profile, an accusation or a bet. But that, increasingly, is what it is.

The asymmetry is total. The seller knows the product's cost, the price it is showing you, the prices it is showing everyone else, the model that produced your figure and the data that fed the model. You know one number. You cannot tell whether personalisation is even happening, because a personalised price and a public one look identical: both are simply digits on a screen. The market was supposed to be an information system, aggregating dispersed knowledge into a public signal that let strangers coordinate their behaviour. Surveillance pricing inverts that function. It turns the price from a signal the market sends to you into a signal the seller secretly extracts from you, while you carry on reading the number as though it still carried its old, shared meaning.

What the Models Choose to Notice

There is a comforting story the industry tells about all this, and it deserves a fair hearing before it is dismantled. In theory, the argument runs, the ability to price each customer individually can expand a market rather than merely milk it. A seller who can identify price-sensitive buyers can profitably offer them a discount that brings them into the market, while charging more to those who can comfortably pay. On a whiteboard this looks almost progressive, a kind of automated means-testing that funds cheaper goods for the poor out of the wallets of the rich.

The world runs the logic in reverse. The signals a machine-learning system finds most useful for estimating willingness to pay are, with grim reliability, the same signals that track vulnerability and constraint. A shopper in an area with no rival supermarket within reach has fewer alternatives, and an algorithm can learn to read that absence of competition and charge for it. A household ordering nappies and repeat prescription items has predictable, inelastic demand, and inelasticity is precisely what a pricing model is built to detect and exploit. The customer with limited mobility, least able to drive between shops to compare, is the one least able to escape and therefore, on the model's own cold accounting, the one most worth charging extra. The system does not optimise for fairness. It optimises for revenue. The people with the least room to push back are exactly the people from whom there is most to extract.

A price built from inferred willingness to pay is, in the end, a price built from a model of who you are, and the characteristics that feed such a model are chosen for their predictive power, not their moral acceptability. If income predicts what you will pay, the model uses income, and if it can infer your income from your postcode, your phone and the brands you buy, it is charging you according to your wealth without ever asking your salary. If household size predicts inelastic demand for essentials, the model uses household size, which can mean that a larger and often poorer family faces systematically higher prices on the very goods it cannot do without. Some of these are characteristics that anti-discrimination law has spent a century learning to treat as illegitimate grounds for differential treatment. None is one that an ordinary shopper would knowingly hand over as a reason to be charged more for milk. And here the loyalty penalty completes its circle, for the most predictive signal of all is your own history. The longer the relationship, the richer the profile, the more confident the model becomes about exactly how much you will bear. Devotion, fed into the machine, comes out as leverage.

The Year the Law Began to Notice

For most of this story the law was simply absent. There is still no comprehensive federal statute in the United States governing surveillance pricing, and enforcement has leaned on the general prohibition of unfair or deceptive practices in Section 5 of the FTC Act, an authority written long before anyone imagined a retailer inferring your income from your shopping habits. That gap has begun to narrow at its edges rather than its centre. On 14 April 2026 the Commission issued an advance notice of proposed rulemaking on unfair and deceptive fee practices in online food and grocery delivery, taking comments until 18 May, and among its questions was whether platforms tell customers when the price in front of them has been personalised; the agency has also signalled work towards a policy statement on when failing to disclose the use of personal data to set an individual price becomes a Section 5 problem. That is rulemaking about disclosure in a single sector, not a prohibition, and it leaves the federal position roughly where it has always been.

The states moved faster, and the first of them moved earlier than the headlines of 2026 suggest. New York's Algorithmic Pricing Disclosure Act took effect on 10 November 2025, obliging a seller that uses personal data to set a price to say so, clearly and at the moment of the price, in a notice reading THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA, with civil penalties of up to a thousand dollars for each violation. The same Act bars the use of protected-class data where the effect is a price different from the one offered to others, and Attorney General Letitia James issued a public warning to New Yorkers as it came into force. What changed in 2026, then, was not that the law noticed surveillance pricing for the first time. It was what the law began to ask of it: the move from compelling a seller to confess the practice to forbidding the practice outright. And the centre of gravity is the food on your table.

Maryland moved first on prohibition. On 28 April 2026, Governor Wes Moore signed House Bill 895, the Protection From Predatory Pricing Act, the first law in the country to restrict surveillance pricing in the grocery sector. It takes effect on 1 October 2026. The statute prohibits food retailers above a certain size and third-party delivery services from using a consumer's personal data to set individualised prices, and pairs this with a rule requiring that displayed prices remain fixed for at least one business day, a direct check on the prospect of electronic shelf labels being used to surge the price of essentials minute by minute. Violations are treated as unfair or deceptive trade practices, carrying civil penalties of up to 10,000 dollars for a first offence and 25,000 dollars for repeat conduct. Enforcement rests solely with the state attorney general, who must give a company 45 days to cure a violation before acting, and consumers have no right to sue on their own behalf.

Those limits are not incidental, and Consumer Reports, while welcoming the law, criticised what it called weak enforcement provisions. Analysts at the International Association of Privacy Professionals catalogued the loopholes plainly: the ban bites only on prices customised for an individual, not on prices set for narrow segments of consumers; it establishes no baseline or standard price against which a personalised figure could be measured; and it explicitly exempts loyalty programmes, the very mechanism through which, in the British insurance saga, the loyalty penalty was built. A law that targets surveillance pricing while carving out loyalty schemes is a law with a gap shaped exactly like the original problem.

Connecticut went broader in scope and slower in delivery. Its Senate Bill 4 passed by overwhelming margins, 141 to 6 in the House and 31 to 4 in the Senate, Governor Ned Lamont signed it on 27 May 2026, and it is now Public Act 26-64. Where Maryland confined itself to groceries, Connecticut's law defines surveillance pricing as setting a customised price for a consumer or group of consumers based on personal data collected through any technology, and bans it across retail sellers and third-party delivery services. Businesses outside those categories that price this way are not forbidden to do it, but must label it, in wording substantially similar to THIS PRICE WAS INCREASED USING YOUR PERSONAL DATA, which is New York's device sharpened by a verb. The act also reaches into the machinery that makes surveillance pricing possible, establishing a state registry of data brokers, building towards a single universal mechanism by which a consumer can demand deletion of their records across the whole industry, and prohibiting the sale of precise geolocation data, the very signal that pushed the price of a television up in a Target car park. By attacking the data supply rather than only the pricing output, Connecticut aimed at the root as well as the fruit. Its timetable, though, is staggered, and the pricing ban is the slowest-arriving piece of it: the privacy act amendments, including the prohibition on selling precise geolocation, begin on 1 October 2026, data brokers must register from 1 January 2027, the surveillance pricing restrictions do not bite until 1 July 2027, and the universal deletion mechanism is not in working use until late 2028. The practice was outlawed in Connecticut with more than a year's notice.

Connecticut also carved out much of what Maryland carved out. Loyalty and rewards programmes are permitted provided their terms are clearly posted and available to every eligible consumer, as are discounts offered to retain a customer, and price differences traceable to legitimate factors: delivery cost, timing, supply and demand, pricing errors, network outages. Insurance-licensed entities sit outside the pricing provisions altogether, as do credit decisions governed by the Fair Credit Reporting Act and the Equal Credit Opportunity Act. The loyalty carve-out, then, is not a Maryland eccentricity. It is a defining feature of this first generation of statutes, and it is the most awkward thing about them, because the loyalty penalty is where the story started. Britain spent the better part of a decade establishing that a rewards relationship is precisely the instrument through which a seller learns to price your captivity, and the first American laws against surveillance pricing have written that instrument into the list of things they do not cover.

New Jersey came third, and went at the thing the other two left alone. Governor Mikie Sherrill signed the Fair Price Protection Act on 23 July 2026, banning personalised algorithmic pricing based on personal data, browsing history, location or protected-class status in the grocery sector, while preserving, as its predecessors did, loyalty programmes and bona fide group discounts. It takes effect on 1 August 2027. What makes it the most consequential of the three is not its reach but its enforcement. Maryland and Connecticut hand the whole job to a state attorney general; New Jersey is the first jurisdiction in the country to give the shopper a weapon of their own. A consumer who believes they were charged more than others for identical groceries because of their personal data may sue under the New Jersey Consumer Fraud Act, individually or as a class, and recover treble damages where the conduct was wilful. The attorney general may still sue as well, for actual damages or fifty thousand dollars for each violation, whichever is the greater. That alters the arithmetic of compliance in a way a cure period and a capped penalty do not, because it turns an invisible practice into a litigable one.

These three are the leading edge of a wave rather than its conclusion, and the wave is now large enough to measure. More than forty bills across at least twenty-four states were introduced in 2026 to regulate personalised algorithmic pricing, already outpacing the whole of 2025. New York, having gone first on disclosure, is poised to go further: its One Fair Price Act, which would ban the practice outright rather than merely oblige a seller to admit to it, passed the legislature on 10 June 2026 and awaits the governor's signature, carrying penalties of five thousand dollars for a first offence and twenty thousand for those that follow. Vermont has taken a narrower route, permitting electronic shelf labels but barring price increases within the day except to correct a documented error, which is Maryland's one-business-day freeze rewritten as a rule about the shelf rather than the shopper. Not all of it will land. The wider point is that the law is responding at the level of state lines while the technology operates at national, indeed global, scale. The result, for the time being, is a map in which the legality of being charged a personalised price for a tin of beans depends substantially on which side of a state border you happen to be standing, and across most of the country the practice remains lawful, undisclosed and unmeasured.

Eight Companies and the Industrial Scale of Knowing

The clearest measure of how far this has already gone came when the FTC stopped theorising and started compelling. In July 2024, using its 6(b) authority, which lets it order companies to hand over internal documents whether or not it suspects wrongdoing, the agency issued orders to eight firms that sit, mostly unseen, in the machinery between retailers and shoppers: the payments network Mastercard, the consultancies Accenture and McKinsey and Company, the banking group JPMorgan Chase, and the pricing and personalisation specialists Revionics, Bloomreach, PROS and Task Software. The orders sought to map an opaque market in which intermediaries claim to use advanced algorithms, artificial intelligence and personal information, location, demographics, credit history, browsing and shopping behaviour, to categorise individuals and set a targeted price for each. The Commission voted unanimously to issue them.

When the preliminary findings landed in January 2025, the picture was not of a fringe experiment but of an established industry. The intermediaries examined were, between them, working with at least 250 clients, selling everything from groceries and clothing to health and beauty products and hardware. The capability to price an individual, in other words, is not waiting to be invented. It has been built, sold and installed at scale, wired into the systems of hundreds of well-known sellers. This is why the denials of any particular company, Delta insisting it does not use personal data for fares, the grocery platform Instacart insisting it merely assigns customers to random pricing cohorts, miss the point that industry analysts keep returning to. Instacart's own year makes the point for them. On 18 December 2025 it agreed to pay sixty million dollars in consumer refunds to settle an FTC lawsuit alleging deceptive delivery-fee and subscription practices, and was reported at the same time to be winding up its AI-based price testing. Neither fact is a finding that it ever charged anyone a surveillance price. Both establish that it had the apparatus and the appetite, and that a federal action was what changed its behaviour. Once the machinery is in place, the difference between not profiling you today and profiling you tomorrow is a single configuration change. The capacity is the danger. A loaded weapon does not need to be aimed at you to have altered the room you are standing in.

When a Price Stops Being a Public Fact

It is tempting to frame all of this as a story about money, about whether you personally end up paying a few pounds more or less. That framing is too small. The deeper casualty of surveillance pricing is something most of us have never had to think about because we have never lived without it: the public price, the single shared number that anyone can see, compare, refuse and undercut.

Consider what that shared number does. When a price is public, a shopper who thinks it too high can walk to a competitor. A rival firm that spots an inflated price can undercut it. A journalist can report it, a regulator can investigate it, and a neighbour can compare notes over the fence. The discipline of the market, the pressure that is supposed to keep prices honest, depends entirely on the price being a fact that more than one person can see. Surveillance pricing dissolves that fact. When your price is calculated for you alone, invisible to everyone else including the competitors, journalists and regulators who might otherwise discipline it, the price becomes a private transaction between you and a model. There is nothing for a rival to undercut, because they cannot see it. There is nothing for you to refuse on principle, because you cannot tell whether anyone else would have been charged the same. The very mechanism that makes markets self-correcting is switched off, quietly, one personalised quote at a time.

This is why disclosure remedies, useful as they are, feel inadequate to the scale of the thing. The European Union, through its Omnibus Directive, already requires a trader to tell a consumer when a price has been personalised on the basis of automated decision-making, and since November 2025 New York has required much the same in blunter language. That requirement has already survived the challenge you would expect. The National Retail Federation sued to have the New York label struck down on First Amendment grounds, arguing the state was compelling retailers to recite a misleading, government-scripted opinion about their own conduct. On 8 October 2025 the United States District Court for the Southern District of New York dismissed the case, holding the compelled disclosure plainly factual and not rendered controversial merely because the regulated business would rather not make it. That is a more important precedent than its narrow subject suggests, because it settles the proposition that a company has no constitutional right to conceal that it priced you from your own data. But being told that a price has been tailored to you, without being told from what data, by what logic, or to what end, restores only a sliver of the lost information. It is like being informed that a stranger has formed an opinion of your character, without being told what the opinion is or what evidence it rests on. The grievance is not merely that the price was personalised. It is that it was built from a portrait of you that you did not sit for, that you cannot see, that you cannot correct, and that may be wrong, unfair, or assembled from precisely the characteristics you would have refused to be judged by.

The ordinary intuition that there is something improper here is not economic naivety. It is an accurate perception that a hard-won feature of how markets are supposed to work is being removed, with nothing adequate put in its place. People do not, on the whole, object to a shop making a profit. They object to being individually appraised by a machine and charged according to a secret estimate of how much they can be made to bear, on the basis of data they did not knowingly surrender, with no way to see the number anyone else is paying. That objection is close to universal, and it is the bedrock on which any durable response will be built.

Switching the Lights On

So what is the customer at the invisible checkout actually to do? Honesty requires conceding that individual self-defence is mostly futile. Clearing your cookies, browsing in a private window, comparing prices across two devices: these are the folk remedies of a cruder era. The man who deleted his cookies on Amazon in 2000 found a cheaper DVD because the discrimination then was primitive. It is not primitive now, and a system that fuses hundreds of inferred signals cannot be reliably evaded by a shopper toggling settings. A person should not have to conduct counter-surveillance against their own grocer to be charged a fair price for bread, and the burden of evasion cannot honestly be placed on them.

The more truthful answer is that this is a collective problem requiring collective tools, and the encouraging part of the story is that those tools are beginning, unevenly, to appear. They arrive in three reinforcing layers. The first is sunlight: the dogged work of investigators, researchers and regulators in dragging an invisible practice into view, because surveillance pricing is a practice that struggles to survive being seen. The journalists who watched a television's price climb in a car park, the FTC compelling eight companies to open their books, the senators demanding answers from an airline, are all performing the same act of making the hidden visible so that it can be argued about. The second layer is disclosure as a legal default, the European and now the New York requirement to declare when a price has been personalised, imperfect but better than silence, and now tested and upheld in court. The third, on which the others depend, is substantive law of the kind Maryland, Connecticut and New Jersey enacted in 2026: rules that do not merely require a disclosure to be ignored but forbid the use of certain data and inferences to price the essentials of life, and give an enforcer real teeth. The loopholes in those first laws, the loyalty-scheme carve-out, the silence about consumer segments, the long lead times before the bans actually bite, show how much remains to be built. One of them has already been closed. The most cited weakness of the Maryland and Connecticut statutes was that they left the shopper with nowhere to go, and New Jersey's private right of action, treble damages and all, answers that objection directly. Everywhere else the enforcement gap remains what it was: one attorney general, a cure period, and a civil penalty a national retailer can treat as a rounding error.

What sets the direction of travel is a fact no amount of optimisation can engineer away. People do not want to be charged according to a secret guess about their desperation, and they do not want their loyalty read as a vulnerability to be priced. The British loyalty penalty was banned not because the maths was wrong but because the public found the principle intolerable once it was named. The same naming is now happening to its digital successor, in legislatures, in regulators and in newsrooms, and it is happening because the practice depends, in the end, on staying unnamed.

That naming is no longer a forecast. On 4 August 2026, two days before this piece went to press, the Senate Judiciary Subcommittee on Crime and Counterterrorism held a hearing titled Your Data, Their Profit: The Consumer Cost of AI Surveillance Pricing. Senator Josh Hawley chaired it, Senator Dick Durbin sat as ranking member, Lindsay Owens of the Groundwork Collaborative gave evidence, and Hawley opened by naming companies out loud: Staples, Target, Lyft, Amazon. Durbin, on a day when the two parties agreed about very little else, said he had little to add to it. A hearing is not a law, and nothing said in that room obliges anyone to change a single price. But the practice has now been described in public, on the record, by people who disagree about nearly everything, which is the condition under which a thing that survives on obscurity begins to lose.

The next time you confirm an order and the total looks about right, hold for a second the thought that you can no longer verify it is right, because right has quietly stopped meaning the same thing for everyone. The price you see may be the price everyone sees. It may be the price reserved for you alone, the loyal customer the system has finally finished learning. That you can no longer tell the difference is the whole of the problem. Reclaiming the ability to tell, the right to a price that is a public fact rather than a private verdict, is the whole of the answer.

References

1. Federal Trade Commission. “FTC Issues Orders to Eight Companies Seeking Information on Surveillance Pricing.” 23 July 2024. https://www.ftc.gov/news-events/news/press-releases/2024/07/ftc-issues-orders-eight-companies-seeking-information-surveillance-pricing
2. Federal Trade Commission. “FTC Surveillance Pricing Study Indicates Wide Range of Personal Data Used to Set Individualized Consumer Prices.” January 2025. https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-surveillance-pricing-study-indicates-wide-range-personal-data-used-set-individualized-consumer
3. Federal Trade Commission. “Issue Spotlight: The Rise of Surveillance Pricing.” January 2025. https://www.ftc.gov/system/files/ftc_gov/pdf/sp6b-issue-spotlight.pdf
4. Fast Company. “Lina Khan says the FTC is investigating surveillance pricing. Here's how that could affect you.” September 2024. https://www.fastcompany.com/91195551/lina-khan-ftc-federal-trade-commission-chair-surveillance-pricing-explained-what-is-it
5. Citizens Advice. “Citizens Advice issues super-complaint as loyal customers continue to be penalised by over £4 billion a year.” 28 September 2018. https://www.citizensadvice.org.uk/about-us/media-centre/press-releases/citizens-advice-issues-super-complaint-as-loyal-customers-continue-to-be-penalised-by-over-4-billion-a-year/
6. Competition and Markets Authority. “Loyalty Penalty: Response to super-complaint.” 19 December 2018. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/766795/Loyalty_Penalty.pdf
7. Financial Conduct Authority. “FCA confirms measures to protect customers from the loyalty penalty in home and motor insurance markets.” 28 May 2021. https://www.fca.org.uk/news/press-releases/fca-confirms-measures-protect-customers-loyalty-penalty-home-motor-insurance-markets
8. Which?. “Despite insurance 'loyalty penalty ban', existing customers are still being disadvantaged versus new customers, Which? finds.” January 2024. https://www.which.co.uk/policy-and-insight/article/despite-insurance-loyalty-penalty-ban-existing-customers-are-still-being-disadvantaged-versus-new-customers-which-finds-atL8s9k5bLmz
9. MoneySavingExpert. “Direct Line Group to pay £30 million in refunds after overcharging some car and home insurance customers.” September 2023. https://www.moneysavingexpert.com/news/2023/09/direct-line-compensation/
10. KARE 11. “Target changes app after KARE 11 investigation.” 2019. https://www.kare11.com/article/money/consumer/target-changes-app-after-kare-11-investigation/89-40ee0e76-9a0f-425d-93b0-b0eb89150f6c
11. United States Senate, Office of Senator Mark R. Warner. “Warner, Colleagues Demand Answers from Delta on Use of AI to Set Individualized Ticket Prices.” 22 July 2025. https://www.warner.senate.gov/public/index.cfm/2025/7/warner-colleagues-demand-answers-from-delta-on-use-of-ai-to-set-individualized-ticket-prices
12. New York State Office of the Attorney General. “Attorney General James Warns New Yorkers About Algorithmic Pricing as New Law Takes Effect.” November 2025. https://ag.ny.gov/press-release/2025/attorney-general-james-warns-new-yorkers-about-algorithmic-pricing-new-law-takes
13. Akerman LLP. “Regulating Protected Class Data and Algorithmically Derived Data: New York's Algorithmic Pricing Act Takes Effect.” November 2025. https://www.akerman.com/en/perspectives/regulating-protected-class-data-and-algorithmically-derived-data-new-yorks-algorithmic-pricing-act-takes-effect.html
14. Troutman Pepper Locke. “New York Algorithmic Pricing Disclosure Act Upheld as Constitutional.” October 2025. https://www.troutmanprivacy.com/2025/10/new-york-algorithmic-pricing-disclosure-act-upheld-as-constitutional/
15. Federal Trade Commission. “Instacart to Pay $60 Million in Consumer Refunds to Settle FTC Lawsuit Over Allegations it Engaged in Deceptive Tactics.” 18 December 2025. https://www.ftc.gov/news-events/news/press-releases/2025/12/instacart-pay-60-million-consumer-refunds-settle-ftc-lawsuit-over-allegations-it-engaged-deceptive
16. The Washington Times. “Instacart to pay $60 million in FTC settlement, end AI-based price tests.” 22 December 2025. https://www.washingtontimes.com/news/2025/dec/22/instacart-pay-60-million-ftc-settlement-end-ai-based-price-tests/
17. Federal Trade Commission. “FTC Seeks Public Comment on Unfair and Deceptive Fee Practices in Online Food and Grocery Delivery Services.” 14 April 2026. https://www.ftc.gov/news-events/news/press-releases/2026/04/ftc-seeks-public-comment-unfair-deceptive-fee-practices-online-food-grocery-delivery-services
18. Office of Governor Wes Moore. “Governor Moore Signs Legislation to Protect Marylanders' Pocketbooks in Grocery Stores.” 28 April 2026. https://governor.maryland.gov/news/press-releases/governor-moore-signs-legislation-protect-marylanders-pocketbooks-grocery-stores-safeguard-voting
19. International Association of Privacy Professionals. “Maryland enacts a first-of-its-kind surveillance pricing law, but there are loopholes.” May 2026. https://iapp.org/news/a/maryland-enacts-a-first-of-its-kind-surveillance-pricing-law-but-there-are-loopholes
20. Consumer Reports. “Consumer Reports statement on the signing of Maryland's Protection from Predatory Pricing Act into law.” April 2026. https://advocacy.consumerreports.org/press_release/consumer-reports-statement-on-the-signing-of-marylands-protection-from-predatory-pricing-act-into-law/
21. Covington and Burling LLP, Inside Privacy. “Connecticut Enacts Omnibus Privacy Law.” June 2026. https://www.insideprivacy.com/state-privacy/connecticut-enacts-omnibus-privacy-law/
22. Skadden, Arps, Slate, Meagher and Flom LLP. “New Jersey Enacts First-in-the-Nation Private Right of Action for Surveillance Pricing.” July 2026. https://www.skadden.com/insights/publications/2026/07/new-jersey-enacts-first-in-the-nation
23. Covington and Burling LLP, Inside Privacy. “State Lawmakers Introduce New Wave of Personalized Algorithmic Pricing Bills.” 2026. https://www.insideprivacy.com/artificial-intelligence/state-lawmakers-introduce-new-wave-of-personalized-algorithmic-pricing-bills/
24. Holland and Knight LLP. “Surveillance Pricing and Dynamic Pricing: What General Counsels Need to Know.” August 2026. https://www.hklaw.com/en/insights/publications/2026/08/surveillance-pricing-and-dynamic-pricing-what-general-counsels
25. United States Senate Committee on the Judiciary. “Your Data, Their Profit: The Consumer Cost of AI Surveillance Pricing.” 4 August 2026. https://www.judiciary.senate.gov/committee-activity/hearings/your-data-their-profit-the-consumer-cost-of-ai-surveillance-pricing

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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 decisive document in the fight over western Coweta County, Georgia, was not a protest sign or a petition. It was a line on a zoning map. For years, 829 acres of pine and hardwood about thirty-five miles south-west of Atlanta had carried the designation Rural Conservation, a category the county's own comprehensive plan reserved for what it called rural places and complete communities. In April 2026, on a three-to-two vote of the county board of commissioners, that land became Industrial. The change took seconds to enact and will take a decade to build out: nine data centre buildings, two substations, a campus valued at seventeen billion dollars and rated at nine hundred megawatts, roughly the output of a nuclear reactor, dropped into a landscape whose defining feature until that afternoon had been that almost nothing was there.

The residents who had spent fifteen months trying to stop it were, by then, extremely well organised. Laura Beth, the chairwoman of a group called Citizens for Rural Coweta, had learned in early 2025 that a hyperscale campus of more than 4.9 million square feet was proposed to run past her back garden, and had started a Facebook group, Stop Project Sail, which TIME reported in July had grown beyond eight thousand members. They read the ordinances. They turned up to every hearing. They had no financial interest in the outcome beyond the water in their wells and the value of their houses. And they lost, by one vote, on a body of five people.

I keep returning to that margin, because it exposes what the national argument about artificial intelligence tends to skip. We debate whether the machines are worth their cost. The question Coweta County actually had to answer was procedural: who possesses the authority to trade a place's groundwater, its grid capacity and its rural character for a facility it did not ask for and will not use? That has an obvious legal answer in most American jurisdictions, and a deeply unsatisfying one. The bodies with formal power to decide are frequently not the bodies containing the people who bear the consequences. The harm has one footprint and the franchise has another, and the AI build-out has made the gap between them impossible to ignore.

A Country Learning to Say No

The gap is being noticed at scale. On 18 July 2026, according to Reuters reporting carried in newspapers across the country, opponents staged 142 protests across 42 states, coordinated by a group calling itself HumansFirst and described as the first nationally organised day of action against the AI build-out. Texas hosted eighteen events, more than any other state; Georgia eleven; California eight. HumansFirst was co-founded by Amy Kremer, a veteran of the Tea Party and of Women for Trump, who has compared the mood to 2009 while insisting the campaign is nonpartisan, and who blames both parties for letting the companies build at this scale without troubling to consult the people who will live beside it.

The protests are the visible edge of something larger. The Information reported in June 2026 that more than three hundred American cities, towns and counties had adopted bans or moratoriums on data centre development. Data Center Watch, the tracker maintained by the AI research firm 10a Labs, counted 833 active opposition groups across forty-nine states by March 2026, up from 396 at the end of 2025, and calculated that in the first quarter of 2026 alone at least seventy-five projects worth a combined 130 billion dollars were blocked or delayed. Taken cumulatively since the start of 2025, the tracker puts the value of projects blocked or delayed at roughly 286 billion dollars.

Public opinion has moved with it. A Reuters/Ipsos poll of 4,531 Americans, published on 11 June 2026, found that just fourteen per cent were comfortable with a data centre being built near them, and that fifty-seven per cent would oppose one in their community, including two-thirds of Democrats and half of Republicans. Only a third agreed that building data centres at the current pace was mainly a good thing. Seventy-seven per cent were worried that AI would make electricity more expensive, and Republicans, Democrats and independents worried about it in close to equal proportion. This is not a partisan split so much as a broad, cross-cutting discomfort, and it has begun to register at the level of state executives. On 14 July 2026, Governor Kathy Hochul signed Executive Order No. 62, imposing what her office described as the first statewide moratorium in the United States on new hyperscale data centres, pausing discretionary state environmental permits for facilities drawing fifty megawatts or more for up to a year while regulators build a framework. New York's average residential electricity price has climbed by close to sixty-eight per cent since 2019. “They drive up costs for local ratepayers,” Hochul said of the facilities, “and I refuse to let those costs get passed down to New Yorkers.”

What happened in New York before that order is the more revealing half of the story, and it is this essay's question in miniature. On 4 June 2026 the state legislature passed the Responsible Data Center Development Act, a one-year moratorium on state permits for data centres drawing twenty megawatts or more, with dedicated rate classes, transparency standards and impact studies attached. The Senate carried it forty-four to sixteen, the Assembly one hundred and two to thirty-nine. Hochul did not sign it. Six weeks later she issued an executive order of her own, and commentators now expect the Act to be vetoed. The thresholds are the tell: the chamber closest to the affected districts drew its line at twenty megawatts, the single executive at fifty, more than twice as permissive. Both instruments are lawful, both get reported as a moratorium, and they are not the same policy. The choice between them was settled by which office holds the pen.

One widely repeated figure deserves care. Reporting on the backlash, including TIME's, has cited electricity price rises of up to 267 per cent in states with dense data centre activity. That number originates in a Bloomberg analysis of wholesale prices at tens of thousands of individual grid nodes, which found that in some locations near major clusters the monthly wholesale cost of power was as much as 267 per cent higher than five years earlier. It is a wholesale, node-level finding rather than a residential bill, and when Senator Elizabeth Warren used it as a household figure in June 2026 PolitiFact rated the claim mostly false. The distinction matters, because the ease with which the number has migrated into the shorthand of the debate is itself a symptom of how little reliable, locally specific information reaches the people asked to accept these facilities.

The Difference Between By-Right and By-Permission

To understand where authority actually sits, you have to understand a distinction almost nobody outside land-use law thinks about until it lands on them. In American zoning, a use is either permitted by right in a given district, meaning a developer who meets the technical standards is approved administratively through site-plan review, or it requires a discretionary act, a rezoning or special exception, which triggers public hearings and a legislative vote.

For most of the industry's history, in most of the places it grew, it was by right. Loudoun County, Virginia, the densest concentration of data centres on earth, permitted them by right across much of its industrially zoned land, which is precisely how the corridor filled so fast and so quietly. That changed on 18 March 2025, when the board of supervisors voted seven to two to make data centres a special exception use in districts where they had been by right, converting an administrative rubber stamp into a legislative process with staff review and public hearings. Even then, the board grandfathered applications submitted before 12 February 2025 for projects more than five hundred feet from homes. The reform arrived after the pipeline had already formed.

The by-right question determines whether there is a public decision at all. Where a data centre is by right, there is no hearing to attend, no vote to lobby, no record to appeal. The community's only formal opportunity to shape the outcome came years earlier, when the zoning map was drawn, in a proceeding almost nobody attended because nobody imagined what would eventually be built there. The decision had, in effect, already been taken by the county's past self on behalf of its future one.

Where discretion does exist, the procedural rules become the whole battlefield. The most consequential legal defeat the industry has suffered in Virginia turned on a technicality of notice. On 7 August 2025, Prince William County Circuit Court Judge Kimberly Irving voided the rezoning for the Prince William Digital Gateway, a project of more than two thousand acres beside the Manassas battlefield, holding the ordinances void ab initio because the county had failed to comply with the state code's public-notice requirements: the advertisement for the December 2023 hearing was published three days before it. The Court of Appeals of Virginia affirmed unanimously on 31 March 2026. The county and the developer Compass Datacenters withdrew from the litigation in April. QTS, the Blackstone-owned operator, petitioned the Supreme Court of Virginia in May through an affiliate, then withdrew the petition on 2 July 2026, leaving the lower rulings standing, and after a Court of Appeals order on 29 July the rezonings are void and the land reverts to the district it held before. The largest data centre campus ever proposed in the United States, some 2,100 acres of it, is dead. Years of civic argument about water, viewsheds and historic land hinged on the calendar arithmetic of a legal notice, because that was the only lever the law actually handed the objectors. The lever worked. The project was not defeated on water, or on viewshed, or on any ground the community had spent years assembling. It was defeated on the date a newspaper advertisement ran.

Ken Confidential

The second structural feature that determines who decides is information, and here the asymmetry has been engineered deliberately. In July 2026, Public Citizen published a report by Deanna Noël titled The Secret Data Center Buildout, documenting the routine use of non-disclosure agreements between developers and the public officials who will vote on their projects. It found that eighty per cent of Virginia localities with proposed or existing data centres have NDAs in place, and that at least ten states have introduced bills restricting the practice. Some agreements forbid officials from revealing that discussions are occurring, that confidential information has been received, or even that an NDA exists.

The Minnesota Star Tribune documented the same machinery in its own state, reporting that eight cities, two counties, at least one state agency and a school district had signed such agreements; of twelve cities the paper approached with known data centre proposals, only four had never signed one. Leslie Krueger, the University of Minnesota's assistant vice president for planning, space and real estate, spent August 2023 corresponding about Meta's Rosemount campus, code-named Project Bigfoot, with a company representative who identified himself to her only as “Ken Confidential”. Further north, in Hermantown near Duluth, city officials knew from September 2024 that a large project was proposed and declined to confirm publicly what it actually was until the Star Tribune obtained the city's emails roughly a year later, at which point the scheme filed as Project Loon turned out to be Google's. A community can be kept from knowing not merely who is building but what is being built, for a year, by the officials it elected. In Tucson, negotiations over a 290-acre campus proceeded for roughly two years behind NDAs that concealed the end user's identity from the public and from most of the city council. “Giant corporations prefer to operate in the shadows,” the council member Lane Santa Cruz said, “but Tucson is not for sale. We deserve transparency and accountability.”

Coweta County shows how the two asymmetries compound. Investigative reporting by DeSmog, based on public records requests, established that Project Sail's public face, an entity called Atlas Development, LLC, was fronting for Prologis, the San Francisco-based logistics real estate giant, whose involvement was concealed until May 2025 even as its logos appeared on project illustrations. The records showed a steady programme of private access: a meeting between a Prologis vice-president and one commissioner at a Newnan law office in July 2025, another with a second commissioner in August, personalised email updates to all five the day before a public hearing, further emails in October. The lobbyist Arthur “Skin” Edge IV submitted recommendations for the county's data centre ordinance on 7 July, writing that he did so on behalf of the Project Sail team and stating that Atlas Development had retained him as an attorney for the project; both Atlas Development and Prologis subsequently told DeSmog that they were not represented by Edge. Somebody's account of who was speaking for whom is wrong, and the instructive part is that the public record does not allow anyone outside those rooms to establish whose. After a CBRE vice-president testified at the November 2025 hearing, commissioners amended their draft to allow data centres on collector roads, raise the height limit from sixty to seventy feet, and replace a requirement that utilities demonstrate adequate capacity with a letter of intent showing preliminary coordination.

Set against that, Laura Beth's account of her group's access is stark. “Coweta County has not met with us specifically about the ordinance,” she told DeSmog, describing commissioners who would accept only group emails and declined the telephone conversations residents requested. The rules governing the facility were being drafted in one conversation and defended in another, and only one of those conversations had a transcript.

The Lawsuit That Followed the Vote

Suppose a community clears every hurdle: it learns about the project in time, secures a discretionary hearing, persuades its elected board, and votes no. What happens then is the sharpest test of where authority really lies, and the answer arrived in a farming township of about 2,200 people in Washtenaw County, Michigan.

In September 2025, after months of contentious meetings, the Saline Township board voted four to one to deny the rezoning sought for a Stargate data centre campus backed by Related Digital, Oracle, OpenAI and Blackstone, with DTE Energy contracted to supply roughly 1.4 gigawatts. Two days later the developers and landowners sued, alleging exclusionary zoning: the claim, under Michigan law, that the township had unreasonably barred a legitimate use, strengthened by the fact that Saline Township had no industrially zoned land at all. Facing litigation costs against an annual municipal budget of roughly a million dollars, the township settled by consent judgment, accepting community benefits worth about fourteen million dollars, including four million for farmland preservation, along with water and noise restrictions. The attorney who advised the settlement observed that zoning power is not absolute.

The sequel is more revealing than the settlement. Residents organised to force a referendum on the rezoning, a right Michigan law affords them. On 14 January 2026 the board formally rezoned the land to industrial to comply with the consent judgment; on 28 January it reversed that action and restored the agricultural designation, reasoning that the judgment itself authorised the project, so no map change was required. Because there had been no formal rezoning, there was nothing to petition against. Tammie Bruneau of the Rural Michigan Defense Fund put the consequence plainly: if the map need not be updated and no notice published, there could be no referendum. The resident Kathryn Haushalter, whose motion to intervene in the settlement had been rejected by a Washtenaw County judge as untimely, asked the question the episode poses: “What does it look like when residents are not able to determine what happens in their communities? What are we expected to do?” On 1 June 2026, Governor Gretchen Whitmer and Sam Altman appeared at the ceremony marking construction of the campus.

Tucson demonstrates the same lesson through geography rather than litigation. On 6 August 2025 the city council voted seven to nil to reject the annexation that Project Blue required, after residents objected to roughly two thousand acre-feet of city water a year. But the land was unincorporated county territory, and the Pima County board of supervisors had already voted three to two in June to sell it and had approved the zoning. The developer, having earlier signalled that the project could not proceed without the city, redesigned the facility for air cooling, which uses far less water and considerably more electricity, and pressed ahead through the county alone. On 3 December 2025 the Arizona Corporation Commission approved the power purchase agreement four to one, Commissioner Rachel Walden the sole vote against; Tucson Electric Power expects to be supplying the campus with two hundred and eighty-six megawatts by 2028. Amazon Web Services withdrew as operating partner. The project continued, and then acquired one more venue. Arizona's attorney general, Kris Mayes, has appealed the commission's approval, contesting the speed of it and arguing that the agreement lets the utility and the developers set electricity rates between themselves, a power the state constitution reserves to the commission alone. For once the proliferation of forums runs in the objectors' favour, though nobody in Tucson could reach that one either. The state's chief legal officer took an interest, which is a different thing from a community having a say.

The pattern in both cases is not that local government was overruled. It is that “local government” was never a single thing. It is a set of overlapping venues — township, county, court, utility commission — in which a developer need prevail only once, while residents must prevail everywhere.

Where the Decision Actually Lives

Step back far enough and the venue keeps moving upward. Justin Kollar, writing in the Journal of the American Planning Association in 2026, describes this as planning under pre-emption: states removing land use, zoning and environmental review authority from cities and counties through statutory overrides, utility governance, fiscal incentives and procedural constraints, so that decisions migrate to state agencies, utilities and corporate actors, and planning becomes administrative coordination rather than deliberation.

West Virginia has gone furthest. Its House Bill 2014 bars counties and municipalities from adopting or enforcing any ordinance limiting a certified microgrid district or a certified high-impact data centre, and exempts those projects from county and municipal zoning and land use ordinances, from building permitting, inspection and code enforcement, and from licence requirements. What is left to the locality is the power to charge fees and to send fire engines and police cars. Nor does the statute restrict what may be generated inside a microgrid district, so a gas, coal or nuclear plant may be sited in a county that has been relieved of any authority to review it. And the property tax revenue such a facility produces does not stay where the facility stands: the statute routes the greater part of it to the state, partly to fund reductions in income tax. That last provision repays attention, because the standard defence of accepting a data centre is that the host is compensated for what it absorbs. West Virginia has legislated away the deliberation and the compensation together, and left the county with the facility. In Pennsylvania, a proposed Data Center Siting and Permitting Act would have the state environmental department designate at least fifteen pre-approved sites. More than three hundred data-centre-related bills were introduced by state legislators in the first six weeks of 2026.

Much of the most consequential decision-making never touches land use at all. It happens at public utility commissions, bodies whose members most residents cannot name. On 15 April 2026 the Louisiana Public Service Commission voted four to one on Entergy Louisiana's application to build the generation for Meta's Richland Parish campus: seven new gas-fired power stations, some two hundred and forty miles of transmission and a set of nuclear uprates, a programme of 21.37 billion dollars, the largest investment in the utility's history, binding ratepayers across the state for the term of a twenty-year agreement. What the commissioners approved that day was not the plan. It was a request to fast-track it, skipping key steps of the standard regulatory review; the vote on the application itself is set for November 2026. The distinction is this essay's subject reduced to a single procedural motion. Before anybody decides the question, somebody decides how much deliberation the question is going to get, and that second decision is taken first, in a proceeding almost nobody is watching. Entergy maintains that Meta will cover construction costs and that customers will save money. The Alliance for Affordable Energy has warned that if Meta departs before the contract ends, the cost of the gas plants could fall on ordinary ratepayers; the Union of Concerned Scientists noted that the commission declined to investigate a financing arrangement under which Meta established an additional parent entity, retaining a twenty per cent stake, and borrowed twenty-seven billion dollars against the project. Whatever one concludes about the risk, a decision of that magnitude about who bears what is being taken by five elected commissioners in a venue with no relationship whatsoever to the parish hosting the buildings.

The tariff question is following the same path. According to Utility Dive, state regulators approved twenty-nine large-load tariffs in 2025, against fourteen in the whole period from 2018 to 2024, and by June 2026 twenty-four states had approved at least one. Minnesota's HF 16, enacted in June 2025, requires the state utility commission to create a very large customer rate class and allocate attributable costs to it. Oregon's POWER Act directs its commission to establish a separate class for facilities of twenty megawatts or more. Texas Senate Bill 6, signed on 20 June 2025, established a regime for loads of seventy-five megawatts and above, requiring new transmission-level connections to install equipment permitting remote curtailment during firm load shed events. These reforms matter enormously for whether the build-out raises household bills. They are also decisions taken hundreds of miles from any affected backyard.

Then there is the fiscal layer, which quietly converts a public choice into an accounting one. Good Jobs First has documented that most states subsidise data centres through sales and use tax exemptions on hardware and software, some lasting as long as forty years, alongside property tax abatements, and calculates that public subsidy runs to roughly two million dollars for every permanent job created. Abatements and payment-in-lieu-of-taxes arrangements are typically negotiated by development authorities rather than legislatures. At the top of the stack sits Executive Order 14318, signed on 23 July 2025, directing federal agencies to streamline permitting, identify federal land for siting and expedite environmental reviews for qualifying projects. Every one of these levers is legitimate in itself. Stacked together, they mean that by the time a proposal reaches a county board, most of the consequential variables have already been fixed elsewhere.

The Boundary Problem in a Rural County

This is where the argument stops being about zoning and becomes something older. Democratic theory has a name for the difficulty at the centre of the data centre fight: the boundary problem. In 1983 the political theorist Frederick Whelan set it out with uncomfortable clarity. Democracy is a method by which a group makes decisions, but it offers no democratic method for deciding who belongs to the group; any vote on the boundaries of the demos must be taken by a demos already constituted, which places the foundational question outside democratic determination. Robert Dahl, who framed the same difficulty a decade earlier, could find no satisfying escape from it.

One candidate answer is the all-affected-interests principle: those affected by a collective decision should have a say in making it. Robert Goodin, examining it in Philosophy and Public Affairs, treated the principle as the most promising basis for constituting a demos while showing how radically expansive it becomes once taken seriously, since almost any decision affects almost everyone at some remove. Sarah Song, writing in International Theory, argued the other way, that the demos should be bounded by the state, precisely because affectedness is too elastic to draw workable lines. The debate is unresolved for good reasons. But the data centre is an unusually clean instance of the mismatch it describes.

Consider the footprints. A hyperscale campus draws on an aquifer that obeys hydrology rather than municipal boundaries; its cooling demand, at the upper end of industry estimates, can reach five million gallons a day, comparable to a city of fifty thousand. Its electrical load falls on a regional transmission system, and PJM's footprint alone spans thirteen states and the District of Columbia. Data centres consumed about 4.4 per cent of American electricity in 2023, according to the Lawrence Berkeley National Laboratory's report for the Department of Energy, and are projected to reach between 6.7 and twelve per cent by 2028. Emissions travel with the wind; xAI sited turbines for its second Memphis supercomputer across the state line in Southaven, Mississippi, while the computing sat in Tennessee. Ratepayer costs fall on a customer class defined by a utility's service territory. Not one of these footprints coincides with the electorate of a county board of commissioners.

The subsidiarity principle, familiar from European constitutional law and Catholic social teaching, holds that decisions should be taken at the lowest level competent to take them. It is usually invoked for localism, and here it is often assumed to settle the matter in favour of the county. But subsidiarity contains its own limit, and the limit is competence. A township of 2,200 people cannot competently allocate a regional grid's capacity, and should not have to. Equally, a state utility commission cannot competently decide what a particular hillside means to the people who live beneath it. The failure is not that decisions are made too high or too low, but that they are distributed across levels in a way that lets each venue treat the questions it is worst placed to answer as somebody else's problem.

Elinor Ostrom, whose fieldwork on commons governance won her the Nobel prize in economics in 2009, derived design principles for institutions that manage shared resources without collapsing. Two are directly relevant: that the boundaries of the resource and of the group entitled to use it be clearly defined and congruent with local conditions, and that most individuals affected by operational rules be able to participate in modifying them. Measured against those principles, American data centre siting fails not because it is too democratic or too little, but because the people bound by the rules and the people entitled to change them are systematically different sets of people.

The Strongest Case Against the Veto

It would be easy, and dishonest, to leave the argument there. The case for local control over land use has a bleak record, and anyone who wants to strengthen community authority over data centres should sit with it.

The most rigorous account is Neighborhood Defenders, the 2019 study by Katherine Levine Einstein, David Glick and Maxwell Palmer, which examined thousands of participants in local land-use proceedings and found that the people who turn up to oppose development are not representative of their communities. They are disproportionately older, whiter, wealthier and far more likely to be homeowners; they oppose new housing considerably more strongly than their neighbours do; and the participatory institutions built to democratise planning reliably amplify their voices. The result has been a decades-long housing shortage borne overwhelmingly by people who never got to attend the meeting because they did not yet live in the town.

The same dynamic has kneecapped the energy transition. The Sabin Center for Climate Change Law at Columbia found that by the end of 2024 at least 459 counties and municipalities across forty-four states had adopted severe local restrictions on siting renewable energy, a sixteen per cent rise in a year, and identified 498 contested projects across forty-nine states. Between 2018 and 2023, at least thirty per cent of utility-scale wind and solar projects were cancelled during siting, largely because of community opposition, local ordinances and zoning. If your principle is that affected communities may refuse infrastructure they did not choose, that principle has already been road-tested, and used most effectively against solar farms and transmission lines.

The pro-build response also has a legitimate core. Matthew de Boer, writing in the Fordham Law Review in 2026, notes that states have aggressively pursued data centres with incentives while declining to regulate their siting, leaving municipalities to manage hyperscale facilities with frameworks designed for warehouses. His proposal is light pre-emption modelled on housing reform: not the elimination of local authority but its discipline through procedural mandates and objective standards. The Michigan exclusionary zoning doctrine that defeated Saline Township exists for a reason. A jurisdiction with no industrial zoning anywhere within it has, in effect, pre-committed to refusing every industrial use, and a legal system that permitted that at every boundary would make nothing buildable anywhere.

The honest position, then, is that “who decides” genuinely cuts both ways, and that a general right of local veto is not a principle anyone should want applied consistently. Which is precisely why the boundary problem is the right frame. The complaint from Coweta County and Saline Township is not, at its strongest, that localities should be able to refuse anything. It is that the procedure by which these decisions are made produces answers no defensible theory of authority endorses: hearings held after entitlements are secured, ordinances drafted by the applicant's lobbyist, referendum rights extinguished by declining to amend a map, consequences distributed across airsheds, watersheds and ratepayer classes that no ballot anywhere encompasses. That is not the tyranny of the neighbourhood defender. It is the absence of any forum in which the actual question can be put.

Making the Franchise Fit the Footprint

What would it take for a decision of this kind to be legitimate rather than merely lawful? The reforms already circulating are more modest than the theory suggests, and worth taking seriously precisely because they are achievable.

The first is informational, and nearly consensual. Public Citizen's recommendations amount to a simple proposition: officials should not sign non-disclosure agreements with private developers about matters they will later vote on, developers should disclose their identity, projected water and energy use, procurement plans and financial incentives before entitlements are granted, and existing agreements should be published. Attorneys quoted in reporting on Oklahoma's secrecy deals have questioned whether such agreements are even enforceable against public bodies. Pima County changed its own policies on NDAs and environmental review in September 2025, after what its supervisors called lessons learned from Project Blue. And the standard objection, that transparency of this kind is commercially unworkable, has now been answered by the only sort of party who could answer it. On 18 March 2026 Microsoft announced that it would stop using non-disclosure agreements with local governments for data centre projects anywhere in the world and would terminate those already in force, saying that transparency with the communities where it operates was paramount. That followed a commitment in January to pay the full cost of the power its facilities draw, decline local property tax breaks, replenish more water than it consumes and invest in local training. Microsoft is so far the exception, and should be called one: Public Citizen records that every other major technology company continues the practice. A single exception is still enough to dispose of the claim that the practice is necessary. Nothing about the AI build-out requires that the people voting know less than the people lobbying them.

The second is jurisdictional, and follows from the boundary problem. If the harms of a facility cross municipal lines, notice, standing and formal comment rights should cross them too. There is no technical obstacle to requiring that a rezoning application within a defined radius of a boundary trigger notice and consultative standing for the adjacent jurisdiction, or that a groundwater withdrawal above a threshold trigger review at the level of the aquifer rather than the parcel. That would not give neighbours a veto. It would give them a seat, the minimum the all-affected-interests principle can be read to demand without becoming unbounded.

The third is fiscal, and has the most momentum. Large-load tariffs and dedicated rate classes of the kind Minnesota, Oregon and Texas have adopted move the ratepayer question into a proceeding where it can be argued explicitly. If a facility's grid costs are ring-fenced to the customer that causes them, the county board's decision becomes one about land, water and noise rather than about everyone else's electricity bill, and the venue mismatch shrinks. The same logic applies to abatements: a forty-year tax exemption negotiated by a development authority binds successor councils and their electorates, and ought to require the deliberative treatment any long-dated public borrowing would.

New York's executive order is a hybrid of all three. It pauses state permits, an exercise of upward authority, while directing Empire State Development to publish a community investment framework within sixty days to guide local negotiations, and the Department of Public Service to prepare a generic environmental impact statement so proposals are assessed against a consistent standard rather than each locality improvising. Whether that produces better decisions or merely relocates them is unclear. That it exists in place of the statute the legislature actually passed, and at more than twice the legislature's threshold, is a reminder that the reforms are governed by the same question as the projects. But it recognises that a county board asked to evaluate a nine-hundred-megawatt load with a planning staff of a dozen is not being empowered. It is being handed a decision it has no capacity to make and then blamed for the outcome.

A Judge in Newnan

On 5 May 2026, Citizens for Rural Coweta filed a petition in Coweta County Superior Court asking a judge to declare the rezoning invalid. Their argument is that the commissioners disregarded the county's own comprehensive plan, which designated the land for rural places rather than industry, and did so despite the site's groundwater recharge areas, wetlands and watershed. It is, in essence, a claim that the county broke a promise to itself.

There is a coda to the vote that is easy to miss, and it may be the sharpest thing in this account. In May 2026, weeks after rezoning the Sargent land, the Coweta commissioners adopted a moratorium on any future data centre project in the county. Residents said it was too little and far too late; one likened it to shutting the barn door once the horses were out. But consider what the sequence establishes. The county was entirely capable of pausing. It held the instrument, understood how to use it and did use it, one decision after the only decision that mattered. Coweta's future self is now protected in a way its present self declined to be, and the difference between those two counties is not one of law, or capacity, or information, or public sentiment. It is a difference of timing, which is to say a difference of nothing at all except that the applicant arrived first.

The venue is telling. Having lost in the only forum where they could vote, the residents have moved to the one forum where nobody votes at all. That is the recurring shape of these disputes. In Prince William County the decisive intervention was a judge's reading of a notice statute, and it killed the largest project in the country. In Saline Township a consent judgment did what the electorate had refused to do, and a second judge declined to reopen it. In Tucson a unanimous rejection was routed around by a county land sale and a corporation commission vote, and the next serious challenge came from a state attorney general. Litigation has become the default civic instrument of the AI build-out because it is the only instrument that reliably crosses the boundaries everything else respects.

There is a version of this story in which the residents are simply wrong: in which a county gains a tax base and a state gains capacity it badly needs, and a few hundred households absorb a view they dislike, which is roughly what infrastructure has always asked of somebody. I do not think that version is obviously false. What is false is the assumption that the current procedure is capable of telling us which version is right. A process in which the applicant's lobbyist helps draft the ordinance, the developer's identity is concealed behind a shell entity, commissioners meet privately with one side and by group email with the other, and a referendum right can be extinguished by declining to amend a map, is not a process that produces a trustworthy answer to a hard question. It produces an answer and calls it settled.

The families in Coweta County who moved out of Atlanta to be surrounded by trees are not, in the end, asking for a veto over American AI policy. They are asking something democratic theory has never fully answered and American land-use law has stopped even trying to: what makes a decision about a place belong to the people who live in it. Nine hundred megawatts is arriving either way. Whether it arrives as something a community decided or as something that happened to it is a difference no amount of tax revenue will retire, and it is being settled right now, county by county, in rooms that most of the affected will never enter.

References

1. Shah, Simmone. “Community Backlash to AI Data Centers Is Growing Across the U.S.” TIME, 22 July 2026. https://time.com/article/2026/07/22/community-backlash-ai-data-centers/
2. Reuters. “Data center opponents stage 142 protests across 42 US states.” 18 July 2026. https://www.spokesman.com/stories/2026/jul/18/data-center-opponents-stage-142-protests-across-42/
3. Reuters/Ipsos. “Americans wary of AI-driven data center boom, Reuters/Ipsos poll shows.” 11 June 2026. https://www.usnews.com/news/politics/articles/2026-06-11/americans-wary-of-ai-driven-data-center-boom-reuters-ipsos-poll-shows
4. Data Center Watch (10a Labs). Data Center Opposition Tracker. https://www.datacenterwatch.org/
5. Office of Governor Kathy Hochul. “Executive Order No. 62: Establishing a Temporary Moratorium on Data Centers in New York.” 14 July 2026. https://www.governor.ny.gov/executive-order/no-62-establishing-temporary-moratorium-data-centers-new-york-while-state-develops
6. Bloomberg. “How AI Data Centers Are Sending Your Power Bill Soaring.” 2025. https://www.bloomberg.com/graphics/2025-ai-data-centers-electricity-prices/
7. PolitiFact. “How much have data centers increased electricity prices?” 12 June 2026. https://politifact.com/factchecks/2026/jun/12/elizabeth-warren/data-centers-rising-electricity-costs/
8. Shehabi, Arman, et al. 2024 United States Data Center Energy Usage Report. Lawrence Berkeley National Laboratory, December 2024. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report_1.pdf
9. DeSmog. “How Data Center Developers Staked Their Claim in Rural Georgia.” 7 April 2026. https://www.desmog.com/2026/04/07/how-data-center-developers-staked-their-claim-in-rural-georgia/
10. Noël, Deanna. The Secret Data Center Buildout: How States Can Stop Big Tech's Abuse of NDAs. Public Citizen, 8 July 2026. https://www.citizen.org/news/the-secret-data-center-buildout-how-states-can-stop-big-techs-abuse-of-ndas/
11. Minnesota Star Tribune. “NDAs, code names and shell companies: how Minnesota officials support data center secrecy.” https://www.startribune.com/ndas-code-names-and-shell-companies-how-minnesota-officials-support-data-center-secrecy/601499182
12. WTOP News. “Digital Gateway data center project dies after final developer withdraws appeal.” July 2026. https://wtop.com/prince-william-county/2026/07/digital-gateway-data-center-project-dies-after-final-developer-withdraws-appeal/
13. Fortune. “A Michigan farm town voted down plans for a giant OpenAI-Oracle data center. Weeks later, construction began.” 6 May 2026. https://fortune.com/2026/05/06/ai-data-center-michigan-saline-politics-farmland/
14. KJZZ. “Rejected by Tucson and abandoned by Amazon, Project Blue data center moves forward.” 9 December 2025. https://www.kjzz.org/the-show/2025-12-09/rejected-by-tucson-and-abandoned-by-amazon-project-blue-data-center-moves-forward
15. Kollar, Justin. “Planning Under Preemption: State Power and Local Authority in the AI Data Center Era.” Journal of the American Planning Association, 2026. https://www.tandfonline.com/doi/full/10.1080/01944363.2026.2618221
16. de Boer, Matthew. “State Intervention in Local Zoning: Lessons from Housing Applied to Data Centers.” Fordham Law Review, Vol. 94, Issue 5, 2026. https://ir.lawnet.fordham.edu/flr/vol94/iss5/12/
17. West Virginia Center on Budget & Policy. “HB 2014 Would Preempt Local Control; Divert Millions Annually in Local Property Tax Revenue for Schools and Public Safety to Fund Income Tax Cuts.” https://wvpolicy.org/hb-2014-would-preempt-local-control-divert-millions-annually-in-local-property-tax-revenue-for-schools-and-public-safety-to-fund-income-tax-cuts/
18. Utility Dive. “Large load tariffs proliferate as states take more active role in data center regulation.” https://www.utilitydive.com/news/large-load-tariffs-proliferate-as-states-take-more-active-role-in-data-cent/816184/
19. Good Jobs First. “Money Lost to the Cloud: How Data Centers Benefit from State and Local Government Subsidies.” https://goodjobsfirst.org/money-lost-cloud-how-data-centers-benefit-state-and-local-government-subsidies/
20. Whelan, Frederick G. “Democratic Theory and the Boundary Problem.” In NOMOS XXV: Liberal Democracy, ed. J. Roland Pennock and John W. Chapman. New York University Press, 1983.
21. Goodin, Robert E. “Enfranchising All Affected Interests, and Its Alternatives.” Philosophy and Public Affairs, Vol. 35, No. 1, 2007.
22. Song, Sarah. “The boundary problem in democratic theory: why the demos should be bounded by the state.” International Theory, Vol. 4, No. 1, 2012. https://polisci.berkeley.edu/sites/default/files/people/u3868/Song%20-%20Boundary%20Problem%20in%20Democratic%20Theory.pdf
23. Ostrom, Elinor. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press, 1990. https://www.cambridge.org/core/books/governing-the-commons/A8BB63BC4A1433A50A3FB92EDBBB97D5
24. Einstein, Katherine Levine, David M. Glick and Maxwell Palmer. Neighborhood Defenders: Participatory Politics and America's Housing Crisis. Cambridge University Press, 2019. https://www.jstor.org/stable/48584947
25. Sabin Center for Climate Change Law. “Opposition to Renewable Energy Facilities in the United States: June 2025 Edition.” Columbia University. https://climate.law.columbia.edu/content/opposition-renewable-energy-facilities-united-states-june-2025-edition

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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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There is a particular kind of phone call that has been going out across the American and British labour markets since the spring. It comes from a number the recipient recognises. It is made by someone who, eighteen months or two years ago, sat across a table and explained that the role was being eliminated because the company was moving to an automated workflow. The tone of the new call is warm, slightly sheepish, careful not to say the word sorry in any way that could later be quoted. The substance is simple. We need you back.

For the person receiving it, the call is not simple at all. It arrives carrying a piece of information they were denied at the moment they most needed it: that the thing they were told about themselves, that a machine could do their job, was not true. It was a forecast dressed as a fact, and the forecast was wrong, and in the interval between the forecast and its correction they lost a salary, a title, a pension contribution, a professional identity and, in a good many cases, a settled sense of what they were for.

The reversal is now large enough to have its own vocabulary. Fast Company has called it the great AI rehire; others call it the AI boomerang. Whatever the label, the underlying phenomenon is a corporate class discovering, at scale and at expense, that the gap between what artificial intelligence was sold as capable of and what it can actually be trusted to do in production is wide enough to swallow a quality programme, a customer service function, an editorial operation, or a bank's call centre. What is far less examined is the cost of that discovery, and specifically who paid it.

The graybeards Ford had to buy back

The most instructive case is not a technology company. It is a car manufacturer.

In late June 2026, Ford disclosed that it had leaned too heavily on artificial intelligence in vehicle quality control and had spent roughly three years bringing back around 350 veteran engineers to repair the consequences. Some of them were former Ford employees. Others had drifted to suppliers. Internally and in the coverage that followed, they were referred to as the graybeards, a term that carries both affection and a certain institutional embarrassment.

Charles Poon, Ford's vice president of vehicle hardware engineering, gave an account of the failure that is unusually candid by the standards of corporate communications. The company, he said, had mistakenly thought that by just introducing artificial intelligence and ingesting the design requirements it had, that would produce a high-quality product. Elsewhere he put it more plainly still: artificial intelligence is a fantastic tool, but it is only as good as the information you use to train it.

What the returning engineers actually did is the detail that matters most, and it is routinely skipped. They did not replace the AI. They rebuilt the data pipelines feeding it, ran weekly design reviews to catch failure points before anything reached the factory floor, mentored junior staff, and functioned as internal auditors of the automated systems. For the 2026 Expedition alone, Ford added around 1,200 new inspections and 203 new inspectors at its Kentucky Truck Plant. The chief executive, Jim Farley, later described the cumulative effect of the quality overhaul as literally hundreds and hundreds of millions of dollars of a tailwind for Ford on cost. In the summer of 2026 Ford topped the J.D. Power Initial Quality Study among mainstream brands for the first time since 2010.

So the story has a happy ending, if you are Ford. Read it from the other end and it looks different. A company removed the people who held the tacit knowledge required to make its automated quality system work, discovered that the system could not function without them, and then spent roughly three years and an unknown sum reassembling that knowledge from the labour market. The engineers who came back are now, in effect, being paid to train their replacement, having previously been dismissed on the assumption that the replacement was already trained.

The account emerged first in Bloomberg on 25 June 2026, reporting that Ford had been rehiring quality inspectors after AI fell short. Kumar Galhotra, the company's chief operating officer, told journalists that Ford had been relying more and more on automated quality systems and not getting the results it wanted. TechCrunch carried Poon's remarks three days later.

Where the fifty-five per cent actually comes from

Every article about the AI rehire leans on a single statistic: 55 per cent of leaders who made AI-driven cuts now regret them. It appeared in Inc. in late July 2026 under the byline of Bruce Crumley, and was widely syndicated from there. It has been attributed variously to Forrester, to Orgvue, and in at least one widely shared instance to Gartner. That last attribution appears to be simply wrong.

There are, in fact, two distinct 55 per cent findings, and their coincidence has caused a good deal of laundering. The first comes from Orgvue, a workforce design software firm, whose survey was run by the research agency Vitreous World between February and March 2025 and published that April. It covered 1,163 C-suite and senior leaders across eight countries in North America, Europe and Asia Pacific. It found that 39 per cent of business leaders had made employees redundant as a result of deploying AI, and that of those, 55 per cent admitted they had made the wrong decision.

The second comes from Forrester's Predictions 2026 report on the future of work, published in October 2025, which found that 55 per cent of employers regretted technology-driven staff reductions and predicted that half of AI-attributed layoffs would be quietly reversed. Forrester's analysts were specific about the form the reversal would take, and the specificity is not flattering: much of this work, the firm predicted, will be given to lower-wage human workers, offshore or at lower salary.

Neither of these is a July 2026 survey. The headline that broke in late July 2026 was, in substantial part, research from spring and autumn 2025 recirculating with fresh urgency because the anecdotes had finally caught up with it. That does not make the finding false. It does mean the reversal has been visible in the data for well over a year while the layoffs continued, which complicates the tidy story of a sudden corporate awakening.

Two genuinely newer datasets do exist and are more useful. Careerminds, an outplacement firm, surveyed 600 HR professionals in February 2026. Of those whose organisations had made AI-led cuts, 32.7 per cent had already rehired between a quarter and a half of the roles they eliminated, and 35.6 per cent had brought back more than half. Add those together and you get 68.3 per cent, which is almost certainly the origin of the widely repeated claim that 68 per cent of firms are rehiring. The same survey found that 52.1 per cent had rehired within six months and 17.8 per cent within three, that 32.9 per cent of HR leaders said their organisations had lost critical skills, and that 28.1 per cent reported remaining staff lacked the capability to fill the resulting knowledge gaps.

The finance is the part that should make boards uncomfortable. Nearly 31 per cent of respondents found that rehiring costs exceeded the initial savings outright. A further 42.4 per cent said savings and rehiring costs roughly cancelled each other out. Only around 27 per cent were financially ahead. That is the actual return on a strategy that was announced to markets as transformation.

The second newer dataset comes from Robert Half, which surveyed more than 2,000 hiring managers in April 2026 and found that 32 per cent had eliminated a role primarily because of AI and later rehired for the same or a similar position. Dawn Fay, Robert Half's operational president, summarised the reason without embellishment: the communication, the judgement, the oversight, the institutional knowledge that employees have, the technology, up until this point, is just not able to replace all of that.

The number that does not say what it is quoted as saying

One figure in circulation deserves direct correction, because it has been assembled from two unrelated statistics and reads as reassurance when the underlying data is not reassuring at all.

The claim is that AI-attributed job cuts in 2026 have exceeded 165,000, and that the pace is 40 per cent slower than in 2025. The 40 per cent figure is real, but it does not describe AI cuts. According to Challenger, Gray and Christmas, whose monthly job cut reports are the standard reference, employers announced 443,604 job cuts in the first half of 2026, down 40 per cent from the 744,308 announced in the first half of 2025. That is total announced cuts across all stated reasons, heavily distorted by the extraordinary government-related reductions that inflated the 2025 baseline.

AI-attributed cuts went in precisely the opposite direction. Challenger recorded 101,743 cuts citing AI through June 2026, roughly 23 per cent of all cuts. The comparison point is 54,836 for the whole of 2025. In other words, in six months of 2026, AI-attributed cuts ran at nearly twice the entire previous year's total. AI was the leading stated reason for workforce reductions for four consecutive months. In May alone, AI was cited in 38,579 cuts, 40 per cent of that month's total and the highest monthly figure since Challenger began tracking the category in 2023. Technology sector cuts through June reached 139,156, an increase of 83 per cent on the same period in 2025.

Andy Challenger, the firm's chief revenue officer, put it flatly when the June report was released on 1 July 2026: tech remains the epicentre of this year's cuts, and AI is the dominant force as companies restructure around it, automate roles and reallocate budgets towards new capabilities.

So the honest framing is this. The rehire is real, and it is happening simultaneously with an acceleration of AI-attributed displacement, not after it. Both trends are running at once, in the same economy, sometimes inside the same organisation. Anyone reading the boomerang coverage as evidence that the danger has passed is reading it wrong.

What the machines could not be told

The reason the reversals cluster around certain functions is not mysterious, and it was described in economics literature a decade before the current wave.

In 2014, the MIT economist David Autor published a working paper for the National Bureau of Economic Research titled “Polanyi's Paradox and the Shape of Employment Growth”. The paradox he named belongs to the chemist and philosopher Michael Polanyi, who captured it in a sentence: we can know more than we can tell. Much skilled work consists of judgements the person making them cannot articulate as rules. Autor's argument was that automation requires exactly the sort of explicit specification that tacit knowledge resists, and that the tasks proving most stubborn to automate are precisely those demanding flexibility, judgement and common sense. He also warned, presciently, that commentators consistently overstate machine substitution while ignoring the complementarities between humans and machines.

Every case in the current reversal is a demonstration of that paradox meeting a quarterly earnings call.

At Ford, the tacit knowledge was in the heads of engineers who could look at a design requirement and know which failure modes it implied. The company assumed those requirements were the knowledge. They were the documentation of the knowledge, which is a different thing, and the AI trained on the documentation inherited the gap.

At IBM, the company's AskHR system handled roughly 94 per cent of routine human resources requests. Arvind Krishna, IBM's chief executive, confirmed in 2025 that AI agents had replaced several hundred people in the HR function. The residual 6 per cent turned out to include the ethically complicated, the legally sensitive and the genuinely distressing, which is to say the part of human resources that is actually the job. IBM's overall headcount rose regardless, because the savings were reinvested in software engineering, sales and marketing, which is a useful reminder that displacement and growth can coexist inside a single organisation.

At Klarna, the Swedish payments firm, the collapse was public and fast. In February 2024 the company announced that an OpenAI-powered assistant was doing the work of 700 customer service agents, handling more than two-thirds of conversations, resolving issues in under two minutes against eleven for humans, and on track to add 40 million dollars in profit. By May 2025 the chief executive, Sebastian Siemiatkowski, was conceding that the drive for cost and efficiency had produced lower quality. The AI handled volume. It could not handle complexity, emotional charge, or multi-step resolution. Klarna began recruiting humans again.

At the Commonwealth Bank of Australia, the failure was almost comic. In July 2025 the bank announced 45 redundancies in its call centre, replaced by an AI voice bot which it said had cut call volumes by around 2,000 a week. The Finance Sector Union disputed the figure, arguing that volumes were in fact rising, that staff were being offered overtime and that team leaders were being directed onto the phones. The union took the matter to the industrial tribunal. In August 2025 the bank reversed the decision, conceded that its redundancy assessment had not properly considered business needs, and offered affected staff the chance to stay, move within the bank or leave. It was not the end of the matter.

Four organisations, four different industries, one shared error: mistaking the codifiable portion of a job for the whole of it.

Coming back is not the same as never leaving

Here is where the boomerang narrative turns from corporate comedy into something closer to a labour story, because the terms of return are not the terms of departure.

Forrester's prediction was explicit that reversal would often mean rehiring offshore or at lower salary. Klarna's approach is the clearest illustration. The company did not simply reinstate its former customer service staff. It launched a pilot recruiting remote agents under what it described as an Uber-type model, targeting students, professionals and entrepreneurs, offering what a company spokesperson called competitive pay and full flexibility, with the eventual aim of replacing its outsourced customer service workforce with this arrangement. Flexibility is a word that does a lot of load-bearing work in that sentence. A permanent contact centre job with a rota, sick pay and a pension is not the same product as a flexible remote gig, even if the tasks performed are identical.

The countervailing evidence deserves its weight. Some reporting suggests returning workers are landing hybrid roles demanding data literacy, prompting skill and change management capability, at pay above the jobs the AI was meant to eliminate. Caroline Castrillon, writing in Forbes on 26 July 2026, advised treating a reversal offer as an entirely new proposition: negotiate higher base pay, signing bonuses, upgraded titles and guaranteed severance, and use the employer's admission of error as leverage. That is sound advice, and it describes a real bargaining position for a particular kind of worker: senior, scarce, holding knowledge the employer has just proved it cannot buy elsewhere.

The graybeards at Ford are in that position. A veteran quality engineer whose absence cost a manufacturer three years and a J.D. Power ranking can name a price. A contact centre agent whose absence produced longer hold times cannot, because the employer's alternative is not to rebuild the function but to source it more cheaply somewhere else. The reversal, in other words, redistributes bargaining power extremely unevenly, and it does so along exactly the lines that already determine labour market power. The scarce get leverage. The substitutable get a flexible contract.

There is also a body of research suggesting that rehiring is not the clean restoration employers imagine. A study published in the Journal of Management in 2021 by John Arnold, Chad Van Iddekinge, Michael C. Campion, Talya Bauer and Michael A. Campion examined 30,714 employees at a large retail organisation, including 1,318 boomerang employees rehired into management roles. Performance before and after rehiring tended to stay flat rather than improve. Boomerang managers performed comparably to internal and external hires in the first year, but both other groups improved more over time. Rehires were also more likely to leave again, and when they did, they tended to leave for reasons similar to those that prompted their first departure.

Read that finding against the current moment and it is faintly ominous. The reason for the first departure was that the employer decided a machine could do the job. That reason has not been retracted so much as postponed.

What the sentence does to the person who receives it

The literature on job loss is old, large and consistent, and it does not support the idea that unemployment is a neutral transition between positions.

Karsten Paul and Klaus Moser's 2009 meta-analysis in the Journal of Vocational Behavior pooled 237 cross-sectional and 87 longitudinal studies and found an average effect size of 0.51 for the impact of unemployment on mental health. Among the unemployed, an average of 34 per cent showed psychological problems, against 16 per cent of the employed. The effects ran across distress, depression, anxiety, psychosomatic symptoms, subjective wellbeing and self-esteem.

The physical consequences are equally documented. Daniel Sullivan and Till von Wachter, writing in the Quarterly Journal of Economics in 2009, matched administrative employment records for Pennsylvanian workers in the 1970s and 1980s to Social Security death records through 2006. For high-seniority male workers, mortality in the year following displacement ran 50 to 100 per cent above expectation. Job loss in a mass layoff, they estimated, reduces life expectancy by one to one and a half years. Effects were still detectable two decades later.

These are the baseline costs of any redundancy. Whether being told that a machine could do your job adds something on top is a separate question, and the answer is more interesting than intuition suggests.

In 2019, Armin Granulo, Christoph Fuchs and Stefano Puntoni published a paper in Nature Human Behaviour reporting eleven studies and surveys involving more than 2,000 participants across Europe and North America. Their finding ran counter to expectation. When people considered other workers being replaced, they preferred those workers to be replaced by humans rather than robots. When they considered their own replacement, the preference reversed: people would rather be replaced by a machine than by another person. The mechanism the authors identified was self-threat. Being displaced by a human invites a direct comparison of worth. Being displaced by a machine does not, because the machine is not a rival in the social hierarchy.

Which suggests that, at the moment of the layoff, the AI explanation may genuinely soften the blow. It removes the sting of having been judged inferior to a colleague. It replaces a personal verdict with a technological inevitability.

And that is exactly what makes the reversal so corrosive. The protective effect depends on the story being true. If you accept that a machine surpassed you, you have absorbed a loss without the humiliation of comparison. When the employer then returns to say that the machine did not surpass you after all, the protective framing collapses and something worse is exposed underneath. You were not replaced by a superior system. You were removed on the basis of an unverified claim about a system, by people who had not checked, in service of a narrative about the company's future that was being told to investors rather than to you. The comfort was borrowed and the loan has been called in.

There is a further injury that has no name in the psychological literature but is obvious to anyone who has lived it. The rehire is an admission of error that arrives without an apology, because an apology would be an admission of liability. The worker is asked to return to an organisation that has demonstrated, in the most concrete way available, that its assessment of their value was not merely mistaken but unexamined. Trust, in the ordinary employment sense, is a bet that the employer's judgement about you is made in good faith and with due care. The reversal is documentary evidence that it was not. Coming back means working inside that knowledge daily.

The demo, the pilot and the shop floor

None of this would have happened if capability claims had been tested before headcount decisions were made rather than after. The record on that testing is poor across the whole ecosystem.

In August 2025, preliminary findings from MIT's Project NANDA, drawing on more than 300 enterprise deployments, 52 case studies and 153 leadership interviews, reported that roughly 95 per cent of enterprise generative AI pilots produced no measurable return, despite tens of billions of dollars of investment. The figure has been misread and overstated in circulation, and the researchers themselves emphasised that the failures were organisational rather than a verdict on model quality, but the direction is not in dispute.

Orgvue's more recent research points the same way, finding that 92 per cent of organisations had invested in AI while 78 per cent reported projects either failed outright or remained stuck in pilot, with around a third saying they still did not understand how to make AI work.

The most methodologically careful evidence, and the most honest about its own limits, comes from METR. In July 2025 the research organisation published a randomised controlled trial in which sixteen experienced open-source developers completed 246 real tasks, some with AI assistance and some without. The developers expected AI to cut completion time by 24 per cent. Afterwards, they estimated it had made them 20 per cent faster. Measured against the clock, they were 19 per cent slower. The gap between perceived and actual productivity, in the group with the strongest professional incentive to judge accurately, was around 39 percentage points.

METR has since revised the picture, and intellectual honesty requires reporting the revision. In February 2026 the organisation published follow-up work covering 57 developers, ten of them veterans of the original study, across 143 repositories and more than 800 tasks. The raw results pointed the other way. Among the newly recruited developers AI produced an estimated 4 per cent speedup, with a confidence interval running from 15 per cent faster to 9 per cent slower, against the original 19 per cent slowdown whose interval ran from 2 to 39 per cent slower. METR also disclosed a selection problem, though not the one it is commonly reported as having. Developers were not refusing to enrol. Between 30 and 50 per cent of them said they were choosing not to submit particular tasks because they did not want to do those tasks without AI, which quietly stripped from the sample the work where the uplift would have been greatest. A rising share also said they would not want to do half their work without AI at all, even at 50 dollars an hour. METR now describes its own headline result as historical and cautions that its newer data is only very weak evidence for the size of any improvement.

The perception side of that gap has meanwhile widened. In May 2026 METR published a survey of 349 technical workers, conducted between February and April, in which the median self-reported change in the value of work produced with AI tools was between 1.4 and 2 times. Respondents put it at 1.3 times for March 2025, 2 times for March 2026, and forecast 2.5 times for March 2027. METR flagged reasons to doubt the magnitude, noting that its own staff returned the lowest estimates of any subgroup, which it attributed to their familiarity with the evidence on the gap between perceived and actual gains.

That sequence is a model of how capability claims ought to be handled: measured, published, revised, hedged. It is also the exact opposite of how they were handled in the decisions that removed 101,743 people from payrolls in the first half of 2026. Those decisions were made on vendor roadmaps and board-level enthusiasm, not on randomised trials. The research community spent a year arguing about whether AI made sixteen developers slightly faster or slightly slower. Corporate management, working from the same underlying technology, concluded that entire functions could be eliminated and acted on it within a quarter.

Why firms cannot stop even when they know

The most uncomfortable analysis of this cycle argues that the reversals will not prevent the next round, because the incentive structure that produced them is intact.

In March 2026, Brett Hemenway Falk of the University of Pennsylvania's Department of Computer and Information Science and Gerry Tsoukalas of Boston University posted a paper to arXiv titled “The AI Layoff Trap”, subsequently revised in June. Its opening premise is the familiar one: if AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on.

The paper's actual contribution is sharper than that summary, and worth stating accurately. Falk and Tsoukalas show that knowing this is not enough for firms to stop it. In a competitive task-based model of a transitioning economy, each firm captures the full cost saving from automation but bears only a fraction of the demand loss it creates, with the remainder falling on rivals. That demand externality traps rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss, they argue, harms both workers and firm owners. More competition and better AI amplify the excess rather than correcting it. Wage adjustment, free entry, capital income taxes, worker equity, universal basic income, upskilling and Coasean bargaining all fail to eliminate it. Their proposed remedy is a Pigouvian automation tax, priced to make firms internalise the demand destruction they cause.

The relevance to the boomerang is direct. If the model is right, the firms conducting reversals are not being corrected by the market in any way that will change their future behaviour. They are absorbing an idiosyncratic operational failure, learning a narrow lesson about one function, and returning to the same competitive pressure that produced the original decision. The rehire fixes the quality problem. It does not touch the externality.

This is a theoretical model, recently posted and not yet through peer review, and its conclusions are contingent on its assumptions. But it offers something the anecdotes do not: an explanation of why sophisticated, well-advised organisations keep making a decision that most of them subsequently regret.

A correction, or a slower version of the same thing

The honest answer is that it is both, and which one dominates depends on where you sit in the labour market.

The genuinely corrective signal is IBM's. In mid-February 2026, at Charter's Leading with AI Summit, the company's chief human resources officer, Nickle LaMoreaux, announced that IBM would triple its United States entry-level hiring in 2026, explicitly for roles that, as the company put it, we are being told AI can do. Entry-level job descriptions were redesigned away from tasks AI automates well, such as routine coding, and towards customer engagement and judgement. LaMoreaux's rationale was strategic rather than sentimental: the companies three to five years from now that are going to be the most successful, she said, are those that doubled down on entry-level hiring in this environment.

That matters because the entry-level damage is the least reversible part of this whole episode. Data from the Burning Glass Institute cited in Forrester's analysis shows the share of postings that are entry-level falling between 2018 and 2024 from 43 to 28 per cent in software development, from 35 to 22 per cent in data analysis, and from 41 to 26 per cent in consulting. Youth unemployment for bachelor's degree holders aged 20 to 24 rose from 5.2 per cent in 2018 and 2019 to 6.2 per cent. You cannot boomerang a graduate who never got hired in the first place. There is no former employee to call.

Set against IBM's example is the pattern Forrester actually predicted, which was not restoration but relocation: work returning to humans, at lower cost, offshore or on worse terms. Klarna's flexible remote model is that pattern in practice. The Careerminds finding that only around 27 per cent of firms came out financially ahead suggests the cost discipline that drove the original decision has not gone anywhere, and will simply be pursued through a different mechanism.

The Commonwealth Bank has since supplied the clearest available test of that prediction, because it is the same employer. In July 2026, less than a year after conceding it had got the redundancies wrong, the bank cut hundreds of customer service chat roles held by contractors supplied by Nutun, a Johannesburg outsourcing firm, as it wound back the contract, and confirmed a further 276 redundancies across technology, operations and human resources. The Finance Sector Union puts the total at around 800 Commonwealth Bank roles over the preceding year, including 176 technology and engineering positions, and alleges that some of the eliminated roles were subsequently advertised through the bank's India-based subsidiary. By May 2026, under a chief AI officer appointed at the start of the year, the bank's Hey CommBank chatbot, running on a messaging platform built with Microsoft, was resolving almost nine in ten customer conversations without human assistance. The union has lodged a formal dispute at the Fair Work Commission and challenged the bank to state its real rationale, given a half-year net profit of 5.44 billion Australian dollars. Forty-five onshore jobs were restored with a public admission of error. Several hundred offshore contractor roles went without one, because a contract that is quietly not renewed requires no consultation and generates no headline.

The Washington Times reported on 10 March 2026 that complaints from frustrated customers had prompted e-commerce and financial technology firms to quietly rehire content writers, software engineers and customer service workers replaced by AI systems, and that IBM, Salesforce, Google and Meta had added undisclosed numbers of workers in redefined roles. The operative words in that reporting are quietly, undisclosed and redefined. A reversal conducted quietly, at undisclosed scale, into redefined roles is not the same thing as a retraction.

What an apology would have to include

For the individual worker holding the phone, the practical questions are narrow and answerable. Is the role permanent or a contract? Is the pay above or below where it was, adjusted for two years of inflation? Is there written severance protection this time? Has the organisation changed how it makes automation decisions, or only which decision it reached about your function? Robert Half's Dawn Fay is right that judgement, oversight and institutional knowledge could not be replaced. The question worth asking the employer is whether they now have a process for finding that out before the redundancy consultation rather than three years afterwards.

The broader accounting is harder, because the costs and the benefits landed on different people. Ford's shareholders got a J.D. Power ranking and hundreds of millions in cost tailwind. The engineers got three years of disruption. Klarna's investors got a widely admired efficiency story in 2024 and a widely admired humility story in 2025. The 700 agents got neither. Across the Careerminds sample, roughly seven in ten organisations rehired, and roughly seven in ten found that the exercise had made them no money at all. The severance was paid, the recruitment fees were paid, the re-onboarding was paid, the quality failures were paid for by customers, and the ledger came out flat. The only unambiguous transfer was from the workers to nobody in particular.

What the cycle reveals about the gap between AI rhetoric and AI capability is not that the technology is useless. Ford still uses it. Klarna's assistant still handles two-thirds of conversations. IBM's AskHR still resolves 94 per cent of routine requests. The gap is not between AI working and AI failing. It is between a claim about what a system will be able to do and evidence about what it currently does, and the fact that in 2025 and 2026 a great many organisations treated the first as though it were the second, then priced human beings out of their livelihoods on the strength of it.

The workers who are being called back were not defeated by a machine. They were removed by a forecast. Those are different things, and the difference is the whole of the injury.

Sources and References

1. Crumley, Bruce. “55 Percent of Leaders Regret AI Layoffs, and a Major Hiring Reversal Has Begun.” Inc., late July 2026. https://www.inc.com/bruce-crumley/55-percent-of-leaders-regret-ai-layoffs-and-a-major-hiring-reversal-has-begun/91380901
2. Forrester Research. “Predictions 2026: The Workforce Muddles Through Ambient Disruption.” Forrester, October 2025. https://www.forrester.com/blogs/future-of-work-predictions-2026-whats-coming-for-work-and-the-workforce
3. Orgvue. “55% of Businesses Admit Wrong Decisions in Making Employees Redundant When Bringing AI Into the Workforce.” PR Newswire, 29 April 2025. https://www.prnewswire.com/news-releases/55-of-businesses-admit-wrong-decisions-in-making-employees-redundant-when-bringing-ai-into-the-workforce-302440611.html; and “92% of Organizations Have Invested in AI but 78% Say Projects Have Either Stalled or Failed.” Orgvue, 2026. https://www.orgvue.com/news/92-of-organizations-have-invested-in-ai-but-78-say-projects-have-either-stalled-or-failed/
4. Careerminds. “AI-Led Layoffs: What HR Leaders Wish They Knew Before Making Job Cuts.” Careerminds, 2026, survey of 600 HR professionals, February 2026. https://careerminds.com/blog/cost-of-ai-layoffs
5. “Employers Who Laid Off Workers Citing AI Are Already Starting to Regret It.” CNBC, 1 July 2026, reporting Robert Half data. https://www.cnbc.com/2026/07/01/employers-who-laid-off-workers-for-ai-are-reversing-their-decisions.html
6. Castrillon, Caroline. “Your AI Layoff Was Reversed. Should You Go Back?” Forbes, 26 July 2026. https://www.forbes.com/sites/carolinecastrillon/2026/07/26/your-ai-layoff-was-reversed-should-you-go-back/
7. “The Great AI Layoff Is Turning Into the Great AI Rehire.” Fast Company, 2026. https://www.fastcompany.com/91571824/the-great-ai-layoff-is-turning-into-the-great-ai-rehire
8. “Companies Rehire Workers After AI Replacements Fail.” The Washington Times, 10 March 2026. https://www.washingtontimes.com/news/2026/mar/10/ai-layoff-reversal-companies-rehire-customer-roles-eliminated/
9. “Ford Has Been Rehiring Quality Inspectors After AI Fell Short.” Bloomberg, 25 June 2026. https://www.bloomberg.com/news/articles/2026-06-25/ford-has-been-rehiring-quality-inspectors-after-ai-fell-short
10. “Ford Rehires 'Gray Beard' Engineers After AI Falls Short.” TechCrunch, 28 June 2026. https://techcrunch.com/2026/06/28/ford-rehires-gray-beard-engineers-after-ai-falls-short/
11. Challenger, Gray and Christmas. “Challenger Report: June Layoffs Cool to 45,849, Down 53% From May; AI Leads Reasons for Fourth Consecutive Month.” 1 July 2026. https://www.challengergray.com/blog/challenger-report-june-layoffs-cool-to-45849-down-53-from-may-ai-leads-reasons-for-fourth-consecutive-month/; and “Job Cut Announcement Report, May 2026.” June 2026. https://www.challengergray.com/wp-content/uploads/2026/06/Challenger-Report-May-2026.pdf
12. Autor, David H. “Polanyi's Paradox and the Shape of Employment Growth.” NBER Working Paper 20485, September 2014. https://www.nber.org/system/files/working_papers/w20485/w20485.pdf
13. Falk, Brett Hemenway, and Gerry Tsoukalas. “The AI Layoff Trap.” arXiv:2603.20617, submitted 21 March 2026, revised 3 June 2026. https://arxiv.org/abs/2603.20617
14. Paul, Karsten I., and Klaus Moser. “Unemployment Impairs Mental Health: Meta-Analyses.” Journal of Vocational Behavior, vol. 74, 2009, pp. 264 to 282. DOI 10.1016/j.jvb.2009.01.001. https://www.sciencedirect.com/science/article/abs/pii/S0001879109000037
15. Sullivan, Daniel, and Till von Wachter. “Job Displacement and Mortality: An Analysis Using Administrative Data.” The Quarterly Journal of Economics, vol. 124, no. 3, August 2009, pp. 1265 to 1306. https://academic.oup.com/qje/article-abstract/124/3/1265/1905153
16. Granulo, Armin, Christoph Fuchs and Stefano Puntoni. “Psychological Reactions to Human Versus Robotic Job Replacement.” Nature Human Behaviour, vol. 3, no. 10, October 2019, pp. 1062 to 1069. DOI 10.1038/s41562-019-0670-y. https://www.nature.com/articles/s41562-019-0670-y
17. Arnold, John D., Chad H. Van Iddekinge, Michael C. Campion, Talya N. Bauer and Michael A. Campion. “Welcome Back? Job Performance and Turnover of Boomerang Employees Compared to Internal and External Hires.” Journal of Management, 2021. https://journals.sagepub.com/doi/abs/10.1177/0149206320936335
18. 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/; and “We Are Changing Our Developer Productivity Experiment Design.” 24 February 2026. https://metr.org/blog/2026-02-24-uplift-update/
19. METR. “Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity.” 11 May 2026. https://metr.org/blog/2026-05-11-ai-usage-survey/
20. “MIT Report Finds Most AI Business Investments Fail, Reveals GenAI Divide.” Virtualization Review, 19 August 2025, reporting MIT Project NANDA “The GenAI Divide: State of AI in Business 2025”. https://virtualizationreview.com/articles/2025/08/19/mit-report-finds-most-ai-business-investments-fail-reveals-genai-divide.aspx
21. “Klarna Changes Its AI Tune and Again Recruits Humans for Customer Service.” CX Dive, 9 May 2025. https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/
22. “Commonwealth Bank Reverses Job Cuts Decision Over AI Chatbots.” Bloomberg, 21 August 2025. https://www.bloomberg.com/news/articles/2025-08-21/commonwealth-bank-reverses-job-cuts-decision-over-ai-chatbots
23. “AI Drives Fresh CommBank Job Cuts.” Information Age, Australian Computer Society, 30 July 2026. https://ia.acs.org.au/article/2026/ai-drives-fresh-commbank-job-cuts.html; and “CBA Axes Contractor Call Centre Jobs After AI Rollout.” Human Resources Director Australia, 29 July 2026. https://www.hcamag.com/au/specialisation/hr-technology/cba-axes-contractor-call-centre-jobs-after-ai-rollout/584060
24. “IBM Plans to Triple Entry-Level Hiring in the US in 2026.” Bloomberg, 12 February 2026. https://www.bloomberg.com/news/articles/2026-02-12/ibm-plans-to-triple-entry-level-hiring-in-the-us-in-2026
25. “IBM Replaced Hundreds of HR Workers With AI, According to Its CEO.” Entrepreneur, 2025. https://www.entrepreneur.com/business-news/ibm-ceo-ai-replaced-hundreds-of-human-resources-staff/491341

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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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Section 5.2 of Nanit's terms of service is not written about babies. It is written about content, in the flat contractual register every consumer technology company has used since roughly 2007. By submitting User Content through the Services, it says, you grant the company “a worldwide, non-exclusive, perpetual, royalty-free, sub-licensable and transferable license to use, process, store, edit, modify, truncate, aggregate, reproduce, transfer, modify, distribute, prepare derivative works of, display, perform, and otherwise fully exploit the User Content in connection with the Site, the App and the Services and our (and our successors' and assigns') businesses.”

User Content, the same document explains, means all content added, created, uploaded, submitted, distributed or posted to the services by users. In the case of a Nanit Pro, the content in question is overhead infrared video of a sleeping infant, captured from a camera mounted above the cot, running most nights for the first several years of that infant's life. The person who grants the licence is the parent. The person the licence is about cannot yet hold up their own head.

On 2 August 2026, Sapna Maheshwari of The New York Times published a profile of Nanit, the New York company whose crib-mounted camera logs the moment a baby's eyes open and close and issues a score for the night on a scale up to 100. The company told the Times it has around one million daily active users and annual revenue above 100 million dollars. The reporting also surfaced a detail no amount of policy analysis could improve upon: the branding firm that built Nanit's identity described its own brief as transcending the negative connotations of surveillance.

That is a candid sentence, and it points at the wrong problem. The interesting thing about the AI nursery camera is not that it is surveillance. Parents have always watched their children, and a camera that lets you check on a sleeping baby without opening a creaking door is a small, genuine good. The interesting thing is what the category has become. Nanit's chief executive describes the value proposition with unusual clarity, and what she describes is not a better way for a parent to watch. It is a system that watches instead.

That substitution is the story. A parent's attention is forgetful, reciprocal and impossible to transfer. A system's attention is total, permanent, non-reciprocal and, per section 5.2, transferable to successors and assigns. Swapping one for the other is not an upgrade to an existing practice. It is the creation of a permanent evidentiary record about a person who will not be able to read it, contest it, or ask who else holds a copy, for approximately eighteen years.

What a Sleep Score Is Actually Made Of

Start with the artefact, because the artefact is stranger than the debate around it.

Nanit describes Sleep Score as the first AI-driven, science-backed sleep score designed specifically for babies, delivered each morning as a single number up to 100 alongside a comparison to the baby's own typical range. It is assembled from four components. Sleep duration is total actual sleep across the night. Sleep timing measures how well bedtime and waking align with age-appropriate biological rhythms. Sleep continuity covers night wakings and time spent awake between sleep periods. The fourth component is parent visits, defined as the frequency with which a parent has to come in and settle the baby.

Read that fourth component again. A metric marketed as a measure of infant sleep quality is partly a count of how often the adult entered the room. Whatever the design intention, the artefact grades the caregiver, and grades them downward for the thing caregiving consists of. Nanit's explainer page cites no peer-reviewed validation of Sleep Score itself, pointing instead to its in-house Nanit Lab and a scientific advisory board.

The breathing feature is more elegant and more revealing. Rather than strap a sensor to the infant, Nanit sells cotton garments, a swaddle and a band, printed with a bespoke pattern. The camera watches the pattern rise and fall, infers breathing motion from the deformation, and alerts the parent if the motion stops. There are no electronics in the garment at all. The company has solved the wearable comfort problem by moving the entire sensing apparatus into computer vision. The nursery is not instrumented. It is watched by a model that has learnt what a breathing infant looks like.

The commercial architecture sits on top. Live video, sound and motion alerts and two-way audio are free. The analysis is not. Nanit's Insights tiers gate the data behind subscription: 120 dollars a year buys thirty days of sleep analysis, two days of continuous video history, 200 saved memories and up to ten caregiver profiles, while 300 dollars a year unlocks unlimited sleep analysis, seven days of continuous video history, growth tracking, and an unlimited number of people on the parenting team.

Unlimited. Not a number at all, because the vendor saw no reason to set one. That tells you what the product understands itself to be. Not a monitor. A distribution platform for footage of a child, with tiered access, retention windows set by the vendor, and an audience list with no upper bound.

Owlet Found the Regulatory Boundary by Crossing It

The other pole of this market took the opposite technical route and, in doing so, mapped the regulatory terrain for everybody else.

Owlet's Smart Sock wrapped a pulse oximeter around an infant's foot and reported heart rate and blood oxygen saturation to a phone. On 5 October 2021 the US Food and Drug Administration issued the company a warning letter, catalogued as MARCS-CMS 616354. The agency's reasoning was narrow and precise. Products measuring blood oxygen saturation and pulse rate become devices when they are intended to identify desaturation and bradycardia and to alarm when measurements fall outside preset values. Owlet had marketed the Smart Sock without clearance, in violation of the Federal Food, Drug, and Cosmetic Act. The company pulled the product.

What followed is the most instructive sequence in this industry. Owlet did not abandon the category. It went through the front door. BabySat, a prescription pulse oximetry sock, was cleared in June 2023. On 9 November 2023 the company announced De Novo clearance for Dream Sock, which it described as the first and only over-the-counter medical-grade pulse oximeter cleared for infants, indicated for healthy babies aged one to eighteen months weighing between six and thirty pounds. Both launched in 2024, and the FDA closed out the warning letter that June.

So Owlet is now, in the relevant respects, a regulated medical device company, with indications, contraindications, a cleared intended use and clinical testing behind it. And here is the asymmetry that should trouble anyone thinking about this market: Nanit, which points a camera at a child every night for years, extracts breathing motion, motor patterns, position and activity from the video, scores the result, and now proposes to project developmental trajectories from it, sits entirely outside that regime. Not because it collects less. Because it claims less.

The line between a consumer gadget and a medical device here is drawn by marketing copy. Say your sock detects desaturation and you enter a world of clinical validation and post-market surveillance. Say your camera provides insights and you do not. The volume, intimacy and longitudinal depth of the data are irrelevant to which side of the line you land on.

The Paediatric Literature Never Signed Off on Any of This

Long before the FDA acted, paediatricians were saying the quiet part in journals.

On 24 January 2017, Christopher Bonafide, Elizabeth Foglia and David Jamison published a Viewpoint in JAMA titled “The Emerging Market of Smartphone-Integrated Infant Physiologic Monitors”. They examined five models introduced over the preceding two years, priced between 150 and 300 dollars, and noted that because the manufacturers did not directly claim to treat, diagnose or prevent disease, the monitors were not regulated as medical devices. Their conclusion was blunt. There was no publicly available evidence supporting the safety, accuracy or effectiveness of these products, and no medical indications for their use in healthy infants.

The following year, Bonafide's group tested two of them. Published in JAMA in August 2018, the study put the Owlet Smart Sock 2 and the Baby Vida on thirty hospitalised infants at the Children's Hospital of Philadelphia, against a hospital-grade reference monitor. The Smart Sock 2 showed 88.8 per cent sensitivity and 85.7 per cent specificity for hypoxaemia, and zero per cent sensitivity for bradycardia. The Baby Vida managed zero per cent sensitivity for hypoxaemia. Bonafide's summary to the press was the correct one. If something is going wrong with a sick infant, you would want to know that one hundred per cent of the time.

Then, in 2022, the American Academy of Pediatrics updated its safe sleep policy statement, produced by its Task Force on Sudden Infant Death Syndrome. The recommendation is one line and has not changed since. Do not use home cardiorespiratory monitors as a strategy to reduce the risk of SIDS. No monitor holds FDA authorisation to prevent sudden infant death, and there is no evidence that commercial infant vital sign devices reduce these deaths. The concern is not merely that they fail to help. It is that false alarms drive unnecessary anguish and clinical workup, while normal-looking readings offer a false reassurance that can substitute for the safe sleep practices that do work.

None of this makes Nanit's Sleep Score dangerous in the way an unreliable oximeter is dangerous. It is an argument about epistemic standing. The paediatric literature has repeatedly declined to certify that consumer infant monitoring measures anything clinically meaningful in healthy babies. Nanit is building a prediction engine on that substrate.

Care Turned Into Record-Keeping and the Chief Executive Said So

On 16 December 2025, Nanit announced a 50 million dollar growth round led by Springcoast Partners, taking total funding to 125.1 million dollars according to Forbes. The money is earmarked for what the company calls a Parenting Intelligence System, rolling out through 2026.

Anushka Salinas, who became Nanit's chief executive in March 2024 after serving as president and chief operating officer of Rent the Runway, framed the opportunity in the announcement. “Adults now track sleep, stress, and health patterns to spot trends and intervene early,” she said, “yet babies, the group that benefits most from early insight, have no comparable system for monitoring or predictive guidance.”

And then, to Forbes, the sentence that ought to be the epigraph for this entire market. “What you don't know is often what creates anxiety. When the system is doing the monitoring and interpretation, parents don't feel like they have to constantly watch and worry themselves.”

That is not a description of a tool that extends parental attention. It is a description of one that absorbs it. And it is offered, quite reasonably, as a benefit, because parental sleep deprivation is real and the anxiety of a first year is genuinely corrosive. But look at what has been swapped.

A parent watching a baby is doing something with four properties. It is forgetful: almost none of it is retained, and what is retained is retained as memory, which decays and reshapes and is legally inert. It is reciprocal: the baby watches back, and being watched by a face that responds is how infants learn that their signals produce effects in the world. It is bounded: the watching stops when the parent leaves the room and cannot be resumed retrospectively. And it is non-transferable: you cannot assign your recollection of your child's first months to a creditor.

A system watching a baby inverts all four. It retains by default and forgets only by policy. It is non-reciprocal, because a computer vision model registers the infant without responding to them. It is total across whatever window the subscription tier defines. And it is transferable, in explicit contractual terms, to successors and assigns.

Helen Nissenbaum's framework of contextual integrity, set out in the Washington Law Review in 2004, is the cleanest way to name what has changed. Information flows, on her account, are governed by norms specified across five parameters: the data subject, the sender, the recipient, the information type and the transmission principle. Nobody has ever objected to a parent knowing how their baby slept. What has changed is the transmission principle and the recipient. Deborah Lupton and Ben Williamson made the parallel point in New Media and Society in 2017, describing a datafied child subject to dataveillance from in utero onward. The nursery has not become less private in the sense of who can see in. It has become a site of extraction, and the norm violated is not visibility but permanence.

Every Statute We Have Regulates the Wrong Moment

Here is where the regulatory picture gets genuinely awkward, because the laws are not absent. They are numerous, recently strengthened, and pointed at a moment that does not carry the weight.

The Children's Online Privacy Protection Act is the obvious candidate, and the Federal Trade Commission gave it real teeth in January 2025. The amended Rule took effect on 23 June 2025, with a compliance deadline of 22 April 2026 that passed a few months ago. It expands personal information to include biometric identifiers, among them voiceprints, gait patterns, facial templates and faceprints. It mandates a written retention policy stating why data is collected and when it will be deleted, and prohibits indefinite retention outright. Most pointedly, the Commission's own commentary states that disclosing a child's personal information to third parties to train or otherwise develop artificial intelligence technologies is not integral to the service and requires separate consent.

Excellent rules. Now ask whether they bite. COPPA governs operators of services directed to children under thirteen, or operators with actual knowledge that they are collecting personal information from a child. A crib camera is marketed to adults, purchased by adults, configured by adults and operated by adults. Nanit's own privacy policy states that its services are not directed to, nor intended to be operated by, children under sixteen. That statement is true, and it is precisely the problem. The infant is not a user. The infant is the subject matter. COPPA's architecture was built for a child typing into a website, not for a child being filmed by one.

The European instruments have the same shape. Article 8 of the GDPR conditions lawfulness on parental authorisation where a child is below sixteen, with member states permitted to set a floor as low as thirteen, but it governs information society services offered directly to a child. Recital 65 gives the right to erasure special force where consent was given by a data subject when they were a child, and states that the right survives into adulthood. That is exactly the right instinct, and it presumes a child who once gave consent. An infant never did.

The UK's Age Appropriate Design Code comes closest. Its fifteen standards, in force since September 2021, apply to services likely to be accessed by children, explicitly including connected toys and devices, require that the best interests of the child outrank commercial interest, and require profiling to be off by default. But it too is oriented around a child as user, and its enforcement centre of gravity has been social platforms and games rather than nursery hardware. The EU AI Act's prohibitions, applicable since 2 February 2025, ban emotion recognition in the workplace and in education, and biometric categorisation used to infer protected characteristics. Neither reaches a domestic device inferring developmental state from video in a family home.

American state biometric law is the sharpest tool in the box and the least aimed. Illinois' Biometric Information Privacy Act carries statutory damages of 1,000 dollars for negligent and 5,000 for reckless or intentional violations, though an amendment signed in August 2024 blunted even that, limiting a claimant to a single recovery per person however many times the identifier was scanned or disclosed. Texas' Capture or Use of Biometric Identifier statute produced a 1.4 billion dollar settlement with Meta in 2024, the largest ever obtained by a single state. Washington's My Health My Data Act, in force since March 2024, goes furthest, defining consumer health data to include information derived or extrapolated from non-health information through machine learning and algorithms, and providing a private right of action.

That last definition is the closest anything in American law comes to describing what a nursery camera does. But every one of these statutes turns on consent at the point of collection, and consent at the point of collection is the one thing this situation cannot supply.

Consent Cannot Represent Someone Who Has Not Arrived Yet

Consent works as a legitimating device under one condition: the party who consents is the party who bears the consequences. Every argument for informed consent, in medicine, in research ethics, in data protection, rests on that alignment. Remove it and consent stops being a transfer of authority and becomes a transfer of liability.

In the nursery, the alignment breaks in three directions at once, and the breaks compound.

The consenting party is not the data subject. A parent clicking through terms of service exercises a legitimate authority over their child's welfare, and nobody sensible wants to strip parents of it. But the interest traded away is not the parent's. Stacey Steinberg, writing in the Emory Law Journal in 2017, described parents as simultaneously the gatekeepers of their children's personal information and the narrators of their children's stories. Sharenting, though, is disclosure by a parent who chose what to say. This is extraction by a firm that chose what to record, at a resolution and duration no parent can evaluate.

The data subject can never ratify. There is no moment at which the infant becomes able to say yes or no to a decision already taken. A teenager can delete an Instagram account. They cannot un-generate four years of nightly behavioural telemetry, because they were never told it exists, do not know the corporate entity that holds it, and cannot identify what has been derived from it.

And the timing is structurally impossible. The gap between collection and the subject's legal capacity to object is roughly eighteen years. Retention windows are measured in months to years. Corporate acquisitions happen on a timescale of three to seven years. Model training happens once and is, for practical purposes, irreversible. Insolvency can happen at any point. By the time a person acquires standing to object to what was recorded in their cot, every consequential decision about that record has already been made, several times, by entities that may no longer exist.

This is not a complaint about weak consent. Strengthening consent does not help. A perfectly informed, freely given, granular, revocable parental consent still fails, because the failure is not in the quality of the permission. It is in the identity of the person granting it. The consent architecture is structurally incapable of representing the only person whose data it is.

The Precedents Are All About What Happens Afterwards

If you want to know how this ends, the record is not speculative. Every failure mode has already occurred in an adjacent product category, usually more than once.

Start with security. On 4 September 2013 the FTC announced a settlement with TRENDnet over its internet-connected cameras, its first action against a connected consumer product of this kind. According to the Commission's complaint, a hacker exploited a flaw in the company's software and posted links to roughly 700 customers' live feeds, which included babies asleep in cots and young children playing. The company had transmitted and stored login credentials in clear readable text. TRENDnet accepted twenty years of third-party security audits.

Ten and a half years later, in February 2024, roughly 13,000 Wyze users received thumbnail images from cameras that were not theirs as devices reconnected after a cloud outage, and 1,504 of them tapped through. A third-party caching library had mixed up device and user identifiers under load. Nothing malicious happened. The system simply handed strangers pictures of other people's homes.

Now retention. In May 2023 the FTC and the Department of Justice charged Amazon with violating COPPA by keeping children's Alexa voice recordings indefinitely and failing to honour parental deletion requests, resulting in a 25 million dollar settlement. The detail that matters is why the recordings were valuable. Children's speech patterns differ from adults', so retained child voice data was a uniquely useful corpus for teaching Alexa to understand children, and transcripts trained subsequent versions without that practice being disclosed in the privacy policy. Deletion was promised, the data was retained, and the retained data made the product better.

Then ownership. In March 2025, 23andMe filed for Chapter 11 bankruptcy. Regeneron agreed to acquire it for 256 million dollars, pledging to honour existing privacy commitments. More than two dozen state attorneys general sued to stop the sale, arguing that genetic information is categorically unlike the assets that ordinarily change hands in an insolvency. The auction was reopened, Regeneron declined to raise its bid, and on 30 June 2025 a judge approved the 305 million dollar sale of the company and its genetic database to TTAM Research Institute, a nonprofit led by co-founder Anne Wojcicki. Customers who consented to one entity holding their genome ended up with a different entity holding it, through a process in which they were not parties.

Finally, the counterexample. In 2017 Mattel announced Aristotle, an always-on voice and camera hub designed to sit in a child's bedroom from birth through adolescence. Senator Edward Markey and Representative Joe Barton demanded answers on children's privacy, campaigners delivered more than 20,000 petition signatures on 2 October 2017, and two days later Mattel cancelled the product before it shipped, and therefore before it had accumulated anything.

Read those five together and the pattern is unmistakable. The promises are made by an entity that will not necessarily be the entity holding the data. The retention is not an accident but a business rationale. And the only intervention that ever fully worked was the one that happened before collection began.

Prediction Is a Different Product From Observation

The Parenting Intelligence System announcement lists six things the platform will measure. Personalised sleep guidance. Key health metrics. Movement signatures and breathing patterns. Motor skill milestones. Speech and language patterns. And then the sixth, which is a different kind of thing entirely: trends that may predict metabolic, emotional, or cognitive challenges.

Nanit currently covers birth to age five, according to Forbes, and intends to extend its models to ages nought to ten. Its own announcement gestures at a horizon of birth to a hundred years old.

There is a real category difference between a record and a prior, and the marketing language elides it. A record says what a baby did on a given night. A prior says what kind of person a baby is likely to become. The first is a description that can be checked against the world. The second is a claim about the future that is unfalsifiable at the moment of generation, because the future has not happened, and self-confirming afterwards, because the parents who receive it will act on it.

That is the mechanism that should worry people, and it is not a data breach. It is a healthy child assigned a low score on emotional-regulation trend lines at fourteen months, whose parents, being good parents, respond by treating them as a child who needs watching. It is a nursery-derived cognitive flag that follows a family into a paediatrician's appointment, or a nursery application, or, eventually, an insurance underwriting model that has learnt to want it. The prediction does not have to be accurate to be consequential. It has to be delivered, and it has to be believed.

And recall the substrate. We are proposing to build developmental forecasts from a score that counts parental visits as a negative, on a class of measurement the relevant clinical bodies have declined to endorse, at a scale of a million children a night.

The Research Literature Is Asking for a Different Design

Three arXiv papers were put forward as evidence for this argument, and honest handling of them matters more than rhetorical convenience.

The first, arXiv 2602.17418, submitted on 19 February 2026 by Diana Addae, Diana Rogachova, Nafiseh Kahani, Masoud Barati, Michael Christensen and Chen Zhou, is titled “A Privacy by Design Framework for Large Language Model-Based Applications for Children”. It is constructive rather than critical, proposing controls across data collection, model training, operational monitoring and validation, grounded in the UN Convention on the Rights of the Child and the UK Age Appropriate Design Code. It is not a finding that large-scale AI surveillance undermines children's privacy. It is a proposal for how to avoid doing so, and its relevance here is that its guidelines exist and consumer nursery hardware is built to none of them.

The second, arXiv 2606.17957, submitted on 16 June 2026 by Kopo Ramokapane, Rui Huan, Zaina Dkaidek and Awais Rashid, is titled “Children Are Not the Enemy: Child-Fit Security as an Alternative to Bans and Surveillance”. It argues that containment approaches, meaning access restrictions, parental monitoring and screen-time limits, are insufficient, and proposes treating children as legitimate users whose wellbeing, development, privacy, safety, agency and rights are core security requirements, shifting the object of protection from the system to the child-system relationship. That is the strongest academic articulation of the principle this market has inverted.

The third, arXiv 2607.01506, does not say what it has been cited as saying, and pretending otherwise would be indefensible in an article about evidentiary standards. Submitted on 1 July 2026 by Tomohiro Nagashima, Lisa Siegrist, Niklas Scholz, Shintaro Sato, Martina Vincoli and Man Su, it is titled “Mind the Trust Gap: Identifying (Mis)alignments in Teacher-Student Views Toward Control and Agency in K-12 Classroom AI”. It reports a speed-dating study with sixteen students and fifteen teachers in Germany and finds misalignments in trust and in views on the social and emotional dimensions of learning with AI. It does not document children reporting fear of constant AI monitoring. The honest version of the claim it was offered to support is narrower and still useful: where children have been asked, their views on AI oversight diverge measurably from those of the adults deciding on their behalf. Infants cannot be asked at all.

Hold It In Trust or Do Not Hold It At All

The instinct at this point is to demand better disclosure, and it is the wrong instinct. Disclosure assumes a reader who can act on it. There is no such reader here. The right move is to stop treating infant behavioural data as ordinary consumer data governed by contract, and start treating it as property held for a beneficiary who has not yet arrived. Four things follow.

Give the infant statutory data subject status with a fiduciary rather than a consenting parent. A parent authorising collection about a person who cannot object should be held to something closer to a trustee's duty than a customer's tick-box: obliged to act in the child's interests rather than their own convenience, and unable to authorise uses a reasonable adult would later refuse. The UK Children's Code gestures at this when it puts the child's best interests ahead of commercial interest. It needs to bind the transaction, not just the interface design.

Impose hard expiry on the record rather than on the retention policy. Raw nursery video should default to deletion within days, not whatever the subscription tier allows. Derived metrics should expire on a statutory schedule tied to the child's age, not to the company's business need, because the amended COPPA Rule's requirement of a stated business need is satisfied by any business that has one.

Bar transfer in insolvency. The 23andMe case is not an analogy, it is a preview. A firm holding nightly video and behavioural scores on a million children is an acquisition target, and a bankrupt one is an estate. Infant biometric and behavioural data needs to be statutorily excluded from the pool of saleable assets, so that who ends up holding it is never decided by a creditors' committee. Consent given to one company is not consent given to its successors and assigns, whatever section 5.2 says.

And create a right of arrival. At the age of majority, every person should receive a statutory notice naming every entity holding data generated about them before they could consent, describing what was derived from it, and offering erasure through a single form. Recital 65 already contains the moral intuition. It simply has no delivery mechanism, because it assumes the person knows what happened to them.

The counterargument is that this is a lot of machinery for a camera that helps exhausted parents through the first year, and that parents like these products. Both points are true, and nobody should want to take the camera away. But the case for regulation does not rest on the device being harmful. It rests on the fact that the transaction has no representative for the one party whose entire early life is its subject matter, and that every existing legal instrument tries to fix that by asking somebody else to sign.

The generation now being scored will not remember being watched, which is precisely the point. They will encounter their own infancy the way the rest of us encounter a credit file: as a document assembled by strangers, from a period they cannot recall, according to criteria they never saw, held by a company they never chose. Their parents will have consented on their behalf, in good faith, to terms promising nothing more alarming than insights, and reserving, in the ordinary language of commerce, a perpetual and transferable licence to fully exploit the record. The forgetting that used to be the default condition of infancy was not a technical limitation. It was the thing that made being watched by someone who loved you different from being watched by anything else.

Sources and References

1. Sapna Maheshwari, “A profile of Nanit, a maker of AI-equipped baby cameras for hyper-detailed health data tracking, which says it has 1M daily users and $100M+ in annual revenue,” The New York Times, 2 August 2026, as indexed by Techmeme. https://www.techmeme.com/260803/p8
2. Nanit, “Terms of Service,” nanit.com, accessed 5 August 2026. https://www.nanit.com/pages/terms-of-service
3. Nanit, “Introducing: Sleep Score, the First-Ever Holistic Sleep Metric for Babies,” nanit.com, accessed 5 August 2026. https://www.nanit.com/blogs/parent-confidently/introducing-sleep-score-the-first-ever-holistic-sleep-metric-for-babies
4. Nanit Support, “What is Nanit Breathing Wear?” support.nanit.com, accessed 6 August 2026. https://support.nanit.com/en_us/what-is-nanit-breathing-wear-SkTibnAtgx
5. Nanit, “Nanit Memberships: Sleep, Milestones & Insights,” nanit.com, accessed 5 August 2026. https://www.nanit.com/pages/memberships
6. Nanit, “Nanit Raises $50M to Expand Its AI-Powered Systems, Giving Parents Real-Time Insights Into Infant Health and Development,” PR Newswire, 16 December 2025. https://www.prnewswire.com/news-releases/nanit-raises-50m-to-expand-its-ai-powered-systems-giving-parents-real-time-insights-into-infant-health-and-development-302643439.html
7. Gemma Allen, “Nanit Raises $50 Million To Expand AI In Early Childhood Development,” Forbes, 16 December 2025. https://www.forbes.com/sites/gemmaallen/2025/12/16/nanit-raises-50-million-to-expand-ai-in-early-childhood-development/
8. US Food and Drug Administration, “Owlet Baby Care Inc. Warning Letter, MARCS-CMS 616354,” 5 October 2021. https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/owlet-baby-care-inc-616354-10052021
9. Owlet Baby Care Inc., “Owlet Achieves De Novo FDA-Clearance For Dream Sock, The First and Only Over-the-Counter, Medical Grade Pulse Oximeter Cleared for Infants,” investors.owletcare.com, 9 November 2023. https://investors.owletcare.com/news/news-details/2023/Owlet-Achieves-De-Novo-FDA-Clearance-For-Dream-Sock--The-First-and-Only-Over-the-Counter-Medical-Grade-Pulse-Oximeter-Cleared-for-Infants/default.aspx
10. Christopher P. Bonafide, Elizabeth E. Foglia and David T. Jamison, “The Emerging Market of Smartphone-Integrated Infant Physiologic Monitors,” JAMA, 24 January 2017. https://jamanetwork.com/journals/jama/article-abstract/2598780
11. Children's Hospital of Philadelphia, “Two Consumer Baby Monitors Show Worrisome Results in Measuring Vital Signs,” chop.edu, 21 August 2018. https://www.chop.edu/news/consumer-use-baby-monitors-have-little-proven-benefit-healthy-infants
12. Rachel Y. Moon et al., American Academy of Pediatrics Task Force on Sudden Infant Death Syndrome, “Sleep-Related Infant Deaths: Updated 2022 Recommendations for Reducing Infant Deaths in the Sleep Environment,” Pediatrics, July 2022. https://publications.aap.org/pediatrics/article/150/1/e2022057990/188304/
13. Federal Trade Commission, “FTC Finalizes Changes to Children's Privacy Rule Limiting Companies' Ability to Monetize Kids' Data,” ftc.gov, 16 January 2025. https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-finalizes-changes-childrens-privacy-rule-limiting-companies-ability-monetize-kids-data
14. Norton Rose Fulbright Data Protection Report, “FTC's COPPA Rule changes include AI training consent requirement,” dataprotectionreport.com, June 2025. https://www.dataprotectionreport.com/2025/06/ftcs-coppa-rule-changes-include-ai-training-consent-requirement/
15. Information Commissioner's Office, “Introduction to the Children's code,” ico.org.uk, accessed 5 August 2026. https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/childrens-information/childrens-code-guidance-and-resources/introduction-to-the-childrens-code/
16. European Parliament and Council, “Article 8: Conditions applicable to child's consent in relation to information society services,” General Data Protection Regulation. https://gdpr-info.eu/art-8-gdpr/
17. European Parliament and Council, “Article 5: Prohibited AI Practices,” EU Artificial Intelligence Act. https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-5
18. Federal Trade Commission, “Marketer of Internet-Connected Home Security Video Cameras Settles FTC Charges It Failed to Protect Consumers' Privacy,” ftc.gov, 4 September 2013. https://www.ftc.gov/news-events/news/press-releases/2013/09/marketer-internet-connected-home-security-video-cameras-settles-ftc-charges-it-failed-protect
19. Federal Trade Commission, “FTC and DOJ Charge Amazon with Violating Children's Privacy Law by Keeping Kids' Alexa Voice Recordings Forever and Undermining Parents' Deletion Requests,” ftc.gov, 31 May 2023. https://www.ftc.gov/news-events/news/press-releases/2023/05/ftc-doj-charge-amazon-violating-childrens-privacy-law-keeping-kids-alexa-voice-recordings-forever
20. Danielle Abril, “Wyze security issue exposed private cameras to 13,000 users,” The Washington Post, 20 February 2024. https://www.washingtonpost.com/technology/2024/02/20/wyze-camera-security-breach/
21. Jaclyn Diaz, “Judge OKs sale of 23andMe, and its trove of DNA data, to a nonprofit led by its founder,” NPR, 30 June 2025. https://www.npr.org/2025/06/30/nx-s1-5451398/23andme-sale-approved-dna-data
22. Office of the Attorney General of Texas, “Attorney General Ken Paxton Secures $1.4 Billion Settlement with Meta Over Its Unauthorized Capture of Personal Biometric Data,” texasattorneygeneral.gov, 30 July 2024. https://www.texasattorneygeneral.gov/news/releases/attorney-general-ken-paxton-secures-14-billion-settlement-meta-over-its-unauthorized-capture
23. Diana Addae, Diana Rogachova, Nafiseh Kahani, Masoud Barati, Michael Christensen and Chen Zhou, “A Privacy by Design Framework for Large Language Model-Based Applications for Children,” arXiv:2602.17418, 19 February 2026. https://arxiv.org/abs/2602.17418
24. Kopo M. Ramokapane, Rui Huan, Zaina Dkaidek and Awais Rashid, “Children Are Not the Enemy: Child-Fit Security as an Alternative to Bans and Surveillance,” arXiv:2606.17957, 16 June 2026. https://arxiv.org/abs/2606.17957
25. Tomohiro Nagashima, Lisa Siegrist, Niklas Scholz, Shintaro Sato, Martina Vincoli and Man Su, “Mind the Trust Gap: Identifying (Mis)alignments in Teacher-Student Views Toward Control and Agency in K-12 Classroom AI,” arXiv:2607.01506, 1 July 2026. https://arxiv.org/abs/2607.01506

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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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Campbell McHenry, a high school junior from Mississippi, had a word for what happens to a school with no AI policy. Unhinged. “Everyone's gonna stop learning and start relying on AI,” he told NPR during a weekend in Boston in July. “Instead of using it as a tool, they're gonna use it as a brain.”

He was sitting in a 5,500 square foot replica of the United States Senate chamber, on the campus of the University of Massachusetts Boston, built by the Edward M. Kennedy Institute for the United States Senate. It is the only full-scale copy of the chamber in the world. The desks are accurate. The rostrum is accurate. The lighting is accurate. What was not accurate, over the weekend of 17 to 19 July 2026, was the age of the people in the seats, and the fact that they were getting something done.

Ninety-eight high school students, mostly juniors and seniors, one or two from each state, spent thirty-eight hours arriving by 42 inbound flights, by Amtrak, by bus and by car, in order to draft and pass a piece of legislation about artificial intelligence in K-12 schools. On the Sunday morning they voted. The STUDENTS FIRST Act passed 82 to 16.

It is not a law. The students are not senators. The whole thing was a simulation, an expanded version of the Kennedy Institute's Senate Immersion Module, the role-play programme that has run more than 160,000 students from 21 states through the replica chamber, and which in autumn 2024 added technology reform to its slate of debate topics. This particular weekend was branded America's Youth AI Festival and organised with Day of AI, the nonprofit spun out of MIT's RAISE initiative, and AASA, The School Superintendents Association.

And yet the text those teenagers produced is now going somewhere real. AASA has said it will send the bill to its membership: more than 10,000 school system leaders, a great many of whom are, at this moment, writing AI policies of their own with almost nothing to copy from. Which raises a question that ought to be more uncomfortable than it has been. Why is the most specific, most internally coherent piece of K-12 AI legislation in circulation this summer a document written by children?

What Ninety-Eight Teenagers Actually Wrote Down

The bill is real enough to read, and reading it is more instructive than reading the coverage of it. AASA has published the full nine-page text as S.2026, styled after the 119th Congress. The acronym is tortured in the authentic congressional manner: Safe Technology Use, Data Privacy, and Ethical Norms for Teaching, Fostering Integrity and Responsible Systems in Teaching.

It runs to five sections, addressed in turn to everyone, to students, to parents and guardians, to teachers and to administrators. Each section after the first was worked up by a subcommittee with its own strained acronym, then amended and adopted by the full body. The student section is the longest, at fifteen provisions.

What is striking, on the page, is how much of the bill points away from students. Section one opens not with a rule about teenagers but with a rule about vendors: any organisation selling generative AI services to public schools “must address all known biases within their generative AI, and must inform their clients of the biases that their products represent,” and the school must then pass that information to parents. Section four tells teachers that “just as students are expected to be transparent in their AI usage, teachers should be held to the same standard.” Section five forbids administrators from using AI to hire staff, forbids them from entering student or teacher personal information into AI tools, and requires them to “consult with students, staff, and families before drafting AI rules.”

There is a running preoccupation with human contact. Educators, section one says, should omit AI “in situations where human connection is irreplaceable in the learning environment.” Administrators should require that school counsellors do not use AI tools, “particularly in mental health contexts.” Nothing in the act, it adds, shall penalise a teacher for declining to use AI at all.

This is not the document you would predict if you assumed teenagers writing their own AI rules would write themselves a permission slip. It reads instead like something drafted by people who have concluded that the technology's main risk to them is not that they will be caught using it, but that the institution around them will be hollowed out by it.

A Theory of Assessment Sharper Than Most Districts Have

The most consequential thing in the bill is also the easiest to skim past. Provision nine of the student section: “AI use is strictly prohibited on all graded assessments.” Then, immediately: “AI use is permitted for studying.”

That is a theory of assessment, and a defensible one. It says AI is a legitimate part of learning and an illegitimate part of measurement. Use it to get better; do not use it at the moment we are checking how good you got. The students arrived at this in a weekend. Australian higher education took rather longer to arrive somewhere similar. The two-lane approach set out by Adam Bridgeman and Danny Liu at the University of Sydney in July 2024, and since widely discussed across the sector, splits assessment into a secure lane, conducted under controlled conditions to verify learning, and an open lane in which AI use is scaffolded and taught. Australia's tertiary regulator followed in September 2025 with guidance on enacting assessment reform, built on the premise that detection cannot guarantee integrity and that structural redesign is the only durable answer.

Most American district policies have not made this distinction cleanly. They tend to produce a general posture, permissive or restrictive, and leave the assessment question to individual teachers. The student bill instead builds a scaffold: teachers must publish a written AI policy each semester, “explicitly stating when AI is permitted, restricted, or banned for specific assignments,” with “category based rules (essay, worksheet, test),” and those rules must be consistent across sections of the same course. The students, in other words, identified the thing that drives them mad, which is not that AI rules exist but that they differ from classroom to classroom and are frequently unwritten, and legislated against it.

They also drew a line most adults have avoided. Provision four: students “should never use AI for the act of writing,” though editing, brainstorming and studying with it are permitted after eighth grade, and only after non-AI editing has been “taught and assessed thoroughly.” Whether that is the right line is arguable. That it is a line, stated in one sentence, with an age attached and a prerequisite skill named, puts it ahead of a great deal of adult guidance.

The Clause Nobody Asked Them For

Then there is the carbon.

Provision two of the student section requires states to mandate AI literacy instruction from the moment students start using devices in the classroom, and specifies the syllabus: misinformation, plagiarism, bias in AI systems, privacy concerns, appropriate academic use, how AI functions, and “environmental impacts.”

Nothing in the American federal framework centres that last item, largely because the American federal framework barely exists. Executive Order 14277, signed on 23 April 2025 and titled Advancing Artificial Intelligence Education for American Youth, established a White House Task Force on AI Education, directed the Education Secretary to issue guidance on how existing grant funds might be spent, told the National Science Foundation to prioritise research, and launched a Presidential AI Challenge. Its subject matter is capability and workforce readiness. It does not address academic integrity. It does not address assessment. It does not mention the energy and water cost of the systems it wants children to become proficient with.

The students put it in the curriculum unprompted, alongside plagiarism, as a thing you are supposed to know about the tool before you are handed it. It is a small provision and it tells you something about the frame these teenagers are working in. They are not treating AI as a neutral productivity layer that has arrived to help them. They are treating it as an industrial product with costs, made by companies with interests, and they want the cost sheet taught in class.

The literacy mandate itself was the weekend's most contested item, and the debate was not sentimental. It was about money. Om Lala of California objected that requiring schools to teach AI literacy would be a heavy lift because “teachers already are pretty under-resourced.” Tristan Tjetjep of Tennessee worried about how “underprivileged schools” would pay for it. The students had walked directly into the structural funding inequity of American public education, and they responded the way legislatures respond, by writing a funding requirement into the bill: proportional federal funding with a baseline minimum, for enterprise AI, mandatory staff training and a range of student literacy options. They invented an unfunded mandate and then, within the same weekend, tried to fund it.

Viva Voce and the Burden of Proof

The most legally fraught thing in the bill sits in section four, provision six, under a Latin heading. “VIVA VOCE (Oral Defense): Teachers reserve the right to require an oral defense of any paper or project where AI use is suspected or reported, to ensure the student possesses the knowledge displayed in the work.”

Take the pragmatic case first, because it is strong. AI detectors do not work well enough to hang a disciplinary finding on. The most cited evidence is a 2023 study in the journal Patterns by Weixin Liang and colleagues at Stanford, which ran seven widely used GPT detectors over essays written by non-native English speakers and found that more than half of the TOEFL essays were misclassified as AI-generated, while the same detectors were near-perfect on writing by US eighth graders. The mechanism is banal and damning: detectors flag low linguistic variability, and non-native writers, along with anyone writing plainly, produce exactly that.

The students knew this. Their bill says a suspected student is entitled to review by two school officials as well as detection software, and that teachers “must personally investigate the issue before reporting the student to admin.” They wrote in a right to appeal any finding of wrongful AI use, and a right of appeal against any disciplinary action. That is more due process than a lot of real districts currently offer, and it was written by people who expect to be on the receiving end of it. Microsoft's 2025 education survey found the single largest concern among American higher education students was not misinformation or job loss but “potential to be accused of plagiarism or cheating,” named by 32 per cent.

But the viva provision still has a problem, and it is the direction the burden runs. An oral defence triggered by suspicion asks a student to prove authorship rather than asking the school to prove misconduct. Perform well and you are cleared; perform badly and the suspicion is confirmed. The evidence on oral assessment is not reassuring here. A systematic review of the structured viva in health professions education found anxiety to be the most prominent reported limitation, along with weak standardisation and equity problems for students speaking English as an additional language. Layer that on a student with an anxiety disorder, a speech difference, a language barrier, or an individualised education programme, and a mechanism designed to protect the wrongly accused becomes a second way to fail them.

To their credit, the students left an escape hatch of sorts: their own bill requires schools to allow AI-based learning supports in IEP and 504 accommodation plans, and advises students to preserve document history on major assignments as a defence against false positives. That last piece of advice is the most telling line in the entire document. It is a generation coaching itself on how to prove it did its own homework.

What the Adults Produced Instead

Set the student bill beside the adult record and the asymmetry is the story.

There is no national framework for AI in American schools. The April 2025 executive order imposes no obligation on any district. At state level, the picture is guidance-heavy and law-light: by late October 2025, 34 states plus Puerto Rico had issued some form of official K-12 AI guidance, according to the tracker maintained by AI for Education. Guidance is not requirement. An analysis published by ExcelinEd in May 2026 counted just seven states with enacted laws, another twenty or so with bills in progress, and around ten with nothing beyond guidance or a task force. Its summary of the gap was that districts are being left to vet tools without any state-provided evaluation framework.

Congress, meanwhile, spent 2025 trying to make the state patchwork illegal. A ten-year moratorium on state AI regulation was inserted into the budget reconciliation bill, and on 1 July 2025 the Senate voted 99 to 1 to strip it out. The single vote in favour of preemption belonged to Thom Tillis. Federal AI policy for the year was, in substance, a near-unanimous decision not to have one, and to let the states carry on.

So when 10,000 superintendents open an email containing a bill written by teenagers, and from August sit through webinars and school visits built around it, they will be doing so because nothing else has arrived. Jeffrey Riley, who runs Day of AI and was previously Massachusetts commissioner of elementary and secondary education, put the logic plainly to NPR: “The adults themselves haven't been able to come to consensus on this, right? And so now it falls to the kids. Let them lead the way.”

That is a generous reading. A less generous one is that adult institutions have discovered that student voice is cheap, fast and reputationally safe in a way that binding policy is not. Nobody gets sued over a simulation.

The Ghost of Social Media in the Chamber

The students had a theory about why the adults were slow, and they said it out loud all weekend. “We missed the mark with social media and phones,” said Hunter Wurzel, a senior representing Ohio. “That's been a huge talking point already, is how we failed to implement those things into education and teach the youth how to use them.”

It is worth taking that claim apart rather than nodding at it, because the record is more specific than the slogan.

The Surgeon General issued a formal advisory on social media and youth mental health in May 2023, noting that up to 95 per cent of teenagers aged 13 to 17 used a social platform and more than a third said they used one “almost constantly.” In June 2024 the Surgeon General went further and called for a tobacco-style warning label. That step requires an act of Congress. Congress did not act.

The Kids Online Safety Act passed the Senate 91 to 3 on 30 July 2024, then died without a House vote. Reintroduced in the 119th Congress, it was eventually folded into a House package, the KIDS Act, which passed 267 to 117 on 29 June 2026, with KOSA's central duty of care stripped out. Senate co-author Richard Blumenthal promptly declared the House version dead in his chamber. Two years and one week after a 91 to 3 Senate vote, there is still no statute.

Phone bans tell a similar story at a different pace. As of the end of April 2025, only eleven states had statewide restrictions of any kind, four of them full bell-to-bell bans, with Florida first in 2023. Fourteen months later the position had transformed: by June 2026, 22 states and Washington D.C. had adopted full bell-to-bell bans, and a further 19 had enacted more flexible legislation leaving districts to set their own policies, putting roughly 41 states under some form of restriction. Common Sense Media's own framing of the arms race is the sharpest version: it notes that Pew found roughly 73 per cent of online American teenagers were using social networking sites by 2009, about three years after Facebook opened to the public. The policy response to a technology that saturated adolescence by 2009 became widespread state law around 2025.

This is the founding political fact for the cohort in that chamber. Every one of those 98 students was born into a live, uncontrolled experiment, watched adults spend fifteen years arguing about whether it was harmful, and reached the age of legislative role-play just in time to be handed the next one. “We missed the mark” is not adolescent grandstanding. It is a reasonably precise summary of the legislative record, delivered by the test subjects.

The Evidence Behind the Fear They Named

McHenry's line about using AI as a brain rather than a tool is a folk statement of a hypothesis researchers are actively testing, with results that are suggestive rather than settled.

The most publicised is the MIT Media Lab study led by Nataliya Kosmyna, posted to arXiv in June 2025 under the title “Your Brain on ChatGPT,” which had 54 participants write SAT-style essays across three sessions while wearing 32-electrode EEG headsets, with 18 returning for a fourth crossover session. Brain connectivity scaled down with the amount of external help: unaided writers showed the strongest networks, search engine users intermediate, language model users the weakest. Participants who had used the model struggled to quote their own essays back and reported weaker ownership of them. The authors called the effect cognitive debt.

It is a preprint with a small sample and a narrow task, and it has been over-read constantly since publication. It is not proof that AI makes teenagers stupid. What it does establish is that the mechanism the students are worried about is at least physically detectable.

The correlational picture points the same way without proving causation. Michael Gerlich's 2025 study in Societies, surveying 666 participants, found a significant negative association between frequent AI use and critical thinking scores, mediated by cognitive offloading, with the strongest dependence among 17 to 25 year olds. Common Sense Media's 2026 census of 1,204 American children aged 9 to 17, fielded with SSRS on a probability panel, found that 20 per cent of AI users, and 42 per cent of daily users, said going a month without AI would be very or somewhat hard. It also found that children who struggle academically lean hardest on it: 56 per cent of those who have a hard time staying focused on assignments use AI for schoolwork at least weekly, against 45 per cent of those who do not.

That last finding is the uncomfortable one, because it cuts against the student bill's own logic. A restriction-first regime falls heaviest on the students already struggling, who are using the tool most, and who are least likely to have the document history, the articulacy under pressure, or the parental advocacy to survive an oral defence.

Ninety-Eight Is Not a Sample

There is a version of this article that treats the Students First Act as the authentic voice of American youth. That version would be wrong, and the numbers say so.

These students were, in NPR's phrasing, “handpicked by their school leaders.” They are the young people a superintendent thinks of when asked to nominate someone for a civics simulation in Boston: engaged, articulate, available in July, backed by families who can get them to an airport. Selecting for civic enthusiasm selects for institutionalism. A cohort assembled that way is likely to believe rules can work, because believing that is roughly the entry requirement.

Set them beside the general population and the gap opens. The Common Sense census found 86 per cent of 9 to 17 year olds use AI in some form, rising to 92 per cent of 16 and 17 year olds, with 24 per cent using it daily. Among users, 85 per cent use it for schoolwork, 48 per cent at least weekly and 21 per cent every day. Asked in an open-ended question what people their age know about AI that adults do not, the single largest theme, at 21 per cent, concerned schoolwork, including how to cheat with it and, in the researchers' summary, “how kids can get away with using AI for schoolwork even when they are not supposed to.” One respondent's verbatim answer: “You can exploit AI to do all your assignments from school, and use multiple AIs to make it look handmade.”

That is the constituency the Students First Act would govern, and it is not the constituency that wrote it.

There is also the performance problem. A room of teenagers, in a Senate chamber, watched by superintendents, a former state education commissioner and a national broadcaster, is a room with a strong incentive to produce responsibility. The bill's restrictionism may be partly sincere and partly a read of the audience. Both can be true. Adult legislators are not usually granted the assumption that their public positions are unaffected by who is watching, and the students should not be either.

What complicates the cynical reading is the texture of the dissent. Jadelynn Petitjean of Alabama called the bill an “unstable foundation” and voted no. Ethan Liu of West Virginia catalogued the contradictions, including amendments that restricted student access while handing teachers broad discretion, and told the chamber he had “a whole different speech planned out against this bill, actually. It was really good.” He changed his vote only after organisers let the students patch some of the inconsistencies. Zora Nobel of Maine voted for it while describing it on the record as containing “repetitive provisions, prescriptive language, and at times, contradictions.” That is not a room performing consensus. That is a room bargaining.

Why Rules You Wrote Yourself Might Actually Hold

The strongest argument for the exercise has nothing to do with the quality of the text.

Tom Tyler's work, running from Why People Obey the Law onward, is the foundation of procedural justice theory, and its central finding is that compliance tracks perceived legitimacy far more reliably than it tracks fear of punishment. People follow rules they consider fairly made by authorities they consider entitled to make them. Tyler's corollary is the one that should worry school administrators: regulating behaviour primarily through threat can actively erode the internal commitment that produces voluntary compliance. Research on adolescents specifically finds that transparency of rationale and genuine consideration of young people's views have consistent effects on trust in authority, in schools and in government alike.

Applied here, the argument is not that teenagers wrote better AI rules than superintendents would have. It is that a rule a student helped write is a rule a student is more likely to keep, and that AI compliance is almost entirely a voluntary-compliance problem, because the enforcement technology does not work.

The research literature has begun to catch up. A peer-reviewed paper by Kaoru Seki, Manisha Vijay and Yasmine Kotturi, titled “Participatory, not Punitive,” presented at the CHI conference on human factors in computing systems in Barcelona in April 2026, ran a three-part workshop series in a graduate design course at a minority-serving American university, in which two student leaders facilitated discussion with no faculty in the room. The participants co-authored ten policy recommendations. The authors argue that engaging students in AI governance carries value beyond the resulting policies, and describe their model as calling students in rather than calling them out.

It is a small study, eight participants, at university rather than K-12, and should not be asked to carry more than it can. But one of its specific findings maps onto the Boston bill with uncanny precision. The student-led process surfaced a concern absent from top-down governance: the double standard of requiring students to disclose or abstain from AI while faculty face no equivalent expectation. Section four, provision four of the Students First Act, written by teenagers who had not read that paper, says teachers must be held to the same transparency standard as students. Two independent participatory processes, different countries of the academy, different age groups, produced the same demand. That is the sort of convergence that suggests the process is surfacing something real rather than flattering its participants.

When the Text Meets a School Board

None of this survives contact with a school board meeting unaltered, and it is worth being specific about where it breaks.

Start with enforceability. “AI use is strictly prohibited on all graded assessments” is a rule about a device, not a rule about a mind, and the bill's own enforcement mechanism, mandating “one standardized AI detector” per district plus lockdown browsers, leans on precisely the technology the students elsewhere acknowledge is unreliable. There is an unresolved contradiction here: the bill requires teachers to use a detector on written graded assessments while simultaneously requiring that no student be judged on a detector alone. Both things can be operationalised, but only by a district with the staffing to run a two-official review on every flag. That district exists. So does the one with a single overstretched assistant principal.

Then the funding. The bill's answer to inequity is proportional federal funding with a baseline minimum, which is a sentence a student senate can write and a school board cannot. Absent that money, an AI literacy mandate starting at the moment of device issue becomes another compliance burden distributed unequally, heaviest on the districts with the least capacity, which is the exact pattern the students were trying to prevent.

Then the parents. Section three is the weakest part of the bill, and the students knew it. “We cannot legislate parenthood,” Isabella Stringer of New Hampshire told the chamber, and the section duly softens into advice. Except in one place, provision seven, where a parent using AI on a child's graded work without teacher permission exposes the child to suspension or expulsion. That will not survive a single angry school board meeting, and it should not.

The record-keeping is imperfect too, in a way that is almost endearing. The PDF AASA published is filed under a name declaring the bill passed 84 to 14. The page hosting it, NPR's account and AASA's own national announcement in August all say 82 to 16. Somewhere between the chamber and the content management system, four votes moved. Real legislatures have the same problem and solve it with a clerk.

The Line They Drew

The temptation is to end warmly. Ninety-eight teenagers did in a weekend what Congress has not done in three years, and they did it with more precision about assessment, more attention to due process and more interest in the environmental cost of the technology than the sitting federal framework contains.

The temptation in the other direction is equally available. It was a simulation, in a replica, with a non-binding output, at an event underwritten in part by an AI literacy nonprofit, involving students selected for their enthusiasm, producing a document with acknowledged internal contradictions that will be put in front of superintendents who are under no obligation to adopt a word of it.

Laura Lundy, writing in the journal Childhood in 2018, made the case that the children's rights field has been too quick with the word tokenism. Since Roger Hart placed tokenism on the bottom rungs of his 1992 ladder of children's participation, alongside manipulation and decoration, the field has often concluded that imperfect participation is worse than none. Lundy's argument, drawn from what participating children actually report, is that this reasoning is used to justify doing nothing, and that young people who have taken part in flawed processes frequently do not experience them as empty.

The useful question is not whether the Boston weekend was real politics. It is what the students chose when handed the pen, and that answer is not ambiguous. Offered an opportunity to write themselves permission, a cohort that uses this technology more than any group in history wrote itself restrictions, then wrote in appeal rights, then turned round and imposed transparency obligations on the vendors, the teachers and the administrators too. They banned AI on the tests and allowed it in the studying. They asked to be taught what the machines cost the planet. They asked, in the last provision of the bill's last section, to be consulted before adults write rules about them.

Bena Mejia Romero of Connecticut put it to the chamber before the final vote: “This bill is about who gets a seat at the table. Today, students have that seat.” It was, for that weekend, a seat in a room where the desks were accurate and the votes were not counted by anyone who had to act on them.

On 3 August 2026 the organisers announced what happens next, and it is more than an email. AASA will put the Act in front of its full membership, and Day of AI, the Kennedy Institute and MIT's RAISE initiative will follow it into schools with webinars, workshops, public discussions and in-person visits. Sarah Yezzi, the Kennedy Institute's chief educator officer, summarised the students' position in that announcement: “Students are not asking schools to ignore AI, nor are they asking schools to adopt AI without limits. They want clear, fair rules that protect privacy, preserve the ability to think, and ensure this transformative technology supports and enhances human relationships.”

A webinar is not a statute and a school visit is not a school board vote. Nothing in the programme obliges a single district to adopt a single provision, and the bill remains what it was on the Sunday morning it passed: a text with no force behind it but its own reasoning. What the announcement does dispose of is the easy dismissal, which was that the thing would land in ten thousand inboxes and stay there. It is going to be argued about in rooms where the votes are counted by people who have to act on them. The question is no longer whether anyone reads it. It is whether any of it ever becomes binding on anyone.

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Sources and References

1. Cory Turner, “Adults have struggled to set rules for AI in school. These teens figured it out,” NPR, 30 July 2026. https://www.npr.org/2026/07/30/nx-s1-5853571/students-set-ai-policy
2. AASA, The School Superintendents Association, “The Students First Act: A Student-Led AI Policy,” 30 July 2026. https://www.aasa.org/resources/resource/students-first-act
3. 2026 Student Senate, “S.2026, The STUDENTS FIRST Act,” full bill text published by AASA, 19 July 2026. https://www.aasa.org/docs/default-source/resources/reports/17_s-2026-the-students-first-act-final-bill-passed-84-14-july-19-2026.pdf
4. AASA, The School Superintendents Association, “Students from All 50 States Produce National Framework for AI in America's Schools,” 3 August 2026. https://www.aasa.org/news-media/news/2026/08/03/students-produce-national-framework-for-ai-in-america's-schools
5. Edward M. Kennedy Institute for the United States Senate, “Edward M. Kennedy Institute introduces a new AI policy program to support civic and digital literacy,” 2024. https://emkinstitute.org/about-the-institute/edward-m-kennedy-institute-introduces-a-new-ai-policy-program-to-support-civic-and-digital-literacy/
6. Kaoru Seki, Manisha Vijay and Yasmine Kotturi, “Participatory, not Punitive: Student-Driven AI Policy Recommendations in a Design Classroom,” Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26), Barcelona, 13-17 April 2026, DOI 10.1145/3772318.3790691; preprint arXiv:2604.10851. https://doi.org/10.1145/3772318.3790691
7. Harvard Kennedy School, “Many states are banning cellphones in schools. Is it working?,” featuring Paul Peterson, 9 June 2026. https://www.hks.harvard.edu/faculty-research/policy-topics/education-training-labor/many-states-are-banning-cellphones-schools
8. Microsoft Education, “2025 AI in Education Report: Extended Survey Data,” August 2025. https://cdn-dynmedia-1.microsoft.com/is/content/microsoftcorp/microsoft/bade/documents/products-and-services/en-us/education/Survey-Data-2025-Microsoft-AI-in-Education-Report.pdf
9. Eleanor O'Neil, Kristen Soltis Anderson, Baxter White, Laura Zimmermann, Supreet Mann and Michael B. Robb, “The Common Sense Media Census: AI Use by Tweens and Teens, 2026,” Common Sense Media, 2026. https://www.commonsensemedia.org/sites/default/files/research/report/2026-ai-use-by-tweens-and-teens-1.pdf
10. Holland & Knight, “Executive Order: Advancing Artificial Intelligence Education for American Youth,” analysis of Executive Order 14277 of 23 April 2025, April 2025. https://www.hklaw.com/en/insights/publications/2025/04/executive-order-advancing-ai-education-for-american-youth
11. The White House, “AI Education,” ai.gov initiatives page, accessed 2 August 2026. https://www.ai.gov/initiatives/education
12. Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein and Pattie Maes, “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task,” arXiv:2506.08872, 10 June 2025. https://arxiv.org/abs/2506.08872
13. Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu and James Zou, “GPT detectors are biased against non-native English writers,” Patterns, 10 July 2023. https://pubmed.ncbi.nlm.nih.gov/37521038/
14. Michael Gerlich, “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking,” Societies, 3 January 2025. https://www.mdpi.com/2075-4698/15/1/6
15. Abdelhamid Ibrahim Hassan Abuzied and Wisal Omer Mohamed Nabag, “Structured viva validity, reliability, and acceptability as an assessment tool in health professions education: a systematic review and meta-analysis,” BMC Medical Education, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10369684/
16. Adam Bridgeman and Danny Liu, “Frequently asked questions about the two-lane approach to assessment in the age of AI,” Teaching@Sydney, University of Sydney, 2 July 2024. https://educational-innovation.sydney.edu.au/teaching@sydney/frequently-asked-questions-about-the-two-lane-approach-to-assessment-in-the-age-of-ai/
17. AI for Education, “State AI Guidance for Education,” updated 28 October 2025. https://www.aiforeducation.io/ai-resources/state-ai-guidance
18. ExcelinEd, “State K-12 AI Policy in 2026: Milestones, Momentum and Missing Links,” 26 May 2026. https://excelined.org/2026/05/26/state-k-12-ai-policy-in-2026-milestones/
19. Goodwin Procter, “Federal AI Moratorium Dies on the Vine as Senate Passes the One Big Beautiful Bill Act,” July 2025. https://www.goodwinlaw.com/en/insights/publications/2025/07/alerts-practices-aiml-federal-ai-moratorium-dies-on-the-vine
20. Office of the U.S. Surgeon General, “Social Media and Youth Mental Health: The U.S. Surgeon General's Advisory,” U.S. Department of Health and Human Services, 23 May 2023. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf
21. Crowell & Moring, “House Passes KIDS Act H.R. 7757: What Online Platforms Must Know Before Senate Showdown,” July 2026. https://www.crowell.com/en/insights/client-alerts/house-advances-bipartisan-kids-online-safety-bill-but-senate-showdown-looms
22. KFF, “A Look at State Efforts to Ban Cellphones in Schools and Implications for Youth Mental Health,” 2025. https://www.kff.org/mental-health/issue-brief/a-look-at-state-efforts-to-ban-cellphones-in-schools-and-implications-for-youth-mental-health/
23. Roger A. Hart, “Children's Participation: From Tokenism to Citizenship,” UNICEF International Child Development Centre, 1992, summarised by Organizing Engagement. https://organizingengagement.org/models/ladder-of-childrens-participation/
24. Laura Lundy, “In defence of tokenism? Implementing children's right to participate in collective decision-making,” Childhood, volume 25 issue 3, 2018. https://pureadmin.qub.ac.uk/ws/portalfiles/portal/148759327/Tokenismfin.pdf
25. Tom R. Tyler, “Why People Obey the Law,” Princeton University Press, 2006 edition. https://press.princeton.edu/books/paperback/9780691126739/why-people-obey-the-law

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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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Since 2 August 2026, Article 50 of the EU AI Act has been enforceable. Providers of generative systems must mark their outputs in a machine-readable format so that synthetic audio, video and text can be detected as artificially generated. Deployers of deepfakes, defined in the Act as AI-generated or manipulated content resembling real persons, objects or events and appearing authentic, must disclose what they have done. Penalties reach 15 million euros or three per cent of global annual turnover. The marking duty itself is arriving in stages: under the AI Omnibus provisional agreement reached on 7 May 2026, providers whose generative systems were already on the market before 2 August have until 2 December 2026 to embed the machine-readable markings and supply a detection mechanism. Everything else in Article 50 applies now.

Buried in Article 50(2) is a carve-out that will decide more cases than the headline rule. The marking obligation does not apply, the text says, “where the AI systems perform an assistive function for standard editing or do not substantially alter the input data.”

Read that again with an audio engineer's ear. European law now turns, in a large and commercially significant class of cases, on whether a machine-learning system substantially altered the material it was given. That is a question about the internals of a process, asked of an artefact that carries no record of its own manufacture. It is the question the music industry has spent four years answering with a shrug and a press release.

The most celebrated answer arrived on 2 November 2023, in the form of a four-minute single credited to a band that had not recorded together since 1970.

What Was Actually On The Cassette

In January 1994, around the time Paul McCartney inducted John Lennon into the Rock and Roll Hall of Fame, Yoko Ono handed McCartney a set of home cassettes. One of them, according to the surviving Beatles' own account, had “for Paul” scrawled on it in Lennon's handwriting. The tapes contained four demos recorded in Lennon's New York apartment in the late 1970s: “Free as a Bird”, “Real Love”, “Grow Old With Me” and “Now and Then”.

Two became records. Working with Jeff Lynne during the Anthology sessions of 1994 and 1995, McCartney, George Harrison and Ringo Starr built new arrangements around the Lennon vocals on “Free as a Bird” and “Real Love”, the latter requiring substantial preparation in Lynne's studio before overdubs could be added at all. “Now and Then” was attempted and abandoned. There was an electrical buzz on the tape, and, more fundamentally, Lennon had sung while playing piano into a single microphone, welding voice and piano into one mono signal. Every attempt to raise the vocal raised the piano with it. McCartney has put it plainly: they could not lift John's voice without lifting the piano, and the piano clouded the picture.

The tool that eventually solved it was not built for the Beatles at all. It was built for a documentary. During production of The Beatles: Get Back, released in 2021, Peter Jackson's WingNut Films Productions needed to make the 1969 fly-on-the-wall tapes intelligible, much of it mono material with dialogue, instruments and room noise hopelessly entangled. Emile de la Rey, a Wellington-based composer and sound editor who joined WingNut as a machine learning engineer, led development of a de-mixing system the team named MAL, a nod to “machine assisted learning”, to Mal Evans, the Beatles' road manager, and to HAL in 2001: A Space Odyssey. De la Rey worked with the machine learning researcher Paris Smaragdis at the University of Illinois Urbana-Champaign, building the thing from the academic source-separation literature upwards and generating their own training data as they went. The Get Back sound team won an Emmy and an MPSE Golden Reel in 2022.

MAL was then pointed at the catalogue. Giles Martin used it in 2022 to produce a genuine stereo mix of Revolver from four-track masters on which guitar, bass and drums shared a single track, a job that had been considered impossible for fifty-six years. It was used again on the 2023 reissues of the Red and Blue compilations. And it was used on the Lennon demo, extracting a usable vocal from the piano that had defeated Jeff Lynne.

“Now and Then” went to number one in the UK on 10 November 2023, the band's eighteenth British chart-topper and their first since “The Ballad of John and Yoko” in 1969. The Official Charts Company noted that the gap of fifty-four years between number ones broke Kate Bush's record, and that sixty years and six months had elapsed since “From Me To You” first took the band to the top. At the 67th Grammy Awards on 2 February 2025 the record won Best Rock Performance, with Sean Ono Lennon accepting; it lost Record of the Year to Kendrick Lamar's “Not Like Us”. Billboard described it as the first AI-assisted recording to receive a Grammy nomination.

McCartney's framing throughout was consistent and emphatic. Nothing, he said, had been artificially or synthetically created. It was all real, and they all played on it.

That sentence is the restoration alibi in its purest form, and it is worth taking seriously before taking it apart.

A Mask Is Not A Microphone

Source separation became a consumer technology in November 2019, when Deezer's research group released Spleeter under an MIT licence, a TensorFlow library shipping pre-trained models for two, four and five stems. Spleeter works in the frequency domain. It converts audio to a spectrogram, treats separation as something close to an image segmentation problem, and estimates a mask: a set of coefficients deciding, for every time-frequency cell, what proportion of the energy in that cell belongs to the vocal, the bass, the drums, the rest. It then reapplies the phase of the original mixture, because it never estimated phase in the first place. The characteristic failures follow directly from that architecture: metallic ringing, watery smearing, and faint ghosts of one instrument haunting another's stem.

Meta's Demucs took a different route, operating on the raw waveform and later hybridising waveform and spectrogram paths. The 2022 Hybrid Transformer Demucs paper reports a signal-to-distortion ratio of 9.20 dB on the MUSDB benchmark, a substantial margin over spectrogram-only approaches. The competitive frontier moved again at the Sound Demixing Challenge 2023, where a band-split transformer architecture, BS-RoFormer, took first place in the music separation track after training on MUSDB18-HQ plus five hundred additional songs. Commercial services proliferated: AudioShake, which says it won Sony's demixing challenge and whose stems have been used on archival projects involving the Jackson 5, Nina Simone and Whitney Houston; MVSep; and the machine-learning rebalancing module in iZotope RX, now standard equipment in post-production.

Here is the load-bearing point. A mask is a filter. It redistributes energy already present in the mixture; it cannot manufacture information the microphone did not capture. Where two sources occupy genuinely distinct regions of the time-frequency plane, masking approaches truth. Where they overlap exactly, which happens constantly with a voice and a piano playing the same notes in the same room through the same microphone, the mask apportions a shared quantity according to a learned prior about what voices and pianos usually do. It is guessing a ratio. Sometimes it guesses well. The residue of bad guesses is the artefact vocabulary every engineer recognises: bleed, musical noise, pre-echo, transient smear.

This is subtractive work, and it is bounded by the recording. Whatever else you say about it, a masked stem is a rearrangement of evidence.

The second family of techniques is not bounded that way. Diffusion-based refinement, of the sort explored in a body of research published through 2024 and 2025, takes a separated stem and resynthesises it, using a generative model trained on how the target source sounds in general. The stated aim of the better papers is fidelity: consistency constraints are added specifically to keep the refined output anchored to the observed mixture rather than drifting into invention. But the mechanism is categorically different. The model is not filtering the recording. It is producing audio conditioned on the recording. When the evidence thins, as it does in a cassette's collapsing high frequencies or a passage buried under tape hiss, the model supplies what is statistically plausible. Bandwidth extension does the same thing more openly, hallucinating high-frequency detail a domestic cassette deck never captured, because the model has heard ten thousand voices that had it.

The distinction between the two regimes is real, sharp and technically meaningful. It is also invisible in the output. Both processes hand you a file called vocals.wav. Nothing in the waveform announces whether a given millisecond was recovered or inferred. There is no provenance metadata by default, no chain of custody, no equivalent of the raking-light photograph a paintings conservator takes before touching anything.

So when McCartney says nothing was synthetically created, he is making a claim about a process that cannot be audited from the record. He may well be right; everything published about MAL is consistent with masking rather than generation. But the listener is being asked to take a technical assurance on trust, which is a strange basis for settling a question of authenticity.

As it turns out, the assurance is also aimed at the wrong part of the record.

The Beatles Told On Themselves

Giles Martin, who produced “Now and Then” with McCartney, has been refreshingly specific about how the finished track was built, and what he describes is not restoration by any definition a conservator would accept.

The backing vocals are the giveaway. Rather than record new harmonies or generate them, Martin lifted them from elsewhere in the catalogue. “I'm literally taking the multitrack tapes of Eleanor Rigby, some stuff from Because and Here, There, and Everywhere,” he told MusicTech in November 2023. Asked whether AI had been involved in that process, he was blunt: “No, it's not artificial or intelligent.” And on why: “It might have been easier if I used AI, but I didn't.” His justification for the result is a small masterpiece of category confusion. “They feel like they're from the Beatles, and they are from the Beatles.”

They are indeed from the Beatles. They are from three different Beatles songs, recorded between 1966 and 1969, none of which is “Now and Then”. Martin used the same technique he had deployed on Love, the 2006 mash-up album made with his father George for Cirque du Soleil, which nobody has ever mistaken for restoration. Add a string arrangement written in the 2020s by McCartney, Martin and Ben Foster, guitar parts Harrison recorded in 1995 for a song that was then shelved, and a slide guitar solo McCartney played in deliberate imitation of his dead friend's style.

Set out plainly, the 2023 record contains: a vocal captured on a domestic cassette in New York in the late 1970s and separated from its accompaniment by machine learning; drums and bass recorded roughly forty-five years later; rhythm guitar from an abandoned 1995 session; vocal harmonies excised from the multitracks of three unrelated 1960s recordings; an orchestral arrangement composed half a century after the demo; and a pastiche solo in the manner of a musician who died in 2001.

That is not a restored artefact. It is a new composition assembled from authenticated fragments, which is a perfectly respectable thing to be and arguably a beautiful one. But notice the inversion at its heart. The AI operation was the most conservative act on the record: it recovered a signal that genuinely existed on the tape. Every piece of real conjecture was performed by human beings with tape and faders. The moral geometry the public applies here, in which AI equals fakery and human hands equal authenticity, is exactly orthogonal to what happened.

McCartney's defence, that nothing was synthetically created, is true and irrelevant. Nothing was synthesised. Almost everything was composed.

Art Conservation Settled This Argument Sixty Years Ago

Recorded sound has no ethics of intervention. Painting, sculpture and architecture have had one since 1964.

The International Charter for the Conservation and Restoration of Monuments and Sites, drafted in Venice in May 1964 and adopted by ICOMOS the following year, does not attempt to define restoration as ontologically distinct from creation. It concedes the blur immediately and responds procedurally. Article 9 states that restoration “must stop at the point where conjecture begins, and in this case moreover any extra work which is indispensable must be distinct from the architectural composition and must bear a contemporary stamp.” Article 12 requires that replacements of missing parts “must be distinguishable from the original so that restoration does not falsify the artistic or historic evidence.” Article 16 requires precise documentation of every stage, deposited “in the archives of a public institution and made available to research workers”, with publication recommended.

Three obligations, then: stop where you start guessing; if you must guess, make the guess legible as a guess; and write down everything you did, in public. The charter's companion principle, developed through subsequent conservation practice, is reversibility, or as the field now more honestly puts it, retreatability. You should be able to get back to where you started, and where you cannot, you should say so.

The Sistine Chapel shows why documentation matters more than doctrine. The Vatican's cleaning of Michelangelo's frescoes, funded by Nippon Television Network Corporation and running from 1980 to 1994, produced colours so unexpectedly brilliant that they revised art-historical understanding of the artist. James Beck, professor of art history at Columbia University and founder of ArtWatch International, argued the restorers had removed more than soot: that Michelangelo had modified the dried fresco with a secco shadows in a glue medium, and that these finishing touches, indistinguishable by eye from glue-trapped grime, had been stripped away. The Vatican's team disputed it. The argument cannot be settled by looking at the ceiling, because if Beck was right the evidence went into the sponge. Only the documentation can adjudicate, and its adequacy is itself contested.

At the other end of the scale sits the Sanctuary of Mercy at Borja, in Aragón. In 2012 an elderly parishioner, Cecilia Giménez, attempted to touch up a modest 1930s Ecce Homo by Elías García Martínez and produced something so far from the original it became a global meme. It also transformed the town: reported visitor numbers rose from around 6,000 a year to roughly 57,000 by 2016, with accounts of the peak varying wildly above that, and revenue from three-euro tickets and donations funds the church, its guides and the local elderly home. The surge did not hold. Recent reporting puts annual visits back at somewhere between sixteen and twenty thousand. The intervention destroyed the evidentiary value of the object and created enormous new cultural value in the same brushstroke, and of the two only the loss turned out to be permanent. Restoration and creation are not opposites. They are the same act described by people with different interests.

Audio has no equivalent framework. The Audio Engineering Society publishes standards on archival formats, migration and metadata, all excellent and all concerned with carriers rather than interventions. No clause anywhere in the discipline says a de-mixed stem must be distinguishable from a natively isolated one, or that the untouched source must be deposited where a researcher can hear it. The industry's operative doctrine is a producer's word in a press interview.

The Law Pays You For Guessing

If you wanted to design a legal regime that penalised faithful restoration and rewarded invention, you would design the one the United States currently has.

On 20 August 2018 the Ninth Circuit decided ABS Entertainment, Inc. v. CBS Corporation, reversing summary judgment and holding that digitally remastered versions of pre-1972 analogue recordings were not automatically new copyrightable derivative works. To earn a fresh copyright, the court reasoned, a derivative work must be original to its author and non-trivial. Engineering judgment applied in service of an accurate reproduction of an existing performance does not clear the bar. Careful, faithful, expensive restoration produces, in law, nothing you can own.

Then, on 29 January 2025, the US Copyright Office published Part 2 of its report on copyright and artificial intelligence. Its conclusions were narrow and sensible: existing law suffices; prompts alone do not confer authorship, because they do not give sufficient control over the expressive result; but human authors retain copyright in their perceptible contributions within AI-generated outputs, in creative selection, coordination and arrangement, and in creative modifications of outputs.

Put the two together and the incentive is unmistakable. A perfect de-mix of a Lennon cassette is legally inert. A de-mix plus a new string arrangement plus harmonies spliced from three other records plus a pastiche solo is a protectable work with fresh copyright and a fresh royalty stream. Copyright pays for conjecture and refuses to pay for care. That is the precise inverse of the Venice Charter's professional hierarchy, in which the least intervention is the highest standard.

The publicity-rights regime, meanwhile, has been growing at speed, and it asks an entirely different question. Tennessee's ELVIS Act, signed on 21 March 2024 and effective from 1 July 2024, made voice a protected property right, expressly including simulations as well as the actual thing, backed by civil action and Class A misdemeanour liability, and extended to anyone making available a tool whose primary purpose is producing unauthorised voice replicas. California followed with a pair signed together on 17 September 2024 and in force together from 1 January 2025: AB 2602, which voids contract terms permitting digital replicas unless the performer received a reasonably specific description of the intended uses and was represented by counsel or a union; and AB 1836, which creates a cause of action for exploiting a deceased personality's digital replica without estate consent, with a 10,000 dollar statutory minimum and exceptions for news, comment, criticism, scholarship, satire and parody.

Post-mortem protection remains a patchwork. New York's Civil Rights Law section 50-f, in force since 29 May 2021, runs forty years after death, covers only those domiciled in the state at death, reaches digital replicas mainly where they are deceptive and lack a conspicuous disclaimer, and confers no cause of action at all until the estate has registered the claim with the New York Secretary of State. Indiana and Oklahoma run to a hundred years. Some states offer nothing.

Federal law may finally arrive. The NO FAKES Act of 2026, S.4591, was advanced by the Senate Judiciary Committee on 18 June 2026 by unanimous voice vote and awaits floor time. It would create a federal, licensable property right in voice and visual likeness, unassignable during life but surviving death for an initial ten years, renewable in five-year increments up to seventy years provided the rights holder can demonstrate active and authorised public use. It layers on a DMCA-style notice and counter-notice procedure, a 25,000 dollar penalty for knowing misrepresentation, and exclusions for nonprofit libraries, archives and accredited educational institutions. It would pre-empt future state digital replica laws while leaving causes of action existing as of 2 January 2025, including the ELVIS Act, intact.

The United Kingdom has taken the slowest road, and still has no general personality right; claimants rely on passing off and data protection. The government's Report on Copyright and Artificial Intelligence, published on 18 March 2026 under section 136 of the Data (Use and Access) Act 2025, abandoned the previously favoured broad commercial text and data mining exception with rights-holder opt-out after fierce creative-sector opposition. The narrow non-commercial research exception stands, and the stated preference is now licensing-first, supported by statutory transparency duties. Sony Music's submission to that consultation disclosed that it had issued more than 75,000 takedown requests against AI deepfakes of its artists, a figure Music Business Worldwide later reported had passed 135,000.

Notice what none of this legislation asks. Not one provision anywhere in this stack asks whether a work is a restoration or a creation. Every provision asks whether an identity was used with permission. The law has quietly concluded that the interesting question is consent rather than ontology.

The law is right. But consent does not make a record true, and that gap is where the damage will happen.

The Studio Was Always A Fiction Machine

The strongest objection to everything above is that the restoration and creation distinction was never stable, that recorded music has been synthetic since roughly the invention of magnetic tape, and that the current anxiety is nostalgia wearing a lab coat. This objection deserves to be taken seriously, because it is largely correct.

Glenn Gould made the argument in 1966, in an essay for High Fidelity called “The Prospects of Recording”. He described recording the A minor fugue from Book I of Bach's Well-Tempered Clavier a decade earlier, producing two technically acceptable complete takes, numbers six and eight, and then discovering that both shared a defect he had not noticed in the studio: both were monotonous. The released performance is a splice of the two. Gould knew perfectly well that purists called such editing dishonest and dehumanising, and he answered with a line that has not been improved on since. One cannot ever splice style, he wrote; one can only splice segments which relate to a conviction about style.

Teo Macero industrialised the idea. In a Silent Way was recorded in a single session on 18 February 1969 and then constructed in the edit; Macero described reducing dozens of reels of quarter-inch master to about eight and a half minutes a side. On Bitches Brew, by one detailed account of the session tapes, “Pharaoh's Dance” contains seventeen edits, its stop-start opening theme assembled in post-production from looped fragments of fifteen and thirty-one seconds. Miles Davis played the notes. Macero wrote the record. Dub producers made the mixing desk an instrument; hip-hop made other people's records the raw material; comping and pitch correction finished the job. The idea that a commercial recording documents an event that occurred died some time around 1950 and has been receiving flowers ever since.

So yes: the distinction is unstable, and always was. But the conclusion is not that it is meaningless. It is that it was never a claim about the ontology of the object. It was a claim about a process, and process claims are verifiable only if documented.

What genuinely changed is not metaphysics. It is cost and asymmetry. Macero needed Miles Davis in a room. Giles Martin needed a cassette. Separation has collapsed the marginal cost of extracting a usable performance from any recording ever made, which means the constraint that used to enforce collaboration, namely that you had to book the artist, has gone. And the artist, in almost every case that matters, is dead.

Consent Has A Sell-By Date And Everybody Knows It

Prince told Guitar World in 1998 that performing alongside a hologram of a dead musician would be, in his phrase, the most demonic thing imaginable. Twenty years later, when reports circulated that Justin Timberlake would summon him at the Super Bowl LII halftime show in Minneapolis, Sheila E. stated publicly that Prince had told her he did not want a hologram of himself used, and that Timberlake had agreed not to use one. The estate's account replied with a flat “No”. That is about as close to documented refusal as the record industry gets, and it held.

Contrast the Zappa Family Trust, which in 2019 authorised “The Bizarre World of Frank Zappa”, a hologram tour built by Eyellusion around vault recordings from 1974 and staged with surviving members of the Mothers of Invention. Ahmet Zappa, co-trustee, presented it as a world premiere of his father's compositions. Same technology class, same posthumous subject, opposite decisions, both legitimate. The variable is not the tool. It is who inherited the answer.

The cleanest case in this field is also the most generative, which ought to end the argument about whether generativity is what matters. Randy Travis lost the ability to speak and sing after a stroke in 2013 left him with aphasia. In late 2023 his longtime producer Kyle Lehning pulled isolated vocal stems from recordings Travis had made between 1985 and 2013 and sent them to a London-based technology firm, which built two bespoke models, one trained on twelve stems and one on forty-two. The scaffolding underneath was not newly recorded. The country singer James Dupré had cut the song with Lehning around 2011, and that existing take became the base over which the model rendered Travis's voice, credited by Warner Music Nashville as a “vocal bed”, reportedly the first outing for the term. Dupré learned what had been done with his old vocal when he heard the finished record. “I really just thought he recorded it years ago and I never knew about it,” he told Rolling Stone. “Where That Came From” was released on 3 May 2024; a second single, “Horses in Heaven”, followed. Lehning has said the models took about five minutes to analyse the demo vocal once it went in, after which he and the engineer Casey Wood spent the real labour by hand, adjusting vibrato speed and the timing of phrases until the delivery sat where Travis would have put it.

Every note of that vocal is synthetic. Nothing was recovered from anything. By the ontological test it is pure fabrication, and by any reasonable ethical test it is unimpeachable, because Travis is alive, understood what was proposed, and wanted it. The one person who does not appear to have been asked in advance is the living singer underneath. Meanwhile “Heart on My Sleeve”, the Ghostwriter track imitating Drake and The Weeknd that went viral in April 2023, was pulled after Universal takedown notices, submitted for Grammy consideration, declared eligible by Recording Academy chief executive Harvey Mason Jr, and then declared ineligible on the ground that it was no longer commercially available. Nothing about the artefact changed between those two announcements. Its distribution status did.

Consent, permission, availability. The industry's own behaviour has already conceded that the ontological question is not the operative one.

Your Stems Are Already In The Cloud

The forward-looking half of the question answers itself once you look at how contemporary sessions are actually stored.

Nobody will need to de-mix a 2026 recording. It arrives pre-separated. Vocal comps live in their own folders; multitracks are delivered as stems for sync and immersive mixes; Atmos deliverables ship object by object. The archive of the future is not a degraded cassette with a piano welded to a voice. It is a project file in which the voice was never mixed in the first place. The technical alibi that carried “Now and Then”, the claim that only what genuinely existed was recovered, will be trivially satisfiable for every artist working now. When everything is recoverable, recoverability stops distinguishing anything.

The market has already moved to the only question that remains. In November 2025 ElevenLabs launched its Iconic Marketplace, a licensing platform for recognisable voices, announced alongside partnerships with Michael Caine and Matthew McConaughey and populated with more than twenty-five further voices including those of John Wayne, Judy Garland, Laurence Olivier, Liza Minnelli and Maya Angelou. On 27 May 2026 the company added Stan Lee, licensed through Stan Lee Universe, the joint venture between Genius Brands and POW! Entertainment, for use across its creative tools and a narrated book club opening with Treasure Island. It is buying estates methodically.

The record business has followed the same logic. Warner, Universal and Sony have each negotiated licences with the AI music company Klay Vision; ElevenLabs signed training licences with Merlin, representing tens of thousands of independent labels, and with Kobalt on opt-in terms with royalties attached. The majors' 2024 infringement suits against Suno and Udio have resolved only in part, and only where a price could be agreed. Universal settled with Udio in October 2025, on terms that turned the service into a walled garden from which AI-generated music cannot be exported; Warner settled with Suno in November 2025, committing to a licensed model with artist opt-in over voice and likeness. Universal and Sony are still suing Suno. Settlement talks hit an impasse in April 2026, and in May the two labels moved to expand the case from 560 recordings to 61,026 after discovery indicated Suno had trained on millions of their tracks. Consent is being priced rather than conceded, and where the price fails the litigation simply resumes. Live experience is licensed the same way. Elvis Evolution, produced by Layered Reality under an agreement with Authentic Brands Group, which owns the Presley estate, opened at Immerse LDN at ExCeL in London on 18 July 2025 after a delay, and was received poorly.

Estate consent has become the sufficient condition, for the simple reason that it is the only condition that is transactable. An heir can sign. Authenticity cannot.

What A Charter For Recorded Sound Would Have To Say

The distinction between restoration and creation is meaningless as metaphysics and indispensable as procedure. Those two claims are not in tension; the second follows from the first. Because you cannot determine from the artefact what was done to it, the only version of the distinction that can survive is a documented, auditable account of the operations performed. If the industry will not produce that account voluntarily, and it will not, three things need to become mandatory.

First, disclosure at the level of the operation rather than the product. A single label saying “AI was used” is worthless when AI covers everything from a noise gate to a synthesised vocal. What is needed is a per-stem statement of intervention: which material was masked out of an existing mixture, which was generatively resynthesised or bandwidth-extended, which was sampled from other recordings, which was newly performed and by whom. Article 50 marks the artefact and exempts assistive editing, so it will catch the obvious deepfakes and miss every interesting case in the archive. The marking duty is being phased in; the exemption is not. That exemption is where the difficult work lives.

Second, retention and deposit of unmodified sources. This is Venice Charter Article 16 transposed into audio, and it is the only thing that makes the first requirement checkable. The cassette transfer, the raw de-mix before refinement, the pre-arrangement rough: deposited with an institution and accessible to researchers, so a future engineer can hear what the machine started with and judge what it did. Not reversibility, which is a fantasy in digital work, but retreatability. The Sistine controversy is unresolvable because the evidence went into the sponge. Every archival de-mix released without a deposited source manufactures the same irresolvability, cheaply and at scale.

Third, consent instruments that are specific and, wherever possible, executed in life. California's AB 2602 already contains the right standard in embryo: a contract term permitting digital replication is void without a reasonably specific description of the intended uses. Export that standard to estates and to wills. Blanket posthumous discretion vested in whoever happens to inherit is the failure mode, because heirs optimise for revenue and have no institutional duty to the work. Prince got the outcome he wanted only because he had said so, repeatedly, to people who repeated it. That is not a system. It is luck.

None of this would have stopped “Now and Then”, nor should it have. That record is good, its makers were candid about their methods when asked directly, and the surviving Beatles had the one thing no future case will reliably have: two living principals with the standing to say yes. What the record actually established, and what its marketing obscured, is that a sufficiently persuasive technical story lets a new work travel under an old name, collect a Grammy in a performance category, and enter the canon as an act of recovery.

Future generations will not need to seize our legacies. They will not have to unmix anything, because we will hand it over already unmixed, already modelled, already licensed, with the paperwork signed by someone who loved us or merely inherited us. The only thing that will distinguish stewardship from ventriloquism is whether we wrote down, in advance and in detail, what we would not have wanted done, and whether anyone kept the original so the difference could still be heard.

References

1. European Parliament and Council, “Article 50: Transparency Obligations for Providers and Deployers of Certain AI Systems,” EU Artificial Intelligence Act, 2024. https://artificialintelligenceact.eu/article/50/
2. Cooley LLP, “EU AI Act: Transparency Obligations Take Effect 2 August 2026,” cooley.com, 3 August 2026. https://www.cooley.com/news/insight/2026/2026-08-03-eu-ai-act-transparency-obligations-take-effect-2-august-2026
3. Ben Rogerson, “How AI helped us hear the 'final' Beatles song,” MusicRadar, November 2023. https://www.musicradar.com/news/ai-stem-separation-now-and-then-the-beatles
4. Official Charts Company, “The Beatles' Now And Then is UK's Official Number 1 song in record-breaking return,” officialcharts.com, 10 November 2023. https://www.officialcharts.com/chart-news/beatles-now-then-number-1-song-record/
5. Billboard Staff, “The Beatles' 'Now And Then' Is First AI-Assisted Song With Grammy Nomination,” Billboard, November 2024. https://www.billboard.com/pro/the-beatles-now-and-then-first-ai-assisted-song-grammy-nomination-history/
6. Will Groves, “It might have been easier if I used AI, but I didn't: How Giles Martin created the backing vocals for The Beatles' Now and Then,” MusicTech, 3 November 2023. https://musictech.com/news/music/giles-martin-beatles-now-and-then-production-ai/
7. Romain Hennequin, Anis Khlif, Felix Voituret and Manuel Moussallam, “Releasing Spleeter: Deezer Research source separation engine,” Deezer I/O, November 2019. https://deezer.io/releasing-spleeter-deezer-r-d-source-separation-engine-2b88985e797e
8. Simon Rouard, Francisco Massa and Alexandre Défossez, “Hybrid Transformers for Music Source Separation,” arXiv:2211.08553, November 2022. https://arxiv.org/pdf/2211.08553
9. Wei-Tsung Lu et al., “Music Source Separation with Band-Split RoPE Transformer,” arXiv:2309.02612, September 2023. https://arxiv.org/pdf/2309.02612
10. Mingqing Yun et al., “Improving Music Source Separation with Diffusion and Consistency Refinement,” arXiv:2412.06965, December 2024. https://arxiv.org/pdf/2412.06965
11. ICOMOS, “International Charter for the Conservation and Restoration of Monuments and Sites (The Venice Charter 1964),” adopted Venice, 31 May 1964. https://www.icomos.org/images/DOCUMENTS/Charters/venice_e.pdf
12. Encyclopaedia Britannica, “The Restoration of the Ceiling of the Sistine Chapel,” britannica.com. https://www.britannica.com/art/Restoration-of-the-Ceiling-of-the-Sistine-Chapel-The-1324351
13. Jorge Fitz-Gibbon, “Amateur artist who famously botched 'Monkey Jesus' restoration dead at 94,” New York Post, 30 December 2025. https://www.aol.com/articles/amateur-artist-famously-botched-monkey-171812480.html
14. United States Court of Appeals for the Ninth Circuit, “ABS Entertainment, Inc. v. CBS Corp., No. 16-55917,” 20 August 2018. https://cdn.ca9.uscourts.gov/datastore/opinions/2018/08/20/16-55917.pdf
15. United States Copyright Office, “Copyright and Artificial Intelligence, Part 2: Copyrightability,” 29 January 2025. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf
16. California Legislature, “AB-1836 Use of likeness: digital replica,” leginfo.legislature.ca.gov, signed September 2024. https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202320240AB1836
17. Justia, “New York Civil Rights Law Section 50-f: Right of Publicity,” 2025 New York Laws. https://law.justia.com/codes/new-york/cvr/article-5/50-f/
18. UK Intellectual Property Office, “Report on Copyright and Artificial Intelligence,” GOV.UK, 18 March 2026. https://www.gov.uk/government/publications/report-and-impact-assessment-on-copyright-and-artificial-intelligence/report-on-copyright-and-artificial-intelligence
19. Murray Stassen, “Sony Music reveals 75,000 AI deepfake takedowns, criticizes UK's rushed copyright reform,” Music Business Worldwide, March 2025. https://www.musicbusinessworldwide.com/sony-music-reveals-75000-ai-deepfake-takedowns-criticizes-uks-rushed-copyright-reform1/
20. Glenn Gould, “The Prospects of Recording,” High Fidelity, April 1966. https://www.sfu.ca/sonic-studio-webdav/AudioMedia/Readings/Alphabetical/Gould-Prospects_of_Recording.pdf
21. Paul Tingen, “Miles Davis and the Making of Bitches Brew: Sorcerer's Brew,” JazzTimes, 2001. https://www.jazztimes.com/features/profiles/miles-davis-and-the-making-of-bitches-brew-sorcerers-brew/3/
22. Jem Aswad, “No Prince Hologram During Justin Timberlake's Super Bowl Set, Sheila E. Promises,” Variety, 4 February 2018. https://variety.com/2018/music/news/no-prince-hologram-during-justin-timberlakes-super-bowl-set-sheila-e-promises-1202686913/
23. Alexandra Supper, “'More Where That Came From': How the Voice of Randy Travis Helps to Normalize AI,” Popular Music and Society 48:4, 2025. https://www.tandfonline.com/doi/full/10.1080/03007766.2025.2492500
24. Rolling Stone, “Randy Travis AI Song: Who Sang 'Where That Came From'?” rollingstone.com, 9 May 2024. https://www.rollingstone.com/music/music-features/randy-travis-ai-song-where-that-came-from-singer-1235017789/
25. Murray Stassen, “UMG and Sony seek to add over 61k recordings to Suno lawsuit after discovery reveals AI trained on 'millions' of their copyrighted tracks,” Music Business Worldwide, May 2026. https://www.musicbusinessworldwide.com/umg-and-sony-seek-to-add-61000-copyrighted-works-to-suno-lawsuit-after-discovery-reveals-suno-trained-on-millions-of-their-recordings/

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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...

You sit before your laptop, alone in your bedroom, staring at a webcam. The screen displays a question about how you would handle a difficult customer. You have two minutes to respond. As you speak, an algorithm analyses every micro-expression on your face, the cadence of your voice, the words you choose, and even the pauses between your sentences. Somewhere in a data centre, machine learning models are calculating your “employability score.” They are determining whether your vibes are right.

Welcome to the new frontier of recruitment, where “good vibes” have been quantified, algorithmatised, and sold back to employers as objective science.

An estimated 99 per cent of Fortune 500 companies now use some form of automation in their hiring process. The World Economic Forum reported in March 2025 that roughly 88 per cent of companies use AI for initial candidate screening. And an October 2024 survey of business leaders found that approximately seven in ten companies allow AI tools to reject candidates without any human oversight. The promise is efficiency, objectivity, and the elimination of human bias. The reality is proving far more complicated.

The Machinery Behind the Vibe Check

The technical architecture of AI-powered hiring tools reads like science fiction adapted for the human resources department. These systems deploy a constellation of technologies: facial expression analysis, voice tone detection, linguistic pattern recognition, and neuroscience-based games, all working in concert to produce a single output, a score that purports to capture something ineffable about a candidate's personality and potential.

HireVue, one of the most prominent vendors in this space, built its reputation on video interview technology that went far beyond simply recording candidates' responses. According to the Washington Post, the company's system used candidates' computer or cellphone cameras to analyse their facial movements, word choice, and speaking voice before ranking them against other applicants based on an automatically generated “employability” score. The company had been used by over 100 employers to evaluate more than a million job candidates.

The granularity of this analysis was remarkable. HireVue examined what they called “Facial Action Units,” which could make up 29 per cent of a person's interview score. According to HireVue's own documentation, 10 to 30 per cent of a candidate's score was based on facial expressions, with the remainder calculated from language use. The AI claimed to determine everything from how excited someone seemed about work tasks to how they might behave around angry customers, all derived from facial movements and voice patterns.

Pymetrics, another major player in the space (now acquired by Harver), took a different approach, using neuroscience-based games to assess candidates. Founded by Harvard and MIT-trained neuroscientist Frida Polli, the company developed 12 neuroscience mini-games that take less than half an hour to measure 90 cognitive, social, and emotional traits. “The whole idea behind Pymetrics is that instead of using a resume, you are looking at people's cognitive, social, and emotional aptitudes,” Polli has stated. “It's also much more future-facing and potential-oriented, rather than backwards-facing and only talking about your past experiences.”

The appeal to employers is obvious. Traditional hiring is expensive, time-consuming, and riddled with inconsistency. A single corporate job posting can attract hundreds or thousands of applications. Human recruiters are limited by time, attention, and their own unconscious biases. AI systems promise to process vast numbers of candidates quickly while applying consistent criteria. When Unilever adopted an AI-powered recruitment funnel partnering with Pymetrics for its Future Leaders programme, the company processed 250,000 applicants in months. Time-to-hire was reportedly reduced by 90 per cent, from four months to four weeks, saving the company over one million pounds in recruitment costs.

The Scientific Foundation Begins to Crack

Yet the scientific claims underlying these systems have come under withering scrutiny. The most damaging revelation came from HireVue's own research, which showed that facial analysis contributed only 0.25 per cent to actual job performance prediction. Candidates were being scored heavily on factors that had virtually no correlation with their ability to do the job.

AI researchers have been considerably less diplomatic. Some have described the technology as “digital snake oil,” an unfounded blend of superficial measurements and arbitrary number-crunching unrooted in scientific fact. They argued that analysing a human being this way could penalise non-native speakers, visibly nervous interviewees, or anyone else who did not fit the model for look and speech.

Sandra Wachter, Professor of Data Ethics at the Oxford Internet Institute, has been particularly scathing. She has stated that emotion AI has “at its best no proven basis in science and at its worst is absolute pseudoscience.” Her assessment reflects a growing consensus among researchers that the technology reproduces historical forms of pseudoscience based on the concept of quantifiable and unequally distributed emotional capacity.

Lisa Feldman Barrett, Professor of Psychology at Northeastern University and a leading expert on emotion, has highlighted the fundamental problem with these systems. “The topic of facial expressions of emotion, whether they're universal, whether you can look at someone's face and read emotion in their face, is a topic of great contention that scientists have been debating for at least 100 years,” she has observed. The science is nowhere near settled enough to stake someone's livelihood on it.

The criticism extends beyond facial analysis to emotion recognition more broadly. Research published in 2024 found that emotion recognition technologies discriminate on the basis of race, gender, and disability. In one study by Lauren Rhue, emotion AI consistently interpreted Black subjects as having more negative emotions than white subjects, even when facial expressions were identical. Another study found that an emotion recognition system read Black faces as angrier than white faces, even when both were smiling to the same degree.

These are not theoretical concerns. They have real consequences for real people. In a US study published in 2024, workers expressed concern that emotion recognition systems would harm their wellbeing and impact work performance. They were fearful that inaccuracies could create false impressions about them.

Training Data and the Reproduction of Bias

The deeper problem with AI hiring tools lies not in their algorithms per se, but in what those algorithms learn from. Machine learning systems are only as good as the data they are trained on. And when that data reflects decades of hiring decisions made by humans with their own biases, the algorithm does not eliminate bias; it codifies it.

The most notorious example remains Amazon's experimental recruiting engine, first reported by Reuters in 2018. The company had been building computer programmes since 2014 to review job applicants' resumes with the aim of mechanising the search for top talent. The tool used artificial intelligence to give job candidates scores ranging from one to five stars.

The problem was the training data. The AI tool was trained on ten years' worth of resumes the company had received. Because the technology sector is male-dominated, the majority of those resumes came from men. The result was that the system was unintentionally trained to prefer male candidates over female candidates.

The discrimination was not subtle. The system reportedly penalised resumes containing the word “women's” or the names of certain all-women's colleges. Meanwhile, it favoured words such as “executed” and “captured,” which are apparently deployed more often in the resumes of male engineers. Amazon edited the programmes to make them neutral to these particular terms, but that was no guarantee the machines would not devise other ways of sorting candidates that could prove discriminatory. The company ultimately disbanded the team because executives lost hope for the project.

The Amazon case illustrates a fundamental tension in AI hiring. These systems are typically trained on data from “top performers” at a company. If a company's existing workforce lacks diversity, particularly at senior levels, then the algorithm will learn to select candidates who resemble that homogeneous group. As Meredith Whittaker, co-founder of the AI Now Institute, has observed, “Firms that are using such software may not have diverse workforces to begin with, and often have decreasing diversity at the top.”

Whittaker has documented how this pattern repeats across the AI industry. “There are an increasing pile of examples where we see that these systems embed biased and discriminatory logics,” she has stated. “In almost every case, these biases are effectively replicating histories of discrimination, so against women, against Black people, against trans people.”

The AI Now Institute's 2019 report “Discriminating Systems” documented how workforce discrimination in AI labs, dominated by a narrow demographic, causally propagates biases into deployed systems. The problem is not that AI is inherently biased. The problem is that AI amplifies and automates the biases embedded in historical data and in the teams that build these systems.

Cultural Fit Becomes Algorithmic Conformity

Perhaps nowhere is the potential for algorithmic discrimination more acute than in the assessment of “cultural fit.” This concept, long a staple of hiring discourse, has always carried discriminatory risks. AI systems that claim to measure cultural fit take those risks and amplify them.

Katherine Klein, Professor of Management at Wharton, has characterised cultural fit as “an incredibly vague term, and it's a vague term often based on gut instinct.” According to diversity researchers, the vague use of “fit” is one of the top contributors to homogenous hiring.

The problem is that hiring managers often conflate cultural fit with personal similarity. Instead of looking for people who share the company's values, they look for people who share their own background and interests. As one expert noted, “What you're going to get is a copy of your existing employees,” and “in many instances, it is a form of discrimination.”

In a 2012 paper for the American Sociological Review, Lauren A. Rivera investigated this dynamic through 120 interviews with professionals involved in hiring at US investment banks, law firms, and management consulting firms. She found that the most common thing employers looked for at the job interview stage was “similarity” in hobbies, experiences, and self-presentation styles. When interviewers said they “clicked” or “had chemistry” with a candidate, they often meant that they shared a similar background.

When AI systems are trained to identify “cultural fit,” they risk encoding these same preferences in algorithmic form. The system learns from historical data about who was hired and promoted. If past hiring reflected homogeneity, the algorithm will perpetuate that homogeneity, now with the veneer of scientific objectivity. “Good vibes” becomes a proxy for candidates who mirror the demographics and mannerisms of those already in power.

Professor Wachter's research at Oxford has explored how AI creates what she calls “artificial immutability,” using features like opacity, vagueness, instability, involuntariness, and invisibility to make discriminatory groupings seem natural and inevitable. Candidates in online interviews may be assessed by facial recognition software that tracks facial expressions, eye movement, respiration, or sweat. The criteria for evaluation are hidden from candidates, who have no way to know why they were rejected or how to improve.

The Disability and Neurodiversity Gap

The limitations of AI hiring tools become especially stark when applied to candidates with disabilities or neurodiverse traits. These systems are typically trained on data from neurotypical, able-bodied candidates. Anyone who communicates differently, whether due to deafness, autism, or other conditions, is at immediate disadvantage.

This issue came into sharp focus in March 2025, when the American Civil Liberties Union, Public Justice, and ACLU of Colorado filed a complaint with the Colorado Civil Rights Division and the Equal Employment Opportunity Commission against Intuit and HireVue. The complaint was filed on behalf of an Indigenous and Deaf woman who was denied a promotion allegedly due to discrimination based on her disability and race.

The complainant, identified as D.K., communicates using American Sign Language and English with a deaf accent. Despite receiving positive supervisor feedback and bonuses every year since 2019, she was rejected for a seasonal manager position after completing a HireVue video interview. According to the complaint, the HireVue platform did not provide consistent subtitles for all audio content.

When D.K. requested human-generated captioning, Intuit allegedly denied this accommodation, telling her that HireVue's software included subtitling capabilities. When she began the interview, no subtitling option was available, forcing her to rely on Google Chrome's automated captioning, which is sometimes incomplete and inaccurate. She was rejected for the promotion and allegedly received AI-generated feedback recommending that she “practice active listening,” a suggestion that the ACLU argues demonstrates how her hearing disability disadvantaged her in the process.

Research has consistently shown that such AI systems perform worse when evaluating non-white and deaf or hard of hearing speakers. The complaint alleges that HireVue's hiring assessment platform discriminated against deaf and non-white individuals, violating the Colorado Anti-Discrimination Act, the Americans with Disabilities Act, and Title VII of the Civil Rights Act.

HireVue CEO Jeremy Friedman has stated that the complaint “is entirely without merit” and that Intuit did not use a HireVue AI-based assessment in this instance. Intuit has similarly stated that “the allegations in the complaint are entirely without merit.” The case remains ongoing.

The legal implications extend beyond this single case. As one legal expert noted, “employers can still be held responsible for AI-related discrimination, even if the tool was developed and implemented by a third-party vendor.”

The Regulatory Landscape Takes Shape

Regulators around the world are beginning to grapple with the implications of AI in hiring. The most comprehensive framework to date is the European Union's AI Act, which entered into force on 1 August 2024. Under this regulation, AI systems used for recruitment and selection are explicitly categorised as “high-risk” due to their potential impact on individuals' access to employment and their future careers.

The EU AI Act covers AI systems intended to be used for the recruitment or selection of individuals, including to place targeted job advertisements, to analyse and filter job applications, and to evaluate candidates. Usage of AI for deciding on promotions, termination of work-related contractual relationships, allocation of tasks, and monitoring or evaluation of the performance and behaviour of workers is also classified as high-risk.

That classification is settled law. Its consequences are not yet in force. The obligations that attach to high-risk systems, covering risk management, data quality, transparency, human oversight, record-keeping, registration and incident reporting, were due to bind providers and deployers from 2 August 2026. They will not. Under the EU's Digital AI Omnibus, provisionally agreed between the Council and Parliament on 7 May 2026 and subsequently finalised, the compliance deadline for stand-alone Annex III systems, the category containing recruitment, selection, promotion and termination, was deferred to 2 December 2027. For AI embedded in regulated products under Annex I, the deadline moved to 2 August 2028. The stated rationale was that industry needed additional time to prepare for compliance and that the harmonised technical standards on which compliance depends were not yet ready. Whatever the merits of that argument, the practical effect is that the world's most comprehensive AI hiring regime will not constrain a single hiring decision until the closing weeks of 2027.

One part of the Act did not slip. The prohibition in Article 5(1)(f) on inferring emotions from biometric data in the workplace took effect on 2 February 2025 and remains in force. The European Commission's review of the Act in November 2025 specifically declined to soften the list of prohibited practices, and the omnibus delay did not touch it. The asymmetry is worth sitting with. The broad, technical, engineering-heavy obligations on high-risk systems, the risk assessments and documentation and audit trails, slipped by sixteen months. The outright ban on the single most pseudoscientific practice in the sector held. Reading emotions off a candidate's face is unlawful in the European workplace today; building a high-risk hiring algorithm without a risk management system is not, at least not yet. The most indefensible practice is the one that stayed banned, and it arguably stayed banned because it was indefensible. There was no serious industry case to be made for a technology whose scientific foundations Wachter has described as absent.

The regulation has extraterritorial reach, meaning US employers can be covered even without a physical EU presence if AI outputs are intended to be used in the EU. Penalties are tiered, and the tiering matters. Engaging in a prohibited practice, which includes workplace emotion recognition, can attract fines of up to 35 million euros or 7 per cent of global annual turnover, whichever is higher. Breaches of the obligations governing high-risk systems carry a lower ceiling of 15 million euros or 3 per cent. That the harsher of the two tiers is the one already operative is the clearest signal Brussels has sent about which practice it considers beyond redemption.

In the United States, regulation has proceeded in a more patchwork fashion. New York City's Local Law 144, which took effect in January 2023 with enforcement beginning in July 2023, requires organisations using automated employment decision tools to undergo annual bias audits from independent third-party auditors. The audit must assess the tools' disparate impact on employment decisions for candidates based on protected categories such as sex, ethnicity, and race.

The law also imposes transparency requirements. Organisations must make the date of the most recent bias audit, a summary of results, and distribution date of the tool publicly available on their websites. Employers must notify candidates at least ten business days before using such a tool.

However, enforcement has proven challenging. A recent state comptroller audit found that while the New York City Department of Consumer and Worker Protection surveyed websites and bias audits of 32 companies and identified only a single issue of non-compliance, the comptroller's office identified at least 17 instances of potential non-compliance. DCWP officials acknowledged that identifying non-compliance is difficult because if an employer does not take the required steps, it is hard to identify that they are violating the law.

Illinois has been particularly active in regulating AI in employment. The state's Artificial Intelligence Video Interview Act governs employers' use of AI analysis in video interviews, requiring various notices, consents, and data management practices. In August 2024, Governor J.B. Pritzker signed HB 3773, amending the Illinois Human Rights Act to expressly regulate AI for employment decisions. That law took effect on 1 January 2026 and now binds employers in the state. It prohibits the use of AI in ways that discriminate against employees on the basis of protected characteristics, even where the discrimination is unintentional; it bars the use of zip codes as proxies for protected classes; and it requires employers to give notice when AI is used in employment decisions.

The obligations arrived without a map, however. The Illinois Department of Human Rights temporarily withdrew its proposed rules on the use of artificial intelligence in employment, leaving the statutory duties fully in force while the precise contours of compliant notice remain undefined pending further rulemaking. Employers are bound by requirements whose detailed shape has not been settled. This is a familiar pattern in the field, where the duty to be accountable consistently outruns any specification of what accountability actually looks like.

The Illinois Biometric Information Privacy Act adds another layer of exposure. Employers using AI facial recognition in conjunction with video interviews, such as to analyse facial expressions, speech patterns, and other non-verbal cues, could face liability under both the video interview act and the biometric privacy law.

The Legal Reckoning Begins

The courtroom is becoming a new battleground over AI hiring discrimination. The EPIC complaint against HireVue filed with the Federal Trade Commission in November 2019 charged that the company falsely denied using facial recognition and failed to comply with baseline standards for AI decision-making. While HireVue subsequently removed facial analysis from its assessments, the complaint highlighted the opacity surrounding these systems.

In July 2024, CVS privately settled a proposed class action lawsuit filed by a job applicant who claimed the company broke Massachusetts law by requiring prospective employees to undergo what legally amounted to a lie detector test. The lawsuit alleged that applicants were required to take HireVue video interviews using Affectiva's AI technology to track facial expressions and assign an “employability score.”

The most consequential of these cases is Mobley v. Workday, which alleges discrimination against Black, older and disabled applicants arising from Workday's algorithmic screening practices. It has proceeded in California as a nationwide collective action under the Age Discrimination in Employment Act, covering applicants who applied for positions through Workday's platform on or after 24 September 2020 and who were aged forty or over at the time. The court authorised notice to that collective, and the window for applicants to opt in closed on 7 March 2026. A ruling handed down on 6 March 2026 rejected Workday's contention that the ADEA does not reach job applicants at all, an argument that, had it succeeded, would have removed algorithmic screening at the application stage from the statute's reach entirely.

Then came a decision that ought to trouble anyone who believes bias audits are a sufficient answer. On 29 May 2026, the court denied the plaintiffs' motion to compel production of Workday's own bias-testing data, the internal evidence of whether its tools produced disparate outcomes. The material was held to be protected by attorney-client privilege. Workday's lawyers had curated the data used in the testing, and the results had been used in the provision of legal advice rather than for a business purpose. The court also rejected the argument that Workday had waived that privilege by publicly acknowledging outside litigation that it conducted bias testing at all. The case remains in discovery, with no trial date set.

The ACLU has also filed a complaint with the FTC against Aon over AI personality tests.

Ifeoma Ajunwa, Professor of Law at the University of North Carolina and founding director of the AI Decision-Making Research Program, has documented the legal challenges these cases present. “The law requires that you prove either intent to discriminate or show a pattern of discrimination,” she has explained. “Automated hiring platforms actually make it much harder to do either of those. And a lot of times, the algorithms that are part of the hiring system are considered proprietary, meaning that they're a trade secret. So you may not actually be able to be privy to exactly how the algorithms were programmed and also to exactly what attributes were considered.”

The Workday privilege ruling is that warning made concrete, and then some. Ajunwa's concern was that trade secrecy would place the algorithm itself beyond a plaintiff's reach. What the May 2026 decision established is that evidence of the algorithm's effects can be placed beyond reach too, by the straightforward expedient of routing the testing through counsel. A company can test its systems for discrimination, learn the answer, tell the world that it tests, and still keep the findings from the very people whose claims those findings might substantiate. The audit becomes a compliance artefact rather than an accountability mechanism. This is the accountability gap in its purest form: not an absence of scrutiny, but scrutiny sealed by privilege, conducted for the benefit of the party being scrutinised.

In testimony before Congress, Ajunwa argued that government measures are urgently needed to regulate automated hiring systems that often discriminate against women, military veterans, formerly incarcerated people, people with disabilities, and others. She has argued for independent audits and compulsory data retention, contending that an employer's failure to audit its automated hiring platforms for disparate impact should serve as prima facie evidence of discriminatory intent under Title VII.

Ajunwa's paper “Automated Video Interviewing as the New Phrenology” draws an explicit parallel between these technologies and discredited pseudoscientific practices of the past. The comparison is not rhetorical flourish. It reflects a genuine concern that we are watching history repeat itself, now with the authority of silicon rather than callipers.

The Promise of Debiased AI

Despite the mounting criticism, proponents of AI hiring tools argue that the technology, properly designed, could actually reduce discrimination rather than perpetuate it. Frida Polli, who co-founded Pymetrics and went on to serve as Chief Data Science Officer at Harver following the 2022 acquisition, has been a vocal advocate for this position. She has since moved on to other work, holding a position as a Visiting Innovation Scholar at MIT's Schwarzman College of Computing and founding Alethia AI in 2023.

“We are fundamentally biased, and we can't help it,” Polli has stated. “I personally think that if we really want to change diversity in the workforce, we are never going to get there with humans.” She argues that algorithms are more trainable than humans: “It's hard to remove bias from algorithms, but it is possible. It is not possible to remove bias from humans.”

Pymetrics designed its assessments to be “completely free of gender and ethnic bias,” arguing that measuring cognitive skills like memory through actual tests is gender-blind. The company audits its algorithms with a formula made publicly available on GitHub. Polli was instrumental in passing New York City's bias audit law, the first such regulation in the nation.

The Unilever case study is often cited as evidence that AI hiring can promote diversity when properly implemented. The company reported that the use of AI in sourcing led to an increase in talent diversity by 16 per cent. It employed what it called “the most diverse ethnic and gender employee class so far.” The company attributed this success to using bias-free data sets in training AI systems and maintaining human supervision in the use of the systems.

Yet even success stories come with caveats. With smaller companies, top performers may be very similar in how they think and work. Training models to identify candidates based on how they match with top performers could potentially exclude innovative thinkers. The very diversity that makes teams effective might be screened out by systems optimised for conformity.

Research by University of Washington found significant racial, gender, and intersectional bias in how state-of-the-art large language models ranked resumes. A large-scale randomised experiment found that leading AI models systematically favour female candidates while disadvantaging Black male applicants, even when qualifications are identical. The bias is complex and intersectional, making simple fixes inadequate.

Professor Wachter's work has shown that the majority of popular bias tests and tools, 13 out of 20 examined, do not live up to the standards of EU non-discrimination law. In response, she developed a bias test called Conditional Demographic Disparity that meets EU and UK standards. Amazon and IBM have implemented it in their cloud services. In 2024, this test was used to uncover systemic bias in education in the Netherlands, leading to a formal apology from the Dutch Minister for Education.

What Candidates Face

For job seekers, the rise of AI hiring tools creates an opaque and often bewildering landscape. You may never know that an algorithm rejected you, let alone why. The criteria for success are hidden. The appeals process is non-existent.

HireVue does not give candidates access to their assessment scores or the training data, factors, logic, or techniques used to generate each algorithmic assessment. You receive a rejection, or you do not. The system has spoken.

A survey found that only 12.9 per cent of Australian adults support face-based emotion recognition technologies in the workplace. Respondents viewed facial analysis as invasive, unethical, and highly prone to error and bias.

The power asymmetry is profound. Job seekers, particularly those early in their careers or from marginalised backgrounds, have little leverage to question or challenge these systems. They must perform for the algorithm, adapting their self-presentation to what they guess the machine wants to see, without knowing what that actually is.

Some candidates are fighting back. Research published in 2024 documented how smart candidates are learning to detect AI interview bias and protect themselves from algorithmic discrimination. But this places an additional burden on job seekers, requiring them to become experts in a technology that should be serving them fairly.

Rethinking What We Measure

The fundamental question underlying this entire debate is what we are actually trying to measure when we hire someone. Traditional interviews, for all their flaws, at least have face validity. You talk to a person. You assess whether you can work with them. The criteria may be subjective, but they are human.

AI hiring tools promise to replace this subjectivity with objectivity. But objectivity requires valid measures. And the measures these systems use (facial expressions, vocal tone, word choice, neuroscience game performance) have not been validated as predictors of job performance. HireVue's own research showed facial analysis contributed only 0.25 per cent to job performance prediction. What, exactly, are we measuring?

The answer may be nothing more than conformity. Systems trained on historical data learn to identify candidates who look, sound, and behave like people who were hired in the past. In a homogeneous workplace, that means selecting for homogeneity. In a discriminatory system, that means perpetuating discrimination.

Joy Buolamwini, founder of the Algorithmic Justice League and co-author of the landmark Gender Shades study, has documented how commercial AI systems fail to recognise people equally. Her research with Timnit Gebru found that the error rate for light-skinned men was 0.8 per cent, compared to 34.7 per cent for darker-skinned women. IBM ended its facial recognition programme partly in response to this research.

Buolamwini's experience was personal before it was academic. While working on a facial-recognition-based art project at the MIT Media Lab, she discovered that commercial AI systems could not consistently detect her face due to her darker skin. “A white mask was a closer fit to what the system had learned was a face than my actual human face,” she has observed. Fortune magazine named her “the conscience of the AI revolution.”

Gebru, who co-founded Black in AI and later the Distributed Artificial Intelligence Research Institute, has spent years documenting how AI systems replicate discrimination. Named one of Time's most influential people of 2022, she has argued that facial recognition is too dangerous to be used for law enforcement and security purposes at present. The same concerns apply to employment.

The Regulatory Winds Are Shifting

It is tempting to read the past two years as a one-way ratchet, with the era of unaccountable AI hiring drawing steadily to a close. The evidence no longer supports that reading. The trajectory is contested rather than inevitable, and during 2026 it moved in both directions at once.

Consider Colorado. SB 24-205, signed in 2024, was the most ambitious state AI statute in the United States: a duty of care imposed on developers and deployers of high-risk systems to protect consumers from algorithmic discrimination, with employment squarely within scope. It never took effect. In roughly six weeks in the spring of 2026, the entire edifice came down. A federal court stayed its enforcement. The US Department of Justice joined a constitutional challenge to it. The state attorney general announced he would not enforce it pending rulemaking. Then the legislature repealed it outright, replacing it on 14 May 2026 with SB 26-189, a markedly narrower framework governing automated decision-making technology, due to take effect on 1 January 2027. The replacement abandons the original's duty of care around algorithmic discrimination altogether. In its place sit notice requirements, a right to an explanation of an adverse outcome within thirty days, and human review where commercially reasonable. The attorney general has indicated he will not enforce that statute either until rulemaking concludes.

Set that alongside the European Union's sixteen-month deferral of its high-risk obligations, and alongside the New York City enforcement record, where a state comptroller's audit identified seventeen instances of potential non-compliance that the enforcing agency had not caught, and a rather different picture emerges. Binding constraints on AI hiring are arriving more slowly and more thinly than the legislative wave of 2024 appeared to promise, even as the underlying technology proliferates and the vendors multiply.

This is not a counsel of despair. Real constraints did land, and they have held. The EU's prohibition on workplace emotion recognition is in force, carries the Act's heaviest penalties, and survived both a Commission review and an omnibus delay that touched almost everything else around it. Illinois HB 3773 is now law and binds employers in that state today. New York City's disclosure regime, however weakly policed, has at least made bias audits a visible expectation rather than a private courtesy. The direction of travel still points towards accountability. What has changed is that nobody can now say with confidence how fast it is moving, or how far it will go.

HireVue itself has evolved. The company removed facial analysis from its assessments in 2021, acknowledging that the technology “wasn't worth the concern.” The company now states that its video assessments only evaluate language, specifically how candidates talk about their experiences and past actions pertaining to competencies critical to the particular role. But voice analysis and linguistic pattern detection carry their own bias risks.

The market is adapting. Some companies are shifting from “culture fit” to “culture add,” seeking candidates who bring different perspectives rather than candidates who conform to existing norms. Research shows structured interviews are twice as predictive of job performance compared to unstructured ones, and they reduce the scope for bias. There are better ways to hire than asking an algorithm to judge someone's vibes.

But the technology continues to proliferate. New AI tools emerge constantly, promising ever more sophisticated assessments of personality, potential, and fit. The vendors may change. The fundamental problems remain.

A Mirror That Distorts

In the end, AI hiring tools hold up a mirror to the organisations that use them. They reflect the biases embedded in historical data. They reproduce the homogeneity of existing workforces. They encode the preferences of the people who build and train them. They are not neutral arbiters of talent. They are amplifiers of existing power structures.

The promise of eliminating human bias through technology is seductive. It offers absolution from the uncomfortable work of confronting discrimination directly. But there is no algorithmic shortcut to equity. Machines learn what humans teach them. If we teach them our biases, they will apply those biases at scale.

The question is not whether AI should play a role in hiring. Automation has benefits: efficiency, consistency, and the capacity to process far more candidates than any human recruiter. The question is whether we will demand transparency, accountability, and scientific validity from these systems, or whether we will allow “good vibes” to become another way of saying “people like us.”

Meredith Whittaker has called for algorithm audits that include experts, advocacy groups, and academics reviewing them and studying their effects on different populations. “We think that's not happening today,” she has stated, “and it could lead to serious problems as AI takes off.”

Ifeoma Ajunwa has argued that audits should be mandated and that failure to audit should constitute evidence of discriminatory intent. Sandra Wachter has developed bias tests that meet legal standards. Joy Buolamwini and Timnit Gebru have shown that these systems fail the people they are supposed to serve fairly.

The tools exist. The research is clear. The regulations are uneven: deferred in Brussels, repealed in Denver, thinly enforced in New York, and binding in Springfield. What remains to be seen is whether employers will choose accountability over convenience in the absence of anyone reliably compelling them to, and whether job seekers will be protected from algorithms that claim to see into their souls but see only reflections of the status quo.

Your next job interview may be judged by a machine. The question is what that machine has learned about who deserves a chance.

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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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