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Picture the test working exactly as designed. A regulator wants to know whether an insurer's pricing algorithm is quietly discriminating against minority drivers, so it does the thing the textbooks and the model bulletins say to do. It takes the premiums the algorithm produces, it lines them up against the legitimate rating factors the insurer is allowed to use, and it asks a statistical question: once you strip out everything the law permits, is there still a residue that tracks race? The formula runs. A number comes back. The number says no. No residue, no proxy, no problem. The insurer files its attestation, the regulator closes the file, and the consumer in the low-income postcode who is paying more than her identical-risk neighbour two streets over goes on paying it, secure in the knowledge that a fairness test was run and she passed it. Everyone passed it. That is the problem. In May 2026, two researchers ran exactly this test across thirty-four real auto insurers and found that the standard regulatory formula flags zero of them. Not one. Then they corrected the maths, and every single one lit up.

The paper is called Fairness Testing for Algorithmic Pricing, posted to the arXiv preprint server on 12 May 2026 by Fei Huang, an associate professor in the School of Risk and Actuarial Studies at the University of New South Wales Business School in Sydney, and Giles Hooker, a professor of statistics and data science at the Wharton School of the University of Pennsylvania. It is a dry document, dense with asymptotic variance estimators and cross-covariance formulae, the kind of thing that ordinarily circulates among a few hundred actuaries and disappears. What it actually describes is a quiet catastrophe of governance: the principal tool regulators rely on to catch the most insidious form of algorithmic discrimination has been built wrong, and has been returning false negatives the whole time it has been deployed. The detector designed to find the discrimination cannot find the discrimination. It has been telling everyone the building is not on fire while the smoke fills the room.

The Discrimination That Hides in Plain Variables

To understand why this matters, you have to understand the specific thing the test was supposed to catch, because it is not the obvious thing. No reputable insurer in the United States or the United Kingdom puts race into a pricing model. It is illegal, it is reputationally radioactive, and it is also, increasingly, unnecessary. The variable the law forbids can be reconstructed from a dozen variables the law permits. This is the mechanism the field calls proxy discrimination, and it is the central villain of the entire story.

Proxy discrimination occurs when an algorithm uses a legally permitted, facially neutral variable as a statistical stand-in for a protected characteristic, producing a discriminatory outcome without ever encoding the protected characteristic directly. Postcode stands in for ethnicity, because residential segregation means a postcode is often an excellent predictor of the race of the people who live there. Occupation stands in for sex, because labour markets remain heavily gendered and a job title carries a probability of the worker's gender almost as reliably as a form that asked outright. Educational attainment, vehicle type, the make of a phone used to fill in an online quote, the timing of a payment, the shopping history attached to a loyalty card: each of these can carry, encoded within it, the very characteristic the insurer is forbidden to price on. The algorithm never sees race. It does not need to. It sees postcode, and postcode has already done the work.

What makes proxy discrimination so corrosive is that everyone's hands stay clean. The insurer can say, truthfully, that race is not in the model. The actuary can demonstrate, truthfully, that postcode is a genuine predictor of claims cost. The regulator can confirm, truthfully, that no protected characteristic appears in the rating factors. And the driver in the minority postcode still pays more than her risk justifies, because the model has found a route to the same destination by a road the law forgot to close. The harm is real and the discrimination is real, but it is laundered through a chain of individually defensible decisions until no one is responsible for it. This is not a hypothetical worry dreamed up by academics. It is the failure mode that the entire apparatus of modern insurance fairness regulation was constructed to detect.

What the Corrected Maths Reveals

The Huang and Hooker paper takes the standard regulatory audit and asks a deceptively simple question about it: is the statistics actually valid? The conventional approach regresses the pricing output on a protected attribute and the legitimate rating factors, then tests whether the resulting coefficient is statistically significant using ordinary least squares standard errors, the same standard errors you would use on noisy survey data. The trouble, the authors show, is that a pricing algorithm is not noisy survey data. It is deterministic. Feed it the same inputs and it returns the same premium every time, with no random scatter. When you regress against a deterministic system, the residuals you get back do not represent sampling variability, the random noise that classical standard errors are designed to handle. They represent approximation error, a fundamentally different beast. The result, in the authors' own words, is that classical standard errors are invalid in both direction and magnitude. The test is not slightly miscalibrated. It is measuring the wrong quantity with the wrong ruler.

The consequence falls hardest precisely on the proxy discrimination test, the one designed to catch the hidden variety. When the standard proxy discrimination formula is applied to the thirty-four insurers, it flags zero of them. The corrected formula, which the authors derive with the proper cross-covariance terms, identifies all thirty-four as statistically significant, of which sixteen exceed the substantive threshold that would mark the disparity as not merely real but materially large. The gap between zero and thirty-four is not a rounding error or an academic quibble about decimal places. It is the difference between a test that exonerates an entire market and a test that condemns it.

The empirical heart of the paper is its dataset: quoted premiums from thirty-four auto insurers operating in Illinois, examined against the demographic composition of the postcodes those quotes were attached to. Applying a conditional demographic parity test, the one that asks whether two areas of equal risk are charged equally, the researchers found that every one of the thirty-four insurers failed. Minority postcodes were quoted premiums between thirty-four and one hundred and fifty-eight US dollars more per year than comparable-risk areas with whiter populations. Comparable risk. That is the phrase that should stop a reader cold. The extra charge was not explained by the drivers being worse risks, because the comparison was constructed to hold risk constant. It was the residue of something else riding along inside the permitted variables, and it was the very residue the standard test had pronounced absent.

The reason the error matters deserves spelling out, because it explains why no amount of good faith on the part of an individual auditor would have saved them. The classical standard error assumes that if you collected another sample, the numbers would jitter around a little, and it sizes that jitter to decide whether an observed disparity is real or could be a fluke. Against a deterministic pricing engine there is no jitter to size, because the engine does not flip a coin: the same applicant always receives the same quote. What the regression's residuals are actually capturing is how well the auditor's chosen control variables happen to approximate the insurer's true rating formula, a quantity with no relationship whatsoever to the confidence interval the formula then prints. An auditor running the standard procedure is not being careless. They are following the method correctly and arriving, inexorably, at a conclusion the method has no right to draw. That is what makes the finding so unsettling: the failure is baked into the recipe, not the cook. The authors extend the same correction to the generalised linear models that insurers most commonly deploy in practice, not merely the simpler ordinary-least-squares case, which is why the result speaks directly to live pricing systems rather than to a statistical toy.

How the Detector Learned to Look the Wrong Way

There is a second, related failure hiding underneath the first, and it concerns the very thing regulators use to stand in for race when they are not allowed to ask for it. In a companion paper posted to arXiv in March 2026, “How Proxy Race Distorts Regression-Based Fairness Audits,” Huang and Hooker, joined by Xi Xin of UNSW, dissected a method that sits at the foundation of fair-lending and fair-insurance enforcement across the United States. Because firms in many contexts cannot collect race directly, regulators and auditors infer it statistically, most prominently through a technique known as Bayesian Improved Surname Geocoding, which estimates the probable race of an individual from their surname and the demographics of the postcode they live in. This proxy is not a fringe tool. It has been institutionalised in regulatory settings, and it underpinned the most prominent fair-lending actions the Consumer Financial Protection Bureau has brought, including its auto-lending discrimination cases against Ally Bank in 2013 and against Honda and Toyota's finance arms in 2015 and 2016.

What Xin, Hooker, and Huang demonstrate is that swapping inferred race for observed race does not merely add a little noise to the analysis. It systematically transforms what the regression coefficient measures. When proxy race is misclassified, even at apparently high accuracy, the disparities attributed to minority groups are compressed toward the majority baseline, because the confusion between groups bleeds the signal from one into the other. The authors put it precisely: proxy-based regression coefficients can be attenuated or amplified relative to analogous analyses based on self-reported race, depending on how the proxy correlates with the pricing residuals. In the common case, the distortion shrinks the measured disparity, which means the proxy that regulators reach for in the absence of real data tends to make discrimination look smaller than it actually is. Taken together, the two papers describe a pincer. One failure lives in the standard error, telling auditors that a real disparity is not statistically significant. The other lives in the proxy for race itself, telling them the disparity is smaller than it really is. A market audited under both errors at once would look serene almost regardless of how it actually behaved, which is exactly the picture the regulatory record has painted for years.

A Second Lab, the Same Verdict

If the Huang and Hooker result stood alone, a sceptic might reasonably wait for replication before sounding alarms. It does not stand alone. Roughly a month later, in research surfacing in late May and June 2026, a team anchored at Bayes Business School, part of City St George's, University of London, arrived at the same destination by a different route, and proposed a tool to do something about it.

The Bayes work centres on Andreas Tsanakas, professor of risk management at Bayes, working with collaborators including Mathias Lindholm of Stockholm University. Their framework, published in the European Journal of Operational Research in 2026, is a measurement instrument: a way of identifying and quantifying how much of an insurance price is attributable to proxy effects, applicable across most lines of insurance and extending into adjacent financial services such as credit scoring. The framework's findings echo the Illinois numbers with uncomfortable precision. Proxy discrimination in insurance pricing, the Bayes team concluded, is both widespread and measurable. In one of their analyses, young drivers from a particular minority ethnic group were systematically quoted higher motor insurance premiums, a disparity driven in part by proxy effects rather than by any difference in their actual risk.

The Bayes framework also surfaces a complication that the cruder public debate tends to miss, and it is worth holding onto because it cuts against easy intuitions. Some variables, the researchers found, can actually reduce proxy discrimination rather than amplify it, because the interactions between pricing factors are tangled enough that removing a variable naively can make the hidden bias worse, not better. Fairness, in other words, cannot be achieved by simply deleting suspicious-looking columns from the data; a regulator who orders an insurer to drop postcode may, depending on what remains, leave the discrimination untouched or even sharpen it. Tsanakas has long argued that the only way to measure proxy discrimination rigorously is, paradoxically, to collect data on protected characteristics from at least a subset of policyholders, so that the proxy effect can be isolated and stripped out. As he has framed it, insurers need to collect information on protected characteristics, which itself raises privacy concerns that demand strict protocols about how the information is gathered and used. It is an awkward truth at the heart of the field: to prove you are not discriminating, you may first have to gather the very data you are forbidden to price on, and the law's instinct to ban the collection of sensitive data collides head-on with the statistics of detecting its misuse.

Two independent research efforts, in two countries, using different methods, on different markets, converging in the same season on the same conclusion. Proxy discrimination in algorithmic insurance pricing is real, it is measurable, it is widespread, and the standard tools deployed to catch it are not catching it. That is no longer a finding. It is a pattern.

The Machines Are Already Everywhere

The reason this lands with such force in mid-2026, rather than as a theoretical footnote, is the sheer extent to which the decisions in question have already been handed to algorithms. A Reuters analysis published in May 2026 confirmed what anyone working inside the industry already knew: artificial intelligence is now deeply embedded across the core functions of insurance, underwriting, pricing, and claims handling, throughout both the United States and the United Kingdom, with little in the way of standardised oversight binding the practice together.

The scale of the shift is not subtle. Across the sector, underwriting decisions that once took days now resolve in minutes; straight-through processing rates, the proportion of applications handled with no human touching them, have climbed from low double digits to the high eighties and nineties at the more automated carriers. AI systems now read claims, estimate damage from photographs, flag suspected fraud, and set the price that lands on a customer's renewal letter. The industry's own commentary describes 2026 and 2027 as the period in which insurers transition from AI-assisted workflows, where a human adjuster uses an AI tool, to agentic workflows, where the AI orchestrates the process and the human reviews the outcome, if a human reviews it at all. The same trajectory runs through the adjacent markets the research touches: in credit and lending, machine-learning models now decide who is offered a loan, at what rate, and on what terms, drawing on the same kind of behavioural and geographic data, and inviting the same kind of proxy effect.

This is the environment into which the Huang and Hooker result drops. The discrimination-detection tools are failing not in a niche of the market but at its operational centre, governing the prices and the acceptances and the rejections experienced by hundreds of millions of people. And the failure is structural rather than incidental. It is not that a few bad actors gamed a sound test. It is that the test itself, the one written into model bulletins and risk-management frameworks and compliance attestations across the industry, has been returning false negatives by design. Every insurer that ran the standard proxy test and passed has a piece of paper saying so. The paper means nothing. It always meant nothing. The fire alarm was wired to stay silent, and the building filled with people who had been assured the alarm was working.

The Patchwork of Rules That Cannot See In

To grasp why the regulatory response has been so thin, it helps to survey the actual rules, because the gap between their ambition and their machinery is where the consumer falls through.

The most muscular attempt sits in Colorado. Senate Bill 21-169, enacted in July 2021 and billed as the first law of its kind in the United States, prohibits insurers from using external consumer data and information sources, along with the algorithms and predictive models built on them, in any way that produces unfair discrimination against consumers on the basis of race, colour, national or ethnic origin, religion, sex, sexual orientation, disability, gender identity, or gender expression. External consumer data, in the Colorado framing, is sweeping: credit-based insurance scores, purchase histories, social-media signals, geographic data, anything not collected directly from the consumer. The law does not merely prohibit. It imposes affirmative governance duties, requiring insurers to document the data their models use, to maintain a risk-management framework to test whether those models discriminate, to monitor the results, and to attest, through a named officer, that the framework has been put in place. On paper, it is the closest thing to a real answer that exists. In practice, its testing regime leans on precisely the kind of statistical audit that the Huang and Hooker paper shows to be broken, and the race it tests against is precisely the kind of inferred, proxy-based race that the companion paper shows to be biased toward understatement. A governance framework is only as good as the test it runs, and if the test flags zero insurers when the truth is thirty-four, the attestation becomes a ritual rather than a safeguard.

At the national level in the United States, the National Association of Insurance Commissioners adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023, and by early 2026 more than half the states had adopted it or something close to it. The bulletin asks insurers to maintain a formal written AI programme covering governance, consumer notice, risk management, internal controls, and vendor oversight. It is a framework for asking the right questions. It is not, in itself, a method for getting the right answers, and it does not prescribe a corrected statistical test, because at the time of its drafting the field did not yet know the standard one was wrong. A bulletin that tells insurers to test for bias, without specifying a test that works, simply ratifies whatever test the industry already uses.

Across the Atlantic, the European Union's AI Act classifies AI systems used for risk assessment and pricing in life and health insurance as high-risk under Annex III, paragraph 5©, subjecting them to conformity assessments, documentation duties, and human-oversight requirements, with the relevant obligations beginning to bite from August 2026 under current law, though parts of the timetable have been subject to proposed delay. The high-risk designation is significant, but its scope is narrower than the problem: it reaches life and health, and does not extend to the property and casualty lines, motor and home insurance, where the Illinois evidence of proxy discrimination is sharpest. A driver overcharged on her car insurance because of where she lives sits entirely outside the AI Act's high-risk perimeter.

In the United Kingdom, the Financial Conduct Authority governs the territory through its Consumer Duty, in force since 2023, which requires firms to deliver fair value and to put customers' interests at the centre of their decisions. The FCA's general insurance value measures, published annually, show claims costs running at around 54 per cent of premium for motor insurance and 46 per cent for home insurance in 2024, and the regulator's thematic reviews have repeatedly flagged weaknesses in how firms conduct fair-value assessments. But fair value is an outcome-focused principle, not a discrimination-detection algorithm. It tells a firm what result to aim for. It does not hand the regulator a valid test for whether a pricing model is using postcode as a proxy for ethnicity, and the Consumer Duty's machinery was not built to peer inside a deterministic model and isolate a proxy effect. A firm can deliver fair value, in the aggregate, while still loading a quiet surcharge onto one ethnic group, because the aggregate hides the distribution.

The common thread running through all four regimes, Colorado, the NAIC, the EU, the FCA, is that each is a framework for requiring good behaviour rather than a tool for verifying it. They demand that insurers not discriminate, that they test for discrimination, that they attest to having tested. None of them could detect the discrimination the research has now measured, because all of them depend, directly or indirectly, on a statistical test that the research has shown to be returning the wrong answer. The regulators built a doctrine on a detector, and the detector was broken.

What Consumer Protection Means When No One Can See

So we arrive at the question the whole affair forces open. When someone living in a low-income postcode, or working in a particular occupation, pays meaningfully more for car, home, or life cover than a neighbour with an identical risk profile, because the model treats her circumstances as a proxy for something the law forbids it to use directly, and when the systems built to catch that practice are demonstrably failing, what does consumer protection actually mean? What is left of it?

The honest answer is that consumer protection, in an algorithmic insurance market, has been resting on an assumption that no longer holds: that the disparities, if they existed, would be visible to a competent auditor running a standard test. The entire edifice of attestation and governance and model bulletins is built on the premise that the discrimination is detectable, that the regulator can in principle see in. The Huang and Hooker result removes that premise. The discrimination was not detectable, not because it was hidden by bad actors but because the detector was miscalibrated, and so for the years the broken test has been in use, the protection was notional. Consumers were told they were protected by a process that could not have protected them. The reassurance was the harm's best disguise.

There is a particular cruelty in the structure of this harm, and it is worth naming precisely. Proxy discrimination does not fall randomly. It tracks the contours of existing disadvantage, because the proxies that machine-learning models find most useful, postcode, occupation, the cheap phone, the thin credit file, are the same variables that encode who is already poor, already marginalised, already segregated. The driver in the low-income postcode is charged more not despite her circumstances but because of them, and the surcharge compounds the disadvantage that produced it. She pays more for insurance because she is poor, and she is a little poorer because she pays more for insurance. The Illinois figures, thirty-four to one hundred and fifty-eight dollars a year, may sound modest set against a single premium. Multiplied across motor, home, and life cover, compounded over a working lifetime, and concentrated on the households least able to absorb it, they describe a regressive transfer running quietly through one of the most heavily regulated industries in the developed world, invisible to the very regulators charged with policing it.

What the research also makes clear is that the failure is fixable, which is the one genuinely hopeful note in the account. Huang and Hooker did not merely diagnose the broken test; they derived the corrected one, the proper asymptotic variance estimators and the cross-covariance formula that a deterministic pricing model actually requires. The Bayes team did not merely confirm the disease; they built a framework to measure and, in principle, to remove the proxy effect. The mathematics to detect proxy discrimination correctly now exists. The instruments are on the table. What does not yet exist is the regulatory will to swap the broken detector for the working one, to rewrite the model bulletins and the risk-management frameworks and the attestation requirements around a test that returns thirty-four rather than zero, and to compel an industry that has every commercial incentive to prefer the comfortable answer to adopt the uncomfortable one.

That is the choice the May 2026 research lays bare, and it is not a technical choice. The technology works; the corrected formula works; the measurement framework works. The open question is whether the people who write the rules will insist on a detector that detects, knowing that the moment they do, an entire market that has been passing its fairness tests will start, all at once, to fail them. Consumer protection in an algorithmic insurance market does not, in the end, mean trusting the attestation on the file. It means demanding that the test behind the attestation be one that can actually find what it was built to find, and being willing to act on the answer when it does. Until then, the woman in the low-income postcode will keep paying her surcharge, the file will keep saying she passed, and the alarm wired to stay silent will keep doing exactly what it was, however unwittingly, designed to do.

Notes and References

1. Fei Huang and Giles Hooker, “Fairness Testing for Algorithmic Pricing,” arXiv preprint arXiv:2605.11614, submitted 12 May 2026. https://arxiv.org/abs/2605.11614
2. Xi Xin, Giles Hooker and Fei Huang, “How Proxy Race Distorts Regression-Based Fairness Audits,” arXiv preprint arXiv:2603.17106, March 2026. https://arxiv.org/abs/2603.17106
3. Associate Professor Fei Huang, School of Risk and Actuarial Studies, UNSW Business School, University of New South Wales, Sydney. https://research.unsw.edu.au/people/associate-professor-fei-huang
4. Professor Giles Hooker, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania. https://ai-analytics.wharton.upenn.edu/responsible-ai-analytics-for-insurance-workshop/
5. “Framework could deliver fairer insurance deals for customers,” Phys.org, 28 May 2026. https://phys.org/news/2026-05-framework-fairer-customers.html
6. Andreas Tsanakas, Mathias Lindholm and colleagues, framework on proxy discrimination in insurance pricing, European Journal of Operational Research, 2026, DOI 10.1016/j.ejor.2026.01.021.
7. “School creates AI-based method to terminate proxy discrimination in insurance pricing,” Insurance Times, 17 February 2023. https://www.insurancetimes.co.uk/news/school-creates-ai-based-method-to-terminate-proxy-discrimination-in-insurance-pricing/1443666.article
8. “Insurers and regulators must stamp out discrimination in insurance pricing to ensure fairness for consumers, says new study,” Bayes Business School, City St George's, University of London. https://www.bayes.citystgeorges.ac.uk/news-and-events/news/2023/january/insurers-and-regulators-must-stamp-out-discrimination-in-insurance-pricing-to-ensure-fairness-for-consumers-says-new-study
9. Colorado Senate Bill 21-169, “Protecting Consumers from Unfair Discrimination in Insurance Practices,” Colorado Division of Insurance. https://doi.colorado.gov/for-consumers/sb21-169-protecting-consumers-from-unfair-discrimination-in-insurance-practices
10. “Protecting consumers: Implementation of Colorado's antidiscrimination law in insurance,” Milliman. https://www.milliman.com/en/insight/protecting-consumers-colorado-antidiscrimination-law-insurance
11. National Association of Insurance Commissioners, “Model Bulletin: Use of Artificial Intelligence Systems by Insurers,” adopted December 2023. https://content.naic.org/insurance-topics/artificial-intelligence
12. “Nearly Half of States Have Now Adopted NAIC Model Bulletin on Insurers' Use of AI,” Quarles & Brady LLP. https://www.quarles.com/newsroom/publications/nearly-half-of-states-have-now-adopted-naic-model-bulletin-on-insurers-use-of-ai
13. European Union Artificial Intelligence Act, Annex III, “High-Risk AI Systems Referred to in Article 6(2),” paragraph 5© on risk assessment and pricing in life and health insurance. https://artificialintelligenceact.eu/annex/3/
14. Financial Conduct Authority, “General insurance value measures data 2024.” https://www.fca.org.uk/data/general-insurance-value-measures-data-2024
15. Financial Conduct Authority, “Our Consumer Duty focus areas.” https://www.fca.org.uk/publications/corporate-documents/consumer-duty-focus-areas
16. Reuters analysis on the integration of artificial intelligence across insurance underwriting, pricing and claims handling in the United States and United Kingdom, published May 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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The thing about walking down a street is that you have never had to ask permission to do it. You step out of your house, you turn onto the pavement, and you move through the world as one anonymous body among millions, your face an unremarkable fact that nobody records and nobody keeps. That assumption, ancient and quiet and almost never examined, is the thing a class-action lawsuit filed on 2 June 2026 says Amazon has quietly demolished. The complaint, lodged in the United States District Court for the Western District of Washington, does not concern a data breach or a leaked database or a rogue employee. It concerns a feature that works exactly as designed. And what it was designed to do, the lawsuit argues, is take a mathematical print of the face of every person who walks past a Ring camera, whether or not that person has ever heard of the feature, whether or not they consented, whether or not they will ever know it happened.

The feature is called Familiar Faces. Amazon's Ring subsidiary announced it in September 2025 and began rolling it out to doorbell owners across the United States on 9 December 2025. The pitch is the kind of mild convenience that has carried surveillance technology into the home for a decade: instead of a generic alert telling you that motion has been detected at your front door, your phone now tells you who is there. You tag the people who come and go, up to fifty of them, and the system learns to recognise them, greeting your partner, your neighbour, your regular delivery driver by name on the screen in your hand.

To do that, the system has to do something more consequential than its marketing suggests. To decide whether the person at the door is the person you tagged, it has to scan the face of everyone who appears in the camera's field of view, extract a faceprint from each one, and compare it against the saved set. As the Electronic Frontier Foundation explained in a November 2025 analysis written by its staff attorney Mario Trujillo, a faceprint is produced by “taking tiny measurements of your face and converting that into a series of numbers that is saved for later.” That string of numbers, derived from the geometry of a stranger's face, is processed and stored on Amazon's servers. Ring's own support materials describe a retention regime in which unnamed profiles are removed after thirty days without a further sighting and all facial-recognition information is deleted after a hundred and eighty days of no recognition. The lawsuit's contention is brutally simple: every person who walks into frame, the postal worker, the canvasser, the child selling biscuits, the neighbour cutting across the lawn, the stranger merely passing on the pavement, is scanned, measured and stored without ever being asked.

A Plaintiff Who Never Bought the Camera

The man bringing the case is Charles Sigwalt, a Virginia resident who has never owned a Ring device. That detail is the entire architecture of the argument. Sigwalt is not a customer complaining about a product he bought. He is, on his own account, a passer-by, someone whose face was captured and stored while he visited friends and family whose doorbells happened to have Familiar Faces switched on. He represents a proposed nationwide class defined, in the complaint, as everyone in the United States whose facial-recognition data was collected, retained or used by the feature within the relevant statutory period, with a Virginia subclass for residents of his own state. The reporting on the filing describes a class that “could include thousands or millions of people,” and the complaint itself seeks damages exceeding the five-million-dollar threshold that anchors a federal class action of this kind.

The legal theories are an instructive patchwork, because they reveal how poorly the existing law fits the harm. Sigwalt's complaint leans on Virginia consumer-protection law, Virginia's appropriation statute, the Virginia Computer Crimes Act, the common-law tort of intrusion upon seclusion, negligence and unjust enrichment. It also invokes, by way of contrast, the biometric-privacy regimes of the three jurisdictions from which Amazon has conspicuously withheld the feature: Illinois, Texas and Portland, Oregon. That contrast is the rhetorical heart of the case. Familiar Faces is simply not available in those three places, and the complaint argues that this selective deployment proves Amazon “clearly has the ability to follow biometric privacy laws” and chooses, everywhere else, not to. As the filing puts it, the rest of the country does not get the same respect.

It is worth dwelling on how strange this is as a matter of corporate behaviour. A company that genuinely believed its feature was lawful and benign would not need to draw a map of the United States and carve three holes in it. Amazon drew exactly that map. The holes are not random. They correspond precisely to the places where collecting a stranger's faceprint without consent carries a defined, expensive and well-litigated legal penalty. Everywhere the penalty is uncertain, the scanning proceeds. The map is, in effect, a confession rendered in geography: a demonstration that the company knows precisely what consent-based biometric law requires, possesses the technical capacity to comply with it, and has decided that compliance is something it owes only to residents of jurisdictions that thought to legislate.

Why Three Jurisdictions, and Only Three

To understand the map, you have to understand the three laws that drew it, because each represents a different answer to the same question and together they form the entire functioning edifice of American biometric protection.

Illinois passed the Biometric Information Privacy Act in 2008. BIPA is, by some distance, the most consequential privacy statute in the United States, and it owes that status to a single design choice: it gives ordinary people a private right of action. Under BIPA, a private entity may not collect a person's biometric identifier, a faceprint emphatically included, without first informing them in writing, explaining the purpose and duration of the collection, and obtaining written consent. Crucially, an individual whose rights are violated can sue on their own behalf and recover statutory damages, between one thousand and five thousand dollars per violation, without having to prove they suffered any concrete downstream injury. That feature turned BIPA into a machine for accountability. It is why Facebook agreed in 2020 to pay six hundred and fifty million dollars to settle claims that its photo-tagging tool extracted faceprints from Illinois users without consent, a settlement approved by Judge James Donato in the Northern District of California in February 2021 and described at the time as one of the largest privacy settlements in history. Eligible class members received cheques averaging around three hundred and ninety-seven dollars. The number that mattered to every other company watching was the total.

Texas takes a different route to a similar end. Its Capture or Use of Biometric Identifier statute, known as CUBI, also prohibits capturing a person's biometric identifier for a commercial purpose without consent, but it reserves enforcement to the state attorney general rather than to individuals. For years that made CUBI look toothless, a law on the books that nobody enforced. Then the office of Attorney General Ken Paxton began to wield it, and the results were staggering. In May 2025, Texas announced a one-billion-three-hundred-and-seventy-five-million-dollar settlement with Google to resolve allegations that the company had unlawfully collected Texans' biometric data, including face geometry, through products such as Google Photos and the Nest line of cameras, capturing, as the EFF later put it, the face geometry of any Texan who happened to come into view, including non-users. Separately, Meta agreed to pay Texas one billion four hundred million dollars over comparable claims. These are not nuisance settlements. They are among the largest privacy recoveries any government has ever secured, and they were secured under a state law that simply says a company may not take your biometric identity without asking.

Portland, Oregon, supplies the third and most categorical model. In September 2020 the city council voted unanimously to pass what was then the first ordinance in the United States to ban private entities from using facial-recognition technology in places of public accommodation. The ban took effect on 1 January 2021. Portland did not bother with the consent framework at all. It concluded that, in the spaces where members of the public have no real choice about being present, the technology should simply not operate. The ordinance was animated explicitly by concerns about over-surveillance, opacity, and the gender and racial bias documented in facial-recognition systems, and it represents the position that some uses of the technology are not a matter for negotiated consent but a line that should not be crossed.

Three jurisdictions, three philosophies: a private right to sue, an empowered public enforcer, an outright prohibition. What they share is that each one attaches a real and predictable cost to scanning a non-consenting face. Familiar Faces stops at all three borders. Everywhere else in America, the cost is still being litigated, and Amazon has decided to keep scanning until a court tells it the price.

A Company With a Surveillance Record

None of this is happening in a vacuum, and the institutional memory matters, because Ring is not a neutral newcomer stumbling into a privacy question for the first time. It is a company with a documented history of treating the cameras in people's homes as instruments whose reach exceeds their owners' understanding.

In May 2023 the Federal Trade Commission charged Ring with a litany of failures and extracted a settlement requiring it to pay five million eight hundred thousand dollars in consumer refunds. The agency's complaint was lurid. It alleged that Ring had given employees and hundreds of third-party contractors unfettered access to customers' private video feeds, including footage from cameras in bedrooms and children's bedrooms, with the ability to download, view and share those recordings at will. It alleged that lax security allowed hackers to seize control of more than fifty-five thousand US customers' accounts and cameras between early 2019 and 2020. The order forced Ring to build a privacy programme, impose multi-factor authentication, and submit to novel safeguards on human review of video. The episode established a pattern that the Familiar Faces dispute now echoes: a product sold as personal security, operating in practice as something with a far wider and less consensual gaze than its buyers imagined.

Ring's entanglement with policing deepens the picture. For years the company's Neighbors app and its earlier footage-request features functioned as a soft channel through which law-enforcement agencies could solicit video from a vast distributed network of private cameras, a quasi-public surveillance grid assembled from doorbells. In October 2025 Ring announced a partnership with Flock Safety that would let police request footage through Community Requests in the Neighbors app, integrating Ring's cameras into a network already controversial for its automated licence-plate readers. After a public backlash, Ring announced on 12 February 2026 that it was cancelling the Flock partnership following a comprehensive review, saying the integration would require more time and resources than anticipated. The reversal was a pattern in miniature: deploy an expansion of surveillance, weather the criticism, retreat only when the cost becomes visible. Familiar Faces is the same manoeuvre at the scale of the human face itself.

The Warnings That Came First

What distinguishes the Familiar Faces episode from an inadvertent overreach is that the objections were registered, loudly and specifically, before the feature ever shipped. This was not a case of a company surprised by an outcome nobody foresaw. The outcome was foreseen, in writing, by some of the most credible privacy voices in the country, and the feature launched anyway.

The EFF's November 2025 analysis was unambiguous. It walked through the mechanics of how a faceprint is taken and stored, identified the population of non-consenting bystanders the feature would inevitably sweep up, and named the legal precedents, the Google and Facebook settlements, that mapped the exposure with precision. Trujillo's warning went beyond the immediate function to the deeper structural danger: a system built to recognise a friend at the front door, he argued, can be repurposed tomorrow for mass surveillance, because the infrastructure, the cameras, the faceprints, the servers, the matching, is identical regardless of the use to which it is put. The capability is the risk. Once tens of millions of doorbells can extract and compare faceprints, the question of what that capability is pointed at becomes a matter of policy, configuration and corporate discretion rather than engineering.

The political warning was just as explicit. Senator Ed Markey of Massachusetts, a member of the Senate Commerce Committee with a long record of scrutinising Ring, wrote to Amazon's chief executive Andrew Jassy on 31 October 2025, demanding that the company abandon its plan to embed facial recognition in Ring doorbells. In response, Markey's office reported, Amazon revealed something telling: that Ring's privacy protections apply only to device owners, not to the members of the public who appear in front of the cameras. That admission is the whole problem stated in a single sentence. The protections run to the customer. The faces belong to everyone else. When a Super Bowl advertisement showcased the technology in early 2026, Markey wrote again, on 11 February 2026, repeating his call for Amazon to discontinue the feature. The company did not.

This is the sequence that gives the lawsuit its moral force. A regulator-adjacent senator warned. A leading civil-liberties organisation warned. The company's own response confirmed that non-users were unprotected. The relevant precedents were already measured in the billions. And the feature shipped to the rest of the country regardless, with three holes cut neatly out of the map where the law had teeth.

The Asymmetry of Consent

Strip away the legal machinery and what remains is a question about consent that the home-security industry has spent a decade avoiding. The Ring camera is bought, installed and configured by a homeowner for the homeowner's purposes. Every consent that exists in the transaction belongs to that one person. But the camera does not point inward at the person who consented. It points outward, at the street, at the pavement, at the approach to the door, at precisely the space through which other people, who consented to nothing, are obliged to pass.

This is the structural inversion at the centre of the Familiar Faces dispute, and it is what makes the ordinary frameworks of consumer privacy inadequate to it. In the standard model, a user agrees to a product's terms and accepts the trade-offs; if they dislike the bargain, they can decline the product. The delivery driver carrying a parcel up the path has no such option. They cannot read Amazon's terms of service. They cannot toggle a setting. They cannot decline to have their face measured, because declining would mean declining to do their job, or declining to visit their friend, or declining to walk down a public street. Their biometric identity is taken as a condition of their physical presence in the world, and there is no interface through which they could ever have said no.

The numbers turn this from a thought experiment into an infrastructure. Ring is the dominant brand in a market that has saturated American residential life; industry analyses place it at the top of the smart-doorbell category, with millions of active units across US households and smart cameras present in roughly a third of American internet homes. When a single company's outward-facing cameras number in the tens of millions and each one is capable of extracting faceprints, the aggregate is not a collection of private security decisions. It is a distributed biometric sensor network blanketing the residential landscape, assembled house by house, consent by individual consent, into a system that no individual consented to and that surveils, overwhelmingly, people who are not its customers. The lawsuit's phrase for the result, an involuntary biometric database of non-users, is not rhetorical excess. It is an accurate description of what tens of millions of consenting installations produce when their gaze is pooled.

What the Data Could Become

The defenders of Familiar Faces will say, correctly, that the current use is narrow. The feature tells a homeowner who is at the door. The faceprints of strangers are, by Ring's account, discarded within months if they are not matched. Nobody is being tracked across the city. No central index of every passer-by is being compiled and sold. All of that is, for now, true. And all of it misses the point that the EFF and Markey were pressing, which is not about what the system does today but about what the existence of the system makes possible tomorrow.

Consider the components that Familiar Faces requires in order to function at all. It requires cameras at scale, which now exist. It requires the capacity to extract a faceprint from any face that appears, which is the core function. It requires servers that process and store those faceprints, which Amazon operates. It requires a matching engine that compares a new face against a stored set, which is the whole feature. Every one of these components is precisely what a mass-surveillance system needs. The only thing standing between a doorbell that greets your neighbour by name and a network that can locate a specific individual across tens of millions of cameras is a policy decision about how the matching is scoped, and policy decisions can change. They can change because a company updates its terms. They can change because a government compels access, as Ring's history of police entanglement makes far from hypothetical. They can change because a feature is quietly expanded, the way Familiar Faces itself was added to cameras that buyers had installed for an entirely different purpose.

The relevant precedent here is not a privacy abstraction but the recurring lesson of Ring's own conduct: capabilities built for a benign stated purpose tend to find broader application, and the public usually learns about the broader application after the fact. The cameras were sold for parcel theft and they became a police network. The footage was meant for owners and contractors in Ukraine were watching bedrooms. The faceprints are taken to recognise friends, and the question the lawsuit forces is what guarantees, if any, prevent them from one day being used to recognise anyone. The honest answer, under the current legal regime in forty-seven states and most cities, is almost none. There is no general federal biometric-privacy law. Outside Illinois, Texas, Portland and a handful of states with comprehensive privacy statutes, the meaningful limits on how a stranger's faceprint may be used, by whom, and for how long are whatever a company writes into a policy it can revise at will.

The Limits of Settling After the Fact

It is tempting to read the billion-dollar settlements as evidence that the system works, that companies which over-collect biometric data eventually pay, and that the prospect of paying will deter the next firm. The Familiar Faces case is the strongest available evidence that this reading is wrong, because Amazon launched the feature in full view of those very settlements. Google's one-billion-three-hundred-and-seventy-five-million-dollar payment to Texas and Facebook's six-hundred-and-fifty-million-dollar BIPA settlement were not obscure. They were the most prominent biometric-privacy outcomes in the country, and Amazon's own engineers and lawyers plainly knew them well enough to draw the exclusion map. The settlements did not deter the conduct. They merely defined the three zones in which the conduct would be too expensive to attempt.

This is the deep inadequacy of an enforcement model that operates only after the harm, and only where a legislature happened to act in advance. The settlements are vast, but they arrive years after the faceprints were taken, they reach only the jurisdictions with the right statute, and they treat the violation as a cost to be priced rather than a line not to be crossed. For the company, a settlement is a known business expense, payable from the revenue the feature generated in the interim, and discharged without any admission that the underlying conduct was wrong. Google paid Texas its one-and-a-third billion dollars without acknowledging any violation and without being required to change its products. A penalty that can be absorbed, that is confined to a few states, and that need not alter the behaviour going forward is not a constraint on surveillance. It is a tariff on it, and a tariff that most of the country does not even charge.

The reactive model also places the entire burden on the surveilled. To vindicate his rights, a person like Sigwalt must discover that his face was scanned, a thing he was specifically never told, retain lawyers, identify a viable legal theory among the patchwork of state torts and statutes, and litigate against one of the largest companies on earth, all to establish a principle that should never have required litigation: that you may not take a stranger's biometric identity without asking. The default is surveillance, and the only available remedy is an expensive, years-long, after-the-fact lawsuit to claw a fraction of dignity back. Reversing that default is the whole challenge, and it is not primarily a technical one.

What Consent-by-Default Would Actually Require

The question the Familiar Faces case ultimately poses is the one its plaintiff's exclusion-map argument answers by implication: what would it take for the default to be consent rather than surveillance? The Illinois, Texas and Portland carve-outs prove that consent-by-default is achievable, because Amazon already achieves it for tens of millions of people. The task is to make the protection those residents enjoy the floor for everyone, and the components are visible, scattered across the very jurisdictions whose patchwork currently frustrates a coherent answer.

The first requirement is a private right of action grounded in personhood, not purchase. BIPA's defining feature is that the person whose face was taken can sue, and can recover statutory damages without proving a separate downstream loss. That single design choice is what gives the law its bite, because it does not ask the surveilled to quantify a harm that is inherently dignitary, the harm of having your biometric identity seized by a stranger. A federal biometric-privacy law built on that model would do what no settlement can: make the taking itself actionable everywhere, by the people it is taken from, rather than only in the three places that legislated first.

The second requirement is that consent must come from the person whose biometric data is collected, not from the person who bought the device. The entire conceptual error of the current arrangement is that it treats the homeowner's consent as covering the faces the homeowner's camera captures. It does not, and cannot, because those faces belong to other people. A meaningful framework would recognise that the relevant consenting party is the data subject, the person whose face is measured, and that no purchase, no terms of service and no household setting can supply consent on a stranger's behalf. Where obtaining that consent is impossible, as it is for a passer-by on a public pavement, the Portland answer, that the scanning simply should not happen, becomes not an extreme position but the only coherent one.

The third requirement is strict limits on retention and repurposing, written into law rather than policy. The danger of a faceprint database is not exhausted by its first use; it is latent in its existence. A framework adequate to the threat would mandate the minimum retention necessary for any consented function, prohibit the use of biometric data collected for one purpose in the service of another, and bar the kind of capability creep, from recognising a friend to locating a stranger, that the architecture makes trivially easy. It would also confront the policing question directly, foreclosing the quiet conversion of a private camera network into a public surveillance grid that Ring's own history shows is no abstraction.

The fourth requirement is that compliance must not be optional based on geography. The exclusion map is the lawsuit's smoking gun precisely because it demonstrates that selective compliance is a choice. A company able to switch a feature off at the Illinois and Texas borders is able to switch it off everywhere, and a legal regime worth the name would remove the incentive to draw such maps at all by making the strongest available protection national. The current arrangement effectively rewards the country for its legislative gaps, granting Amazon free rein everywhere a state failed to act. A federal floor would convert those gaps from commercial opportunities into the protections they should always have been.

The Street That Used to Be Anonymous

There is a temptation, encountered in every privacy debate of the past two decades, to treat the loss as already complete and the resistance as quaint. The cameras are everywhere; the faceprints are already taken; the database, involuntary or not, already exists. Why fight a war that is over? The answer is that the war is not over, and the exclusion map is the proof. In Illinois, in Texas, in Portland, the war was fought before the technology arrived, and it was won, and the result is that the residents of those places walk past Ring cameras every day without having a faceprint extracted from them. They were not protected by accident. They were protected because a legislature decided, in advance, that a person's biometric identity is not a thing a company may take simply because its camera can see a face.

What the Familiar Faces lawsuit asks the rest of the country to decide is whether that protection is a regional privilege or a human baseline. The stakes are easy to understate, because the immediate harm is invisible. Nobody is arrested. Nobody is denied a loan. A faceprint is taken, stored, and in most cases deleted within months, and the person it was taken from feels nothing and knows nothing. But the absence of a felt injury is exactly what makes the precedent so corrosive. We are being asked to accept, quietly and without ever having been consulted, that the act of walking through public space now generates a biometric record held by a private company, and that the only people exempt are those whose local governments thought to forbid it. The default has shifted from anonymity to identification, and the shift happened not through legislation or public deliberation but through a software update pushed to cameras that people had bought for a different reason.

Charles Sigwalt's lawsuit may succeed or it may fail; the patchwork of Virginia torts it relies on is a fragile substitute for the clean biometric statute the rest of the country lacks. But its central insight does not depend on the verdict. Amazon has already told us, by where it declined to deploy, that consent-based biometric privacy is technically and commercially feasible, that the company can honour it when a law requires, and that it will withhold it wherever a law does not. The only remaining question is who deserves the protection that Illinois, Texas and Portland already guarantee. The honest answer is that a person's face should not be a thing that any company is entitled to measure and keep merely because that person had the temerity to walk down a street. Making that the default, everywhere and for everyone, is the unfinished work the doorbell has forced into view.

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References and Sources

1. Classaction.org, “Ring Lawsuit Claims 'Familiar Faces' Feature Violates Basic Notions of Consumer Privacy”, June 2026. https://www.classaction.org/blog/ring-lawsuit-claims-familiar-faces-feature-violates-basic-notions-of-consumer-privacy
2. Biometric Update, “Amazon Ring sued over facial recognition feature as privacy fight moves to federal court”, June 2026. https://www.biometricupdate.com/202606/amazon-ring-sued-over-facial-recognition-feature-as-privacy-fight-moves-to-federal-court
3. The Register, “Ring gets buzzed by class action for collecting visitors' faces without consent”, 3 June 2026. https://www.theregister.com/personal-tech/2026/06/03/ring_faces_class_action_over_facial_recognition_feature/
4. CBS News, “Amazon faces lawsuit over Ring facial recognition software”, June 2026. https://www.cbsnews.com/news/amazon-ring-lawsuit-facial-recognition-familiar-faces/
5. Electronic Frontier Foundation, Mario Trujillo, “The Legal Case Against Ring's Face Recognition Feature”, 3 November 2025. https://www.eff.org/deeplinks/2025/11/legal-case-against-rings-face-recognition-feature
6. TechCrunch, “Amazon's Ring rolls out controversial, AI-powered facial-recognition feature to video doorbells”, 9 December 2025. https://techcrunch.com/2025/12/09/amazons-ring-rolls-out-controversial-ai-powered-facial-recognition-feature-to-video-doorbells/
7. Office of the Attorney General of Texas, “Attorney General Ken Paxton Secures Historic $1.375 Billion Settlement with Google Related to Texans' Data Privacy Rights”, 9 May 2025. https://www.texasattorneygeneral.gov/news/releases/attorney-general-ken-paxton-secures-historic-1375-billion-settlement-google-related-texans-data
8. CNBC, “Google to pay Texas $1.4 billion in data privacy settlement”, 9 May 2025. https://www.cnbc.com/2025/05/09/google-texas-data-privacy-settlement-paxton.html
9. American Bar Association, Business Law Today, “Historic Biometric Privacy Suit Settles for $650 Million”, February 2021. https://www.americanbar.org/groups/business_law/resources/business-law-today/2021-february/historic-biometric-privacy-settlement/
10. Federal Trade Commission, “FTC Says Ring Employees Illegally Surveilled Customers, Failed to Stop Hackers from Taking Control of Users' Cameras”, 31 May 2023. https://www.ftc.gov/news-events/news/press-releases/2023/05/ftc-says-ring-employees-illegally-surveilled-customers-failed-stop-hackers-taking-control-users
11. TechCrunch, “Amazon's Ring to pay $5.8M after staff and contractors caught snooping on customer videos, FTC says”, 31 May 2023. https://techcrunch.com/2023/05/31/amazon-ring-ftc-settlement-lax-security/
12. U.S. Senator Ed Markey, “Senator Markey Demands Amazon Abandon Plan to Include Facial Recognition Technology in Ring Doorbells”, 31 October 2025. https://www.markey.senate.gov/news/press-releases/senator-markey-demands-amazon-abandon-plan-to-include-facial-recognition-technology-in-ring-doorbells
13. U.S. Senator Ed Markey, “Following Dystopian Super Bowl Ad, Markey Again Calls on Amazon to End Facial Recognition Technology in Ring Doorbells”, February 2026. https://www.markey.senate.gov/news/press-releases/following-dystopian-super-bowl-ad-markey-again-calls-on-amazon-to-end-facial-recognition-technology-in-ring-doorbells
14. CNBC, “Amazon Ring cameras deeper into policing with Flock Safety, Axon deals”, 16 October 2025. https://www.cnbc.com/2025/10/16/amazon-ring-cameras-surveillance-law-enforcement-crime-police-investigations.html
15. 9to5Mac, “After Ring privacy backlash, company abandons police partnership”, 16 February 2026. https://9to5mac.com/2026/02/16/after-ring-privacy-backlash-company-abandons-plans-for-police-partnership/
16. Portland.gov, “City Council approves ordinances banning use of face recognition technologies by City of Portland bureaus and by private entities in public spaces”, 9 September 2020. https://www.portland.gov/bps/com-tech/smart-city-pdx/news/2020/9/9/city-council-approves-ordinances-banning-use-face
17. Hunton Andrews Kurth, “Portland, Oregon Becomes First Jurisdiction in U.S. to Ban the Commercial Use of Facial Recognition Technology”, September 2020. https://www.hunton.com/privacy-and-information-security-law/portland-oregon-becomes-first-jurisdiction-in-u-s-to-ban-the-commercial-use-of-facial-recognition-technology
18. Hunton Andrews Kurth, “Texas AG Announces $1.375 Billion Settlement with Google for Privacy Violations”, May 2025. https://www.hunton.com/privacy-and-information-security-law/texas-ag-announces-1-375-billion-settlement-with-google-for-privacy-violations
19. Telecompetitor, “Video Doorbell Research: Amazon Ring Tops in Market Share with 16% of Households Opting In”. https://www.telecompetitor.com/video-doorbell-research-amazon-ring-tops-in-market-share-with-16-of-households-opting-in/
20. State of Surveillance, “Ring Now Scans Faces at Your Door. Here's What That Means.”, December 2025. https://stateofsurveillance.org/articles/corporate/amazon-ring-familiar-faces-facial-recognition-2025/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

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

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

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

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For four weeks, sixty-seven people sat down with a screen and a question that has come to define the age: is this real? Each was shown a procession of news headlines paired with images, a stream of the genuine and the fabricated mixed together in deliberate confusion. Some of the pictures were authentic. Some were the synthetic offspring of generative models, plausible to the point of menace. And for part of the study, the participants did not face this alone. They had an assistant, a conversational AI willing to weigh in, to reason aloud, to nudge them towards a verdict. With the machine at their side, they grew measurably sharper. They caught more of the fakes. They were, on average, twenty-one per cent more accurate than they had been without help.

Then the researchers took the machine away.

What happened next is the reason the study exists, and the reason it should unsettle anyone who has come to lean on a chatbot to tell the true from the false. When the participants were asked to evaluate fresh headlines on their own, their performance did not merely fail to improve. It fell. By the fourth week, their unassisted accuracy had declined by 15.3 per cent compared with where they had started. The tool that had made them better at the task had, over the same weeks, made them worse at it without the tool. And a striking share of them did not notice. Roughly a quarter reported feeling that they had improved, even as the data recorded the opposite.

The work, conducted by researchers at the MIT Media Lab and presented at CHI 2026, the premier international gathering for human-computer interaction research, carries a title that reads almost like a warning label: “Dialogues with AI Reduce Beliefs in Misinformation but Build No Lasting Discernment Skills.” The team behind it, including Anku Rani, Valdemar Danry, Paul Pu Liang, Andrew Lippman and the senior researcher Pattie Maes, had set out to test a hopeful proposition. If conversing with an AI can durably lower a person's belief in false information, perhaps those same conversations might also teach the person to detect falsehood independently, the way a good tutor leaves a student more capable than they found them. The hope did not survive contact with the evidence.

The design of the study is worth dwelling on, because the architecture of the experiment is what gives the result its force. The researchers did not simply hand participants a verdict-dispensing oracle and measure their satisfaction. They structured the month into phases, taking a baseline measurement of unassisted accuracy at the outset, interleaving sessions of AI-assisted evaluation, and then testing participants again on entirely fresh, previously unseen items without any help. That last detail matters enormously. If the unassisted test had recycled familiar headlines, an apparent improvement might have reflected nothing more than memorisation. By presenting new material, the researchers isolated the thing that actually counts: not whether a participant could recall a particular debunked story, but whether the experience of working alongside the AI had left them better equipped to confront the unknown. It had not. The transfer that defines genuine learning, the carrying of a skill from one instance to the next, simply failed to occur. The machine had functioned as a prosthesis rather than a teacher, and a prosthesis, however effective while it is worn, builds no muscle of its own.

The analogue everyone reaches for

There is a metaphor that the researchers, and almost everyone who has since written about the study, reach for instinctively. It is the satellite navigation system. You have probably lived the small version of it yourself: years of obediently following the turn-by-turn voice, until one day the signal drops in an unfamiliar city and you realise, with a cold little jolt, that you have no idea where you are. You have been to this place a dozen times. You have never once learned the way.

The analogy is more than rhetorical convenience, because the underlying neuroscience is real and unusually well documented. The most celebrated demonstration comes not from a study of GPS users but from a study of the people who represent its precise opposite: the licensed black-cab drivers of London. To earn their badge, these drivers must pass an examination known simply as the Knowledge, a feat of memorisation requiring years of preparation and the internalisation of some twenty-five thousand streets and the tangle of routes between them. In a landmark investigation published in 2000, the cognitive neuroscientist Eleanor Maguire and her colleagues at University College London scanned the brains of these drivers and found that the posterior hippocampus, a region central to spatial memory and navigation, was enlarged relative to that of non-drivers. A later longitudinal study tracked trainees over the years of their preparation and watched the structure grow, but only in those who ultimately passed.

The Knowledge, in other words, leaves a physical signature on the brain that acquires it. The hippocampus responds to demand. And the corollary, the part that should give every habitual user of navigation software pause, is that the relationship runs in both directions. Tissue that is exercised grows; capacity that is delegated does not. Maguire's drivers also paid a price, performing less well on certain other memory tasks, a reminder that the brain is not an infinitely expandable warehouse but an organ of trade-offs. Subsequent research on habitual GPS use has reported associations between heavier reliance on turn-by-turn navigation and poorer performance on spatial-memory measures, with longitudinal work suggesting steeper self-reported decline in navigational ability among the most dependent users. The compass in your hand, used uncritically, becomes the compass you no longer carry inside.

The MIT team's insight was to recognise that misinformation detection might be a faculty of exactly this kind: a skill that strengthens with practice and atrophies with delegation. When you puzzle over whether a headline is genuine, you are exercising something. You are checking the source against memory, interrogating the image for the tell-tale incoherence of a synthetic render, registering the emotional manipulation in the phrasing, recalling whether the claimed event squares with everything else you know. Hand that labour to a machine and the immediate problem is solved. But the faculty goes unexercised. And faculties that go unexercised, as the hippocampus of the lapsed navigator demonstrates, do not stand still. They quietly recede, and the recession is all the more insidious for being silent, because nothing about the smooth experience of asking and receiving an answer signals that anything is being lost at all.

A long lineage of outsourced minds

If the finding feels novel, the anxiety it provokes is anything but. Plato has Socrates fret, in the Phaedrus, that the invention of writing would implant forgetfulness in the souls of those who learned it, because they would cease to exercise their memory and trust instead to external marks. It is fashionable to cite this episode as proof that fears about cognitive offloading are perennial and therefore overblown. That reading is too glib. Socrates was not simply wrong; he was describing, with reasonable accuracy, a genuine trade-off. Literate cultures did substitute external storage for prodigious feats of oral memory. We gained more than we lost, but we did lose something, and pretending otherwise misses the actual lesson, which is that every cognitive tool reshapes the cognition that uses it. The pertinent question is never whether a tool changes us, because all of them do. It is whether the particular change it produces is one we would choose with our eyes open.

The modern empirical literature on this reshaping is substantial. In 2011, the psychologists Betsy Sparrow, Jenny Liu and Daniel Wegner published a paper in Science describing what swiftly became known as the Google effect. Across four experiments, they found that when people expected to be able to look information up again later, they remembered the information itself less well, but remembered better where to find it. The internet, the authors argued, had become a form of transactive memory, an external partner to which we offload the burden of remembering, holding onto the index rather than the entry. We had begun to remember our way to knowledge rather than the knowledge itself. The phenomenon was soon given a popular name, digital amnesia, and it captured something real about the texture of modern thought: the strange confidence of knowing that an answer is retrievable, paired with the quiet erosion of actually holding it.

There is the calculator, too, the example invoked so often it has become a cliché of the genre, and a contested one. The evidence on calculators is genuinely mixed, which is part of why the comparison is instructive rather than damning: a tool that handles arithmetic can free a learner to grapple with higher-order mathematical reasoning, or it can hollow out the numerical intuition on which that reasoning depends, and which outcome prevails turns largely on how the tool is folded into the learning. The instrument is not destiny. The pedagogy around it is. A calculator introduced after a child has internalised the structure of multiplication is an accelerant; the same device introduced before that structure exists can prevent it from ever forming. The lesson generalises with uncomfortable directness to AI, and it is precisely the lesson the MIT study sharpens.

And there is aviation, the field that has stared longest and hardest into the question of what happens when humans cede a complex skill to an automated system. Decades of cockpit automation have delivered enormous safety gains, but they have also produced a documented phenomenon that pilots and regulators call skill fade: the erosion of manual flying ability among aviators who spend the overwhelming majority of their hours monitoring systems rather than hand-flying aircraft. Investigations by bodies including the United States Federal Aviation Administration have repeatedly flagged automation complacency and the degradation of basic stick-and-rudder competence as safety concerns, the danger crystallising in those rare, terrible moments when the automation disengages and a crew must suddenly fly an aeroplane whose feel they have half-forgotten. The aviation world's response is telling, and we will return to it, because it represents one of the few large-scale institutional attempts to deliberately preserve a skill that automation tends to corrode.

The deskilling we forgot to study

What unites the cab driver, the Google user and the airline pilot is a single, under-examined idea: that the most consequential effect of a powerful tool may not be anything it does to the world, but what it does to the person wielding it. This is the argument advanced in a paper published in May 2026 by Ilias Chalkidis and Anders Søgaard, bluntly titled “Brainrot: Deskilling and Addiction are Overlooked AI Risks” and accepted to FAccT 2026, the major conference on fairness, accountability and transparency in computing.

Their contention is structural. The field of AI safety, they observe, has organised itself around a fairly stable taxonomy of harms: discrimination and hate speech, violent or illegal content, information hazards, and the misuse of models by malicious actors for cyberattacks or worse. These are real and serious. But they share a feature, which is that they concern what AI systems output into the world. What the literature has largely neglected, Chalkidis and Søgaard argue, is what sustained reliance on these systems does to their users: the deskilling that follows from chronic cognitive offloading, the slow atrophy of critical thinking, and the dependency and attachment that can shade into something like addiction. These risks are, in their framing, hiding in plain sight, prominent in public conversation yet largely absent from the safety and alignment research that is supposed to anticipate harm. The authors go further, quantifying the discrepancy between how much attention the research community devotes to output harms and how little it devotes to user harms, and arguing that the gap is not an accident but a reflection of where the field's incentives and instruments happen to point.

The distinction they draw is the one that makes the MIT findings so quietly alarming. The danger most people associate with AI and misinformation is that the machines will manufacture convincing fakes faster than we can debunk them, flooding the information environment with synthetic plausibility. That danger is genuine. But it is a supply-side problem, a question of what is poured into the public sphere. Deskilling is a demand-side problem, a question of what happens to the human capacity to process whatever is poured in. The two interact in the worst possible way. The very tool offered as the antidote to the flood of fakes may, through habitual use, be eroding the cognitive immune system that the flood demands. We are, on this account, being handed a crutch precisely as the ground beneath us turns to ice. Worse, the erosion and the flood are likely to accelerate together, because the same advances in generative modelling that make synthetic content more convincing also make the assistant more fluent and more trusted, deepening the reliance at the exact moment the threat intensifies.

This is not the only recent study to point in the direction. In early 2025, researchers at Microsoft Research and Carnegie Mellon University surveyed hundreds of knowledge workers about their use of generative AI and reported that higher confidence in the AI was associated with less critical thinking, while higher confidence in one's own abilities was associated with more. The same survey found that AI-assisted workers tended to produce a less diverse range of outputs for a given task, a possible signature of homogenised, under-interrogated thinking. Around the same period, the researcher Michael Gerlich published a study in the journal Societies, drawing on data from hundreds of participants, that found a significant negative correlation between frequent AI use and critical-thinking scores, mediated by cognitive offloading and most pronounced among the youngest respondents. None of these studies is the last word. Each has the familiar limitations of survey-based and correlational work, and self-reported measures of one's own thinking are notoriously unreliable. But they are beginning to rhyme, and when independent groups using different methods and different populations converge on the same uncomfortable melody, the prudent response is to listen rather than to wait for a single decisive experiment that may never come.

The young, the trusting and the exposed

The demographic dimension is where the abstract risk acquires a sharp social edge. According to data gathered by the Pew Research Centre and cited in the MIT study, roughly one in five American teenagers now turns to AI chatbots for news, and around one in five adults under fifty does so at least some of the time. Pew's broader survey work supports the surrounding picture: about two-thirds of US teenagers aged thirteen to seventeen report using AI chatbots at all, with close to three in ten using them daily, and adults under fifty are roughly twice as likely as their elders to report using a tool such as ChatGPT.

Read those figures alongside Gerlich's finding that the young rely most heavily on AI and score lowest on critical thinking, and a troubling alignment comes into focus. The population most inclined to outsource the work of telling true from false to a machine is, on the available evidence, also the population whose independent capacity to do that work is most at risk of going undeveloped or eroding. This is not a story about people losing a mature skill they once possessed. For many of the youngest users, it may be a story about a skill that never gets built at all, because the scaffolding is removed before anything load-bearing has formed behind it. The lapsed navigator at least once knew the route. The teenager who has only ever asked the chatbot whether a story is true may never lay down the cognitive map in the first place. There is a developmental window in which the habits of scepticism, source evaluation and patient verification are most readily acquired, and a tool that pre-empts those habits during that window may foreclose them in a way that is far harder to reverse than the deskilling of an adult who learned them long ago.

It would be easy, and lazy, to slide from here into a familiar lament about distracted youth. That is not the argument, and the data do not license it. The teenagers turning to chatbots for news are, in many respects, behaving rationally. The information environment they have inherited is genuinely treacherous, thick with manipulated images and algorithmically amplified falsehood, and a tool that promises to cut through it is a reasonable thing to reach for. The problem is not their judgement in reaching for it. The problem is the design of the thing they reach for, and what that design does to them over time. Which raises the question the MIT researchers were ultimately driving at, and the one on which the entire matter turns. Is the deskilling inevitable, a fixed cost of any AI assistance? Or is it an artefact of how these tools happen to be built, and therefore something a different design might avoid?

Tools that tell, tools that ask

The MIT team did not stop at diagnosis. Embedded in their analysis is a distinction that may prove to be the most useful thing to come out of the entire study. There are, broadly, two ways an AI can help a person evaluate a claim. It can tell, or it can ask.

A telling system delivers verdicts. You show it a headline, it informs you that the headline is false and perhaps explains why, and you move on. It is efficient, satisfying, and, on the evidence, corrosive, because it positions the human as a passive recipient of conclusions rather than an active producer of them. As Valdemar Danry, one of the study's authors, put it, AI systems that tell by providing direct answers are more likely to foster reliance, whereas those that ask, through something like Socratic questioning, are better at engaging a person to actually learn. An asking system withholds the verdict. It prompts you to consider where the image might have come from, whether the source is one you recognise, what about the framing is designed to provoke. It hands the cognitive labour back to you, while structuring that labour so you are more likely to perform it well. The asking system is, in a precise sense, less helpful in the moment and more helpful over a lifetime, and the tension between those two timescales is the whole game.

It is worth pausing on a particular detail the researchers reported, because it sharpens the stakes. They identified a subset of participants, around a fifth of the sample, who behaved as what might be called dependency developers, passively accepting the AI's guidance with little independent scrutiny. And it was precisely the gap between felt and actual competence, the quarter of participants who believed they had improved while measurably declining, that should worry us most. A person who knows they have grown dependent can choose to wean themselves. A person who has grown dependent while believing they have grown skilled has no reason to, and every incentive to deepen the reliance. Misplaced confidence is the mechanism by which a temporary aid hardens into a permanent dependency, and it is exactly the mechanism a telling interface cultivates, because nothing about receiving correct answers teaches you to doubt your own unaided judgement.

This is the difference between substituting for a skill and scaffolding it, and the word scaffolding is doing precise work here. In developmental psychology, scaffolding refers to the temporary support a more capable partner provides to a learner, support that is calibrated to the learner's current level and, crucially, gradually withdrawn as competence grows. The point of a scaffold is that it comes down. A scaffold that becomes permanent is no longer a scaffold; it is a crutch, or a cage. The conventional misinformation chatbot, the one that simply renders verdicts, is a crutch by design. It offers no path towards its own obsolescence. The asking system, by contrast, is built to make itself unnecessary, to leave the user more capable than it found them, exactly as Maguire's Knowledge left its drivers with enlarged hippocampi rather than enlarged dependence on a map.

The design vocabulary for this already exists, and it has an appealingly counter-intuitive name: productive friction. The dominant instinct in technology design is to remove friction, to make every interaction as smooth and effortless as possible, and for most purposes that instinct is sound. But learning is not frictionless, and the very smoothness that makes a tool pleasant to use can be what prevents it from teaching. Productive friction is the deliberate reintroduction of effort at the points where effort produces growth: a prompt that asks you to commit to a judgement before the AI reveals its own, a system that requires you to articulate your reasoning, an interface that surfaces the verification heuristics a journalist or fact-checker would apply and invites you to apply them yourself. A growing strand of human-computer interaction research, including recent work on AI provocations designed to restore critical thinking to AI-assisted knowledge work, has begun to demonstrate that such friction can measurably raise the quality of engagement without destroying the tool's usefulness. The trick is that the friction must be productive, targeted at the moments where struggle builds capacity rather than merely irritating the user, and calibrating it is a genuine design problem rather than a slogan.

What aviation already knows

The aviation industry, having confronted skill fade decades before the rest of us, offers a working model of what taking deskilling seriously looks like in practice. The response there was not to abandon automation, which would be absurd given its safety record, nor to pretend the erosion of manual skill was not happening. It was to mandate the deliberate, scheduled exercise of the very skills the automation tends to atrophy. Pilots are required to hand-fly, to practise in simulators the failure modes in which the automation drops out and human competence must take over, to maintain the faculty against the day it is needed. The principle is that a skill worth preserving in a partly automated system must be actively maintained, because the system itself will not maintain it. Left to its own logic, the automation will quietly let the skill decay.

Translate that principle to the epistemic domain and the outlines of a response begin to appear. It implies that media-literacy education cannot treat AI assistance as a neutral convenience to be bolted onto existing curricula, but must reckon with the possibility that the tools students use to check facts are simultaneously shaping, and possibly degrading, the faculties the curriculum is meant to build. Pattie Maes, the senior MIT researcher, drew exactly this conclusion, stressing the importance of raising awareness in schools and academic communities about the shortcomings of AI as a learning tool. It implies that the design of consumer AI products is not an ethically neutral matter of feature optimisation, because the choice between a telling interface and an asking one is, in aggregate and over years, a choice about the cognitive capacities of a population. And it implies, perhaps most provocatively, that we may need the epistemic equivalent of mandatory hand-flying: structured, regular practice at unassisted discernment, built into education and perhaps into the tools themselves, on the understanding that the capacity will wither if it is never exercised.

The analogy is imperfect, of course, and the imperfection is instructive. Aviation could mandate hand-flying because it is a regulated profession with licensing bodies, recurrent training requirements and a safety culture forged by catastrophe. There is no equivalent authority over the billions of casual interactions between ordinary people and consumer chatbots, no licensing regime for citizens evaluating the news. The maintenance of epistemic skill cannot simply be legislated into the daily habits of a population the way it can be written into a pilot's logbook. That makes the design layer more important, not less. If we cannot mandate the practice from outside, the practice must be engineered into the tools themselves, so that the path of least resistance is also a path that keeps the underlying faculty alive. Chalkidis and Søgaard gesture at a complementary lever, suggesting that public information campaigns and regulation might mitigate deskilling much as they have been mobilised against other public-health risks, treating cognitive atrophy as a hazard to be managed rather than an inevitability to be absorbed.

The limits of one study, and the shape of the stakes

Intellectual honesty requires holding all of this at the right distance. The MIT study tracked sixty-seven people over four weeks. That is a serious, well-constructed piece of work, but it is not the foundation for sweeping civilisational pronouncement. Sixty-seven is a modest sample. Four weeks is a short window against which to project lifelong cognitive change. Laboratory and online study conditions are not the messy reality of how people actually consume news, and the artificiality of repeatedly classifying headline-image pairs may exaggerate or distort effects that would look different in the wild. The measured decline, real and statistically significant within the study, is a finding to be replicated and probed, not a law of nature to be enshrined. The authors themselves frame it as evidence that demands further investigation, not as a verdict already delivered.

There are genuine counterarguments, too, and they deserve more than a perfunctory nod. The optimistic case is that AI assistance frees human cognition from drudgery to operate at a higher level, much as literacy freed us from the tyranny of oral memorisation and arithmetic tools can free a mathematician for genuine reasoning. Perhaps a generation that offloads first-order fact-checking to machines will redirect its cognitive energy towards more sophisticated forms of judgement, towards synthesis and meaning-making and the evaluation of the machines themselves. Perhaps. But that hopeful trajectory is precisely the one the MIT data fail to support. The participants did not ascend to some higher plane of discernment; they got worse at the task and, in many cases, did not realise it. The mismatch between their declining accuracy and their rising confidence is the detail that should linger, because a population that is simultaneously less able to detect falsehood and more sure of its abilities is not a population that has traded up. It is a population that has been quietly hollowed while believing itself enriched.

What ties the strands together is the recognition that we are conducting an unplanned experiment on the epistemic capacity of the species, and we are running it backwards, deploying the tools at planetary scale first and asking what they do to us afterwards. The MIT study is one of the early, careful attempts to ask the question with rigour, and its provisional answer is that the relationship between AI assistance and human discernment is not neutral. The default design of these systems, the telling design that simply hands down verdicts, appears to trade long-term capacity for short-term accuracy, and to do so invisibly, beneath the user's own awareness. That is the worst kind of trade, because it offers no signal that a trade is being made at all.

But the same study, read carefully, contains the seed of a more hopeful possibility. The deskilling is not a fixed cost of intelligence in a box. It is, on the evidence, a consequence of a particular and dominant design choice, the choice to substitute rather than to scaffold, to tell rather than to ask, to remove friction rather than to place it where it does some good. A different choice is available. We know what scaffolded discernment looks like, in the Socratic tutor who refuses to give the answer, in the aviation regime that mandates hand-flying, in the developmental scaffold engineered to come down. We have the design vocabulary, the productive friction and the asking interface and the heuristic made visible and practised. What we have lacked, so far, is the will to prefer the tool that strengthens us over the tool that merely serves us, and an industry whose incentives reward engagement and ease rather than the slow, unglamorous cultivation of an independent mind.

The compass in your hand will always be more convenient than the one you must build inside yourself. That has been true of every tool that ever offered to think on our behalf, from the written word to the calculator to the satellite overhead. The question the fading compass poses is not whether to use the tool. It is whether we will insist on tools that, like the best teachers and the hardest examinations, leave us more capable than they found us, or settle for tools that leave us merely more dependent, lost in a familiar city, certain we know the way.

References

1. Rani, Anku, Valdemar Danry, Paul Pu Liang, Andrew B. Lippman, and Pattie Maes. “Dialogues with AI Reduce Beliefs in Misinformation but Build No Lasting Discernment Skills.” arXiv:2510.01537, 2026 (presented at the CHI Conference on Human Factors in Computing Systems, 2026). https://arxiv.org/abs/2510.01537
2. “The consequences of relying on AI for accurate news.” MIT News, Massachusetts Institute of Technology, 9 June 2026. https://news.mit.edu/2026/consequences-of-relying-on-ai-for-accurate-news-0609
3. Chalkidis, Ilias, and Anders Søgaard. “Brainrot: Deskilling and Addiction are Overlooked AI Risks.” arXiv:2605.03512, 2026 (accepted to the ACM Conference on Fairness, Accountability, and Transparency, FAccT '26). https://arxiv.org/abs/2605.03512
4. Maguire, Eleanor A., et al. “Navigation-related structural change in the hippocampi of taxi drivers.” Proceedings of the National Academy of Sciences, vol. 97, no. 8, 2000. https://www.pnas.org/doi/10.1073/pnas.070039597
5. Woollett, Katherine, and Eleanor A. Maguire. “Acquiring 'the Knowledge' of London's Layout Drives Structural Brain Changes.” Current Biology, 2011. https://pmc.ncbi.nlm.nih.gov/articles/PMC3268356/
6. “Changes in London taxi drivers' brains driven by acquiring 'the Knowledge'.” ScienceDaily, 8 December 2011. https://www.sciencedaily.com/releases/2011/12/111208125720.htm
7. Sparrow, Betsy, Jenny Liu, and Daniel M. Wegner. “Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips.” Science, vol. 333, no. 6043, 2011, pp. 776–778. https://www.science.org/doi/10.1126/science.1207745
8. Lee, Hao-Ping (Hank), et al. “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers.” Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Microsoft Research and Carnegie Mellon University, 2025. https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/
9. Gerlich, Michael. “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking.” Societies, vol. 15, no. 1, Article 6, 2025. https://www.mdpi.com/2075-4698/15/1/6
10. “Teens, Social Media and AI Chatbots 2025.” Pew Research Centre, 9 December 2025. https://www.pewresearch.org/internet/2025/12/09/teens-social-media-and-ai-chatbots-2025/
11. “Americans' Views on AI Chatbots, Smart Devices and AI's Impact.” Pew Research Centre, 17 June 2026. https://www.pewresearch.org/internet/2026/06/17/americans-and-ai-2026-chatbots-smart-devices-and-views-on-impact/
12. “The Dangers of Overreliance on Automation.” FAA Safety Briefing Magazine, Federal Aviation Administration. https://medium.com/faa/the-dangers-of-overreliance-on-automation-5b7afb56ebdc
13. “Methods for Preventing the Degradation of Manual Flying Skills in an Automated Cockpit Environment.” The Collegiate Aviation Review International. https://ojs.library.okstate.edu/osu/index.php/CARI/article/view/10345

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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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Somewhere in the American desert, a building the size of a small town hums in the dark. It has no windows and almost no people. Inside, tens of thousands of processors run hot, churning through the requests of strangers half a world away: a marketing executive in London asking for a punchier subject line, a student in Toronto summarising a textbook, a hobbyist in Sydney conjuring a cartoon dragon for no particular reason. Pipes carry water through the building to keep the silicon from cooking itself. Transformers the size of lorries pull electricity off the grid in quantities that would once have powered a city. The dragon appears on the hobbyist's screen in about four seconds. The cost of producing it does not appear anywhere at all.

That invisibility is the point, and it is also the problem. For most of the people typing into a chatbot, generative artificial intelligence feels like the most weightless technology ever invented. There is no exhaust pipe, no smokestack, no spinning meter on the wall. You ask, it answers, and the bill, if there is one, seems to be a few pennies on a subscription. But the bill is real, and it is enormous, and it is being paid in a currency most users never see: water drawn from stressed aquifers, land scraped flat for server halls, electricity wrenched off ageing grids, and a rising tide of toxic electronic waste. The question the technology industry has been remarkably good at avoiding is a simple one. Who, exactly, is footing it?

On 3 June 2026, the United Nations University Institute for Water, Environment and Health published a report that tries to answer that question with the kind of hard numbers the debate has mostly lacked. Its title, “Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints”, is dry. Its findings are not. The report argues that the environmental costs of the AI boom are not only larger than commonly understood, but are being distributed in a way that is profoundly, structurally unjust. The wealthy generate the prompts. Someone else, very often, pays the bill.

The timing was pointed. The report landed in the same week as World Environment Day, an annual fixture in the United Nations calendar, and its authors clearly intended the juxtaposition. While the world's environment ministers issued their usual statements, a team of UN scientists was quietly publishing evidence that one of the fastest-growing pressures on the planet's water, land and atmosphere is a technology that most of those ministers were probably using to draft their speeches. The report is not a polemic. It is an attempt at accounting, an effort to put a defensible number on a cost that the industry has been content to leave uncounted, and then to ask what follows once the number is on the table.

The Myth of the Weightless Machine

There is a stubborn assumption baked into how we talk about software, and it goes roughly like this: bits are cheap, the cloud is somewhere else, and digital things do not have a physical body. Kaveh Madani, director of UNU-INWEH and one of the report's authors, puts the counterargument bluntly. “Though often described as weightless and virtual,” he says, “the reality of AI is profoundly physical.”

That physicality starts with electricity. The International Energy Agency, in its landmark “Energy and AI” analysis published in April 2025, estimated that the world's data centres consumed roughly 415 terawatt-hours of electricity in 2024, about 1.5 per cent of global demand. That figure is already growing at around 12 per cent a year, far faster than overall electricity use. The IEA's central projection is that data centre consumption will roughly double by 2030, reaching around 945 terawatt-hours. That is more than the entire current electricity consumption of Japan. The UNU report adopts the same headline number and spells out what it means: were the world's AI-driven data centres a country, they would rank around eleventh in the world for electricity use, sitting behind France and ahead of Saudi Arabia.

The IEA's analysis is careful to note that AI is the single most important driver of this surge, and that the United States accounts for by far the largest share of the projected increase, with China following. In the United States, the agency found, data centres are on track to account for nearly half of all electricity demand growth between now and 2030. This is the part that ought to alarm anyone who follows the energy transition. The grid was already straining to decarbonise. Now it is being asked to absorb a vast new load on top of everything else, and that load does not wait politely for clean power to come online. It plugs into whatever is available, which in most of the world still means gas and coal.

None of this is hypothetical. The build-out is happening now, in concrete and copper, across Virginia and Texas, across Inner Mongolia and Ningxia, in Ireland and in the Gulf. And here the UNU report makes its first genuinely clarifying move. The public conversation about AI's energy appetite has fixated on training, the months-long, headline-grabbing process of building a large model from scratch. Training is expensive and dramatic, and it makes for good copy. But it is not where most of the energy goes.

It Was Never About Training

The report's central technical insight is that the day-to-day running of AI models, the part engineers call inference, accounts for somewhere between 80 and 90 per cent of the technology's total energy demand. Training a model is a one-off cost, however large. Inference is what happens every single time anyone, anywhere, uses the thing. And the using has become astronomical.

This matters because it reframes the entire problem. If training were the dominant cost, then the environmental footprint of AI would be lumpy and occasional, a series of expensive sprints punctuating long quiet stretches. You could imagine regulating it the way you might regulate a handful of large industrial projects. But inference is not lumpy. It is continuous, ambient and growing without limit, a constant background draw that scales directly with adoption. The more useful AI becomes, the more it is used, and the more it is used, the heavier its footprint, regardless of how cleverly the original model was trained. The cost is not in the building of the machine. It is in the running of it, forever.

Consider a single product. The report notes that ChatGPT alone fields on the order of 2.5 billion prompts a day, and that running it consumes something in the region of 383 gigawatt-hours of electricity a year. That is one application from one company. Multiply the logic across the entire ecosystem of chatbots, image generators, coding assistants, search summaries and the AI features now wedged into every productivity suite on Earth, and the scale of the inference problem comes into focus.

It is also wildly uneven from task to task. The report draws on research showing that the energy cost of an AI interaction depends enormously on what you ask for. A simple text query is relatively cheap. Generating an image is, by some measures, more than a thousand times more energy-intensive than a basic text-classification task. Producing even a short, high-resolution AI video can require an order of magnitude more energy again, the report putting a single clip at over 415 watt-hours. Even the quiet creep of AI into ordinary web search carries a cost: the report notes that an AI-enhanced generative search can use roughly ten times the energy of a conventional one. The casual user has no way of knowing any of this. The interface is identical. A request that boils a notional kettle and a request that barely warms a teaspoon look exactly the same on screen, and cost the same nothing at the point of use.

Mir Matin, another of the report's authors, frames the accumulation problem precisely. “Every prompt, default setting, generated image, video, and query,” he says, “accumulates when multiplied by billions of users.” This is the crux. No single interaction matters. All of them together matter immensely. And because the cost is spread across billions of weightless-seeming moments, it never lands anywhere a user can feel it. The default settings are perhaps the most insidious detail. When a search engine or an operating system switches on an AI feature by default, billions of people begin paying its resource cost without ever choosing to, and without anyone telling them the choice was made.

The Thirst Nobody Mentions

If electricity is the part of AI's footprint that gets the headlines, water is the part that gets buried. Data centres are thirsty in two distinct ways. First, the servers inside them generate prodigious heat, and many facilities use evaporative cooling, which works by turning water into vapour and letting it drift away into the atmosphere. That water is gone from the local system. Second, and less obviously, the electricity that powers the centres is itself water-intensive to produce, because thermal power plants use vast quantities of water for cooling. Every kilowatt-hour drawn from a coal or gas plant carries an invisible water cost upstream, before a single drop touches the servers themselves.

The pioneering work on this hidden cost came from Shaolei Ren, a researcher at the University of California, Riverside, whose 2023 paper bore the memorable title “Making AI Less 'Thirsty'”. Ren and his colleagues calculated that training GPT-3 in Microsoft's state-of-the-art American data centres could have evaporated around 700,000 litres of clean freshwater, and that the figure would have roughly tripled had the training run been done in the company's less water-efficient Asian facilities. To make the number concrete, his team noted that this was comparable to the water used to manufacture hundreds of cars. Crucially, Ren extended the analysis beyond training to the everyday business of answering queries, and projected that global AI demand could be responsible for the withdrawal of between 4.2 and 6.6 billion cubic metres of water in 2027, more than the total annual water withdrawal of a country the size of Denmark several times over.

What makes Ren's work so important is not just the figures but the method. Because operators almost never disclose the water consumption of individual sites, he and his colleagues had to infer it from the efficiency of cooling systems, the local climate, and the water intensity of the electricity feeding each facility. The same prompt, run in a cool and hydro-powered region, might cost a fraction of what it costs in a hot, fossil-fuelled one. The footprint, in other words, is not an intrinsic property of the software. It is a property of where and how the software is run, a point that turns out to matter enormously when you ask who ends up paying.

The UNU report takes this body of work and pushes the timeline to 2030, arriving at a figure designed to stop the reader cold. By the end of the decade, it estimates, the annual water footprint of AI could reach 9.3 trillion litres. To make that abstraction tangible, the authors compare it to the basic annual domestic water needs of every one of the 1.3 billion people who live in sub-Saharan Africa. The image is deliberate and devastating: a technology marketed in Silicon Valley and consumed in the world's richest cities, drinking, in effect, the daily water of an entire subcontinent that has barely been consulted about its construction.

The geography sharpens the injustice. Data centres are frequently sited where land is cheap, energy is abundant and tax incentives are generous, and those conditions often coincide with regions that are already water-stressed. Matin, whose expertise is in exactly this kind of spatial analysis, has pointed to the danger of mapping where data centres are being built against where water is scarce, and finding the two maps overlapping. A facility that evaporates millions of litres a year in a temperate, rain-soaked region is a manageable nuisance. The same facility in a drought-prone basin is a direct competitor with farms and households for a resource there is not enough of. Communities in such places have already begun to push back, querying why a hyperscale operator should be granted the water their own crops are rationed.

Land, Carbon and the Mountain of Waste

Water and electricity do not exhaust the inventory. The UNU report adds two further footprints that rarely make it into the conversation at all.

The first is land. Server farms are not small. The report projects that the physical land footprint of AI infrastructure could exceed 14,500 square kilometres by 2030, an area it likens to roughly twice the metropolitan expanse of Jakarta, home to more than 32 million people. That is land taken out of other uses, reshaped, fenced, paved and wired, often on the rural fringes of communities that gain a handful of permanent jobs in exchange for a permanent neighbour that never sleeps. The footprint extends well beyond the perimeter fence, too, taking in the substations, transmission corridors and access roads that a facility of this scale demands, and in many cases the dedicated power generation built specifically to feed it.

The second is carbon. For all the talk of powering data centres with renewables, the grids they plug into remain substantially fossil-fuelled, and the sheer scale of new demand is, in many regions, keeping coal and gas plants running that might otherwise have closed. The report projects that AI-related activity could be responsible for around 400 million tonnes of carbon dioxide equivalent emissions annually by 2030. To offset that volume of carbon, the authors calculate, would require growing on the order of 6.7 billion trees over a decade. The “green” technology, in other words, is leaning heavily on a decidedly un-green energy system. There is a bitter irony in the fact that some of the same companies championing AI as a tool to fight climate change are, through that very tool, adding materially to the emissions driving it.

Then there is the rubbish. Artificial intelligence runs on specialised hardware, principally graphics processing units, that becomes obsolete with brutal speed as each new generation outperforms the last. When the chips are retired, they become electronic waste, a category laced with lead, mercury and other hazardous materials. The scale of the looming problem was quantified in a 2024 study published in Nature Computational Science, led by Peng Wang of the Chinese Academy of Sciences, which projected that the rapid expansion of generative AI could create between 1.2 and 5 million tonnes of additional e-waste over the period to 2030. Under an aggressive-growth scenario, the study found, the annual e-waste stream attributable to generative AI could reach 2.5 million tonnes by 2030, the very figure the UNU report cites. As with water and land, the burden of dealing with that waste tends not to fall on the cities that generated the demand. The world's discarded electronics have a long and grim habit of ending up in informal recycling yards across the global South, where they are picked apart by hand, often by people with no protective equipment and no choice, releasing toxins into the soil, the water and the bodies of the workers themselves.

The Two-Country Cloud

All of this might be merely alarming if the costs and the benefits were borne by the same people. They are not, and this is where the UNU report moves from environmental accounting into something closer to political economy.

The capacity to build and run frontier AI is astonishingly concentrated. The report finds that more than 90 per cent of the world's AI-specialised computing capacity sits in just two countries. The picture is corroborated by independent analysis: a study of the global data centre landscape drawn on by Oxford University researchers found that only 32 countries host AI data centres at all, and that the United States and China between them operate the overwhelming majority of the specialised facilities. By contrast, more than 150 nations have no significant domestic AI compute infrastructure whatsoever. The IEA's own figures show the United States accounting for the largest single share of global data centre electricity consumption, followed by China, with Europe a distant third and the rest of the world barely registering.

The concentration runs deeper than geography. The same handful of corporations that own the compute also own the leading models, the training data, the cloud platforms on which everyone else builds, and increasingly the energy deals that keep the whole edifice powered. Oxford's analysts have argued that this clustering of compute, talent, data and capital means that even mid-sized economies, let alone poor ones, face barriers to independent frontier development that are close to insurmountable. The result is a world in which AI is not a general-purpose technology that diffuses outward to everyone, the way electricity or the internet eventually did, but a service piped out from a couple of national hubs, on terms set by their owners.

Think about what that distribution actually means. The intelligence is manufactured in a tiny number of places, owned by a tiny number of companies, and rented out to the rest of the planet as a service. The economic returns, the share prices, the productivity gains, the strategic advantage, accrue overwhelmingly to those two countries and the firms headquartered in them. But the environmental costs, as the report documents, are not so neatly contained. They leak. The carbon enters a shared atmosphere that warms everyone. The water is drawn from local basins that, increasingly, sit in the very regions least able to spare it. The discarded hardware migrates down the global waste stream to the poorest places on Earth.

This is the asymmetry at the heart of the report, and it deserves to be stated plainly. When someone in a wealthy country generates an image, summarises a document or asks a chatbot for advice, the water, land and energy costs of that interaction are being distributed across communities and ecosystems that had no say in the choice and will see little of the benefit. The convenience is privatised. The cost is, in large part, socialised, and socialised on to exactly the populations with the least power to refuse it. It is a near-perfect inversion of the polluter-pays principle that environmental law spent half a century trying to establish: here, the polluter mostly does not pay, and the payer mostly does not pollute.

Why Efficiency Will Not Save Us

There is a comforting story the industry likes to tell about all this, and it goes like this: the chips keep getting more efficient, the models keep getting leaner, and so the problem will shrink itself out of existence. Every generation of hardware does more computation per watt. Every clever algorithmic trick squeezes more capability from less silicon. Surely, the argument runs, efficiency will win.

It will not, and the reason has a name. It is called the Jevons paradox, after the nineteenth-century English economist William Stanley Jevons, who noticed something counterintuitive about coal. As steam engines became more efficient and burned less coal per unit of work, Britain did not use less coal. It used vastly more, because cheaper, more efficient steam power made coal worth using for a thousand new purposes. Efficiency did not curb consumption. It unleashed it.

The same logic stalks artificial intelligence, and the parallel is not merely rhetorical. A 2025 paper presented at the ACM Conference on Fairness, Accountability and Transparency, titled “From Efficiency Gains to Rebound Effects”, examined precisely how Jevons' paradox applies to AI, arguing that the efficiency improvements the industry trumpets as environmental wins are systematically reinvested to expand markets, stimulate new demand and drive aggregate resource consumption upward rather than down. When a model gets cheaper to run, it does not get used the same amount more cheaply. It gets used more. Features that were too costly to ship get shipped. AI gets stuffed into products that never had it. The summary, the autocomplete, the always-on assistant proliferate precisely because each one became cheap.

The episode that made this concrete for the whole industry arrived in early 2025, when the Chinese firm DeepSeek released a model that matched the performance of far costlier systems at a fraction of the computational expense. The market's first instinct was to assume this would mean less demand for chips. The more sophisticated reading, which gained ground quickly, was the opposite: drastically cheaper AI would mean drastically more of it, everywhere, all the time. Cheaper inference is not a brake on consumption. It is an accelerator. Madani's co-authored framing captures the trap exactly: more efficient and more affordable AI does not mean less consumption, it means more.

This is why the report insists that judging AI's sustainability by efficiency metrics alone, or by carbon alone, is a category error. A model that uses half the energy per query but is used twenty times as often has not solved anything. It has made the problem worse while looking, on the relevant dashboard, like progress. The footprint that matters is the total one, and the total one is going up. The rebound effect is not a quirk to be engineered away. It is the structural reason that efficiency, on its own, can never be the answer, and that some external limit, whether regulatory, economic or physical, will eventually have to do the work that efficiency cannot.

The Trouble With Measuring Anything

If the costs are this large and this skewed, an obvious question follows: why has it taken so long for anyone to say so clearly? Part of the answer is that the numbers are genuinely hard to pin down, and the companies that hold the best data have shown little appetite for sharing it.

Operators rarely disclose the water consumption of individual facilities, the energy mix powering them, or the per-query resource cost of their models. Researchers like Ren have had to reverse-engineer estimates from patchy public filings, regulatory disclosures and educated assumptions about cooling systems and grid composition. The result is a literature full of ranges rather than precise figures, and those ranges are routinely weaponised by industry defenders who point to the uncertainty as a reason to wait. The argument is circular and convenient: the companies decline to publish the data, then cite the resulting uncertainty as grounds for inaction, all while the build-out accelerates.

Aczel, the report's lead author, locates the deeper hazard in the metrics themselves, warning that the choices which look greenest on a narrow accounting can disguise real costs that a fuller reckoning would expose. Judge AI's sustainability by carbon alone, and you will systematically miss the water and the land and the waste. Her broader point is that the environmental footprint of AI is not a fixed fact of nature. It is shaped, she argues, by infrastructure decisions, by the energy sources chosen, and by how models are designed, which means it can be shaped differently. That is, in its way, an optimistic claim. If the footprint were destiny, there would be nothing to do but despair. Because it is the product of choices, it is open to better ones.

The opacity is not accidental. A technology whose costs are invisible to its users and unmeasured by its public is a technology that faces very little pressure to change. The first act of accountability, then, is simply measurement. You cannot govern what you refuse to count, and for the moment the people best placed to count have every incentive not to.

What Accountability Would Actually Look Like

The UNU report does not stop at diagnosis. It proposes a framework built on six principles: transparency, efficiency by design, equity and environmental justice, lifecycle responsibility, global cooperation, and sustainable use. The list can read as the usual policy boilerplate, but underneath it sits a genuinely radical proposition, which is that the relationship between AI's beneficiaries and its bill-payers should be made visible and then made fair.

Transparency comes first because nothing else works without it. If operators were required to disclose, in standardised and audited form, the energy, water and carbon footprint of their facilities and ideally of their models, the entire debate would shift from contested estimates to verifiable fact. Users could, in principle, see the resource cost of a request the way a car displays its fuel consumption. Regulators could site facilities with full knowledge of local water stress. Investors could price environmental risk properly. The information asymmetry that currently protects the industry would begin to close. None of this requires a technological breakthrough. It requires a disclosure regime, and the political will to impose one.

In the weeks after the report appeared, something close to that political will began, tentatively, to surface. On 23 June 2026, at London Climate Action Week, the United Nations Secretary-General, António Guterres, launched what he called the AI Environmental Transparency Initiative, a charter inspired directly by the UNU-INWEH findings. It asks every major AI company to do two things: to measure and publicly disclose the full carbon, water and land footprint of its systems, and to commit to powering every data centre with renewable energy by 2030. “No more hidden costs,” Guterres said. “If AI is to help build a better future, it must be honest about what it costs us now.” Madani, whose report had supplied much of the initiative's intellectual scaffolding, called it “a gift” and “an opportunity to be proactive instead of reactive”, returning to the principle that had animated the whole project: “We cannot properly manage what we do not measure.” The significance is real, and so are the limits. The initiative is a voluntary call rather than a binding rule, an invitation to companies to come clean rather than a mechanism that compels them to. It is the sound of the political will clearing its throat, not yet the disclosure regime itself, and the distance between an industry being asked to disclose and an industry being made to is precisely the distance this report has spent its length measuring.

Efficiency by design and lifecycle responsibility push the engineering upstream. The e-waste research is instructive here: Wang's team found that extending the working life of AI hardware, and refurbishing and redeploying ageing chips for less demanding tasks rather than scrapping them, could cut projected e-waste dramatically, by more than 40 per cent in some scenarios. The point generalises. A great deal of AI's footprint is the product of decisions, where to build, what to cool with, how long to run the hardware, whether to bolt an AI feature on to a product that did not need one, and decisions can be made differently. Lifecycle responsibility means the firm that profits from a chip's first life is also accountable for its last, rather than letting the carcass become someone else's problem in a recycling yard half a world away.

But the principles that carry the real moral weight are equity, environmental justice and global cooperation, because they speak directly to the asymmetry the rest of the report documents. If 90 per cent of the compute and almost all of the profit sit in two countries, while the water stress, the e-waste and the climate impact land disproportionately on the 150-plus nations with no AI infrastructure of their own, then any honest framework has to grapple with redistribution. That might mean siting standards that steer facilities away from water-scarce regions and on to genuinely surplus renewable power. It might mean the wealthy beneficiaries of AI financing water security, grid resilience and proper e-waste recycling in the places absorbing the downstream costs. It might mean giving those nations a real voice in the governance of a technology that is reshaping their environment without their consent. At minimum, it means refusing to pretend the costs are not there.

What the report stops short of, sensibly, is pretending that any of this will be easy. Each principle cuts against a powerful commercial interest. Transparency threatens a competitive secret. Lifecycle responsibility threatens a margin. Equity threatens a status quo from which the powerful benefit enormously. A framework is not a mechanism, and the gap between the two is where most well-meaning governance goes to die. But the value of naming the principles is that it makes the trade-offs explicit. It turns a set of invisible, deferred costs into a visible political question, and visible political questions can at least be argued over, which is more than can be said for costs nobody admits exist.

Paying the Bill

Return, for a moment, to the windowless building in the desert, and to the four-second dragon. There is nothing wrong with wanting the dragon. The case against the hidden bill is not a case against artificial intelligence, which is already delivering genuine value in medicine, science, accessibility and a hundred mundane corners of working life. The case is against the invisibility. A technology this physical, this thirsty and this geographically lopsided should not be allowed to present itself as weightless, because the weightlessness is a kind of accounting trick, and the trick has victims.

The deepest finding of the UNU report is not any single number, alarming as the numbers are. It is the structure those numbers reveal: a global system in which the pleasure of generating is decoupled, almost completely, from the pain of providing. The user in London or Toronto or Sydney experiences AI as frictionless because the friction has been exported, to an aquifer in a dry country, to a grid burning fossil fuel to meet demand it never planned for, to a recycling yard where someone breaks apart a dead processor with their bare hands. The friction did not disappear. It moved to where it could not be seen and could not be refused.

Building accountability into that relationship means, in the end, putting the friction back where it belongs. It means the price of a prompt, somewhere, somehow, reflecting the water it evaporated and the carbon it emitted. It means the firms reaping the trillion-dollar valuations carrying the cost of the cleanup, the refurbishment and the repair, rather than letting it flow downhill to people who never typed a word into a chatbot. It means measuring honestly, siting responsibly, and granting the communities on the receiving end something they have never been offered: a say.

The world is not about to stop using artificial intelligence. The 2.5 billion daily prompts will become more, not fewer, and the rebound effect guarantees that every efficiency gain will be spent on more usage rather than less impact. The only real question is whether the bill will keep arriving, silently, at the doorsteps of people who never ordered anything, or whether the world musters the will to redirect it to the address where the dragon was actually conjured. The report's authors have done the arithmetic, and in the weeks since, a first move has been made: a United Nations Secretary-General asking the industry to come clean. But it remains an asking, not a requiring, a voluntary charter rather than a bill redirected, and the choice about who pays is still, for now, ours to make. It is worth remembering that someone is already paying, and they are not the ones holding the phone.

References

1. United Nations University Institute for Water, Environment and Health (UNU-INWEH). “The Environmental Cost of Artificial Intelligence: Carbon, Water, and Land Footprints.” 3 June 2026. https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints
2. United Nations University. “Rising Emissions, Depleting Water and Vanishing Land: UN Scientists: AI Is Threatening Natural Resources for Billions.” 3 June 2026. https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints
3. EurekAlert! / UNU-INWEH. “Beyond AI's surging energy use: UN details escalating water, land, and CO2 emission consequences.” 3 June 2026. https://www.eurekalert.org/news-releases/1128642
4. UN News. “AI's environmental costs threaten water, land and climate.” 3 June 2026. https://news.un.org/en/story/2026/06/1167658
5. International Energy Agency. “Energy and AI: Executive Summary.” April 2025. https://www.iea.org/reports/energy-and-ai/executive-summary
6. International Energy Agency. “Energy demand from AI: Energy and AI.” April 2025. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai
7. Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren. “Making AI Less 'Thirsty': Uncovering and Addressing the Secret Water Footprint of AI Models.” arXiv, 2023. https://arxiv.org/abs/2304.03271
8. UC Riverside News. “AI programs consume large volumes of scarce water.” 28 April 2023. https://news.ucr.edu/articles/2023/04/28/ai-programs-consume-large-volumes-scarce-water
9. Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren. “Making AI Less 'Thirsty'.” Communications of the ACM. https://cacm.acm.org/sustainability-and-computing/making-ai-less-thirsty/
10. Vinuesa, R., et al. “From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate.” Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. https://arxiv.org/abs/2501.16548
11. NPR Planet Money. “Why the AI world is suddenly obsessed with Jevons paradox.” 4 February 2025. https://www.npr.org/sections/planet-money/2025/02/04/g-s1-46018/ai-deepseek-economics-jevons-paradox
12. Wang, P., et al. “E-waste challenges of generative artificial intelligence.” Nature Computational Science, 2024. https://www.nature.com/articles/s43588-024-00712-6
13. Scientific American. “Generative AI Could Generate Millions More Tons of E-Waste by 2030.” https://www.scientificamerican.com/article/generative-ai-could-generate-millions-more-tons-of-e-waste-by-2030/
14. TechRepublic. “Global Divide: Only 32 Countries Host AI Data Centers.” 2025. https://www.techrepublic.com/article/news-only-32-countries-host-data-centers-2025/
15. Oxford Martin AI Governance Initiative. “A Blueprint for Multinational Advanced AI Development.” 2025. https://aigi.ox.ac.uk/publications/a-blueprint-for-multinational-advanced-ai-development/
16. S&P Global. “Global data center power demand to double by 2030 on AI surge: IEA.” 10 April 2025. https://www.spglobal.com/energy/en/news-research/latest-news/electric-power/041025-global-data-center-power-demand-to-double-by-2030-on-ai-surge-iea
17. United Nations University Institute for Water, Environment and Health (UNU-INWEH). “UN Secretary-General Launches AI Environmental Transparency Initiative, Calling on AI Companies to Disclose Carbon, Water and Land Footprints.” 23 June 2026. https://unu.edu/inweh/news/un-secretary-general-launches-ai-environmental-transparency-initiative-calling-ai
18. Fortune. “'It is time to come clean': UN Secretary General calls out AI companies on their climate impact.” 23 June 2026. https://fortune.com/2026/06/23/un-guterres-ai-climate-impact-disclosure-data-centers/

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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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In Boxtown, a neighbourhood on the southern edge of Memphis where the streets run flat toward the Mississippi and the air carries the metallic tang of the refinery, the noise arrived before anyone knew what it was. A low, continuous mechanical drone, the sound of dozens of gas turbines spinning around the clock. By the summer of 2025, residents of this predominantly Black community could stand in their gardens and watch the heat shimmer rising off a sprawling industrial site that had appeared, almost overnight, behind a chain of fences and non-disclosure agreements. The site was Colossus, the supercomputer built to train the artificial intelligence models of Elon Musk's company xAI. To power it, the company had installed as many as thirty-five portable methane gas turbines, most of them operating without the air permits that, as one veteran environmental lawyer put it, every set of turbines he had ever encountered was required to hold. It was the opening chapter of a fight that, by the spring of 2026, would harden into a federal lawsuit.

The people of Boxtown did not ask for a data centre. They were not consulted about it in any meaningful way. They derive almost none of the economic benefit from the chatbots and image generators that the facility's tens of thousands of graphics processors were assembled to produce. What they got instead was the exhaust: an estimated two thousand tonnes a year of smog-forming nitrogen oxides, according to filings cited by the Southern Environmental Law Center, layered onto a neighbourhood that the American Lung Association had already graded an F, in a part of Memphis that the local state representative Justin Pearson describes as hosting twenty-two of the thirty largest industrial polluters in the state of Tennessee. South Memphis has child asthma hospitalisation rates among the highest in the country and cancer rates that researchers have linked to its decades of accumulated industrial emissions. The turbines were simply the newest insult in a very long sentence.

Boxtown is not an aberration. It is a preview. As the AI boom collides with the physical limits of the electricity grid and the water table, the pattern visible in South Memphis is repeating across the United States with grim consistency. The communities absorbing the noise, the diesel particulates, the groundwater draw and the rising electricity bills of AI infrastructure are, again and again, the communities with the least political power to refuse it and the least access to the technology that demand is supposedly serving. The cloud, that weightless metaphor we use for the digital economy, turns out to have a very specific postcode, and it is rarely a wealthy one.

The Arithmetic of an Unequal Burden

Begin with the bills, because the bills are where the abstraction becomes a number on a household's kitchen table. In February 2026, the Environmental and Energy Study Institute, a non-partisan body founded by members of the United States Congress, published an analysis by its researcher Miguel Yañez-Barnuevo laying out the disparity in stark terms. Low-income residents, renters, and Black and Hispanic households in the United States can spend as much as twenty per cent of their income on energy, the institute found, against roughly three per cent for higher-income households. That is not a marginal gap. It is the difference between energy as a line item and energy as a recurring crisis, the kind that forces a choice between cooling the home and filling the fridge.

This is what researchers call energy burden, the share of household income consumed by keeping the lights on, the home warm in winter and survivable in summer. The American Council for an Energy-Efficient Economy, which has tracked the metric for years, has found that one in four low-income households spends more than fifteen per cent of its income on energy, with the figure climbing far higher in particular cities. In Baltimore, the council reported, the most burdened quarter of low-income households pay an average of around a quarter of their income on energy bills alone. As of early 2026, roughly twenty-one million American households, about one in six, were behind on their utility payments.

Now layer the AI boom on top of that. The institute noted that utilities received requests in 2025 for at least seven hundred gigawatts of new power connections, a figure that exceeds the entire electricity consumption of the United States in 2023. Data centres are the engine of that demand. They do not simply consume electricity; by competing for scarce generation and transmission capacity, they push the wholesale price of power upward for everyone connected to the same grid. The national average electricity price had climbed to nineteen cents per kilowatt-hour by the end of 2025, a twenty-seven per cent jump from 2019, and the institute projected residential prices could rise by up to forty per cent by 2030 against 2025 levels. Utilities filed for more than twenty-nine billion dollars in rate increases in just the first half of 2025.

The crucial point is who pays. When a utility builds a transmission line or a gas plant to serve a hyperscale data centre, the cost is frequently socialised across the entire ratepayer base rather than borne by the company that triggered the spending. The household already spending a fifth of its income on energy has no buffer to absorb the increase. The trillion-dollar corporation behind the data centre does. The burden flows, predictably, downhill.

Two Hundred and Sixty-Seven Per Cent

The clearest single illustration of this dynamic came from a Bloomberg analysis published in 2025. Its reporters examined wholesale electricity prices across tens of thousands of locations on the American grid, using monthly nodal data aggregated by the energy analytics platform Grid Status. The finding was arresting: in some areas near significant data centre activity, wholesale electricity cost as much as two hundred and sixty-seven per cent more for a single month than five years earlier. More than seventy per cent of the nodes recording the steepest increases sat within fifty miles of major data centre clusters.

Those wholesale costs do not stay wholesale. They are passed through to households and businesses, padded with the charges utilities levy to maintain and expand the network. The Bloomberg figure and the energy-burden figure are two ends of the same wire: the data centre boom raises the price of the commodity, and the people least able to absorb a rise pay the largest share of their income for it.

Virginia offers the textbook case, because Virginia is where the modern data centre industry was effectively born. The corridor running through Loudoun, Prince William and Fairfax counties in the state's north, known to the industry as Data Center Alley, hosts around four-fifths of Virginia's data centre capacity and a substantial fraction of the world's internet traffic. In December 2024, the state's own Joint Legislative Audit and Review Commission, a non-partisan watchdog known as JLARC, delivered a sober assessment to legislators. If the industry continued to grow at an unconstrained pace, the commission warned, Virginia would struggle to supply enough power, and ratepayers would help foot the bill for the infrastructure the buildout required. The average residential customer of Dominion Energy, the report estimated, could see generation and transmission costs rise by between fourteen and thirty-seven dollars a month by 2040, independent of inflation. Virginia, unsurprisingly, was among the regions Bloomberg identified as having seen wholesale increases of up to two hundred and sixty-seven per cent over five years.

The same pressure shows up in the wholesale capacity markets that keep the grid reliable. PJM Interconnection, the operator responsible for the grid across thirteen states and the District of Columbia, ran its most recent capacity auction in December 2025. Prices hit a record high of 16.4 billion dollars, the third record-setting auction in a row. PJM's independent market monitor calculated that data centre load accounted for around 6.5 billion dollars of that total, roughly forty per cent, much of it relating to data centres not yet built. The bill for that demand lands on every household in the region, including the one already a payment behind.

The Squeeze on Time and Steel

There is a second, less visible mechanism through which the AI boom inflates bills, and it has to do with the physics of building power plants faster than the world can supply the parts. The surge in demand has collided with a supply chain that simply cannot keep pace, and the resulting scarcity radiates outward as cost. The Environmental and Energy Study Institute's analysis traced the squeeze in unsettling detail. The cost of constructing a new natural gas plant, it reported, had roughly tripled since 2022, to around two thousand dollars per kilowatt of capacity. Refurbishing an ageing coal plant to keep it running could now run to as much as 1.3 billion dollars. The wait for a single large gas turbine, the workhorse component of new fossil generation, had stretched to as long as seven years, and the time needed to build a gas plant from start to finish had grown from roughly four and a half years to at least six.

Each of those numbers is, in effect, a tax on every household sharing the grid. When utilities must pay triple the price for new generation and wait years longer to bring it online, they recover those costs through the rates they charge, spread across the broad base of customers rather than the data centres whose appetite created the shortage. The household already spending a fifth of its income on energy does not get to opt out. It pays the premium embedded in every kilowatt-hour, one it had no hand in creating and draws no benefit from.

The timing dimension matters because it converts a temporary surge in demand into a long-lived cost. Generation built today at inflated prices will sit on the rate base for decades, its expense amortised across a generation of bills. A community that absorbs a data centre in 2026 is not signing up for a one-year inconvenience; it is committing its children to paying down the infrastructure for years. The asymmetry between the speed at which AI demand materialises and the slowness with which the grid can answer it guarantees that the gap will be filled, in the interim, by the cheapest and dirtiest expedient to hand. In Memphis, that expedient was a field of unpermitted gas turbines. Elsewhere it is the deferral of coal-plant retirements that public-health advocates had spent years fighting to secure. The machines need power now, and now is precisely when clean power is hardest to build.

A Map of Old Wounds

Here is where the story turns from arithmetic to geography, and the geography is not random. The single most revealing document of the past year is a report published in December 2025 by the Kapor Foundation, an Oakland-based organisation focused on equity in technology. Titled The Unequal Burden of Data Centers, it mapped California's operational and planned data centres against the state's environmental health data and produced figures that ought to be impossible to ignore.

Eighty-two per cent of California's data centres, the foundation found, are sited in communities already classified as facing poor air quality, as measured by diesel particulate levels. Sixty-five per cent sit in areas with the highest level of groundwater threat. Seventy-nine per cent are in census tracts carrying the greatest burden of hazardous waste. These facilities are not being dropped into pristine landscapes. They are being stacked on top of communities that have already been designated, by the state's own screening tools, as the places carrying the most pollution.

The report's three case studies read like a tour through the history of American environmental racism. Bayview-Hunters Point in San Francisco, scoring in the seventy-fifth to ninety-second percentiles on California's CalEnviroScreen tool, hosts a colocated forty-five-megawatt data centre and a thirty-six-megawatt standalone facility. This is a neighbourhood whose Black population grew during the Second Great Migration to work the Hunters Point Naval Shipyard, a site so contaminated by chemical and radiological waste that the federal government designated it a Superfund site in 1989. Generations of redlining concentrated Black residents there, in what historians and residents alike describe as a sacrifice zone, with cancer rates and chronic-disease hospitalisations running well above regional averages. Now the cloud has come to Bayview, and it has come because Bayview was already deemed a place where industry goes.

Del Paso Heights in Sacramento, scoring in the eighty-fourth to ninety-fourth percentiles, sits near two data centre campuses. Hawthorne, in Los Angeles County, hosts a twenty-eight-megawatt facility in a community scoring as high as the ninety-fifth percentile. The pattern across all three is the same: the infrastructure of the most futuristic industry on earth is being routed, with near-mechanical precision, into the neighbourhoods that an earlier century's discriminatory policies already hollowed out. The Kapor Foundation projected that diesel-generator emissions from these facilities could contribute to a meaningful share of asthma deaths in affected communities by 2030, and noted that California's data centres consumed around seventeen billion gallons of water in 2023, roughly the annual usage of more than four hundred thousand residents.

This is the heart of the matter. The decisions about where to build AI infrastructure are not being made on a blank map, but on one already scarred by a century of choices about whose neighbourhoods could be sacrificed. Land is cheaper where the air is already bad. Political resistance is weaker where residents have been told for generations that their objections do not count. The cold logic of site selection, optimising for cheap land, available power and minimal friction, reliably points the bulldozers toward the communities with the least power to say no. The industry does not have to be malicious to produce this outcome. It only has to be efficient.

The Thirst of the Machines

Electricity is the burden that makes headlines, but water may be the one that bites hardest where it can least be spared. Cooling tens of thousands of densely packed processors generates enormous heat, and the cheapest way to shed it has long been evaporative cooling, which consumes water directly. Estimates across the industry suggest a single large data centre can draw up to five million gallons a day, the equivalent of a town of tens of thousands. Loudoun County, the heart of Virginia's Data Center Alley, used around nine hundred million gallons across its roughly two hundred facilities in 2023.

The xAI Colossus facility in Memphis was reported to draw up to a million gallons a day for cooling. Memphis sits atop the Memphis Sand aquifer, a source of unusually pure drinking water that residents have long regarded as a civic birthright. The prospect of a supercomputer drinking from it, alongside the gas turbines fouling the air above it, sharpened the sense among residents that something they held in common was being quietly enclosed for a purpose that served someone else.

The scale of the coming water demand is only beginning to be understood. A research team at the University of California, Riverside, working with Caltech and led by the associate professor Shaolei Ren, modelled the additional water infrastructure that American communities will need to absorb the peaks in data centre cooling demand. Without significant efficiency gains, the team projected, data centre cooling within four years could require between 697 million and 1.45 billion gallons of additional peak water capacity per day, a figure roughly equivalent to the entire daily water supply of New York City. The cost of building that capacity, the researchers estimated, could run anywhere from ten to fifty-eight billion dollars. As with electricity, the question is not only how much, but who pays, and the answer once again tends toward the ratepayers rather than the corporations driving the demand.

The cruelty of the geography compounds here too. Many of the communities targeted for new data centres sit in water-stressed regions of the American South and West, where drought is a recurring fact of life and the residents competing with the machines for the aquifer are, disproportionately, the ones with the least. To draw down a community's water for cooling, where that water is already scarce and unequally distributed, is to convert a shared resource into a private input in precisely the places least able to absorb the loss.

When the Lawsuit Becomes the Only Voice

The communities on the receiving end of this are not passive. Boxtown organised. By mid-2025, residents had submitted more than two thousand comments to the Shelby County Health Department, the great majority opposing the gas turbines and demanding that xAI power its facility with something cleaner. The Southern Environmental Law Center, acting on behalf of the NAACP, issued a sixty-day notice of intent to sue over the original Colossus facility, alleging that xAI had violated the Clean Air Act by installing and operating turbines that, under the law's Prevention of Significant Deterioration requirements, should have been treated as a major source of pollution requiring full permitting and public oversight. That notice, it turned out, was only the opening move. In response, xAI removed the unpermitted turbines at Colossus and obtained permits for the fifteen that remained, and for a moment the pressure appeared to have worked.

It had not. Rather than abandon the strategy that had drawn the legal fire, xAI exported it. The company built a second facility, Colossus 2, to power its Grok chatbot, and this time installed the gas turbines across the state line in Southaven, Mississippi, while the data centre itself sat in South Memphis, Tennessee. Twenty-seven turbines went up, capable of as much as four hundred and ninety-five megawatts, and once again they were switched on before any air permit had been obtained, the same copy-and-paste approach carried one jurisdiction over. The Mississippi Department of Environmental Quality granted a permit for them in March 2026, but only after they had already been running. By then xAI had added six more unpermitted turbines, bringing the total to thirty-three and the estimated emissions to around two thousand five hundred and eight tons a year of smog-forming nitrogen oxides, which the plaintiffs call potentially the single largest industrial source of NOx in the greater Memphis area.

So the litigation escalated to match. In February 2026, the Southern Environmental Law Center and Earthjustice, acting on behalf of the Mississippi State Conference of the NAACP and the national NAACP, sent a fresh notice of intent to sue over Colossus 2. On the fourteenth of April 2026, the NAACP filed an actual lawsuit in the United States District Court for the Northern District of Mississippi, naming xAI and its subsidiary MZX Tech and alleging Clean Air Act violations for installing and operating the turbines before any permit was granted. On the sixth of May, the plaintiffs asked the court for a preliminary injunction to halt the unpermitted pollution at once. “A data center should not be a potential death sentence,” said Abre' Conner, the NAACP's director of environmental and climate justice, accusing the company of “a blatant disregard for the law” in expanding an unpermitted power plant despite decades of clear direction for permitting. Laura Thoms, an enforcement director at Earthjustice, put the emergency motion plainly: “We're asking the judge to halt all unpermitted pollution and make sure xAI follows the law.”

The voices in that fight are worth recording precisely, because they belong to real people speaking for a real place. KeShaun Pearson, who directs the group Memphis Community Against Pollution, framed the failure as one of accountability: “Our local leaders are entrusted with protecting us from corporations violating our right to clean air, but we are witnessing their failure.” Patrick Anderson, a senior attorney at the Southern Environmental Law Center, captured the absurdity of the permitting claim plainly: “Every single time I've ever seen turbines anywhere, they have an air permit.” His colleague Amanda Garcia put the equity stakes in a single sentence: “No one should be above the law, and it is Memphis communities who have been paying the price for xAI's unpermitted pollution.” Dorthy Seawood, a resident whose mother died of cancer, reduced it to the human floor beneath all the legal argument: “It's not fair to us that we have to deal with whatever comes out of this plant.”

There is something telling in the fact that litigation became the community's primary instrument of voice. A neighbourhood excluded from the planning, bound out of the conversation by non-disclosure agreements, that learned of the turbines from the noise and the satellite imagery rather than any public process, was left with the courts as its main avenue of objection, and the live federal case now pending in Mississippi is the measure of how far that avenue has had to be pushed. That is a symptom of a deeper failure, not a sign the system is working. When the only way a community can register its interests is to sue after the fact, and then to sue again when the same company relocates the same conduct over a state line, the decision-making process has already failed the test of fairness. The harm was done first, and the process invoked afterwards.

The same script is playing in dozens of other places. Across the country, residents show up to county zoning meetings, file public-records requests, form coalitions and discover, often, that the deals were struck before they were ever told. The asymmetry is structural. On one side sit corporations with effectively unlimited legal and lobbying budgets, the promise of jobs and tax revenue, and the ear of local officials eager to land a marquee investment. On the other sit residents with day jobs, a folding table of leaflets and the slow machinery of administrative complaint.

The Vocabulary of Energy Justice

To name what is happening here, it helps to borrow a framework that scholars have spent the past decade refining. The energy researcher Benjamin Sovacool and his colleagues have argued that questions of energy can be assessed through the lens of energy justice, which they break into distinct components. There is distributive justice, concerning how the benefits and burdens of the energy system are spread across society. There is procedural justice, concerning whether the people affected by energy decisions get a genuine say in making them. And there is recognition justice, concerning whether marginalised and vulnerable communities are seen and given special consideration rather than treated as invisible or expendable.

Map the data centre boom onto that framework and the failures line up with uncomfortable neatness. Distributively, the benefits of AI, the productivity gains, the valuations, the convenience of the tools, accrue overwhelmingly to affluent users and shareholders, while the burdens, the particulates, the noise, the water draw, the higher bills, settle on low-income communities of colour. Procedurally, those communities are routinely excluded from the decisions, sometimes literally bound to silence by non-disclosure agreements, and left to litigate after the fact. And in terms of recognition, the entire logic of siting depends on these neighbourhoods having already been classified, by an earlier era's policies, as places where pollution is acceptable. All three forms of justice fail at once, and they fail in the same direction.

This is not an argument against artificial intelligence, nor the infrastructure that runs it. The grid will be built; the demand is real. The argument is about whose interests sit at the centre of the decisions about where and how it goes up. At present, the answer is plainly the companies building the facilities and the officials competing to host them. The residents who breathe the air and drink the water are, at best, an afterthought to be managed, and at worst an obstacle to be routed around. Taking the burden seriously means inverting that order of priority, and it is worth being concrete about what that would require.

What Taking It Seriously Would Look Like

The first and most obvious lever is who pays. If a data centre triggers new generation, transmission or water infrastructure, the cost should fall on the company that caused it rather than being smeared across every household in the region. Regulators call this cost causation, and it is not a radical idea; it is simply the principle that the party generating a cost should bear it. Several states have begun moving this way, creating special rate classes for very large electricity users designed to insulate ordinary ratepayers from the AI buildout. The household spending a fifth of its income on energy should not be subsidising the cooling of a supercomputer. That single reform, applied consistently, would change the economics of siting overnight, because much of the appeal of a given location lies precisely in the ability to externalise these costs onto others.

The second lever is procedural, and it goes to the heart of the recognition failure. Communities asked to host this infrastructure should have a genuine, early and binding voice in the decision. That means an end to the non-disclosure agreements that kept Boxtown in the dark until the turbines were already running. It means meaningful public hearings before permits are issued rather than litigation after harm is done. It means transparency about water draw, emissions and grid impact as a condition of approval, not a fact prised loose by journalists and lawyers months later. A process in which the affected community learns of the project from the noise in their gardens is no process at all.

The third lever is distributive, and it asks a harder question: if a community is going to bear the burden, what does it get in return? Genuine community benefit agreements, legally enforceable rather than rhetorical, could direct a share of the value back to the host neighbourhood, as funded energy efficiency and weatherisation, rooftop solar, lowered bills, clean-up of legacy contamination, or direct investment in the schools and clinics that sit in the turbines' shadow. There is a particular logic to using the infrastructure to reduce the host community's own energy burden, closing the loop between the demand the facility creates and the bills the neighbours pay.

The fourth lever is recognition itself, the most demanding because it requires looking at the map differently. The screening tools California and other states already use, the very tools that revealed eighty-two per cent of the state's data centres sitting in poor-air-quality communities, could be turned from a diagnosis into a constraint. A siting regime serious about justice would treat a high cumulative pollution burden not as a green light, a sign of cheap land and weak resistance, but as a red one, a reason to look elsewhere or demand far more in return. The communities that have already given the most to a century of industry are precisely the ones that should be asked to give the least to the next.

None of this is technically difficult. The water can be recycled; xAI itself proposed an eighty-million-dollar grey-water reclamation plant in Memphis once the pressure mounted, which rather proves the point that cleaner approaches were available all along and simply not chosen until someone forced the question. Cooling can be made far more efficient. Clean generation can be built ahead of demand rather than gas turbines bolted on in a hurry. The obstacles are not engineering ones. They are obstacles of cost, speed and political will, resolved at present in favour of whoever is building fastest and against whoever is breathing hardest.

The Postcode and the Promise

Return, at the end, to Boxtown, and to the woman standing in her garden listening to a sound she did not invite, breathing air made worse by a machine she will likely never use to produce intelligence she will likely never own. Her postcode was poisoned long before xAI arrived; the shipyards and refineries and gas plants saw to that, decade by decade, decision by decision. The data centre is only the latest layer, but it is a revealing one, because it shows that the most advanced industry humanity has yet built is reproducing the oldest pattern of harm rather than escaping it. And the fight has not ended with the first turbines; it has followed the company across a state line into Mississippi, where a federal judge is now being asked whether the law still means what it says.

The promise of artificial intelligence is routinely framed in the language of universal benefit, a rising tide of productivity and discovery that will lift everyone. But a tide does not arrive everywhere at once, and the physical foundation of this one is being laid in specific places, on specific people, who are absorbing the costs of a future from which they have been largely excluded. The defining question of the AI build-out is not whether the machines will think. It is whose lungs, whose water table and whose electricity bill will pay for the thinking, and whether the people answering that question can be persuaded that a community's powerlessness is not the same thing as its consent.

There is nothing inevitable about the geography of the cloud. It was chosen, node by node, permit by permit, and what was chosen can be chosen differently. To take the burden seriously is, in the end, a simple proposition: to insist that the people who breathe the exhaust of the AI economy be treated as something more than the terrain on which it is built. Boxtown, and now Southaven, are asking that question already, in courtrooms and council chambers and comment letters. The rest of the country will be asking it soon enough, because the turbines are coming, and the only thing still undecided is whose garden they will hum behind next.

References

1. Yañez-Barnuevo, Miguel. “Data Center Power Demands Are Contributing to Higher Energy Bills.” Environmental and Energy Study Institute, 24 February 2026. https://www.eesi.org/articles/view/data-center-power-demands-are-contributing-to-higher-energy-bills
2. Kapor Foundation. “The Unequal Burden of Data Centers: An Examination of the Environmental and Public Health Impacts on Communities in California.” December 2025. https://kaporfoundation.org/datacenters-envt-health/
3. Bloomberg. “How AI Data Centers Are Sending Your Power Bill Soaring.” 2025. https://www.bloomberg.com/graphics/2025-ai-data-centers-electricity-prices/
4. American Council for an Energy-Efficient Economy. “Study: One in Four Low-Income Households Spend Over 15% of Income on Energy Bills.” September 2024. https://www.aceee.org/press-release/2024/09/study-one-four-low-income-households-spend-over-15-income-energy-bills
5. Ugwa, Jennifer. “In South Memphis, Elon Musk's Colossus Operated Gas Turbines Without Appropriate Permits, Residents and Activists Claim.” Inside Climate News, 17 July 2025. https://insideclimatenews.org/news/17072025/elon-musk-xai-data-center-gas-turbines-memphis/
6. Southern Environmental Law Center. “xAI Built an Illegal Power Plant to Power Its Data Center.” 2025. https://www.selc.org/news/xai-built-an-illegal-power-plant-to-power-its-data-center/
7. NAACP. “Elon Musk's xAI Threatened with Lawsuit Over Air Pollution from Memphis Data Center.” 2025. https://naacp.org/articles/elon-musks-xai-threatened-lawsuit-over-air-pollution-memphis-data-center-filed-behalf
8. Southern Environmental Law Center. “Civil Rights Group Sues xAI for Illegal Pollution from Data Center Power Plant.” April 2026. https://www.selc.org/press-release/civil-rights-group-sues-xai-for-illegal-pollution-from-data-center-power-plant/
9. Earthjustice. “NAACP Sues xAI for Illegal Pollution from Data Center Power Plant.” April 2026. https://earthjustice.org/press/2026/xai-sued-for-illegal-power-plant
10. Earthjustice. “NAACP Asks Court for Emergency Action to Stop Illegal Air Pollution from xAI's Data Center Power Plant.” May 2026. https://earthjustice.org/press/2026/naacp-asks-court-for-emergency-action-to-stop-illegal-air-pollution-from-xais-data-center-power-plant
11. Joint Legislative Audit and Review Commission. “Data Centers in Virginia.” Commonwealth of Virginia, December 2024. https://jlarc.virginia.gov/pdfs/reports/Rpt598-2.pdf
12. Utility Dive. “Data Centers Were 40% of PJM Capacity Costs in Last Auction: Market Monitor.” December 2025. https://www.utilitydive.com/news/data-centers-pjm-capacity-auction/808951/
13. UC Riverside News. “Data Center Water Spikes Could Cost Billions.” University of California, Riverside, 9 March 2026. https://news.ucr.edu/articles/2026/03/09/data-center-water-spikes-could-cost-billions
14. Sovacool, Benjamin K., and Michael H. Dworkin. “Energy Justice: Conceptual Insights and Practical Applications.” Applied Energy, 2015. https://www.sciencedirect.com/science/article/abs/pii/S0306261915000082
15. Carleton College. “History and Legacy of Environmental Racism in the Bayview Hunters Point Neighborhood.” https://www.carleton.edu/chemistry/diversity-equity-inclusion-and-respect/our-actions-as-chemists-have-consequences/history-and-legacy-of-environmental-racism-in-the-bayview-hunters-point-neighborhood/
16. World Resources Institute. “From Energy Use to Air Quality, the Many Ways Data Centers Affect US Communities.” https://www.wri.org/insights/us-data-center-growth-impacts
17. TechPolicy.Press. “Data Center Boom Risks Health of Already Vulnerable Communities.” https://www.techpolicy.press/data-center-boom-risks-health-of-already-vulnerable-communities/

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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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When a company tells the world that artificial intelligence has made your role redundant, it hands you a story about yourself. The story is tidy and modern and faintly heroic in its fatalism: the future arrived, the machine learned to do what you did, and there was nothing anybody could have done. You were not failed by your employer or by the economy. You were simply standing where the tide came in.

The trouble is that, on the evidence now accumulating, the story is frequently untrue. And a false story about why you lost your job is not a harmless thing. It is a map. It tells you which skills have become worthless, which industries to flee, and what to retrain into if you want to eat next year. If the map is wrong, every decision you make from it is wrong too. You will run from sectors that were never under threat, abandon skills that were never obsolete, and spend scarce money and scarcer time learning things that will not save you. You will also, very often, blame yourself.

This is the quiet scandal underneath the loud one. The loud scandal is that companies are firing people and pointing at AI. The quiet scandal is what that pointing does to the people on the receiving end, and whether the law or our collective ethics recognise any difference at all between being replaced by a machine and being made redundant by a spreadsheet wearing a machine's clothes.

The data that broke the story

The framing began to wobble in earnest at the start of 2026. On 7 January, Fortune published an analysis built on research from Oxford Economics that landed with the force of a deflating balloon. The headline finding was blunt: firms do not appear to be replacing workers with AI on any significant scale. The macroeconomic data, the consultancy argued, simply did not support the idea of a structural shift in employment driven by automation.

The numbers told the story. AI was cited as the reason for nearly 55,000 US job cuts in the first eleven months of 2025, a figure that accounted for more than three-quarters of all AI-attributed cuts reported since 2023. That sounds dramatic until you set it against the whole. Those 55,000 cuts represented just 4.5 per cent of total reported job losses. Redundancies blamed on ordinary market and economic conditions ran four times higher, at roughly 245,000. And every month, in the normal churn of the American labour market, somewhere between 1.5 and 1.8 million workers lose their jobs. Against that ocean, the AI cuts were a puddle.

Oxford Economics offered an unsentimental reading of why companies might reach for the AI explanation anyway. Attributing staff reductions to automation, the firm noted, conveys a more positive message to investors than admitting to weak consumer demand or, more awkwardly still, to having over-hired during the cheap-money years. As the analysts put it, they suspected some firms were trying to dress up layoffs as a good news story rather than a bad one. A redundancy is a confession of error. An AI transformation is a strategy. Same severance cheque, very different press release.

The consultancy also proposed a test that is hard to argue with. If AI were genuinely replacing labour at scale, productivity growth should be accelerating: fewer people producing the same or more output is, by definition, a productivity gain. Generally, it is not accelerating. The machines that have supposedly displaced all these workers have left almost no fingerprints on the output statistics.

The paradox that economists keep digging up

That absence has a name, or at least a precedent. In February 2026, the National Bureau of Economic Research published a survey of around 6,000 chief executives, chief financial officers and other senior managers across the United States, the United Kingdom, Germany and Australia. The result, reported by Fortune in an article by Sasha Rogelberg, was startling in its flatness. Nearly 90 per cent of firms said AI had made no impact on either employment or productivity over the previous three years.

This was not a survey of sceptics. Around two-thirds of the executives said they used AI. But that usage amounted to roughly 1.5 hours per week, and a quarter of respondents reported not using AI at work at all. The people running the companies, the ones with every incentive to talk up their digital transformation to shareholders, were quietly admitting in an academic survey that the revolution had not yet arrived in any measurable form. They still expected it to: forecasts pencilled in a 1.4 per cent productivity gain and a 0.8 per cent output gain over the next three years, alongside a 0.7 per cent cut to employment. The gains were always just over the horizon.

To economists, this had a familiar shape. In 1987, the Nobel laureate Robert Solow made an observation that has haunted every technological boom since. Despite the spread of computers through the economy, productivity growth had actually slowed, falling from 2.9 per cent in the post-war decades to around 1.1 per cent afterwards. You could see the computer age everywhere, Solow remarked, except in the productivity statistics. The gap between the visible presence of a technology and its invisible economic contribution became known as the Solow productivity paradox.

The parallel is not lost on the people watching the data now. Torsten Slok, the chief economist at Apollo, captured the present moment with a near-direct echo of Solow: AI, he observed, is everywhere except in the incoming macroeconomic data. There are signs the picture may be shifting. The Federal Reserve Bank of St Louis noted in November 2025 a productivity increase of around 1.9 per cent since ChatGPT's launch in late 2022, and the MIT economist Daron Acemoglu has projected a more modest gain of around half a per cent over a decade. But a half-per-cent productivity bump over ten years is not the sound of a labour market being demolished. It is the sound of a useful tool being slowly absorbed, the way spreadsheets and email and search engines were absorbed before it.

The vocabulary of the redundancy letter

If the productivity gains are not there, the language certainly is. By March 2026, the disconnect between AI's omnipresence in corporate communications and its near-absence from corporate output had become a recurring theme in technology writing, including at HackerNoon, which through its March coverage tracked how the rhetoric of machine intelligence had saturated the language of management while the efficiency it promised stayed stubbornly theoretical. AI had become the foundation on which policies, training programmes and strategic announcements were built, even where the underlying work had not changed at all.

The Wharton management professor Peter Cappelli put his finger on the sleight of hand. Companies, he has pointed out, announce layoffs that they never actually carry out, harvesting the favourable stock-market reaction to a leaner-sounding workforce. And on the AI claims specifically, he noticed something telling in the wording. The headline says it is because of AI, he observed, but when you read what the companies actually say, they tend to say they expect that AI will cover this work. Expect. Future tense. The work has not been automated. It has been earmarked for automation, at some unspecified point, by some unspecified system, and in the meantime the humans who did it are already gone.

This is the heart of what has come to be called AI washing, the workforce cousin of greenwashing. The term migrated from financial regulation, where it described companies overstating the role of AI in their products to attract investors, into the language of redundancy, where it describes companies overstating the role of AI in their cost-cutting to soften the blow and burnish the brand. By early 2026, compliance specialists were warning that AI washing carried real legal and reputational risk, and that it had arguably overtaken greenwashing as the corporate communications hazard of the moment.

The most striking confirmation came from inside the industry that has the most to gain from the displacement narrative. At the India AI Impact Summit in February 2026, Sam Altman, the chief executive of OpenAI, was asked about the wave of AI-attributed layoffs. He did not reach for the triumphal line. There is some AI washing, he conceded, where people are blaming AI for layoffs that they would otherwise do, and then there is some real displacement by AI of different kinds of jobs. He could not say what the exact percentage was. But the man whose company sells the picks and shovels of the AI gold rush was openly acknowledging that some of the gold was fake. Within weeks, by late May 2026, Altman was going further still, telling interviewers he had been pretty wrong about the speed of AI's economic impact, a notable reversal of his earlier warnings that entry-level roles were in serious jeopardy.

There is a complicating truth here, and the better analysts have insisted on it. Andy Challenger, of the outplacement firm Challenger, Gray and Christmas, made a point that cuts through the binary. Regardless of whether individual jobs are being replaced by AI, he noted, the money for those roles is. Capital that companies might once have spent on hiring is being diverted into AI infrastructure: the data centres, the chips, the licensing deals, the eye-watering capital expenditure that the hyperscalers have committed to. Through April 2026, AI was cited as justification for nearly 50,000 US job cuts, according to Challenger data. By late May 2026, technology-sector layoffs for the year had passed 142,000, and reporting noted that many of the firms doing the cutting were profitable companies trimming headcount to help fund AI infrastructure spending running into the hundreds of billions of dollars. A worker can be a casualty of AI spending without ever being replaced by an AI system. The job did not go to a machine. It went to the bill for the machines somebody else is building.

There is a further wrinkle that should make anyone pause before accepting the displacement story at face value. Even where firms have genuinely rolled out AI tools, the productivity returns have been ambiguous and sometimes negative. A Boston Consulting Group study of nearly 1,500 American workers found that productivity rose when people used one to three AI tools but fell sharply once they were juggling four or more, with workers reporting a kind of brain fog and an uptick in errors. The picture this paints is not one of clean substitution, a human swapped out for a more efficient machine. It is messier and more human: tools half-adopted, workflows half-rebuilt, gains that arrive in one place and evaporate in another. A labour market being smoothly automated would not look like this. It would look like the productivity statistics climbing while headcount fell. Instead, headcount is falling while the productivity statistics barely twitch, which is precisely the pattern you would expect if the cutting were driven by cost and capital allocation rather than by machines actually doing the work.

What a false map does to the person holding it

For the individual worker, these distinctions are not academic. They determine the shape of the next several years of a life.

Consider what the AI explanation actually communicates to the person receiving it. It says: the specific thing you were good at can now be done by software, therefore it has no future value, therefore you should retrain into something a machine cannot do. That instruction sounds responsible. It is the standard advice handed to displaced workers in every wave of automation since the power loom. But it is only sound advice if the premise is true. If your role was eliminated because your employer over-hired in 2022, or because a private-equity owner wanted to juice margins before a sale, or because demand for the product softened, then the skill you possessed has lost none of its market value. The job that used it has simply moved, or shrunk, or relocated to a cheaper labour market. Retraining away from that skill is not adaptation. It is a self-inflicted wound, dressed up as foresight.

The research on displaced workers is unforgiving about how costly these wrong turns are. Workers whose skills lie in declining industries already earn less even after they find new work, because their old competencies are hard to transfer. Studies of American retraining schemes have found that participants in some programmes remained underemployed and earning slightly less than comparable non-participants even four years after losing their jobs. Retraining is not a magic bridge across the labour market. It is a slow, expensive, uncertain crossing, and the single most important factor in whether it succeeds is whether the worker is retraining away from something genuinely obsolete and towards something genuinely in demand. A false map corrupts that calculation at its root. It can send a perfectly employable person sprinting away from a skill the market still wants, towards a future the market has not actually promised.

The damage is compounded by timing. Retraining decisions are made fastest in the weeks immediately after a job loss, when redundancy money is fresh, anxiety is highest and the instinct to do something, anything, is strongest. That is exactly the window in which the company's explanation has the most power, because it is the only authoritative-sounding account the worker possesses. If the leaving manager said the role was automated, that sentence becomes the seed of every subsequent choice: the course enrolled in, the sector written off, the contacts not called because that line of work is finished. By the time the worker discovers, months later, that a cheaper replacement was quietly hired or that the team was simply folded into another department, the money is spent and the new direction is half-travelled. The cost of the false map is not paid all at once. It compounds, quietly, in the form of a recovery aimed at the wrong target.

Then there is the damage that does not show up in earnings data. The psychology of job loss has been studied for decades, and the findings are consistent and grim. Unemployment inflicts stress, a collapse in perceived control, loss of self-esteem, shame, loss of social status, and a grieving process that resembles bereavement. Work, the research repeatedly finds, supplies purpose and identity as much as income; its removal produces feelings of helplessness, isolation and worthlessness.

How a person explains their job loss shapes how much of that damage they absorb. There is a well-documented divide in how people attribute redundancy. Those who blame themselves tend to feel worse about who they are, but stay oddly optimistic about their ability to learn new skills and recover. Those who blame the system suffer less self-reproach but feel more trapped, more convinced that nothing they personally do will change their situation. The AI redundancy narrative does something peculiar and corrosive: it manages to deliver the worst of both attributions at once. It is systemic, in that the machine is presented as an unstoppable historical force, which breeds the fatalism of the external-blame group. And yet it is intimately personal, because the message is that you, specifically, have been rendered obsolete by a technology, that your particular abilities have been surpassed. The worker is invited to feel both powerless against the tide and personally outdated. It is difficult to imagine a more demoralising combination, and it is built on a premise that, in a great many cases, is simply false.

This is the specific human cost the displacement narrative imposes when it is misapplied. It is not only that people lose jobs. People lose jobs in every downturn. It is that they are handed an explanation that misdirects their recovery and corrodes their sense of self, and they are handed it precisely because it was the most convenient thing for someone else to say.

Does the distinction carry legal weight?

If being told AI took your job is materially different from being made redundant by cost-cutting, you might expect the law to take an interest. The answer depends enormously on which side of the Atlantic you are standing.

In most of the United States, the doctrine of at-will employment means an employer generally needs no reason at all to end an employment relationship, provided the real reason is not an illegal one such as discrimination on the basis of a protected characteristic. There is no legal requirement to accurately state why a worker is being let go, and certainly none to disclose whether AI was involved. The principal federal protection, the Worker Adjustment and Retraining Notification Act, requires larger employers to give sixty days' notice of mass layoffs and plant closings, but it is a notice law, not a justification law. It governs the timing of the bad news, not its honesty. There is no federal requirement that an employer disclose whether a layoff is genuinely AI-driven, a gap that has not gone unnoticed; a proposed overhaul of WARN, introduced as the Fair Warning Act in early 2026, would represent the first significant rewrite since 1988, but the core architecture remains a question of notice rather than rationale. In the American legal frame, the AI explanation is largely a public-relations choice with little statutory consequence. A company can say almost anything about why it is shrinking, because in most states it does not have to say anything at all.

The United Kingdom is a different country in more than the obvious sense. Here, redundancy is one of a small number of potentially fair reasons for dismissal under the Employment Rights Act 1996, and the law cares a great deal about whether the stated reason is the real one. A genuine redundancy exists where an employer has ceased the business, or no longer needs employees to do work of a particular kind, or needs fewer of them. Crucially, if an employer dismisses someone and then immediately hires a replacement to do the same job, that is not a genuine redundancy at all. It is potentially an unfair dismissal. The role has to have actually disappeared, not merely changed hands.

This is where the AI framing becomes legally consequential rather than merely rhetorical. An employer in England or Wales can lawfully make staff redundant because it has introduced automation that genuinely removes the need for a role. But the reason has to withstand scrutiny. Employers may be required to explain, in clear and human terms, how an automated system has actually changed staffing levels or work design, and if that explanation cannot be coherently justified, defending the dismissal in a tribunal becomes considerably harder. A dismissal based purely on an automated recommendation, without proper assessment of the individual or genuine consideration of alternative roles, risks being found unfair. British employers also carry obligations to consult, to apply fair selection criteria, and to consider redeployment before reaching for redundancy. From 6 April 2026, the financial stakes rose: the maximum protective award for failing to comply with collective consultation obligations doubled from ninety to a hundred and eighty days' pay, sharply increasing the cost of getting the process wrong in a large-scale restructure.

So in the British context, the distinction the question asks about does carry weight, though perhaps not the weight one might hope. The law does not punish dishonest framing as such. There is no statutory offence of AI washing a redundancy. But the framing can become a liability, because a worker who suspects the AI story is a cover can challenge it. If a tribunal finds that the role did not really vanish, that a replacement was quietly hired, that the automation was aspirational rather than actual, or that the process was a pretext for getting rid of a particular person, the AI narrative collapses and the dismissal may be unfair. A worker has, in principle, three months less a day from the date of dismissal to bring such a claim, and the remedies can include compensation for lost earnings on top of statutory redundancy pay. The convenient story, in other words, can become the thread that unravels the whole decision if it does not match the facts on the ground.

That said, the protection is uneven and easily evaded. It applies to employees with sufficient qualifying service, not to the growing population of contractors, gig workers and the recently hired. It requires the worker to recognise that something is amiss, to absorb the cost and stress of a legal challenge, and to gather evidence about internal decisions they were never shown. The asymmetry of information is total. The employer knows whether the AI story is true. The worker can usually only guess. And a guess, however well-founded, is a thin basis on which to stake a tribunal claim while also trying to find a new job and pay the rent. The legal distinction, in short, exists in Britain and barely exists in America, but even where it exists it favours the party with the documents, the lawyers and the institutional memory, which is never the person who has just been shown the door.

The ethics of the convenient explanation

Strip away the legal scaffolding and an ethical question remains, and it is sharper than the legal one. Is it wrong for a company to attribute a layoff to AI when the real driver is something more ordinary, even if doing so breaks no law?

The case for leniency runs roughly as follows. Companies have always smoothed the language of bad news. Restructuring, rightsizing, streamlining, synergies: the corporate lexicon is a museum of euphemisms for sacking people, and AI transformation is merely the newest exhibit. Workers, the argument goes, know to read between the lines. No real harm is done by a gentler framing, and the alternative, brutal honesty about over-hiring or declining demand, might be worse for the morale of those who remain and the share price that funds everyone's pension.

The case against is more persuasive, and it turns on the specific nature of this particular lie. Most corporate euphemisms obscure the fact of the decision while leaving its meaning intact: everyone understands that streamlining means job cuts. The AI explanation is different in kind, because it does not merely soften the news. It actively misinforms the worker about the cause, and the cause is precisely the information the worker needs to plan a recovery. Telling someone they were streamlined leaves their understanding of the labour market undamaged. Telling someone they were replaced by AI, when they were not, plants a false belief about the value of their own skills, the safety of their own profession, and the direction in which their future lies. It is a lie that keeps working long after the person has left the building, steering their retraining, their job search and their self-image down a path laid by someone else's convenience.

There is also a collective harm that compounds the individual one. Every false AI redundancy adds to a public narrative of inevitable, accelerating, machine-driven displacement, a narrative that the productivity data does not currently support. That narrative has consequences well beyond the firms telling it. It shapes how governments think about retraining budgets and which sectors they prioritise. It influences which degrees school-leavers choose and which they avoid. It feeds a generalised anxiety about the future of work that the actual evidence, for now, does not justify. When companies AI wash their layoffs, they are not only misleading their own former employees. They are subsidising a public misunderstanding of the economy, and doing so for the narrow purpose of a better quarterly story.

The deepest ethical objection is about dignity. A worker who is made redundant for ordinary reasons retains a true account of what happened to them. They can be angry at the right target, grieve the right loss and plan around the real facts. A worker who is falsely told a machine surpassed them is denied even that. They are made to carry a story about their own obsolescence that is not true, told to them by people who knew better, for reasons that had nothing to do with them. There are few more basic things one person owes another than an honest account of why they are being harmed. The convenient explanation withholds exactly that, and calls the withholding progress.

Reading the map for what it is

None of this means AI will never displace workers. It almost certainly will, in some roles, on some timeline, and the real cases deserve real attention and real policy. Altman himself was careful to say that alongside the washing there is genuine displacement, and the diversion of capital from payrolls to AI infrastructure is reshaping hiring in ways that hurt people whether or not a model ever touches their old tasks. The point is not that the machine is innocent. The point is that the story has run far ahead of the evidence, and that the gap between the two is being filled with the cheapest available narrative.

For the worker handed that narrative, the most valuable instinct may be a sceptical one. The map you were given was drawn by someone with an interest in how it reads. Before you flee a sector or abandon a skill, it is worth asking the questions the company would prefer you did not: Did the role actually disappear, or was someone hired to do it? Is there a working system that does what I did, or only a slide deck that says one is coming? Did the firm over-hire, lose a contract, change owners, or simply decide its margins should be fatter? The honest answer to those questions is the real map. It tells you what is genuinely obsolete and what is merely inconvenient to keep paying for, and those are not the same thing at all.

The companies have learned that AI is a comfortable thing to blame, because it is no one's fault and everyone's future. The least we can do for the people on the wrong end of that sentence is to insist on the difference between the technology that took the work and the spreadsheet that took the worker. One of those stories is sometimes true. The other is just easier to tell.

References and sources

1. Lichtenberg, Nick. “AI layoffs are looking more and more like corporate fiction that's masking a darker reality, Oxford Economics suggests.” Fortune, 7 January 2026. https://fortune.com/2026/01/07/ai-layoffs-convenient-corporate-fiction-true-false-oxford-economics-productivity/

2. “Evidence of AI-driven job losses remains limited, says Oxford Economics report.” Workplace Insight, January 2026. https://workplaceinsight.net/evidence-of-ai-driven-job-losses-remains-limited-says-oxford-economics-report/

3. Rogelberg, Sasha. “Thousands of CEOs admit AI had no impact on employment or productivity, and it has economists resurrecting a paradox from 40 years ago.” Fortune, April 2026. https://fortune.com/article/why-do-thousands-of-ceos-believe-ai-not-having-impact-productivity-employment-study/

4. “A Huge Survey of CEOs and Other Execs Just Found Something Damning About AI's Effects on Productivity.” Futurism, February 2026. https://futurism.com/artificial-intelligence/survey-ceos-ai-workplace

5. “Over 80% of companies report no productivity gains from AI so far despite billions in investment.” Tom's Hardware, 2026. https://www.tomshardware.com/tech-industry/artificial-intelligence/over-80-percent-of-companies-report-no-productivity-gains-from-ai-so-far-despite-billions-in-investment-survey-suggests-6-000-executives-also-reveal-1-3-of-leaders-use-ai-but-only-for-90-minutes-a-week

6. Rogelberg, Sasha. “OpenAI CEO Sam Altman warns 'AI washing' is real.” Fortune, 19 February 2026. https://fortune.com/2026/02/19/sam-altman-confirms-ai-washing-job-displacement-layoffs/

7. “Sam Altman and Dario Amodei are both walking back their AI jobs apocalypse prophecies as they eye blockbuster IPOs.” Fortune, 26 May 2026. https://fortune.com/2026/05/26/sam-altman-dario-amodei-walking-back-ai-jobs-apocalypse-prophecies-ipo/

8. “Sam Altman says some companies are 'AI washing' by blaming unrelated layoffs on the technology.” TechRadar, 2026. https://www.techradar.com/pro/sam-altman-says-some-companies-are-ai-washing-by-blaming-unrelated-layoffs-on-the-technology-but-admits-things-may-get-worse-soon

9. “Who the AI Works For.” HackerNoon, 16 March 2026. https://hackernoon.com/who-the-ai-works-for

10. “The HackerNoon Newsletter: Who the AI Works For (3/17/2026).” HackerNoon, 17 March 2026. https://hackernoon.com/3-17-2026-newsletter

11. “2026 Operational Guide to Cybersecurity, AI Governance and Emerging Risks.” Corporate Compliance Insights, 2026. https://www.corporatecomplianceinsights.com/2026-operational-guide-cybersecurity-ai-governance-emerging-risks/

12. “Tech Layoffs Reach 142,000 in 2026: Profitable Companies Cut Jobs to Fund $700B AI Infrastructure.” TechTimes, 29 May 2026. https://www.techtimes.com/articles/317392/20260529/tech-layoffs-reach-142000-2026-profitable-companies-cut-jobs-fund-700b-ai-infrastructure.htm

13. “The toll of job loss.” American Psychological Association, October 2020. https://www.apa.org/monitor/2020/10/toll-job-loss

14. “AI labor displacement and the limits of worker retraining.” Brookings Institution. https://www.brookings.edu/articles/ai-labor-displacement-and-the-limits-of-worker-retraining/

15. “The interplay between structure and agency in shaping the mental health consequences of job loss.” National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3573919/

16. LaLonde, Robert. “Retraining Displaced Workers.” The Hamilton Project. https://www.hamiltonproject.org/assets/legacy/files/downloads_and_links/10_displaced_workers_lalonde.pdf

17. Cakali, Samira. “I've Been Made Redundant Due to AI, Can I Claim Compensation?” Winston Solicitors. https://www.winstonsolicitors.co.uk/blog/ive-been-made-redundant-due-ai-can-i-claim-compensation

18. “AI and Redundancy: Is UK Employment Law Keeping Pace?” Bellevue Law. https://www.bellevuelaw.co.uk/insights/ai-and-redundancy-is-uk-employment-law-keeping-pace/

19. “Can I Replace Staff With AI and Make Them Redundant?” Pearce Legal. https://pearcelegal.co.uk/blog/can-i-replace-staff-with-ai

20. “UK Employment Rights Act 2025: What's new from April 2026.” Bird & Bird. https://www.twobirds.com/en/insights/2026/uk/uk-employment-rights-act-2025--whats-new-from-april-2026

21. “Unfair dismissal.” Acas. https://www.acas.org.uk/dismissals/unfair-dismissal

22. “What Is the WARN Act? Employee Rights and Layoff Notice Requirements.” FindLaw. https://www.findlaw.com/employment/losing-a-job/what-is-the-warn-act-employee-rights-and-layoff-notice-requirements.html

23. “Plant Closings and Layoffs.” US Department of Labor. https://www.dol.gov/general/topic/termination/plantclosings

24. “Congress Proposes Major Overhaul of WARN: What Employers Need to Know About the Fair Warning Act.” Law and the Workplace, January 2026. https://www.lawandtheworkplace.com/2026/01/congress-proposes-major-overhaul-of-warn-what-employers-need-to-know-about-the-fair-warning-act/

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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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Picture the moment of the mistake. Not a dramatic one, because the dramatic ones are easy to litigate. Picture instead the small, plausible failure: an AI agent embedded in your phone, told to “sort out the trip to Lisbon,” books a non-refundable fare for the wrong week. Or it renews a subscription you meant to cancel. Or it accepts terms and conditions on a checkout page, ticks a box authorising the sharing of your data with a dozen marketing partners, and moves on to the next task at a speed no human would bother to match. By the time you notice, the money has moved, the consent has been given, and the commitment has hardened into something you cannot easily reverse.

Now ask the question that the entire emerging architecture of agentic commerce is currently unable to answer cleanly: who is responsible? You issued the instruction, but you did not approve the specific action. The platform built the agent, but it acted within the bounds you set. The developer trained the underlying model, but cannot foresee the millions of paths it might take. The merchant accepted the order, but had no way of knowing a human had not reviewed it. Four parties, each holding a fragment of the responsibility, and no settled rule for assembling those fragments into a single answerable entity. That gap, between what the software can now do and what accountability exists when it does it wrong, is the most under-examined risk in consumer technology today. And it is widening fast.

The shoppers who already feel it

The unease is not theoretical, and it is not confined to commentators. It has shown up in the numbers. In its first-quarter 2026 study of consumers across the United States and the United Kingdom, the fraud-prevention firm Riskified found a population that is enthusiastically adopting AI for shopping while quietly refusing to let go of the wheel. A clear majority, 61.5 per cent, said they had already used AI tools for product discovery and recommendations. Yet 55.0 per cent said they were not comfortable with AI agents making purchases on their behalf, and 53.9 per cent believed AI could increase their exposure to online fraud.

Read those figures together and a coherent psychology emerges. People are happy to let an agent browse, compare, and suggest. They are markedly less happy to let it buy. The line they are drawing falls precisely at the point where browsing becomes a binding transaction, the point where money leaves the account and an obligation is created in their name. Riskified's data sharpens this further: 46.5 per cent said they did not trust any company to manage purchases for them, and 73.9 per cent expected strong safeguards, such as biometric checks or one-time passwords, before they would consent. Most revealing of all, 50.8 per cent said they believed the AI platform itself should bear responsibility for unauthorised purchases.

That last statistic is the hinge on which this whole subject turns. Half of consumers have already, intuitively, assigned the liability. They want the platform to pay when the platform's agent gets it wrong. The trouble is that the law has made no such assignment, and there is no guarantee it will arrive at the same answer that consumers consider obvious. The shift in sentiment is recent, too. Riskified noted that concern had grown compared with its late-2025 survey, with anxieties around fraud, security, and accountability actively slowing the path toward the fully autonomous, agent-driven checkout that the industry has been promising. The technology is racing ahead. Trust is not following at the same pace.

From novelty to infrastructure

It would be comforting to treat all of this as a problem for later, a speculative concern about a future that has not yet arrived. It is not. Agentic commerce has already crossed the threshold from demonstration to deployment. The major payment networks have built the rails. Mastercard's Agent Pay, announced in 2025, allows verified AI agents to transact on a consumer's behalf using what it calls Agentic Tokens, an extension of its existing tokenisation infrastructure, with early issuing partners including Citi and US Bank. Visa has extended its intelligent-commerce programme to cover AI-initiated payments. PayPal has adopted the Agentic Commerce Protocol to embed payments directly inside conversational AI, so that a user can complete a purchase without ever leaving the chat window.

The model developers have built the hands that operate the rails. OpenAI's computer-using agent and Anthropic's Claude integrations can navigate websites, fill in forms, and complete checkouts. These are no longer laboratory toys. By 2026 they were processing meaningful daily transaction volumes, embedded not in some separate “AI app” that a user consciously opens, but woven into operating systems, browsers, and financial apps as a default capability. That is the crucial detail. An agent that lives inside your bank's app or your phone's assistant is not a product you chose to trust. It is infrastructure, and infrastructure is something you stop noticing.

OpenAI has been candid about the failure modes. The company acknowledges that its agent can take an action the user did not intend, with consequences ranging in severity from a typo in an email through to buying the wrong item or permanently deleting an important file. That candour is welcome, but it also frames the problem precisely. The errors are not edge cases to be engineered away in the next release. They are an intrinsic property of systems that act autonomously across an open-ended web of websites, each with its own design quirks and dark patterns.

The early dispute data bears this out. Industry analysis in 2026 found that disputes on agent-initiated transactions were running at roughly 2.4 times the rate of comparable human-initiated, card-not-present transactions. Tellingly, the composition was different. There was proportionally less outright fraud and proportionally more of two other categories: “did not authorise” and “not as described.” In other words, the distinctive harm of agentic commerce is not the stolen card. It is the legitimate agent, operating within its mandate, doing something the human behind it never actually wanted. That is a category of harm that existing fraud systems were never designed to catch, because it is not fraud. It is a faithful machine faithfully executing a flawed plan.

A law built for people who can be found

Here is the deeper problem, the one the legal community spent the spring of 2026 trying to articulate. A legal analysis published by Reuters in April 2026 documented the accountability gaps that open up when agentic systems, capable of booking travel, authorising payments, placing orders, and managing subscriptions without a human confirming each individual step, make errors or cause harm. The central observation was disarmingly simple. Existing consumer protection law was designed for transactions between identifiable human or corporate parties. It was not designed for autonomous software agents acting in a user's name.

Consider what consumer protection law quietly assumes. It assumes there is a buyer and a seller. It assumes that when something goes wrong, you can identify who did what, establish what they knew and what they intended, and direct a remedy at a party who can be reached and held to account. The whole apparatus, from the right to a refund to the rules on unfair contract terms, rests on a knowable, reachable responsible party. Strip that away and the machinery seizes. When the “buyer” is a piece of software, intent becomes a slippery concept. When the action was taken by an agent built by one company, running a model trained by another, operating on instructions from a third, and transacting with a fourth, the chain of responsibility does not break in one obvious place. It dissolves across several.

Regulators have begun to respond, though their response so far has been to reach for the tools they already have. On 9 March 2026 the United Kingdom's Competition and Markets Authority published two linked documents: practical guidance on complying with consumer law when using AI agents, and a wider policy paper on agentic AI and consumers. The CMA's headline principle was clear and, for businesses, uncomfortable: the same consumer law rules apply regardless of whether a customer interacts with a human or an AI agent. Crucially, it stated that businesses remain fully responsible for what their AI agents say and do, even where the technology is supplied or designed by a third party. Delegating a decision to software, in the regulator's view, does not delegate the legal accountability that comes with it. The teeth behind this are real. Under the Digital Markets, Competition and Consumers Act 2024, the CMA can levy fines of up to 10 per cent of a firm's global turnover.

The CMA's instinct is sound, and it points toward concentrating accountability rather than letting it scatter. But notice what it does and does not solve. It tells a merchant deploying a customer-service agent that it cannot hide behind its vendor. That is a meaningful clarification. It does much less for the messier scenario at the heart of consumer anxiety, in which the agent is not the merchant's tool at all but the consumer's own, acting on the consumer's side of the transaction, and getting it wrong in a way that harms the very person who deployed it. When your agent books the wrong flight, the merchant did nothing improper. The platform's agent behaved as designed. There is no obvious villain to point the existing rules at.

The European position is, if anything, more exposed. The EU AI Act, the most comprehensive AI regulation yet written, was not drafted with agents in mind. It does not explicitly define agentic AI, instead sweeping such systems into broad, catch-all categories. More fundamentally, its architecture rests on the idea of a system having a static “intended purpose” and a band of “reasonably foreseeable misuse.” Agentic systems make a mockery of both. They work through an iterative execution loop, dynamically generating novel, unprogrammed routes to a goal. The intermediate actions an agent takes to satisfy an instruction are, by design, not foreseeable by the developer who built it. A regulatory framework anchored to foreseeability is poorly equipped to govern a technology whose defining feature is that its specific behaviour cannot be foreseen.

That gap is not merely an academic complaint. As of early 2026, despite the EU AI Act's prohibited-practice provisions having been enforceable since August 2025, no enforcement actions had been announced, while the powers to fine providers of general-purpose AI models were not due to engage until August 2026. The regulation was racing to define categories even as the technology slipped between them. Legal commentators tracking the field through the spring of 2026, including practitioners at firms such as Venable, reached a strikingly consistent conclusion: deploying an AI agent does not transfer accountability to the agent. It concentrates accountability on the party that deploys it. The recurring failure they identified was not a shortage of rules but a failure of legal and policy definitions to capture what agentic systems actually are, how they work, and where the genuine locus of risk and responsibility lies. A gap opens, in their phrasing, wherever the original human instruction is remote from the final, potentially harmful output. Agentic systems are engineered, deliberately, to widen exactly that distance, because the entire value proposition is that the human gives a goal and the machine handles the steps in between.

The trouble with putting it right

Allocating blame is only half of consumer protection. The other half is recourse: the practical ability to undo a harm once it has happened. Here the distinctive shape of agentic failure becomes a problem in its own right. Recall that the disputes piling up are not predominantly fraud claims but “did not authorise” and “not as described” claims. Those are far harder to adjudicate than a stolen card, because they hinge on a question that is genuinely murky: what, precisely, did the consumer authorise? When you hand an agent a loose instruction such as “find me a good deal on a weekend away,” you have plainly authorised some purchases and plainly not authorised others, with a vast grey zone in between. A non-refundable booking might fall on either side of that line depending on facts that no one recorded at the time.

This is why the technical question of how an agent captures and proves consent is not a back-office detail but the foundation on which any recourse must rest. Without a record of the mandate, every dispute collapses into one person's word against a black box, and the consumer, lacking visibility into the agent's reasoning, is structurally disadvantaged. Schiavi's essay, to which we will return, made exactly this point about knowledge and consent: users cannot meaningfully consent to choices they cannot understand, and an agent's decision-making is frequently both opaque and complex. The recourse problem and the consent problem are the same problem wearing different clothes. Solve the record of intent, and you give both the disputing consumer and the adjudicating system something concrete to reason about. Leave it unsolved, and the protections that exist on paper become unreachable in practice, which for the harmed consumer is indistinguishable from having no protection at all.

The seductive, dangerous metaphor

Faced with something genuinely new, the human reflex is to reach for the nearest familiar analogy. For agentic AI, the analogy everyone reaches for is the employee, or its legal cousin, the agent in the principal-agent sense: a proxy who acts on your behalf, within your authority, with consequences that flow back to you. It is an appealing frame. We already have centuries of law and intuition about delegated authority, about what a principal owes for the acts of an agent, about when a worker's mistake becomes the employer's liability. Why not simply pour the new wine into these old bottles?

Because, according to research published by Harvard Business Review in May 2026, the bottles leak in ways that produce real damage. The study, conducted by Matthew Kropp, Julie Bedard, Megan Hsu and Lisa Krayer of Boston Consulting Group together with Emma Wiles of Boston University's Questrom School of Business, examined what happens when organisations treat AI agents as though they were employees, complete with the conventional accountability structures that surround human staff. In a large-scale experiment involving more than 1,200 managers, the framing alone changed behaviour, and not for the better. When the AI was presented as an employee, managers identified 18 per cent fewer errors in its work. Individual accountability for errors dropped by nine percentage points, while the accountability that managers attributed to the AI itself rose by eight percentage points.

Sit with what those numbers describe. Simply by dressing an agent in the costume of a colleague, the humans around it became less vigilant, less willing to own its mistakes, and more inclined to treat the software as a locus of blame in its own right. The metaphor did not just mislead. It actively eroded the human oversight that is the only thing standing between a flawed plan and a harmful outcome. The researchers argued that the employee analogy hides three things humans supply for free that agents do not: a stable sense of context across the day, an instinct to escalate when something feels wrong, and accountability that survives a bad outcome. An agent will keep executing a flawed plan with serene confidence long after a human teammate would have stopped to ask whether the plan still made sense.

The conclusion the authors drew is the single most important sentence in this entire debate, and it deserves to be stated plainly. Accountability does not transfer to a model. It stays with the humans who deployed it. The agent cannot be a defendant. It owns no assets, feels no consequence, and carries no reputation into the next transaction. Any framework that lets responsibility settle on the agent is not allocating responsibility at all. It is abolishing it, while preserving the comforting appearance that someone, somewhere, is answerable. The HBR finding matters beyond the office, too, because the same psychological reflex that makes a manager relax their scrutiny of an “employee” agent makes a consumer relax their scrutiny of an assistant that feels like a helpful person rather than a piece of fallible software. The friendlier the framing, the looser the vigilance, and the wider the gap into which harm can fall.

Responsibility laundering

This is precisely the failure that the bioethicist Adam Schiavi warned about in an essay published by Undark in March 2026. Schiavi, an anaesthesiologist and neurocritical care specialist at the Johns Hopkins Hospital who also works as a biomedical ethicist studying synthetic personas in AI systems, gave the problem a name that ought to enter the general vocabulary: responsibility laundering. His worry is that granting AI any form of personhood or quasi-agency, even a limited one, formalises the most dangerous escape hatch of the agentic era. It creates a clean, respectable mechanism for a human to say, in effect, “It was not me. The agent did it.” The harm becomes everyone's problem and nobody's fault.

Schiavi's framing is valuable because it comes from medicine, a field that has spent a very long time thinking rigorously about responsibility when the stakes are life and death and the actors are fallible. He draws on the clinical concept of moral residue: the lingering weight of responsibility that a person continues to carry even after taking a justified action with a bad outcome. A clinician who makes a defensible decision that nonetheless harms a patient feels that residue, and that felt weight is part of what keeps the profession careful. An AI agent feels nothing. It cannot bear moral residue, which means that if responsibility is allowed to flow to the agent, it does not accumulate anywhere. It simply evaporates.

His prescription is not to ban the technology but to keep responsibility within reach of a human at every stage. He proposes a framework of what he calls authorised agency, and its components map almost perfectly onto what meaningful consumer protection would need to look like. There should be an authority envelope, a bounded and explicit scope of what the agent is permitted to do. There should be a human-of-record, a named person who authorised the agent and remains answerable for it. There should be interrupt authority, an unqualified right to stop the agent at any moment. And there should be an answerability chain, a traceable path that leads from any action the agent takes back to the human who authorised it. The unifying principle, in Schiavi's words, is that human accountability must precede autonomous capability. We have, so far, deployed the capability first and left the accountability to catch up.

The strength of Schiavi's approach is that it refuses the question everyone else gets bogged down in. The interesting debate, he argues, is not whether an agent deserves personhood or some lesser flavour of legal status. That debate is a distraction, and worse, it is the very mechanism by which responsibility gets laundered. The questions worth asking are blunt and operational: who authorised this agent, what was it allowed to do, who can stop it, and who will answer when it causes harm? Notice that all four are questions about humans, not about software. They reframe the entire problem away from the metaphysics of machine agency and toward the practical engineering of human accountability. That reframing is the most useful thing anyone has contributed to this argument, because it is buildable.

What protection could actually look like

If the diagnosis is that responsibility dissolves across layers of software, platforms, and developers, then the cure cannot be a single new rule. It has to be an architecture, one that is partly legal, partly technical, and partly commercial. The encouraging news is that the outlines of that architecture are already visible in the work of regulators, payment networks, and researchers. The discouraging news is that nobody yet owns the job of assembling them, and the harms are accumulating while the pieces sit in separate boxes.

Start with the technical layer, because it is the most tractable and the furthest advanced. The payment networks have arrived at an instinct that aligns neatly with what consumers told Riskified they want. Visa has indicated that its zero-liability protection, which shields cardholders from unauthorised charges, applies to AI-initiated transactions just as it does to any other. Mastercard's approach keeps the consumer's chargeback rights intact and follows its established tokenisation rules, under which the card issuer carries fraud liability when a token is validly issued. The mechanism that makes this workable is the tokenised, verifiable mandate: a cryptographic record of what the user actually authorised the agent to do. Mastercard's “verifiable intent” layer is an attempt to build exactly this, a provable record of user authorisation that can be inspected after the fact.

This matters enormously, because it begins to reconstruct the thing the law assumes and agentic commerce destroyed: a knowable record of intent. If every agent action carries a verifiable mandate showing the scope the user granted, then the question “did the human authorise this?” becomes answerable rather than philosophical. An agent that buys within its mandate has acted legitimately, and the consumer wears the result the way they would wear any considered purchase. An agent that strays beyond its mandate has produced an unauthorised transaction, and the existing zero-liability and chargeback machinery can engage, just as it does for a stolen card. The verifiable mandate is, in effect, the technical implementation of Schiavi's authority envelope and answerability chain. It is the bridge between a legal system that needs a knowable responsible party and a technology that had threatened to abolish one.

The legal layer then has to do the work the CMA has started, and extend it. The principle that deploying an agent concentrates rather than dilutes accountability is the right foundation, but it needs to be applied to the consumer's own agent as well as the merchant's. That points toward a tiered allocation of responsibility rather than a single answerable party, which is uncomfortable for a legal tradition that likes to find one defendant, but probably unavoidable given that the harm genuinely is distributed. Within an agent's verified mandate, the consumer reasonably bears the outcome, with platforms obliged to make mandates clear, revocable, and not buried in dark patterns. Beyond the mandate, or where the agent malfunctioned, the platform that built and operated it should bear the loss, which is both what half of consumers already expect and what creates the correct incentive for platforms to make their agents safer. Where a model defect causes systematic harm at scale, the developer's product liability should be engaged. And merchants should retain their existing obligations around honest description and fair terms, regardless of whether the buyer on the other side was flesh or code.

The commercial layer, finally, is where competitive pressure can do work that regulation cannot do quickly enough. Riskified's finding that 73.9 per cent of consumers expect strong safeguards before they will trust an agent to buy is not just a warning. It is a market signal. Trust is the binding constraint on agentic commerce, which means that the platform that offers the most legible guarantees, the clearest interrupt authority, the most generous reversal window, and the most transparent mandate, will win the customers that the technology cannot otherwise persuade. The firms building these systems have a commercial reason, not merely a moral one, to build the accountability in. The danger is the classic one of a race to deploy, in which the pressure to ship the capability outruns the patience to ship the protections, and the harms land on consumers in the interval.

Who builds it, and when

So who is responsible for building meaningful consumer protection before the harms accumulate? The honest answer is that no single actor can, and that is exactly why the gap has been allowed to widen. The platform can build the verifiable mandate and the interrupt button, but it cannot rewrite consumer law. The regulator can insist that accountability concentrates rather than dissolves, but it cannot, on its own, supply the technical means of proving what a user authorised. The payment network can extend zero-liability protection, but only within the transactions it touches. The developer can make the underlying model more cautious and more legible, but cannot foresee every path its agent will take across an open web. Each holds one piece. None holds the whole.

What this demands is not a single owner but a deliberate stitching-together, a decision by each layer to design for the others rather than to push responsibility outward and hope it lands somewhere else. That is the precise opposite of responsibility laundering, and it is achievable. The verifiable mandate gives the law its knowable party. The law's insistence on concentrated accountability gives the platform its incentive. The platform's incentive aligns with the consumer trust that the market is demanding. The pieces want to fit. What is missing is the urgency to fit them before the dispute rate, already running at well over twice the human baseline, climbs higher and ordinary people start absorbing losses that the system was supposed to absorb for them.

The window for getting this right is the brief moment we are in now, while agentic commerce is large enough to matter but not yet so embedded that its failure modes have calcified into the way things are. The shoppers in Riskified's survey have already told us where the line sits. They will let the agent browse. They are not yet ready to let it buy without a safety net, and they have already decided, by a narrow majority, that the platform should catch them when it falls. The technologists have built the cryptographic tools to honour that expectation. The ethicists have supplied the vocabulary, from moral residue to the authority envelope, to keep a human within reach of every action. The regulators have laid down the founding principle that software does not get to be the one who pays.

The work that remains is the unglamorous business of assembly, of treating accountability not as someone else's department but as a design requirement that every layer owns a share of. An autonomous agent acting in your name is, at bottom, a promise: that the thing buying on your behalf is still, in every way that matters, you. A promise needs someone to keep it. The achievement of the next few years, if there is one, will be ensuring that when the agent gets it wrong, the answer to “whose fault is it?” is never allowed to be the one Schiavi warned us about. Not the comfortable, dangerous answer of nobody.

References

1. Riskified. “Riskified Study Finds Consumers Aren't Ready to Hand Over Control as AI Transforms Shopping, with Over Half Afraid of Online Fraud.” Business Wire, 27 April 2026. https://www.businesswire.com/news/home/20260427900819/en/Riskified-Study-Finds-Consumers-Arent-Ready-to-Hand-Over-Control-as-AI-Transforms-Shopping-with-Over-Half-Afraid-of-Online-Fraud

2. StockTitan. “Most shoppers use AI to browse, but 53.9% fear more online fraud.” https://www.stocktitan.net/news/RSKD/riskified-study-finds-consumers-aren-t-ready-to-hand-over-control-as-1s2lkwwo0muk.html

3. TLT LLP. “Agentic AI: CMA publishes guidance on consumer law and DMCCA risks.” https://www.tlt.com/insights-and-events/insight/agentic-ai-cma-publishes-guidance-on-consumer-law-and-dmcca-risks

4. Thomson Reuters Institute. “Agentic AI following GenAI's growth trajectory in legal, but with unique oversight challenges, new report shows.” 7 April 2026. https://www.thomsonreuters.com/en-us/posts/technology/agentic-ai-oversight-challenges/

5. Kropp, Matthew; Bedard, Julie; Wiles, Emma; Hsu, Megan; Krayer, Lisa. “Research: Why You Shouldn't Treat AI Agents Like Employees.” Harvard Business Review, 6 May 2026. https://hbr.org/2026/05/research-why-you-shouldnt-treat-ai-agents-like-employees

6. Schiavi, Adam. “Opinion: Autonomous AI Agents Have an Ethics Problem.” Undark, 5 March 2026. https://undark.org/2026/03/05/opinion-ai-agents-ethics/

7. Johns Hopkins Anesthesiology and Critical Care Medicine. “Adam Schiavi, MD, PhD, MS.” https://anesthesiology.hopkinsmedicine.org/faculty/adam-schiavi/

8. Venable LLP. “Agentic AI Is Here, Legal, Compliance, and Governance Risks You Need to Know.” February 2026. https://www.venable.com/insights/publications/2026/02/agentic-ai-is-here-legal-compliance-and-governance

9. TechPolicy.Press. “The EU AI Act is Not Ready for Agents.” https://www.techpolicy.press/the-eu-ai-act-is-not-ready-for-agents/

10. OpenAI. “Computer-Using Agent.” https://openai.com/index/computer-using-agent/

11. Eco. “What Is Mastercard Agent Pay? AI Agent Commerce Protocol in 2026.” https://eco.com/support/en/articles/15192001-what-is-mastercard-agent-pay-ai-agent-commerce-protocol-in-2026

12. ALM Corp. “Visa AI-Initiated Payments: What Fintech Must Know in 2026.” https://almcorp.com/blog/visa-ai-initiated-payments-agentic-commerce-fintech/

13. CNBC. “Payment giants are preparing for a world where AI agents book flights and shop for you.” 29 December 2025. https://www.cnbc.com/2025/12/29/ai-agentic-shopping-price-discounts-cheap-sales-commerce-visa-mastercard-chatbots.html

14. TrustSphere. “When the Agent Gets It Wrong: Liability, Consent and Recourse in AI-Initiated Commerce.” https://www.trustsphere.ai/post/when-the-agent-gets-it-wrong-liability-consent-and-recourse-in-ai-initiated-commerce

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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 dread that arrives in an email. Not the dramatic, cinematic kind, but the bureaucratic sort, the message with a case number in the subject line and a portal link in the body, the message that informs you that an allegation has been raised against you and that you are required to respond. For a growing number of university students around the world, that email is the opening move in a process they did not start, cannot easily understand, and may struggle to survive with their academic record intact. The allegation is that they cheated. The accuser is a piece of software. And the evidence, frequently, is nothing more than a number generated by a model that its own manufacturer admits can be wrong.

We have built an apparatus of suspicion and pointed it at the people we are supposed to be educating. The apparatus does not deliberate. It does not weigh context, intent, or the particular cadence of a sentence written by someone who learned English as a third language. It produces a percentage. And in institutions stretched thin and frightened of being left behind by generative artificial intelligence, that percentage has quietly hardened into something resembling a verdict. The question that follows is not technical. It is moral, procedural, and increasingly legal. If a machine is the first-instance judge of a student's honesty, and that machine is demonstrably biased against particular kinds of writers, what does an institution owe the people it accuses? And is a detector's output ever, on its own, enough to justify punishing anyone at all?

The number that became a verdict

Begin with the scale of the thing, because scale is what turns an individual injustice into a systemic one. In October 2025, the Australian Broadcasting Corporation reported on internal documents from Australian Catholic University showing that the institution had logged close to six thousand academic misconduct referrals in a single year. Roughly nine in ten of those referrals concerned suspected misuse of artificial intelligence. The university operated across nine campuses, and across all of them the same pattern repeated itself: a student submitted work, a detection tool returned a score, and a process began.

What the documents revealed was not merely the volume of accusations but the thinness of the evidence underpinning many of them. According to the reporting, around a quarter of all referrals were dismissed once they were actually investigated. The university acknowledged that any case resting solely on a Turnitin AI report was dismissed immediately, an admission that, read carefully, is extraordinary. It means the institution understood that the tool's output, by itself, proved nothing. And yet thousands of students had been put through the machinery of accusation on precisely that basis.

The human texture of those cases is where the abstraction collapses into something harder to look at. One student, referred to in the reporting as Madeleine, was accused of using AI to write an assignment in the final year of her nursing degree, while she was on placement caring for patients. She was cleared, but only after roughly six months during which her results were withheld. She believes the delay cost her a graduate role. A paramedic student described being told that eighty-four per cent of his essay had been flagged as machine-written, despite having laboured over every line of it. Students told the ABC that the process was opaque, rushed, and weighted against them, that they were given little time to respond, and that they were asked to produce search histories and handwritten notes to prove a negative. The burden, in other words, had been inverted. It was not for the university to prove that a student had cheated. It was for the student to prove that they had not.

This inversion is the single most important fact in the entire story, and we will return to it, because it is the precise point at which a tool becomes an injustice. Australian Catholic University, to its credit, eventually stopped using the Turnitin AI indicator in March 2025. But internal documents indicated it had been aware of the tool's unreliability for more than a year before pulling it. The damage to the students processed in the interim was already done.

A model that is wrong in a predictable direction

It would be one thing if AI detectors were simply unreliable in a random, scattershot way, flagging the innocent and the guilty in roughly equal and indiscriminate measure. Random error is bad, but it is at least democratic. The deeper problem is that these tools are wrong in a direction. They fail more often, and more confidently, against specific groups of people. And the people they fail are, with a bleak predictability, those least equipped to fight back.

The foundational evidence here comes from a study led by researchers at Stanford University, published in 2023 and titled, with admirable bluntness, “GPT detectors are biased against non-native English writers.” The team, whose co-authors included the computer scientist Weixin Liang, ran a set of essays written by non-native English speakers through seven widely used GPT detectors. The detectors flagged those essays as AI-generated 61.3 per cent of the time. Nearly ninety-eight per cent of the essays were flagged by at least one detector. On around a fifth of the papers, every single detector agreed, unanimously, that a human-written essay had been produced by a machine. Every one of those essays had, in fact, been written by a person.

The mechanism behind this bias is not mysterious, and understanding it matters because it tells us the problem cannot be engineered away with a software update. Many detectors lean on a statistical property called perplexity, a measure of how surprising or predictable a sequence of words is. Text that uses common words in common arrangements scores as low-perplexity, and low-perplexity text looks, to the model, like machine output. The trouble is that writing in a second or third language tends naturally toward exactly this profile. A non-native writer often reaches for the reliable word rather than the idiomatic one, favours regular syntax over flourish, and produces prose that is more formal, more concise, and more evenly structured. These are the marks of someone writing carefully in a language not their own. They are also, by unhappy coincidence, the statistical fingerprints the detector has learned to associate with a chatbot.

The Markup, the investigative technology newsroom, examined the consequences of this in reporting by the journalist Tara García Mathewson, also published in 2023. The piece documented how Turnitin's tool was markedly more likely to flag the writing of international students, and it laid out the stakes with clarity. There are close to a million international students in the United States alone. For many of them, an academic misconduct finding is not merely an academic matter. It can imperil a visa, and with it an entire future built around the right to remain and study. The newsroom quoted Liang's assessment directly: “The design of many GPT detectors inherently discriminates against non-native authors.” The reporting also captured the helplessness of those caught in the net. One New York University senior, Heewon Yang, put it plainly: “If it's the AI picking up on our language patterns and automatically deciding, I don't know how I can prevent that.”

That sentence deserves to be sat with. I don't know how I can prevent that. It is the voice of someone who has understood that the accusation against them is not really about anything they did. It is about how they write, which is to say about who they are, and there is no behaviour they can adopt to make themselves safe.

The student who paid to prove a negative

In law, there is a reason the burden of proof rests with the accuser. Proving that something did not happen is, in the general case, close to impossible. You cannot produce evidence of an absence. This is why the inversion documented at Australian Catholic University is so corrosive, and why a case from Garden City, New York, has come to feel like a landmark.

In early 2026, Newsday reported on a student named Orion Newby, a history student at Adelphi University. The episode that upended his first year began when a professor ran one of his papers, written for a World Civilizations course, through Turnitin's AI detection tool. The tool returned a result indicating the paper was one hundred per cent AI-generated. On the strength of that single number, the professor awarded a zero and a misconduct finding followed.

Newby said he had not used AI to generate the essay. The grammatical help he had received came from a human tutor through Bridges to Adelphi, a university programme that supports students with neurodevelopmental differences. Two other AI detectors, run on the same paper, indicated it had been written by a human. In other words, the evidence was not even internally consistent. One tool said machine, two said human, and the institution proceeded on the word of the one that condemned him.

What Newby's family did next is what most families cannot do. They went to court, and they reportedly spent more than one hundred thousand dollars doing it. The matter came before Justice Randy Sue Marber in the State Supreme Court in Nassau County, and on 28 January 2026 she ruled against the university, ordering Adelphi to reverse its sanctions and expunge the finding from Newby's record. The student's attorney, Mark Lesko, described the decision as groundbreaking and urged higher education to look hard at what it had been doing. Newby's own account of the moment he was first accused is the kind of thing that lingers: “I felt shocked. I felt like that was it. I felt like my life was over.”

The Newby case is a victory, but it is a victory that should frighten us rather than reassure us, because of what it took to win. It took six figures. It took a family with the resources, the resolve, and the time to mount a sustained legal campaign against an institution. It took a sympathetic judge. The overwhelming majority of students accused on the basis of a detector score have none of these things. They have a portal link, a deadline, and a number they cannot argue with. For every Orion Newby whose name is cleared in a courtroom, there are, by the logic of the Australian numbers, a great many others who simply absorbed the finding, accepted the lesser penalty to make the nightmare stop, or quietly carried a mark on their record that they did not earn. Justice that is only available to those who can spend a hundred thousand dollars is not a system of justice. It is a lottery with an entry fee.

A climate of fear, measured

If you want to understand what the constant possibility of false accusation does to a population, you do not have to rely on anecdote. It has been measured. In February 2026, Times Higher Education reported on survey work conducted by YouGov and commissioned by the student support company Studiosity, drawing on responses from 2,373 students in the United Kingdom. The numbers describe a generation studying under a low, persistent hum of anxiety.

Seventy-one per cent of respondents said they used AI tools for assignments or study, a figure up from sixty-four per cent the previous year. But the striking finding was not adoption. It was dread. Three-quarters of the students who used AI reported stress about being wrongly flagged for plagiarism by detection tools. Sixty per cent experienced stress while using the tools at all. Fifty-two per cent of everyone surveyed cited being accused of cheating when they had done nothing wrong as a source of stress. And the burden was not evenly distributed: international students were twice as likely to report experiencing a lot of stress, the very students the Stanford research had already identified as most likely to be falsely flagged. The fear, in other words, is not irrational. It is a rational response to a measurable risk that falls hardest on those already most exposed.

There is something quietly devastating in that distribution. The detection regime was sold, implicitly, as a way of protecting the integrity of honest work. What the survey describes is the opposite: honest students, the ones with nothing to hide, living under a suspicion they cannot dispel, and the heaviest share of that suspicion landing on the students who are furthest from home and have the most to lose. Universities UK chief executive Vivienne Stern was among those whose organisation engaged with the findings, and the report's own recommendations were unambiguous, urging university leaders to reconsider detection tools that produce false positives and to establish pathways to protect students from wrongful accusations. The recommendation is sensible. That it needed to be made at all is the indictment.

The algorithm starts writing the student

Here is where the story curves into something genuinely strange, a development that would be funny if it were not a quiet catastrophe for the whole purpose of education. Students have begun to write badly on purpose.

In April 2026, The Washington Post published an opinion piece arguing, in its headline, that AI detectors are hurting honest students and that schools should ban them. The phenomenon it described had already been surfacing elsewhere. The technology writer Mike Masnick, drawing on an account from the writing instructor Dadland Maye published in the Chronicle of Higher Education, documented students deliberately degrading their own prose to avoid the detector's gaze. They introduced typos. They roughened their grammar. They stripped out the sophisticated vocabulary and the clean structure that a detector might read as too polished to be human. They spent hours rewriting work that was already good, not to make it better, but to make it look worse, because looking worse was the only way to look safe.

The logic, once you trace it, folds back on itself in a way that should be deeply alarming to anyone who cares about teaching. Maye described a student who began using generative AI for the first time not to cheat but to defend herself, running her own honest writing through AI tools to see how it would register, because she had heard that certain stylistic features, the em dash among them, were rumoured to trigger detectors. A tool deployed to stop students using AI had become the specific reason a student started using it. The detector did not catch a cheat. It manufactured one, out of someone who had been entirely honest, by making honesty feel unsafe.

Strip away the technology for a moment and look at what is actually happening here. The entire project of education is to help a person write more clearly, think more precisely, and express themselves with greater command of their language. We assess this growth because we want to encourage it. And now we have built an assessment apparatus whose statistical preferences run in exactly the opposite direction, that reads clarity as suspicious and precision as evidence of guilt, and that is therefore teaching students, through the crude instrument of fear, to write less clearly and think less precisely on the page. The algorithm's limitations have begun to shape the work. The tail is wagging the dog, and the dog is the intellectual development of a generation.

Who is judging, exactly?

To call an AI detector a judge is, strictly, a category error, and the error is illuminating. A judge, in any system worthy of the name, does several things a detector cannot. A judge gives reasons. A judge can be questioned. A judge weighs the specific circumstances of the case before them, the context, the history, the plausibility of competing accounts. A judge operates within a framework of appeal, so that error at one level can be corrected at another. And a judge, crucially, is accountable: a bad decision can be traced to a decision-maker who can be asked to justify it.

A detector does none of this. It returns a number. It cannot tell you why a particular sentence raised its score, in any sense a human being could interrogate or rebut. It has no concept of the student in front of it, of the fact that this is a non-native speaker writing carefully, or a neurodivergent student who works with a tutor, or simply a person whose natural prose happens to be tidy. It cannot hear an explanation. It produces the same output whether the truth is innocence or guilt, because it is not, in any meaningful sense, assessing truth at all. It is assessing resemblance, the statistical likeness between one body of text and another, and resemblance is not evidence of authorship any more than sharing a surname is evidence of a crime.

Turnitin itself, the company whose tool sits at the centre of so many of these cases, cautions that its AI indicator may not always be accurate and should not be used as the sole basis for action. This is the manufacturer telling the buyer not to do the exact thing the buyer keeps doing. The detection score is meant to be a flag, a prompt for a human being to look more closely, the beginning of an inquiry rather than the end of one. What the cases at Australian Catholic University and Adelphi reveal is an institutional failure to honour that distinction. The flag became the finding. The prompt became the verdict. The thing that was supposed to start a conversation ended it.

This collapse is not, at root, a failure of the technology. It is a failure of process, and the difference matters enormously, because it locates the responsibility squarely with the institutions rather than with the tools. A detector cannot expel anyone. A detector cannot withhold a degree, derail a graduate job offer, or place a permanent mark on a record. Only an institution can do those things, and only an institution can decide to do them on evidence it has been explicitly warned is insufficient. When a university treats a detector's output as dispositive, it is not being overruled by a machine. It is choosing to outsource its judgement to one, and then disowning the consequences. The algorithm makes a convenient defendant precisely because it cannot be held to account. The institution behind it can.

What is actually owed

So what, concretely, do institutions owe the students they accuse? The answer is not exotic. It is mostly a matter of importing into the disciplinary process the elementary protections that any fair system of adjudication already takes for granted, and that these processes have somehow been allowed to discard.

The first obligation is the most fundamental, and it is the one inverted at Australian Catholic University. The burden of proof must rest with the accuser. It is for the institution to demonstrate misconduct, not for the student to perform the near-impossible feat of proving they did not cheat. A demand that a student produce browser histories, draft documents, and handwritten notes to establish their innocence is not a fair process. It is a presumption of guilt dressed in the language of due diligence, and it should be recognised as such.

The second obligation follows directly from everything the research has established. A detector score cannot, on its own, constitute sufficient evidence for any disciplinary action whatsoever. This is not a cautious or hedged position. It is the position the tools' own makers articulate, the position the Stanford findings compel, and the position a New York court effectively enforced when it overturned a finding built on a single score while two other detectors disagreed. A number that is wrong sixty-one per cent of the time against a known group of writers, and that the manufacturer warns against using alone, is not evidence on which a person's academic future can justly be staked. If a detector flag is to play any role at all, it can only be as the trigger for a genuine human inquiry, one that gathers independent evidence, hears the student's account in full, and reaches a conclusion a person is willing to put their name to and defend.

The third obligation concerns transparency and appeal. A student accused must be told what they are accused of, on what basis, and by what process the matter will be decided. They must have a real and timely opportunity to respond, before sanctions bite rather than months afterward while their results sit frozen and their job offers evaporate. And there must be a route of appeal that does not require a six-figure legal budget to walk. The lesson of the Newby case is not that the courts work. It is that they should never have had to, that the protections he eventually secured in a courtroom should have been available to him, and to every student, inside the institution from the start.

The fourth obligation is to confront the bias directly rather than treating it as an unfortunate externality. An institution that knows, as the evidence now makes it impossible not to know, that its detection regime systematically misfires against international students, non-native English speakers, and neurodivergent students, and that deploys that regime anyway without correction, is not a neutral party caught out by a flawed vendor. It has chosen to run a process it understands to discriminate. The students most likely to be falsely accused are frequently those least able to absorb the consequences, the ones for whom a misconduct finding can mean a lost visa, a severed future, a return home in disgrace for something they did not do. To know this and to proceed regardless is a decision with a moral weight that no procurement contract can offload onto a software company.

The cost, and who pays it

Every system of accusation produces error. The relevant question is never whether mistakes will happen, because they always will, but who is made to bear them. The deepest failure of the current arrangement is that it has loaded its errors onto the people least able to carry them and least responsible for the system's design.

Consider the distribution one more time, because it is the whole argument in miniature. The detector is most likely to be wrong about non-native speakers and international students. Those same students are, by the survey evidence, the most stressed by the threat of false accusation. They are also, frequently, the ones facing the gravest consequences if a finding sticks, because their right to study and to stay can hang on a clean record. So the technology's errors flow, with a kind of grim efficiency, precisely toward the people who can least afford them. This is not a system that happens to be unfair at the margins. It is a system whose unfairness is concentrated and predictable, aimed with statistical precision at the already vulnerable.

And the cost is not only borne by the individuals falsely accused, real and severe as that cost is. It is borne by the educational relationship itself. A university works on trust, on the basic assumption that the institution and the student are engaged in a shared project rather than locked in an adversarial contest. Every false accusation withdraws a little from that account. The student who learns to write worse to stay safe, the honest student running her own essays through a chatbot to see if she will be flagged, the international student who has concluded there is nothing she can do to make her own prose look human enough: each of them has been taught a lesson the curriculum never intended, which is that the institution does not trust them and cannot be trusted in return. That is a debt that compounds, and it is not clear how it is ever repaid.

The defenders of detection will say that the alternative is a free-for-all, that without these tools academic integrity collapses under a flood of machine-written work. The concern is real, and nothing here suggests that AI misuse is imaginary or that universities should simply surrender. But the choice was never between flawed detectors and anarchy. The choice is between treating a detector's output as the beginning of a fair inquiry and treating it as the end of one. The former is difficult, demanding human time, judgement, and the willingness to be wrong and correct it. The latter is cheap, fast, and unjust. We have, in too many places, chosen the cheap and unjust thing and called it integrity.

The presumption of innocence is not a technicality that fairness can afford to suspend when the caseload climbs and the tools get convenient. It is the load-bearing wall of any process that claims the authority to judge. When an institution lets a machine that cannot explain itself, cannot be questioned, and is known to be biased stand in for that judgement, it has not modernised its disciplinary process. It has abandoned the principle that gave the process its legitimacy in the first place. The students caught in the gap between the tool's confidence and its competence are not collateral damage in an otherwise sound system. They are the evidence that the system was unsound from the moment a number was permitted to mean guilt.

A finding by an AI detector is not, and cannot be, sufficient basis for disciplinary action. Not because the technology will one day improve, though it may, but because the question of whether a human being cheated is not the kind of question a resemblance score can answer, and the cost of pretending otherwise is paid, every term, by the students least able to pay it. Until institutions accept that, the email with the case number in the subject line will keep arriving, and the people opening it will keep discovering that in the eyes of the machine they have already been found guilty, and that proving a machine wrong is a thing most of them will never be given the means to do.

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References and Sources

1. Australian Broadcasting Corporation reporting on Australian Catholic University misconduct referrals, as summarised in “University wrongly accuses thousands of students of AI cheating: major scandal at ACU,” The Australian Business Journal, 10 October 2025. https://theabj.com.au/2025/10/10/acu-ai-cheating-scandal-false-accusations/

2. Liang, W., et al., “GPT detectors are biased against non-native English writers,” Stanford University, arXiv preprint, 2023. https://arxiv.org/abs/2304.02819

3. Stanford Institute for Human-Centered Artificial Intelligence, “AI-Detectors Biased Against Non-Native English Writers.” https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers

4. Mathewson, Tara García, “AI Detection Tools Falsely Accuse International Students of Cheating,” The Markup, 14 August 2023. https://themarkup.org/machine-learning/2023/08/14/ai-detection-tools-falsely-accuse-international-students-of-cheating

5. CBS New York, “Adelphi student Orion Newby sues over AI plagiarism accusation and wins. Why it's being called a 'groundbreaking' case,” 9 February 2026. https://www.cbsnews.com/newyork/news/orion-newby-adelphi-university-ai-plagiarism-accusations/

6. Inside Higher Ed, “Adelphi Student Wins AI Plagiarism Lawsuit,” 11 February 2026. https://www.insidehighered.com/news/quick-takes/2026/02/11/adelphi-student-wins-ai-plagiarism-lawsuit

7. Plagiarism Today, “Adelphi Student Wins AI Plagiarism Lawsuit,” 11 February 2026. https://www.plagiarismtoday.com/2026/02/11/adelphi-student-wins-ai-plagiarism-lawsuit/

8. EdScoop, “Student accused of AI plagiarism wins lawsuit against Adelphi University,” 10 February 2026. https://edscoop.com/adelphi-university-student-ai-lawsuit/

9. Williams, Tom, “Fear of being flagged by AI detectors drives stress among students,” Times Higher Education, 23 February 2026. https://www.timeshighereducation.com/news/fear-being-flagged-ai-detectors-drives-stress-among-students

10. Inside Higher Ed, “Fear of Being Flagged by AI Detectors Drives Student Stress,” 25 February 2026. https://www.insidehighered.com/news/faculty-issues/learning-assessment/2026/02/25/fear-being-flagged-ai-detectors-drives-student

11. The Washington Post, “Opinion | AI detectors are hurting honest students. Schools should ban them,” 13 April 2026. https://www.washingtonpost.com/opinions/2026/04/13/ai-detectors-students/

12. Masnick, Mike, “We're Training Students To Write Worse To Prove They're Not Robots, And It's Pushing Them To Use More AI,” Techdirt, 6 March 2026. https://www.techdirt.com/2026/03/06/were-training-students-to-write-worse-to-prove-theyre-not-robots-and-its-pushing-them-to-use-more-ai/

13. Inside Higher Ed, “Students Embrace AI but Fear False Accusations,” 31 March 2026. https://www.insidehighered.com/news/student-success/academic-life/2026/03/31/students-embrace-ai-fear-false-accusations

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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

Listen to the free weekly SmarterArticles Podcast

Discuss...

In Bengaluru, a software career was never just a job. It was a contract written across generations. A family in a small town in Maharashtra or Telangana would sell farmland, mortgage a house, or empty a lifetime of savings to put a child through an engineering degree, on the understanding that the degree was a bridge. Cross it, and you reached the other side: a salaried position at a multinational, a flat with reliable electricity, a marriage proposal that mentioned the company's name like a credential, an ageing parent's medical bills quietly absorbed. The promise was specific and it was widely believed, because for two decades it largely held. An engineering degree in Bengaluru, Hyderabad, or Chennai was the most reliable path that India's emerging middle class had ever been offered.

That contract is now being torn up by the same technology those cities helped build.

In January 2026, the publication Rest of World reported on a wave of suicides, layoffs, and automation-driven despair moving through India's IT sector. The reporting documented something that the productivity dashboards of Silicon Valley do not capture: that when an algorithm absorbs the code-writing, testing, and maintenance work that defined a profession, it does not simply free up labour for higher-value tasks. It can dismantle an entire architecture of expectation, one that entire neighbourhoods, marriage markets, and family survival strategies were built around. The crisis is concentrated, communal, and intergenerational. And it is unfolding in some of the cities least represented in the rooms where the future of artificial intelligence is actually decided.

This is a story about who counts.

The Contract That Held for Twenty Years

To understand the scale of what is being undone, you have to understand how much was built. India's technology industry is not a sector in the ordinary sense. It is closer to an economic organ. According to the industry body Nasscom, the technology sector reached roughly 282 billion dollars in revenue in the 2025 financial year and was on track to approach the 300 billion dollar mark in the year following. It employs in the region of 5.8 million people directly, and many millions more indirectly through the dense web of housing, transport, catering, retail, and services that grows up around any concentration of salaried workers. The United States accounts for the majority of India's IT outsourcing revenue, which means that for years the prosperity of a young engineer in Pune was tethered to procurement decisions made in boardrooms in New Jersey and California.

The human meaning of those numbers is easy to lose. For the generation that came of age after India's economic liberalisation, the IT services boom offered something close to a guaranteed escape route. The work was often unglamorous: maintaining legacy systems, writing and testing routine code, staffing the support functions that kept Western enterprises running through the night. The phrase that recurs in the industry is the round-the-clock cycle, the 24/7 model in which Indian engineers handled the maintenance and overflow that clients in other time zones did not want to do themselves. Krishnakumar Natarajan, a former chairperson of Nasscom who co-founded the services firm Mindtree, has described how the economics of outsourcing depended on this always-on availability. It was demanding, but it was stable, and stability was the entire point.

The trouble is that routine, rules-bound, well-documented work is precisely what large language models are best at absorbing. The very characteristics that made Indian IT services scalable and exportable, their standardisation and their predictability, are the characteristics that make them automatable. The sector built its success on doing enormous volumes of structured technical work reliably. AI systems are now extremely good at exactly that.

The Year the Promise Broke

The numbers from late 2025 onward read like a structural rupture rather than a cyclical dip. In the autumn of 2025, Tata Consultancy Services, widely regarded as India's largest private-sector employer, cut close to 20,000 jobs in what was described as its biggest layoff round ever, framed by the company as part of an AI-driven overhaul and a response to a skills mismatch in its workforce. The figure ending the September 2025 quarter showed the company's headcount falling below 600,000 for the first time in recent memory, with nearly 20,000 employees leaving in a single three-month period.

The cuts did not go uncontested. The Nascent Information Technology Employees Senate, a union known as NITES, accused TCS of illegal termination and unlawful layoffs, alleging forced resignations, denial of statutory dues, and coercive practices. Its president, Harpreet Singh Saluja, claimed that the true number of departures was thousands higher than the company publicly admitted, suggesting deliberate underreporting. In November 2025, the Pune Labour Commissioner summoned TCS over the complaints, and the union wrote to the Maharashtra Chief Minister alleging the illegal layoff of around 2,500 mid-to-senior employees in Pune alone. Whatever the precise figures, the dispute revealed how little legal cushioning India's IT workforce actually has when the contract breaks.

This was not an isolated Indian event. It was the local edge of a global contraction. Across the technology industry worldwide, the first quarter of 2026 alone saw well over 73,000 jobs cut, according to tallies drawn from layoff trackers, with some counts running closer to 80,000 by early in the second quarter. The defining feature of this wave, distinguishing it from previous rounds of tech belt-tightening, was attribution. Where in 2025 employers had cited AI as a factor in a small minority of layoff announcements, by early 2026 close to half of the quarter's cuts were being attributed to reduced need for human workers because of AI and workflow automation. The companies were no longer being coy about it. The machines were named in the redundancy notices.

For India, the exposure is structural and disproportionate. Aditya Vashistha, who works on the Global AI Initiative at Cornell University, has argued that India's outsourcing sector is more vulnerable to AI disruption than many Western labour markets precisely because so much of its value rested on the kind of standardised technical work that automation handles most readily. The country that became the backbone of global software services did so by specialising in the tasks that are now first in line to be absorbed. The startup ecosystem offered no shelter either. Through 2025, thousands of additional jobs disappeared across Indian startups, including layoffs at AI-focused ventures, which carried its own bitter irony: people building the automation losing their livelihoods to the automation.

The Bottom Rung Disappears First

The cruelty of this transition is that it lands hardest on the youngest. The work that artificial intelligence absorbs most readily is the entry-level work, the routine code-writing, the unit testing, the bug-fixing and minor upgrades, that historically formed the first rung of the career ladder. That is the rung where a fresh graduate learns the craft, builds a track record, and earns the experience that protects more senior workers. When automation removes the bottom rung, it does not merely cut current jobs. It severs the pipeline by which the next generation was supposed to enter the profession at all.

The data on this is stark, and it is not confined to India. Research from economists at Stanford University, drawing on payroll records covering tens of millions of workers, found a roughly 13 percent relative decline in employment among early-career workers in occupations most exposed to AI, even as employment for older and more experienced workers in the same fields held steady or grew. The pattern is consistent across software engineering and other knowledge work, and the explanation is intuitive: the book learning that a recent graduate brings is precisely the kind of generalised, codified knowledge that large language models reproduce most easily, whereas the tacit judgement of an experienced worker is harder to automate.

In India, this dynamic collides with a demographic and educational reality that magnifies the harm. The country produces well over a million engineering graduates a year, and even before the current AI wave, the proportion who could secure a job in their field was a fraction of the total, with one widely cited figure suggesting only around a tenth of a recent graduating cohort was likely to find suitable employment. Entry-level salaries, by the assessments of career analysts, had barely moved in real terms across a decade and a half, even as the cost of the education that produced those graduates, and the cost of the housing and food they needed to take up the work, rose steeply. The bargain was already fraying. AI is now pulling the loose thread.

A Life Plan, Collapsed

Statistics describe the shape of a crisis. They do not convey its texture. For that, consider a single case that the publication American Bazaar documented in April 2026, describing it as a stark reminder that AI-driven job losses carry profound human costs that do not appear in productivity statistics.

The account concerned Banu Chandra Reddy, a 32-year-old man from Telangana who had built a software career in the United States, and his wife, Bibi Shaziya Siraj, who had worked at IBM. Reddy lost his American job to AI-driven restructuring. He returned to Bengaluru and spent close to a year searching, without success, for work in a market that had absorbed thousands of returnees and freshers all competing for a shrinking pool of roles. Their interfaith marriage, the reporting noted, had already strained ties with family on both sides, depriving the couple of the financial and emotional buffer that, in Indian society, often makes the difference between a setback and a catastrophe. The story, as American Bazaar told it, ended in tragedy, with their deaths framed as the consequence of prolonged unemployment compounded by isolation.

What that case makes visible is the load-bearing role a tech salary plays in an Indian family system. In much of the West, a job loss is, however painful, frequently an individual or household event, cushioned to varying degrees by unemployment insurance, savings, and a social safety net. In the communities that fed India's IT boom, a software job is a node in a web of obligation. It pays the school fees of a younger sibling. It services the loan that funded the degree. It underwrites a parent's diabetes medication. It is the reason a marriage was arranged. When that node fails, the failure propagates outward through the entire web, and it does so in a cultural context where professional failure carries a heavy stigma. There is no anonymity in it. The neighbours know. The extended family knows. The matchmaker knows.

The Stigma That Makes a Setback a Catastrophe

That stigma is not incidental to the crisis. It is one of its engines. The Bridge Chronicle, a publication based in Pune at the heart of Maharashtra's IT belt, reported in June 2025 on rising suicides in India's IT sector, tracing them to a combination of work stress, job insecurity, and a social weight that falls especially hard in communities where a technology career carries the expectations of an entire family across generations. When a young person from a modest background becomes the first software engineer in their family, they do not simply hold a job. They hold a thesis: that the sacrifices made to put them there were justified, that the family's bet on education paid off, that mobility is real. The collapse of the job can feel like the collapse of the thesis.

The mental health picture that emerges from the reporting is alarming, and that word is not chosen lightly. Jayanta Mukhopadhyay, a senior professor of computer science and engineering at the Indian Institute of Technology Kharagpur, told Rest of World that tech workers face a huge uncertainty about their jobs because of AI, and described the situation as very alarming, noting that job insecurity had worsened markedly since the pandemic. Sanjeev Jain, a professor of psychiatry at the National Institute of Mental Health and Neurosciences in Bengaluru, observed that suicide is increasingly reaching into a professional class whose jobs have become precarious, a class that the old models of risk did not expect to be vulnerable.

The conditions surrounding the work compound the danger. Rest of World's analysis of local news reporting identified 227 reported cases of suicide among Indian tech workers between 2017 and 2025. Surveys cited in the reporting suggested that a large majority of Indian tech workers experience burnout and that a substantial share routinely work well beyond 70 hours a week. This is an industry whose most prominent leaders have publicly championed extreme hours. The Infosys co-founder Narayana Murthy called for a 70-hour working week. SN Subrahmanyan, chairperson of the engineering conglomerate Larsen and Toubro, pushed the idea of a 90-hour week. Bhavish Aggarwal, the founder of Ola, advocated a 70-hour week and characterised the idea of work-life balance as a Western cultural import. The result is a working culture that prizes endurance, layered on top of a job market that has suddenly stopped guaranteeing the reward.

Bino Paul, a professor of human resources at the Tata Institute of Social Sciences, has described the pandemic as a point of inflection that dissolved the boundary between work and home. When that boundary disappears and is then combined with the fear that the work itself may vanish, the psychological terrain becomes treacherous. Union organisers describe workers who feel trapped, unable to speak out for fear of being blacklisted across an industry that, as the Uni Global Union general secretary Christy Hoffman has noted, offers comparatively weak legal protections and where outspoken employees risk being shut out permanently.

When the Pillar Was Also a Marriage Plan

The collapse of the IT promise does not fall evenly across a household, and it is reshaping social arrangements that extend well beyond the workplace. In the communities that fed the boom, a software job was woven directly into the marriage market. A position at a recognised multinational was a desirable credential in arranged matches, a signal of stability and earning power that families weighed as carefully as caste, education, and horoscope. Reporting on the sector's restructuring has noted how the crisis is unsettling not only careers but the gender roles and matrimonial expectations that were built on top of them, as the assumed reliability of a tech salary, long treated as a near-certainty, becomes a question mark.

For women in the sector, the squeeze can be sharper still. India's IT industry was, for many women, one of the more accessible routes into formal, well-paid professional work, a sphere where merit and qualification could, at least relatively, outweigh the constraints they faced elsewhere in the labour market. As that route narrows, the loss is not only of income but of one of the few escalators of independence available to a generation of women whose families had finally been persuaded that a daughter's engineering degree was worth the investment. The retreat of those jobs risks pushing hard-won gains backward, returning to households a question they had thought settled about whether such an education pays off.

The case of the Bengaluru couple captures how these threads tangle together. Here was a marriage that had already cost both partners the support of their families, sustained instead by two incomes in a sector everyone had assumed was secure. When AI restructuring removed one of those incomes and a saturated market refused to replace it, the couple lost not only money but the very thing the careers were supposed to guarantee: a basis for independence from the family networks they had stepped away from. The job was never just a job. It was the foundation a whole life had been built to stand on, including the parts of that life that had nothing to do with writing code.

The People Building the Machine

There is a deeper layer of irony here, and it concerns who actually builds artificial intelligence. The popular image of AI is of brilliant systems that learn on their own. The reality is that they learn from oceans of human-labelled data, and a great deal of that labelling is done in exactly the kind of communities now being displaced. India is one of the central hubs of the global data annotation industry, the painstaking and largely invisible work of tagging images, transcribing audio, moderating content, and rating model outputs that makes machine learning possible. From a market worth a few hundred million dollars at the start of the decade, India's share of the global annotation business is projected to reach several billion dollars by 2030, with estimates suggesting that around a million data annotators may be active in the country by 2028.

These ghost workers, as researchers have come to call them, are often paid little, work without protections, and remain absent from the story the industry tells about itself. Among the most marginalised are Adivasi women, drawn from tribal communities that have long been excluded from formal employment, who now label the data that trains systems they will never be consulted about. The structure is stark. The labour that teaches the machine is sourced from the global majority. The decisions about how the machine is governed are made elsewhere. And when the machine is ready to replace human work, it is frequently the labour of the global majority that it replaces first. Data workers across the global South, in India, Kenya, the Philippines, and elsewhere, have for years performed the human effort behind nominally automated systems, sometimes for only a couple of dollars an hour and frequently in psychologically gruelling conditions involving the review of disturbing content. They are the part of the supply chain that the marketing never mentions, and they sit at the precise intersection of the two harms this article describes: undervalued while building the technology, and exposed when it matures.

This is the pattern that the Western conversation about AI has been slow to see. The communities most acutely harmed by AI displacement are, in significant part, the same communities that supplied the human effort to build it.

Whose Displacement Counts

Here is the question that the Indian crisis forces, and that the brief at the heart of this article asks directly: whose displacement counts in the global reckoning with AI?

The dominant Western framing of AI and labour tends to imagine displacement in particular bodies and particular places. It pictures a warehouse worker in the American Midwest, a lorry driver anticipating autonomous vehicles, a mid-career professional in Europe whose white-collar role is being hollowed out. These are real and serious harms. But they share a geography, and that geography is also where the policy gets written. The displaced worker in Ohio and the policymaker drafting the AI strategy in Washington or Brussels inhabit the same political space. Their concerns can travel relatively short distances to reach the rooms where decisions are made.

The displaced engineer in Bengaluru has no such proximity. The research on AI governance is blunt about the imbalance. Studies of the international AI governance landscape have found that the overwhelming majority of AI policy and governance activity is concentrated in Europe and North America, with one analysis putting the European and North American share at around 58 percent against roughly 1.4 percent for the entire African continent. Work presented at the academic conference on Fairness, Accountability, and Transparency has documented how grassroots organisations, indigenous communities, speakers of low-resource languages, workers in the AI data supply chain, and young people who will inherit these decisions are routinely marginalised in the global dialogues that set the agenda. The Brookings Institution and other bodies have catalogued how the coloniality of power persists in AI governance, with the priorities of richer countries shaping the ethics debate far more than those of the societies bearing much of its cost.

The consequence is not merely symbolic. Representation determines what gets measured, and measurement determines what gets addressed. When the harms of AI are framed primarily as a problem of Western workers, the policy instruments designed to respond, the retraining programmes, the disclosure requirements, the social protections, are calibrated to Western labour markets. The Keep Call Centers in America Act, debated in the United States in 2025, sought to protect American customer service jobs from both offshoring and automation. There is no remotely comparable political momentum to protect the Indian worker whose job is being automated, in part because the rooms where such momentum would be generated do not contain many people who can speak to that worker's reality.

This is what it means that the communities most acutely harmed are also among the least represented. It is not only that they suffer. It is that their suffering does not register in the instruments built to track and mitigate the technology's effects. A productivity statistic that records a 30 percent efficiency gain at a multinational does not record the engineer in Hyderabad who can no longer pay his sister's tuition. The human cost is real, but it falls outside the frame, and a cost outside the frame is, for policy purposes, a cost that did not happen.

The Weight a Career Was Made to Carry

It would be a mistake to treat the suicides as a separate tragedy, a mental health footnote to an economic story. They are the most extreme expression of the same structural fact: that in India, a tech career was loaded with more meaning, and more weight, than any single job should reasonably be asked to bear.

You can see the same load operating at the top of the educational pyramid, in the institutions that feed the industry. A 2026 investigation by Al Jazeera documented around 160 student suicide deaths recorded across the Indian Institutes of Technology over two decades, with 69 of them in the most recent five years. These are the country's most elite engineering schools, the apex of the very aspiration that defines the sector, and the pressure inside them is lethal. In 2024, by the figures the investigation cited, 38 percent of IIT graduates went unplaced, a remarkable number given that admission to these institutions is treated across India as the ultimate validation of a young person's worth. More than a million school leavers sit the entrance examination each year for a few tens of thousands of seats. The funnel is brutal at every stage, and the reporting found that students from Scheduled Castes, Scheduled Tribes, and Other Backward Classes were significantly overrepresented among the suicides, the same communities for whom the engineering degree was supposed to be the great equaliser.

What connects the IIT student and the laid-off TCS engineer and the returnee couple in Bengaluru is a single cultural fact. In these communities, a technology career was never sold as one option among many. It was sold as the option, the load-bearing pillar of an entire mobility strategy that families pursued at enormous personal cost across decades. When AI restructures that career, it does not remove a pillar from a building with many pillars. It removes the pillar that the whole structure was balanced upon.

What a Fairer Reckoning Would Require

None of this is an argument that the technology should not exist, or that automation can be wished away. The data annotation projections, the productivity gains, the sheer momentum of the systems make clear that the transformation is underway and will not reverse. The question is not whether AI reshapes work but whether the reshaping is governed by anyone who can see the full cost of it.

A fairer reckoning would begin by widening the frame of measurement. The human costs that do not appear in productivity statistics, the family obligations broken, the medical bills unpaid, the mental health crises, are invisible largely because no one with power has chosen to count them. They are countable. India keeps records. Unions like NITES are documenting layoffs that companies prefer to underreport. Researchers at NIMHANS and at institutions like IIT Kharagpur and the Tata Institute of Social Sciences are studying the patterns. The information exists; what is missing is its incorporation into the instruments of global AI governance.

A fairer reckoning would also require changing who is in the room. The structural underrepresentation of the global majority in AI policy is not a regrettable accident to be acknowledged and then ignored. It is the mechanism by which the harms of AI become invisible. As the governance researchers argue, inclusion is only the first step toward a redistribution of agenda-setting and decision-making power. Until the engineer in Bengaluru, the union organiser in Pune, the data annotator in a tribal district, and the psychiatrist in a Bengaluru hospital have a route into the conversations where AI's labour effects are weighed, those conversations will continue to mistake a partial view for the whole.

And it would require a kind of honesty about reciprocity. The communities now bearing the sharpest edge of AI displacement are not bystanders to the technology. They wrote its code, staffed its support lines, and labelled its training data. They were the backbone of the global software economy and a substantial part of the human scaffolding on which modern AI was built. There is a particular injustice in a system that draws so heavily on a community's labour while excluding that community from any say in how the resulting technology is deployed against it.

The contract that held in Bengaluru, Hyderabad, and Chennai for twenty years was, in the end, a promise made by a global economy to the people who agreed to do its most exportable work. That promise is being rewritten by machines those same people helped create, in rooms those same people are rarely allowed to enter. Whose displacement counts is not an abstract question of ethics. It is a question about whose pain shows up in the data, whose voice shapes the policy, and whose generational bet on the future is allowed to fail in silence. So far, the answer has been written by the people least likely to pay the price. The communities of India's tech cities are asking, with growing urgency, for that to change while there is still something left to save.

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References and Sources

1. Rest of World, “India's tech workers in crisis amid suicides, layoffs, and AI,” January 2026. https://restofworld.org/2026/india-tech-workers-crisis-suicide/
2. American Bazaar, “Indian couple's American dream turns sour, highlighting AI-driven job loss,” 1 April 2026. https://americanbazaaronline.com/2026/04/01/indian-couples-american-dream-turns-sour-highlighting-ai-driven-job-loss-478071/
3. The Bridge Chronicle (Pune), reporting on rising suicides and stress in India's IT sector, June 2025. https://www.thebridgechronicle.com/
4. Latestly, “Tech Layoffs 2026: Over 73,000 Jobs Cut As Big Tech Firms Accelerate AI Pivot and Workforce Reduction,” 2026. https://www.latestly.com/technology/tech-layoffs-2026-over-73000-jobs-cut-as-big-tech-firms-accelerate-ai-pivot-and-workforce-reduction-7399032.html
5. Tom's Hardware, “Tech industry lays off nearly 80,000 employees in the first quarter of 2026, almost 50% of affected positions cut due to AI,” 2026. https://www.tomshardware.com/tech-industry/tech-industry-lays-off-nearly-80-000-employees-in-the-first-quarter-of-2026-almost-50-percent-of-affected-positions-cut-due-to-ai
6. Futurism, “Wave of Suicides Hits as India's Economy Is Ravaged by AI,” January 2026. https://futurism.com/artificial-intelligence/suicides-india-economy-ai
7. Outlook Business, “TCS Cut Nearly 20,000 Jobs in Q2, Union Alleges Forced Resignations,” 2025. https://www.outlookbusiness.com/corporate/tcs-cut-nearly-20000-jobs-in-q2-union-alleges-forced-resignations
8. Outlook Business, “Pune Labour Commissioner Summons TCS over Complaints of 'Illegal' Layoffs,” 2025. https://www.outlookbusiness.com/corporate/pune-labour-commissioner-summons-tcs-over-complaints-of-illegal-layoffs
9. Nascent Information Technology Employees Senate (NITES). https://nites.co.in/
10. Nasscom, “Technology Sector in India: Strategic Review 2025.” https://nasscom.in/knowledge-center/publications/technology-sector-india-strategic-review-2025
11. DD News, “Indian tech industry to hit $300 billion revenue in FY26, workforce to reach 5.8 million in FY25.” https://ddnews.gov.in/en/indian-tech-industry-to-hit-300-billion-revenue-in-fy26-workforce-to-reach-5-8-million-in-fy25/
12. India Brand Equity Foundation (IBEF), “Indian Information Technology Sector and Its Growth.” https://www.ibef.org/industry/information-technology-india
13. Al Jazeera, “'The other side' of IITs: Student suicides haunt India's top tech schools,” 11 March 2026. https://www.aljazeera.com/news/2026/3/11/the-other-side-of-iits-student-suicides-haunt-indias-top-tech-schools
14. Carrasco, Mira, et al., “At the Tensions of South and North: Critical Roles of Global South Stakeholders in AI Governance,” ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. https://facctconference.org/static/pdfs_2022/facct22-3533200.pdf
15. Brookings Institution, “AI in the Global South: Opportunities and challenges towards more inclusive governance.” https://www.brookings.edu/articles/ai-in-the-global-south-opportunities-and-challenges-towards-more-inclusive-governance/
16. Ideas for India, “India's data workers: The human labour making machines learn.” https://www.ideasforindia.in/topics/productivity-innovation/india-s-data-workers-the-human-labour-making-machines-learn
17. Centre for Development Policy and Practice, “Ghosts in the Machine: Adivasi Women and the Hidden Labours of AI.” https://www.cdpp.co.in/articles/ghosts-in-the-machine-adivasi-women-and-the-hidden-labours-of-ai
18. Brynjolfsson, Erik, Bharat Chandar, and Ruyu Chen, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” Stanford Digital Economy Lab, 2025. https://digitaleconomy.stanford.edu/
19. WION, “Anxiety from AI driving Indian IT workers towards suicide: Report,” 2026. https://www.wionews.com/trending/artificial-intelligence-job-threat-layoffs-india-it-workers-suicide-1769766292342

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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

Listen to the free weekly SmarterArticles Podcast

Discuss...

Consider the moment you do not see. It is an ordinary Tuesday evening, and you open a grocery app to order the week's essentials. Nappies, milk, bread, the brand of coffee you always buy, the painkillers a household runs through unnoticed. You add the items, glance at the total, tap to confirm. The total seems about right. You have nothing to compare it against, because there is nothing to compare it against. The price you see is the only price you will ever see. You do not know, and have no way of finding out, that the shopper in the next postcode, ordering the identical basket from the identical store at the identical minute, has been quoted a figure several pounds lower. You do not know that a piece of software has looked at what it can infer about you, your past behaviour, your location, the predictability of your needs, the apparent absence of alternatives, and concluded that you, specifically, will pay a little more. No negotiation, no notice. There was only a number, presented as if it were the number, and you accepted it because the entire architecture of shopping has trained you to assume that a price is a fact about a product rather than a judgement about you.

This is not a thought experiment. In December 2025, a joint investigation by Consumer Reports, the Groundwork Collaborative and More Perfect Union pulled back the curtain on exactly this practice, running inside Instacart, the largest grocery delivery platform in the United States. The investigation found that roughly three-quarters of products checked were being offered to different customers at different prices, for the same item, from the same store, at the same time. The variations ran from a few pennies to more than two dollars per item. Extrapolated across a typical household's annual spend, the swing came to around 1,200 dollars a year. The engine behind it was an artificial intelligence pricing platform called Eversight, which Instacart had acquired in 2022, and which the company marketed to retailers as a way to lift sales and squeeze out incremental margin. Within days of the story being published, Instacart announced that, effective immediately, it was ending all item price tests on its platform. The lab, as one campaigner put it, had been closed only because someone finally switched on the lights.

The episode is not an aberration. It is a preview. The capacity to set a different price for every customer, calibrated to the maximum each will tolerate, has been the holy grail of commerce for as long as commerce has existed, and for almost all of that history it has been impossible at scale. What has changed is that the impossibility has dissolved. Cheap data, behavioural tracking and machine learning have made it not merely feasible but routine to estimate, in real time, how much a particular human being is likely to pay, and to charge them precisely that. The question this raises is not technical. The technology works. It is what it means to live in a market where the price is no longer a shared fact about the world but a private message addressed to you alone, written in a language you cannot read, by a system that knows things about you that you have not agreed to disclose and may not even know yourself.

The Oldest Dream in Retail

Economists have a name for what Instacart's software was reaching towards, and it is not new. They call it first-degree price discrimination, or perfect price discrimination, and it describes the seller's fantasy of charging each buyer exactly their maximum willingness to pay. The market trader who sizes up a customer's shoes and accent before naming a figure is practising a crude, intuitive version of it. The theory has been in textbooks for a century. What it has lacked, until very recently, is a mechanism. To charge everyone their personal maximum, a seller must somehow know everyone's personal maximum, and individual human beings have historically been quite good at concealing it. The posted price, the same number on the same shelf for everyone, emerged in part because sellers could not do better. It was a technological limit dressed up as a social norm.

The first sign that the limit might be lifting came in September 2000, when shoppers on Amazon noticed something strange. A man buying a DVD found that when he deleted the cookies from his browser, the price dropped. Amazon, it turned out, had been running an experiment in which the price of certain titles varied according to what the company could infer about the shopper from their browsing and purchase history. Loyal customers, the kind least likely to wander off, were in some cases being shown higher prices than newcomers. The discovery produced a wave of public fury, and Amazon retreated almost at once, insisting the variations had been a random test rather than deliberate profiling, and refunding the difference. The episode entered the folklore of e-commerce. The lesson the industry drew was not that personalised pricing was wrong. The lesson was that it must never again be visible.

For the better part of two decades the dream advanced quietly, in forms ordinary shoppers had been trained to accept. Airlines pioneered the art, charging fares that lurched with demand, with the day of the week, with how close the departure loomed, and, as many travellers suspected, with how many times a route had been searched from a given device. Ride-hailing apps normalised the idea that a price could surge in real time, rising when it rained or when a concert let out, framed as a neutral response to supply and demand rather than a calculation about the rider's desperation. Streaming services and online retailers learned to offer a discount to one customer that never materialised for another. Each of these was a step away from the posted price and towards the personalised one, and each was small enough, and dressed in enough economic respectability, that it provoked little sustained alarm. The frog, to borrow the old image, was being warmed by degrees.

What the Machine Sees

The leap from dynamic pricing, where the figure moves with the market, to surveillance pricing, where the figure moves with the customer, is a leap in kind and not merely degree. A surge fare is at least the same for everyone standing on the same wet pavement at the same moment. Surveillance pricing is the surge fare turned inward, aimed not at the conditions but at the person. The raw material it runs on is the vast, largely invisible economy of behavioural data that has accreted around every digital interaction we have.

In January 2025, the United States Federal Trade Commission published the initial findings of a study into precisely this market. Acting under its Section 6(b) authority, which lets it compel companies to hand over internal documents, the agency had sent orders to a clutch of intermediaries that sit, mostly unseen, between retailers and shoppers: Mastercard, Accenture, the pricing-software firm PROS, the personalisation company Bloomreach, the pricing optimiser Revionics and the consultancy McKinsey and Company. What the staff found, even in a preliminary cut, was a thriving and shadowy infrastructure for setting individualised prices. The intermediaries drew on a remarkable breadth of signals, both data volunteered by consumers and data inferred about them from first and third party sources. The behaviours that could be tracked and fed into a price ranged from the movements of a mouse across a webpage to the specific products a shopper abandoned, unpurchased, in an online basket. One example in the documents was a cosmetics company targeting promotions by a customer's skin type and skin tone. The intermediaries the FTC examined were, between them, working with at least 250 clients selling everything from groceries to clothing. The market for knowing what you will pay was already industrial in scale.

The Instacart investigation gave that abstraction a face. When Consumer Reports and its partners examined the patent filings that Instacart and Eversight had lodged from 2017 onward, they found the ambition spelled out in the dry language of intellectual property. The patents referenced setting prices using previous purchase history, buying behaviour, and characteristics such as age, gender, household size and household income. One metric flagged was whether a shopper was new to a brand or returning to it. The investigation also documented what it called phantom discounts, in which different customers were shown different inflated original prices for the same item, manufacturing the impression of a bargain where none existed. A box of premium saltine crackers, in one example, was presented with an original price of 5.93 dollars, 5.99 dollars or 6.69 dollars depending on the shopper, before a sale price of 3.99 dollars that was in fact the same for everyone. The discount was theatre. The variation was real.

Instacart denied that it currently used personal or demographic data to set prices, maintaining that customers were randomly assigned to pricing cohorts by product category and location rather than profiled as individuals. But the denial, even taken at face value, missed the point the industry's own analysts kept returning to. Phil Lempert, a grocery analyst who runs the site SupermarketGuru, put it plainly: once the technology is in place, even if a company is not profiling shoppers today, the capacity to start is a button-press away. The machinery of individualised pricing does not need to be aimed at you to be pointed in your direction. Its mere existence changes the relationship between buyer and seller, removing the floor of the posted price and replacing it with an open question about how much, in your case, the seller thinks it can get.

The Survey Nobody in Retail Wanted to Read

Defenders of personalised pricing tend to argue that consumers do not really mind, or that they accept it as the price of convenience, or that the discounts it enables for the price-sensitive outweigh the premiums it imposes on the rest. The data does not support this. As part of its investigation, Consumer Reports ran a nationally representative survey of 2,240 American adults in September 2025. Among those who had used Instacart in the previous year, 72 per cent did not want the company to charge different users different prices for any reason. Not for some reasons. Not unless the reasons were fair. For any reason at all. The aversion was close to universal, and it cut against the entire logic of the surveillance-pricing business.

This exposes the gap between what the practice does and what it claims to do. The economic defence of first-degree price discrimination holds that it can, in theory, expand the market, letting sellers profitably reach price-sensitive buyers who would otherwise be excluded while extracting more from those who can afford it. On a whiteboard this looks almost progressive, a kind of automated means-testing. In the world it works the other way around. The signals a machine-learning system finds most useful for estimating willingness to pay are precisely the signals that track vulnerability. A shopper in a food desert, with no rival supermarket within reach, has fewer alternatives, and the algorithm can learn to read that constraint and charge for it. A household ordering nappies and prescription items has predictable, inelastic needs, and inelasticity is exactly what a pricing model is built to exploit. The customer with limited mobility, least able to drive between shops, is least able to escape and therefore most worth charging more. The system does not optimise for fairness. It optimises for revenue, and the people with the least room to push back are the ones from whom there is the most to extract.

Lina Khan, who chaired the FTC from 2021 to 2025 and now teaches at Columbia Law School, framed the stakes in a sentence that has stuck to the debate. We are moving, she said, from a transparent market with public prices to an opaque world where we are alone against secret algorithms. The phrasing identifies the precise thing that is lost. It is not simply that some people pay more; markets have always produced unequal outcomes. It is that the mechanism becomes unknowable. In a market of posted prices, a high price is public information that competitors can undercut and shoppers can refuse. In a market of personalised prices, it is a private transaction between you and a model, invisible to everyone else, including the regulators, journalists and rival retailers who might otherwise discipline it. The discipline of the market depends on the price being a shared fact. Surveillance pricing dissolves the shared fact, and with it the discipline.

The Fairness Nobody Consented To

Set aside the question of whether you pay more or less. Ask instead the question the practice never lets you ask: what, exactly, is being used to decide. This is where personalised pricing stops being a story about money and becomes one about discrimination in the older and graver sense of the word.

A price built from inferred willingness to pay is a price built from a model of who you are, and the characteristics that feed such a model are not chosen for their moral acceptability. They are chosen because they predict. If income predicts willingness to pay, the model uses income, and if it can infer income from your postcode, your device, your browsing and the brands you buy, then it is charging you according to your wealth without ever asking your salary. If household size predicts inelastic demand, the model uses household size, which means a larger family, often a poorer one, may face systematically higher prices on essentials it cannot do without. The Instacart patents named age, gender, household size and income directly. Some are characteristics anti-discrimination law has spent a century learning to treat as illegitimate grounds for differential treatment. None is one an ordinary shopper would knowingly hand over as a reason to be charged more for milk.

The trouble is that the shopper never gets to decide. The whole design of surveillance pricing is that the grounds of differentiation are hidden. You cannot object to being priced on your gender if you do not know your gender is in the model. You cannot contest a markup based on the inference that you are housebound if you never learn the inference was made. The ordinary apparatus of fairness, the ability to know the reason for a decision and to challenge it, simply does not engage, because the reason is buried in a proprietary system and the decision arrives disguised as a fact of nature. A price, to the shopper, looks like something the world has handed down. It does not look like an accusation, a profile or a bet. But that is what, increasingly, it is.

This is the argument that Veena Dubal, professor of law at the University of California, Irvine, has developed across both the consumer and the labour sides of the same phenomenon. Writing in Governing magazine in April 2026, Dubal set out why AI should not be setting prices or wages, and why states needed to push back. The techniques now spreading through consumer pricing were pioneered on workers, in the ride-hailing and food-delivery platforms, where her earlier research documented what she named algorithmic wage discrimination: the practice of paying different workers different amounts for substantially the same work, with the wage personalised in real time according to dozens of behavioural signals invisible to the worker. The platform companies, she has observed, have been at the cutting edge of experimenting with ways to control people without it being obvious, and when those experiments work, they leach into other industries. Surveillance pricing is the consumer-facing twin of surveillance pay. Both rest on the same engine of behavioural inference. Both produce outcomes the affected person cannot predict, cannot explain and cannot contest.

The Revenue Nobody Mentions

Dubal's Governing piece adds a dimension that rarely surfaces in the consumer-protection framing: the state's own balance sheet. When algorithmic systems reclassify what would once have been straightforward taxable wages into a shifting patchwork of bonuses and incentives, calibrated worker by worker, the effect is not only to make individual incomes unpredictable. It is to erode the tax base on which public insurance depends. Dubal cites Connecticut, estimating that the state stands to lose around 60 million dollars a year in unemployment-insurance contributions as wages are restructured into forms that escape the payroll levy. The same opacity that lets a company extract a few extra pennies from a vulnerable shopper lets it shrink its obligations to the commons, and because the mechanism is granular and individualised, it is fiendishly hard for any tax authority to see, let alone challenge.

This is the quiet scandal beneath the loud one. The visible harm of surveillance pricing is the markup on your groceries. The invisible harm is what the same techniques do to the institutions that depend on legible, shared economic facts: tax systems, labour statistics, consumer-price indices, the apparatus by which a society measures and governs its own economy. An economy of personalised prices and wages becomes progressively harder to measure, because measurement assumes that prices and wages are public things. The spread of these tools from the gig platforms into healthcare, retail, logistics and customer service threatens not only individual fairness but the informational foundations of governance itself. An audit of 500 AI vendors her research points to found at least 20 at high risk of enabling surveillance-based pay, most already wired directly into employers' payroll and HR systems. The leach is well underway.

A Legal Framework That Is Mostly a Vacuum

If this sounds like the sort of thing the law would surely prohibit, the uncomfortable answer is that, for the most part, it does not. In April 2026, the legal-analysis service JD Supra carried a clear-eyed assessment, written by attorneys at the firm Holland and Knight. Their conclusion was blunt: there is no comprehensive federal statutory framework in the United States governing surveillance pricing. What exists instead is improvisation, the stretching of older authorities to cover a practice their drafters never imagined. Enforcement, where it happens at all, leans on Section 5 of the FTC Act, which prohibits unfair or deceptive practices, and on the agency's rule against unfair or deceptive fees. The authors noted that pricing-enforcement risk was no longer theoretical but an active priority. Yet active priority is not clear law. Section 5 was written to police deception in the abstract, not to answer whether a retailer may infer your income from your shopping habits and charge you accordingly, and the absence of a statute on that question leaves enormous room for argument, delay and retreat.

The patchwork that fills the federal vacuum is uneven and young, but filling fast. New York has moved fastest on disclosure, with a law requiring that when a price has been personalised using a consumer's data, the shopper must be told, in some versions with a stark warning that an algorithm set the price. The disclosure approach restores a measure of the visibility that surveillance pricing destroys, but it does not prohibit the practice, and a warning that everyone learns to ignore is a thin protection. The decisive shift in 2026 has been towards bans. By the spring, state lawmakers had introduced more than forty bills across at least twenty-four states to regulate personalised algorithmic pricing, outpacing the whole of 2025.

Maryland was the first to enact one, its governor, Wes Moore, signing the Protection From Predatory Pricing Act on 28 April 2026, effective 1 October. The statute, the first of its kind in the food sector, prohibits large food retailers and third-party delivery services from using personal data to set higher prices for particular consumers or classes. It carries penalties of up to 10,000 dollars per violation, rising to 25,000 dollars for repeat offenders, enforced by the state attorney general. It also carves out the practices the industry was most anxious to protect: loyalty schemes, voluntary membership discounts, genuine promotional offers and price differences attributable to objective costs such as shipping or tax.

Connecticut followed within weeks. Its bill, SB 4, passed the legislature on 4 May 2026, 141 to 6 in the House and 31 to 4 in the Senate, and Governor Ned Lamont signed it on 27 May as Public Act 26-64. Where Maryland's law reaches only the food sector, Connecticut's prohibits surveillance pricing, defined as setting a customised price for a consumer or group of consumers on the basis of personal data gathered through any technology, across retail generally and binding third-party delivery services. The act goes further still, establishing a state data-broker registry, a one-request mechanism for wiping a consumer's records across the whole industry, and a ban on the sale of precise geolocation data. Its provisions take effect on 1 October 2026, the same day as Maryland's.

The same season showed how easily such laws fail to arrive. Colorado's legislature passed the most ambitious measure of all, HB26-1210, which would have banned individualised price and wage setting based on surveillance data across every industry, on 8 May 2026. Governor Jared Polis vetoed it. His objection was not to the principle but to the reach: the bill, he wrote, took too broad an approach, capturing any technology that incidentally influences a price or wage amount rather than targeting unethical conduct, and would punish lower prices as readily as higher ones. The veto is an instructive counterpoint to the bills that passed: the friction these tools provoke reaches all the way to the governor's desk. California, meanwhile, kept moving: its surveillance-pricing bill cleared a key vote on 15 May 2026 and is still in progress.

Governing magazine's April 2026 reporting, and Dubal's argument within it, treated these state moves as the leading edge of a necessary legislative response rather than a settled solution. The pattern is familiar from the early history of data protection and of antitrust in the digital economy. The technology arrives at national, indeed global, scale. The law responds at the level of individual states, slowly, unevenly and with vigorous lobbying against every clause. The result, for now, is a map in which the legality of charging you a personalised price for a tin of beans depends substantially on which state line you happen to be standing behind, and in most of the country the answer remains that the practice is lawful, undisclosed and unmeasured.

What Europe Did Differently, and Did Not

Across the Atlantic, the legal starting point is different, though it is a mistake to imagine it amounts to a clean prohibition. The European Union confronted personalised pricing earlier and built a disclosure obligation into its consumer law through the Omnibus Directive of 2019, which took effect across member states in 2022. Under it, a trader must inform a consumer whenever the price they are being shown has been personalised on the basis of automated decision-making and profiling. The obligation is narrower than it sounds. It requires the seller to say that the price is personalised; it does not forbid the personalisation, and it does not require the seller to reveal what data went into it or how. A consumer told that a price has been tailored to them learns that they are being profiled without learning anything about the profile.

The heavier weapon in the European arsenal is data-protection law, and here the picture is genuinely contested. Article 22 of the General Data Protection Regulation gives individuals a right not to be subject to decisions based solely on automated processing that produce legal or similarly significant effects on them. Whether a personalised price counts as such a decision, and whether the regulation can therefore be read to require explicit consent before a shopper is priced by algorithm, is a question on which European lawyers have argued for years without settling. Some scholars contend that the GDPR, read seriously, enshrines something like a right to an impersonal price, a right to be quoted the same figure as everyone else unless you have genuinely agreed otherwise. Others regard that reading as aspirational. What is not in dispute is that European anti-discrimination law forbids using certain protected characteristics, of the kind that pricing models are perfectly capable of inferring, as the basis for differential treatment. The European framework, in other words, contains stronger raw materials than the American one, but it has not yet been assembled into a coherent answer to the specific harm. The United Kingdom, having left the EU before the Omnibus Directive bound it, is under no obligation to mirror even the disclosure rule, and its Competition and Markets Authority has approached the question through its broader work on online choice architecture and the manipulative design of digital interfaces rather than through a dedicated pricing statute.

The comparison yields a sober conclusion. No major jurisdiction has yet produced a settled, comprehensive answer to the question of when, if ever, a company may charge you a price calculated from a secret model of who you are. Europe has more tools and more disclosure. America has more enforcement appetite in some states and almost nothing in most. Everywhere, the technology is ahead of the law, and everywhere the burden of that gap falls on the individual shopper, who has neither the information to know what is happening nor the standing to do much when they find out.

The Asymmetry at the Heart of It

Strip the subject to its bones and what remains is an asymmetry of knowledge so steep that it makes a mockery of the idea of a transaction between equals. The seller knows the cost of the good, the price it shows you, the price it shows others, the model that produced your figure and the data that fed the model. You know the price it shows you. That is all. You cannot see the distribution of prices around you, the inputs, or the inference. You cannot even reliably tell whether personalisation is happening at all, because a personalised price and a non-personalised one look identical: both are just numbers on a screen. The market, classically conceived, was supposed to be an information system, aggregating dispersed knowledge into a public signal that coordinated the behaviour of strangers. Surveillance pricing inverts it. It turns the price from a signal the market sends to you into a signal the seller extracts from you, and does so silently, so that you go on reading the number as though it still carried its old public meaning.

This is why disclosure remedies, useful as they are, feel inadequate to the scale of the thing. Telling a shopper that their price has been personalised restores a sliver of the lost information, but it leaves the deeper asymmetry intact. It is rather like being told 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 tailored. It is that it was tailored using a portrait of you that you did not sit for, that you cannot see and that may be wrong, unfair or built from characteristics you would never have agreed to be judged by. The ordinary person's intuition that there is something improper here is not naivety about how markets work. It is an accurate perception that a hard-won feature of how markets are supposed to work, the shared and public price, is being quietly removed, with nothing put in its place to protect them.

Switching the Lights Back On

What, then, is the ordinary person at the invisible checkout to do? Honesty requires admitting that individual self-defence is mostly futile. Clearing cookies, browsing privately, comparing prices across devices: these are the folk remedies of a simpler era of price discrimination, and against a system that fuses dozens of inferred signals they offer little. The man who deleted his cookies on Amazon in 2000 found a cheaper DVD because the discrimination then was crude. It is not crude now, and the burden of evading it cannot reasonably be placed on the shopper. A person should not have to conduct counter-surveillance against their grocer to be charged a fair price for bread.

The more honest answer is that this is a collective problem requiring collective tools, and the encouraging part of the story is that those tools are beginning, haltingly, to appear. The Instacart episode is the clearest demonstration of the mechanism that actually works, which is exposure. The company did not stop because the law compelled it. There was, in the relevant sense, no law to compel it. It stopped because an investigation made the practice visible, and visibility was intolerable to a business that depended on shoppers believing the price was the price. Lindsay Owens of the Groundwork Collaborative put the dynamic with precision when she said that once the curtain was pulled back, the company had no choice but to close the lab. Surveillance pricing is a practice that cannot survive being seen. That is its great vulnerability, and it points directly at the remedy.

The remedy has three reinforcing layers. The first is sunlight, the dogged work of investigators, researchers and regulators in dragging an invisible practice into view, because each exposure raises the reputational cost of doing it. The FTC study, the Consumer Reports investigation and the work of scholars like Dubal are instances of the same act: making the hidden price visible so it can be argued about. The second is disclosure as a legal default, the New York and European approach of requiring sellers to declare when a price has been personalised, imperfect but better than silence. The third, on which the rest depend, is substantive law of the kind Maryland and Connecticut have now enacted: rules that do not merely require disclosure but forbid the use of certain data and inferences to set the price of essentials, and give a public enforcer the teeth to make the prohibition real. Colorado's veto shows that this third layer is the hardest to lay, the one over which the fight is fiercest.

None of this will arrive quickly or cleanly, and the lobbying against every line of it will be intense, because the prize for the seller is enormous and the constituency for the shopper is diffuse. But the direction of travel is set by a simple fact that no amount of optimisation can engineer away. People do not want to be charged according to a secret estimate of how much they can be made to bear. The Consumer Reports survey found the objection close to unanimous, and it cut across every reason a company might offer. That near-universal refusal is the political bedrock on which any durable response will be built. The invisible price depends, in the end, on staying invisible. The work of the coming years, in legislatures and regulators and newsrooms alike, is to ensure that it cannot.

The next time you tap to confirm an order and the total looks about right, hold for a second the thought that you cannot verify it is right, because right has quietly stopped meaning the same thing for everyone. That second of doubt is not paranoia. It is the appropriate response of a citizen to a market that has learned to read them and has not asked permission. The price you see may be the price everyone sees. It may not. That you can no longer tell is the whole problem, and reclaiming the ability to tell is the whole of the answer.

References

1. Consumer Reports. “Instacart's AI-Enabled Pricing Experiments May Be Inflating Your Grocery Bill, CR and Groundwork Collaborative Investigation Finds.” December 2025. https://www.consumerreports.org/money/questionable-business-practices/instacart-ai-pricing-experiment-inflating-grocery-bills-a1142182490/
2. Consumer Reports. “New Report Exposes Instacart's Hidden Price Games.” Press release. December 2025. https://www.consumerreports.org/media-room/press-releases/2025/12/new-report-exposes-instacarts-hidden-price-games/
3. Consumer Reports. “Instacart Stops AI Pricing Tests.” December 2025. https://www.consumerreports.org/money/questionable-business-practices/instacart-stops-ai-pricing-experiments-a1176475852/
4. CBS News. “Instacart to end AI price tests for retailers following investigation.” 23 December 2025. https://www.cbsnews.com/news/instacart-ends-ai-price-testing-tool-eversight/
5. Grocery Dive. “Instacart ends controversial price tests.” December 2025. https://www.grocerydive.com/news/instacart-ends-controversial-price-tests/808490/
6. Veena Dubal. “AI Shouldn't Be Setting Prices or Wages. States Need to Push Back.” Governing. 20 April 2026. https://www.governing.com/workforce/ai-shouldnt-be-setting-prices-or-wages-states-need-to-push-back
7. Veena Dubal. “On Algorithmic Wage Discrimination.” Columbia Law Review 123(7), 2023. https://columbialawreview.org/content/on-algorithmic-wage-discrimination/
8. Christopher Armstrong, Benjamin Genn, Ashley Joyner Chavous and Kwamina Thomas Williford (Holland and Knight LLP). “Surveillance Pricing, AI Pricing Tools and the Push for Price Transparency.” JD Supra. 28 April 2026. https://www.jdsupra.com/legalnews/surveillance-pricing-ai-pricing-tools-8489430/
9. 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
10. 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
11. 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
12. Skadden, Arps, Slate, Meagher and Flom LLP. “Maryland Becomes the First State to Restrict Surveillance Pricing in the Food Industry.” May 2026. https://www.skadden.com/insights/publications/2026/05/maryland-becomes-the-first-state-to-restrict-surveillance-pricing
13. Greenberg Traurig LLP. “Maryland Enacts Food-Sector Personalized Pricing Law.” May 2026. https://www.gtlaw.com/en/insights/2026/5/maryland-enacts-food-sector-personalized-pricing-law
14. Future of Privacy Forum. “Third Time's the Charm: Connecticut Enacts Annual Privacy Update.” May 2026. https://fpf.org/blog/third-times-the-charm-connecticut-enacts-annual-privacy-update/
15. Covington and Burling LLP. “Connecticut Enacts Omnibus Privacy Law.” Inside Privacy. May 2026. https://www.insideprivacy.com/state-privacy/connecticut-enacts-omnibus-privacy-law/
16. Colorado Newsline. “Colorado bill to ban surveillance prices, wages vetoed by Gov. Polis.” May 2026. https://coloradonewsline.com/briefs/surveillance-pricing-bill-vetoed/
17. CalMatters. “Why surveillance pricing bans are suddenly gaining traction this year (and not just in California).” May 2026. https://calmatters.org/economy/technology/2026/05/why-surveillance-pricing-bans-are-suddenly-gaining-traction-this-year-and-not-just-in-california/
18. Covington and Burling LLP. “State Lawmakers Introduce New Wave of Personalized Algorithmic Pricing Bills.” Inside Privacy. 2026. https://www.insideprivacy.com/artificial-intelligence/state-lawmakers-introduce-new-wave-of-personalized-algorithmic-pricing-bills/
19. European Parliament. “Personalised pricing.” Study, IPOL, 2022. https://www.europarl.europa.eu/RegData/etudes/STUD/2022/734008/IPOL_STU(2022)734008_EN.pdf
20. BEUC (The European Consumer Organisation). “Price personalisation.” 2023. https://www.beuc.eu/sites/default/files/publications/BEUC-X-2023-097_Price_personalisation.pdf
21. Frederik Zuiderveen Borgesius and others. “The GDPR enshrines the right to the impersonal price.” Computer Law and Security Review, 2022. https://www.sciencedirect.com/science/article/pii/S0267364922000085
22. Washington Center for Equitable Growth. “How artificial intelligence uncouples hard work from fair wages through 'surveillance pay' practices, and how to fix it.” 2026. https://equitablegrowth.org/how-artificial-intelligence-uncouples-hard-work-from-fair-wages-through-surveillance-pay-practices-and-how-to-fix-it/
23. OECD. “Personalised Pricing in the Digital Era.” DAF/COMP(2018)13. 2018. https://one.oecd.org/document/DAF/COMP/WD(2018)150/en/pdf

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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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