When you have no money, sometimes it just means you have some money but it is not enough. My Homeowners Association is still relentlessly pursuing me while I am trying to survive. The HOA terror has me in a constant state of stress. Stress activates my illness. And that illness landed me in the hospital at 3 am on a Saturday because I wasn’t sure if I was going to die or if I was just going to go ahead and walk in front of the next car in the dark on highway 301*.

After several rounds of OTC medicine in the late afternoon, evening, and night, I awoke in excruciating pain. I booked the first available appointment at TGH Urgent Care, but it wasn’t until 08:30. I knew that would be my cheapest option. But I didn’t make it to TGH because waiting five more hours was a near guarantee that highway 301 would be the better option. So, I went to South Bay Hospital in hot tears. I hate hospitals. But more than that, I hate ER bills.

It took less than 10 minutes for me to get into a room. I was in so much pain I forgot how cold it was in hospitals, so I thought my shivering was the beginning of convulsions. And then the hospital forgot about me for almost an hour while I sat erect and motionless wondering if my ancestors were taking me to the promised land. The nurse apologized…said someone was coming in via ambulance. But that person still wasn’t there. And there was all of maybe 2 patients in the ER.

You know who I did see? That lady who comes to collect insurance info and payment. I had neither. I continued to sit there…like a mannequin waiting to come alive. They finally gave me meds, two warmed blankets the thickness of a cotton round, and eventually sent me on my way before dawn. Good, because I was dying to get into my own warm bed.

Two hours later, I was questioning my life. The pain did not subside. I knew something was wrong in the hospital because I still felt a distant-like pain. When this scenario would happen in the past, I would be pain-free but not un-sick. The pain increased in intensity. I called TGH and asked if I could still come in. I appeared, crying hotter tears. They asked for payment upfront: $250. Ouch! I paid it, but I didn’t “have it” because it was for something else…anything else…like food and shit.

I could barely talk to the doctor through my streams of tears and inability to look at him continuously. He asked if my symptoms were typical. Yes. And I had been to TGH in the past so it wasn’t extraordinary. He asked what South Bay gave me and I could recite 3 out of 5. I got more drugs. They sent me on my way on a promise that I would go directly home. There was nothing else I wanted more to do.

By Sunday, I was better, but sensitive. By Monday I felt brand new. It goes like this every time. I got a text from South Bay saying if I paid immediately, the cost for the ER visit would only be $130. I almost had a heart attack. One-hundred and thirty dollars? Surely they meant $1300. But I didn’t have $130 to pay immediately…because I gave TGH $250, remember? Then, I got a text from TGH. It said I owed $238. Surely that wasn’t correct. I already paid them. The charge went through, I swear.

Whatever the cost, I don’t have the money. But you know what costs more than both of my visits combined? My medication. That’s $5k for 15 pills. With the discount card, it comes to $3500.

*(Someone did do that—one woman and one man—in front of my neighborhood. One of them was killed, or so I was told.)

The room, in the scenario its designer envisages, is small and clean. It contains a chair, a screen, a microphone, and nothing else. The person who has come to die is asked to sit. The screen flickers on. A face appears, rendered in synthetic colour, with a voice that has been trained for warmth. The face asks why the person is there. It asks about pain. It asks about the alternatives the person has considered. It asks about the family. It asks again, in a slightly different way, about the alternatives. The conversation continues for perhaps an hour. At the end the avatar pauses, and a value somewhere in its underlying network resolves into one of two outcomes: capacity granted, or capacity denied. If granted, the door to the next room unlocks. In that room sits a 3D-printed nitrogen capsule. Twenty-four hours later, if the person still wishes to proceed, the capsule will let them die.

That room does not yet exist. It is the proposal the Australian euthanasia advocate Philip Nitschke set out in January 2026, when he announced that he was developing artificial intelligence software to replace psychiatrists in assessing whether a person seeking assisted dying possesses the mental capacity to make the decision. Nitschke is sixty-eight, no longer a registered doctor (his medical licence was permanently suspended in 2015 by the Medical Board of Australia), and the founder of Exit International. He is also the inventor of the Sarco pod, the device used for the first time in Switzerland on 23 September 2024, when a sixty-four-year-old American woman with a severely compromised immune system died inside it in a forest in the canton of Schaffhausen. Swiss authorities arrested Florian Willet, chief executive of the affiliated organisation The Last Resort, on suspicion of inducing and aiding suicide. The serious charge of intentional homicide was withdrawn within weeks. Willet himself died by suicide in Germany in May 2025. The pod has not been used again.

Nitschke's case for the AI assessor is presented as a complaint against human inconsistency. “I've seen plenty of cases where the same patient, seeing three different psychiatrists, gets four different answers,” he told reporters in January 2026. “There is a real question about what this assessment of this nebulous quality actually is.” His proposed alternative is a conversational avatar that interviews the candidate, draws inferences about their reasoning, and arrives at a binary outcome. If the AI grants capacity, the Sarco unlocks after a twenty-four-hour cooling-off period. If it denies, the candidate has no further recourse within the system.

Two months later, on 26 March 2026, Noelia Castillo Ramos died by legal euthanasia at a healthcare centre in Sant Pere de Ribes, in the Province of Barcelona. She was twenty-five. She had survived a suicide attempt in October 2022 that left her paraplegic, and she had been diagnosed with obsessive-compulsive disorder and borderline personality disorder. Her euthanasia request had been approved on 18 July 2024 by the Catalonia Guarantee and Evaluation Commission. It had then been delayed for 601 days by her father's appeals, which travelled through a Barcelona court, the High Court of Justice of Catalonia, the Spanish Supreme Court, the Constitutional Court and finally the European Court of Human Rights. Every one of those bodies, at every level, found that she had the capacity to decide. Uniladtech, reporting on the case in March 2026, noted that Castillo's twenty-month legal battle had revived a debate that until recently had been hypothetical: whether, in a system where capacity is the gate through which the entire procedure passes, the gate-keeper might one day be a machine.

In the jurisdictions that permit assisted dying (Switzerland, the Netherlands, Belgium, Luxembourg, Spain, Canada, Colombia, New Zealand, parts of Australia, ten US states plus the District of Columbia), the law requires that the person making the request have decision-making capacity. The form of the requirement varies. In Spain it is set out in Organic Law 3/2021 and assessed by the responsible physician and a consulting physician, with a Guarantee and Evaluation Commission as procedural backstop. In the Netherlands and Belgium, two decades of practice have produced a clinical literature in which capacity is most often presumed and only formally tested when doubt arises. In Canada, the Medical Assistance in Dying regime requires a capacity assessment by two practitioners. The United Kingdom's most recent attempt at a statute, Kim Leadbeater's Terminally Ill Adults (End of Life) Bill, would have written capacity testing on at least five separate occasions into the procedure, including a panel review by a psychiatrist, a social worker and a senior judge. That bill ran out of parliamentary time in 2025 and did not become law.

What unites these regimes is that the moment of capacity assessment is the load-bearing column of the entire structure. Everything else, the prognosis, the suffering, the documentation, the medical opinion, the cooling-off period, depends on the prior finding that the person before the clinician understands what they are choosing and can hold the choice steady. To propose that this assessment be performed by a machine is to propose that the column itself be replaced. The question is not whether such a substitution is technically possible. The question is what standard of evidence, accountability and explainability it would have to meet, who would set that standard, and who would be liable when the system was wrong.

What Capacity Actually Is

The clinical standard for decision-making capacity is older than most AI systems by several decades. The MacArthur Competence Assessment Tool for Treatment (MacCAT-T), developed by Thomas Grisso and Paul Appelbaum at the University of Massachusetts Medical School and published in 1997, identifies four abilities a person must demonstrate: the ability to communicate a choice; the ability to understand the relevant information; the ability to appreciate the situation and its likely consequences; and the ability to reason with the information in a way that is internally coherent. The MacCAT-T is administered as a semi-structured interview, takes fifteen to twenty minutes, and is calibrated against the patient's own clinical situation rather than a generic script. Its inter-rater reliability is high. It is the closest thing the field has to a gold standard, and it is what most of the formal clinical literature on capacity assessment for assisted dying assumes.

What the MacCAT-T cannot do, and what no successor instrument has succeeded in doing, is remove the human judgement at its centre. The clinician administering the interview has to decide whether the patient's articulation of their understanding is genuinely their own; whether their appreciation of consequences extends to the morbidity of their own affect; whether their reasoning is shaped by a depression that is itself a treatable condition. The Dutch literature on assisted dying for psychiatric suffering is unsparing on this point. A 2016 study in JAMA Psychiatry by Scott Kim and colleagues at the United States National Institutes of Health, reviewing sixty-six cases of euthanasia for psychiatric reasons in the Netherlands, found that in only a minority were the capacity assessments documented in any structured form. Survey research published among Dutch psychiatrists found that sixty-five per cent believed they could determine capacity in a patient with a psychiatric disorder requesting assisted dying; twelve per cent thought they could not; twenty-three per cent had doubts.

Nitschke takes this variability as evidence that the existing assessment is incoherent and that an AI could do better by being consistent. The inference is half right. The variability is real. The conclusion that consistency is the same as correctness, however, is the mistake at the centre of his proposal. A model that returns the same answer every time can be reliably wrong. The variability between psychiatrists is, in part, a feature of a genuinely contested judgement being made under uncertainty. To collapse that variability into a deterministic algorithm is to mistake the noise of human judgement for the signal of the underlying problem. Codifying the disagreement away does not resolve it. It only conceals it inside a model.

There is then the related problem of what the AI would actually be measuring. A capacity assessment is not a quiz. It is a relational interaction in which the clinician reads the patient's affect, hesitations, repetitions and changes of mind across time. The Dutch psychiatrists writing in Frontiers in Psychiatry in 2022 describe capacity in psychiatric euthanasia cases as a temporally extended judgement: not a snapshot but a moving picture, sometimes assembled over months. An avatar that speaks to a candidate for an hour cannot perform that kind of assessment, regardless of how richly trained its conversational model. Even a system fine-tuned on transcripts of human capacity assessments would inherit the structural limits of its training distribution: it would replicate the documented patterns of those assessments rather than independently verify the underlying capacity. If a substantial portion of the training data records cases in which capacity was presumed without rigorous test, the model will learn to presume.

The Bias That Lives in the Data

Nitschke's claim that AI is “less subject to personal bias” than a human clinician is the part of the proposal that has aged worst in the seven years since the most authoritative work on AI bias in medicine was published. The position is not novel. It is the same claim that has been made for AI in criminal sentencing, hiring, child welfare and visa adjudication, and in each domain the claim has not survived contact with the data. Models do not invent their judgements from first principles. They infer them from training distributions that reflect the prejudices of the institutions whose records they were trained on. The 2018 Gender Shades study by Joy Buolamwini and Timnit Gebru documented commercial facial classification systems with error rates of up to 34.7 per cent on darker-skinned women, against 0.8 per cent on lighter-skinned men, an asymmetry that arose not from any flaw in the architectures but from the demographic skew of the data on which they had been trained.

The clinical AI literature has reproduced the pattern in fine detail. A 2025 systematic review in Oxford Open Digital Health found that of 390 clinical AI studies examined, eighty-four per cent failed to report the racial composition of their training data and thirty-one per cent failed to report sex. A 2025 study in npj Digital Medicine on racial bias in AI psychiatric diagnosis found that large language models propose differential treatments when patient race is implicitly indicated, and that descriptive language describing Black male patients diverges in ways that align with documented patterns of involuntary hospitalisation. None of these findings is exotic. They are now baseline expectations of the field.

If the AI that Nitschke proposes were trained on the records of past capacity assessments, it would inherit any structural patterns those assessments contained. Spanish psychiatric data, Dutch end-of-life records, Belgian dossiers: each carries the demographic, linguistic and cultural particularities of the system that produced it. A model trained on European data and asked to assess capacity in a candidate whose first language is not the language of the training corpus, whose cultural framing of illness or family or suffering differs from the modal record, will not be neutral. It will be biased in ways that the model itself cannot articulate. The relational competence that a human psychiatrist brings to a difficult bilingual capacity assessment, the ability to ask the question in a different register, to wait for the second answer, to read silence as a signal rather than a missing data point, is precisely the competence that the model has not been trained to perform.

The Paper on Trust

On 29 April 2026, three authors from the Ukrainian computer-science community, Serhii Zabolotnii, Viktoriia Holinko and Olha Antonenko, posted to arXiv a paper that addresses the structural question Nitschke's proposal raises without ever naming his project. The paper, “From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy”, argues that clinical AI trustworthiness cannot be inferred from accuracy benchmarks, fluency of generation, or the subjective confidence of human users. Trust, the authors write, must be engineered as a measurable property of the system, with explicit evidence trails, supervised escalation, and graduated action rights that depend on demonstrated calibration.

The paper's substantive proposal is the framework named in its title. A trustworthy clinical AI system, on this account, is built from a deterministic clinical logic core (the parts of the decision rule that can be written as code and audited line by line), a patient-specific assistant that validates the deterministic decision against the patient's individual context, a multi-tier escalation mechanism that routes uncertain cases upwards through a hierarchy of models and humans, and a human supervision layer that retains the right of final adjudication. Around these structural elements the paper specifies a set of trust metrics drawn from metrology: measurement uncertainty, calibration error, evidence trail completeness, autonomy boundary compliance, operational stability. The point is that an AI is not granted autonomy by fiat. It is granted autonomy by demonstrating, on instruments that can be inspected, that it deserves it.

The phrase the paper deploys for the governing principle is “staged autonomy”. A system begins life under tight human supervision, with its decisions advisory only. It progresses, if and only if its performance on the trust metrics warrants the progression, through stages of expanded autonomy. At each stage the evidence threshold is higher. The right to act without immediate human review is earned, not assumed. The framework is not specific to assisted dying, and the authors are careful not to claim domain-particular expertise. The framework is, however, exactly the framework against which a proposal like Nitschke's most usefully fails. A capacity-assessment AI deployed at the highest tier of autonomy, granting or denying access to an irreversible procedure on its own authority, would, on the paper's logic, need to clear an evidence threshold that no clinical AI to date has cleared, in a domain where the metrics themselves are contested.

The arXiv paper is a serious attempt to specify what would actually be required, in measurable terms, before a clinical AI is granted autonomous decision-making authority. It is also an implicit indictment of the practice that has tended to prevail in the field, in which AI tools are deployed with the language of “decision support” and then drift into operational use as decision-makers, on the back of confidence scores that have not been calibrated against any externally validated baseline. The drift is documented in domain after domain. There is no reason to think it would not occur in capacity assessment. There is every reason to think it would, because the surrounding economic and political pressures all point the same way: faster, cheaper, less litigable, more deniable.

The Two Errors

The categorical human stakes are easily stated. An AI capacity-assessor that wrongly grants approval to a person who lacks genuine capacity authorises a death that cannot be reversed. The reversal cannot be partial. There is no appeal procedure that returns the dead to their families. An AI that wrongly denies approval to a person who does have capacity denies them a legal right at the moment of maximum suffering. The denial is also categorical in its way: a person whose end-of-life decision has been refused does not, in any general sense, get to try again under different circumstances. They live the time they live, and they suffer what they suffer, with whatever options were available before the algorithmic refusal. Both errors are irreversible. The first is irreversible in the metaphysical sense. The second is irreversible in the human one.

This is the asymmetry that distinguishes assisted dying from almost every other domain in which clinical AI is being proposed. A misdiagnosis in radiology can, in most cases, be corrected by a second opinion or a subsequent test. A bad triage decision in an emergency department can be revisited as new information arrives. A wrong recommendation by a clinical decision support tool can be overridden by a clinician who notices something the system missed. The Zabolotnii, Holinko and Antonenko framework relies, throughout, on the existence of a human in the loop who can revise the system's output. Nitschke's proposal explicitly removes that human. The AI's answer is the answer. The pod, in his architecture, then enforces the answer without further review.

A defensible deployment of AI in capacity assessment, on the paper's logic, would begin as advisory only. It would generate an output. A trained clinician would review the output, would interview the candidate, would arrive at an independent finding, would compare. Only when the AI's outputs had been demonstrated to converge with skilled clinical judgement across a representative cohort, with measurable calibration and a documented evidence trail, would the question of expanded autonomy even arise. Even then, the irreversibility of the underlying procedure provides a principled reason to retain final human authority. The asymmetry of error makes the cost of one wrong call so high, and so unrecoverable, that no defensible trust metric is likely to be permissive enough to justify removing the human entirely.

The Regulatory Vacuum

The systems that have actually been built and deployed in clinical AI live within a regulatory regime that, with respect to autonomous life-ending decisions, does not yet exist. In the European Union, the AI Act entered into force on 1 August 2024, with the main applicability date for high-risk AI obligations set for August 2026. Medical devices that incorporate AI are classified as high-risk by default and required to comply with both the AI Act and the existing Medical Device Regulation. The Act mandates risk management, transparency, technical documentation, post-market monitoring, and meaningful human oversight. It does not, in its current form, contemplate the use of AI as the autonomous final adjudicator in an assisted-dying procedure. The category does not exist in the regulatory taxonomy. Whether such a system would be permitted at all, under the AI Act's prohibitions and high-risk provisions taken together, is an open question that has not been litigated because no one has yet tried.

The United States is more fragmented. The Food and Drug Administration regulates Software as a Medical Device through its Digital Health Center of Excellence, and has cleared hundreds of AI-enabled tools for clinical use. Almost all of them are deployed in a decision-support mode in which a clinician retains authority. The legal status of an autonomous AI that itself decides eligibility for medical-aid-in-dying in the states where the practice is permitted has never been adjudicated. The state statutes were written to govern the conduct of physicians, not algorithms. A model that produced an eligibility decision would not, on its face, be the kind of actor the statutes contemplate.

The United Kingdom is in the awkward position of having no current statute for assisted dying and a fragmented regulatory regime for clinical AI. The Medicines and Healthcare products Regulatory Agency has issued software-as-a-medical-device guidance and is developing the AI Airlock sandbox for testing of higher-risk AI applications. The Ada Lovelace Institute, in its May 2025 report on facial recognition governance and in subsequent publications on clinical AI, has argued that the UK lacks the statutory framework required to govern the deployment of high-risk biometric AI in any setting, let alone in life-ending decisions. There is no UK regulator with the authority, at present, to license or refuse the deployment of an AI capacity-assessor for an assisted-dying procedure if such a procedure were to be permitted by future legislation.

Switzerland, where Nitschke's pod first operated, is in a stranger position again. The country has long permitted assisted suicide under the relatively permissive provisions of Article 115 of the Penal Code, which criminalises assisting suicide only when done for selfish motives. There is no specific Swiss statute that governs the eligibility assessment for assisted dying, which is in practice carried out by clinicians within the right-to-die associations. After the September 2024 use of the Sarco pod, the Swiss minister for health, Elisabeth Baume-Schneider, said in parliament that the device did not meet the requirements of product safety law and that the use of nitrogen was not legally compliant. The prosecution then collapsed when the homicide charge against Willet was withdrawn. The pod has not been used since, but the absence of a clear regulatory determination means that no court has authoritatively decided whether a future capacity-assessment AI integrated into such a device would be permissible. The vacuum is real. It is the vacuum into which Nitschke's January 2026 announcement was made.

Who Is Accountable When the System Is Wrong

If a Spanish psychiatrist working under Organic Law 3/2021 wrongly assesses capacity, the responsibility chain runs through professional regulation, civil liability, and, in serious cases, criminal investigation. The clinician is identifiable. Their training is documented. Their professional indemnity insurer is on the hook for compensable harm. The Guarantee and Evaluation Commission is the procedural oversight body. The system has its critics, but it has actors who can be named and held to account.

The chain is not the same for an AI assessor. A model is, in any meaningful legal sense, not a person. It cannot hold a professional registration. It cannot be deposed. It cannot be struck off. The candidate liable parties are the developer who built and trained the model, the operator who deployed it, the clinician (if any) who reviewed its output, the regulator who licensed its use, and the procedural body that integrated it into the assessment workflow. The history of liability in clinical AI, such as it is, suggests that none of these is currently a satisfactory locus. Developers point to terms of service that disclaim responsibility for clinical decisions. Operators argue that they followed the manufacturer's instructions. Clinicians, where present, often defer to the algorithmic output and treat it as authoritative. Regulators license tools at the level of the device rather than the deployment.

This pattern of distributed and diluted accountability has been documented in domains as varied as algorithmic hiring, predictive policing, child-welfare screening and welfare fraud detection. The pattern arises not by accident but by design. The procurement structures of public administration favour the procurement of tools whose vendors carry the technical expertise and the legal liability disclaimers, and where the deploying institution can present the algorithmic output as merely advisory while in practice treating it as binding. The drift is consistent with the structural pressures that make a capacity-assessment AI attractive in the first place: it is cheaper than a psychiatric consultation, it is faster than a panel review, it is more deniable than a human judgement, and the responsibility for its errors can be allocated across a chain of actors none of whom carries the whole weight.

A defensible accountability regime for an AI capacity-assessor would have to invert most of those incentives. It would have to require named clinical responsibility for every deployment. It would have to mandate publication of model cards, training data composition, demographic performance, and calibration curves. It would have to provide the candidate with a meaningful right of contest before, not after, the procedure is enacted. It would have to assign liability for catastrophic error to a party that has the resources and the legal exposure to take the design choices seriously. None of these requirements is technically infeasible. None of them is currently in place.

What the Standard Would Have to Be

What standard of evidence, accountability and explainability should be required before AI is permitted to substitute for clinical human judgement in assisted-dying eligibility, and who bears responsibility when the system errs? The components of an honest answer can be sketched.

The first component is independent validation on the population to which the system would be applied. Not on a generic clinical cohort, not on the records the model was trained on, but on a representative sample of candidates with their own demographic, linguistic and diagnostic particularities. The validation has to include stratified performance reporting: by age, sex, ethnicity, diagnosis, language of assessment, socioeconomic background. The Buolamwini and Gebru paradigm applies here as elsewhere. An AI that performs well in aggregate while performing badly on identifiable subgroups is, for the purposes of an irreversible decision affecting members of those subgroups, an unsafe instrument.

The second component is calibrated and explainable confidence. The Zabolotnii, Holinko and Antonenko framework offers a vocabulary for this. The system must report not only its decision but the calibration of that decision against external evidence. It must articulate the reasoning chain in a form that a human reviewer can audit. The contemporary literature on explainable AI in clinical decision support is unsparing on the limits of post-hoc explanation: saliency maps and attention visualisations are widely accepted within the machine-learning community to be unreliable as faithful accounts of model behaviour. A capacity-assessment AI that cannot produce a contemporaneous, auditable reasoning chain that a clinician can independently verify is not a candidate for autonomous deployment.

The third component is meaningful human authority. The staged-autonomy framework is, on its own terms, a framework for graduated reduction of human oversight as the system earns the right. In the highest-stakes application, an irreversible procedure with categorically asymmetric error costs, the principled reading of the framework is that the highest stage is not reached. The human stays in the loop, with final authority, throughout the system's operational life. The AI's role is to enrich the clinical judgement, to flag inconsistencies, to surface the patterns that a tired clinician might miss. The role is not to displace the judgement.

The fourth component is real contestability. The candidate, before the decision is acted upon, must have the right to know that AI was used, what it concluded, what the underlying evidence was, and to obtain a substantive review of the decision by a different clinician or panel that is not bound by the system's output. The review has to be funded. Legal aid for capacity disputes in assisted-dying cases has, in most jurisdictions, never been adequately resourced even for human-only decisions. It would have to be restored as a precondition of any AI deployment.

The fifth component is the accountability regime described above: named clinical responsibility, mandated transparency, clear liability allocation, and an independent regulator with audit powers. The European Union's AI Act is the closest existing instrument to the kind of framework this implies, and even the AI Act does not yet contemplate the specific case. The work of writing the regime is, at the moment, work that has not been done.

Against this five-part standard, Nitschke's January 2026 proposal does not even rise to a starting position. There is no independent validation. There is no published calibration. The human authority has been explicitly removed. There is no contestability mechanism. There is no accountability regime, because there is no statute, no regulator, and no jurisdiction that has agreed to host the system. What there is, instead, is a press conference, an underlying ideology that locates the right to die in the autonomy of the individual to the exclusion of every other social good, and a 3D-printed capsule sitting in a workshop somewhere in continental Europe.

The Castillo Ramos case in Spain illuminates the alternative. Her capacity was assessed, contested, re-assessed, litigated through five levels of courts, and finally confirmed not because the system was efficient but because the system included multiple human decision-makers, each accountable to a professional regime and a public, who could be made to defend their conclusions in open court. The proceedings were slow, painful, and at moments inhumane. They were also the proceedings the law specifies, and the proceedings whose existence makes the eventual finding of capacity legible as a finding rather than as a verdict from inside a sealed box. To replace that process with a conversation between a vulnerable person and an avatar, with no appeal and no accountability and no audit trail beyond what the developer chooses to disclose, is not a refinement of the existing system. It is a different proposition. It belongs to a different jurisprudence.

The choice the next few years will pose is not a choice between human fallibility and machine reliability. It is a choice between two different kinds of fallibility, in a domain where both kinds are categorical, and where one kind comes attached to a chain of accountable persons and the other kind does not. The Zabolotnii, Holinko and Antonenko framework, by insisting that trust is something to be measured rather than asserted, offers the beginning of an answer to the question of when the substitution might be defensible. That answer, applied honestly to assisted dying, is: not yet, possibly not ever in the autonomous form, and only under a regime of staged authority and human supervision that nobody has yet built. The room described at the opening of this article, with its chair and its screen and its avatar, is not a future the law currently authorises in any jurisdiction on earth. The interesting question is whether the law will continue to refuse to authorise it once the technology is sold to states as an efficiency. The Sarco pod sits in a workshop. The avatar exists in beta. The case for the standard, against the case for the procurement, is what the next legislative cycle will decide.

References

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19. Joy Buolamwini and Timnit Gebru. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of Machine Learning Research, vol. 81, 2018. https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
20. Oxford Open Digital Health. “Gender and racial bias unveiled: clinical artificial intelligence (AI) and machine learning (ML) algorithms are fanning the flames of inequity.” https://academic.oup.com/oodh/article/doi/10.1093/oodh/oqaf027/8279897
21. npj Digital Medicine. “Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models.” https://www.nature.com/articles/s41746-025-01746-4
22. European Union. Regulation (EU) 2024/1689 (the AI Act). Entered into force 1 August 2024. https://artificialintelligenceact.eu/
23. Ada Lovelace Institute. “An eye on the future: Examining the UK's approach to facial recognition technology governance.” May 2025. https://www.adalovelaceinstitute.org/report/an-eye-on-the-future/
24. PMC. “Explainable AI in Clinical Decision Support Systems.” https://pmc.ncbi.nlm.nih.gov/articles/PMC12427955/
25. Swiss Federal Council. Article 115, Swiss Penal Code. https://www.fedlex.admin.ch/eli/cc/54/757_781_799/en

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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

In Summary: * A quiet Wednesday winds down. It was fun, and very surprising, to listen to the New York Mets win their MLB game this afternoon against the Seattle Mariners. Having followed that one professional sports event today, I don't feel so obligated to listen to the Spurs / Knicks game tonight. I can work quietly through the night prayers now and head to bed early without the excitement of an NBA Finals game. I DO hope the Spurs win, but they can manage without me tonight.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Starting Ash Wednesday, 2026, I've added this daily prayer as part of the Prayer Crusade Preceding the 2026 SSPX Episcopal Consecrations.

Health Metrics: * bw= 232.15 lbs. * bp= 138/82 (68)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 06:15 – 1 banana, seafood salad on saltine crackers, 1 barbacoa taco * 11:20 – 1 cookie * 13:00 – fried chicken, meat loaf

Activities, Chores, etc.: * 04:00 – listen to local news talk radio * 04:50 – bank accounts activity monitored. * 05:30 – read, write, pray, follow news reports from various sources, surf the socials, nap * 13:00 to 14:00 – watch old game shows and eat lunch at home with Sylvia * 14:30 – listening to Pregame Show ahead of thie afternoon's MLB Game between the Seattle Mariners and the New York Mets * 17:20 – And the Mets win, 7 to 1. * 17:30 – follow news reports from various sources

Chess: * 11:40 – moved in all pending CC games

Heavenly Father

I am purchasing the dawn And no more rain Nor Earth in Water- as a substance to remain And I, the closing witness- as expression to our pain And belittle to existence I cried forever on this day And laid new tracks at enemy Grum For betrothed to anew I knew this copy wasn’t round Nor paranoid in filth But blessing every courage- and sound of the alarm- in every calf to behold We slipped upon our death For chrysalis and church,- But mercy called and our unfathom- crossed a billfold of every heart And why the sum of carry- ever North where water was And how about We just get off to Heaven where it was In dining maids of Windsor For this call to undo favour- but our part And in this query-people I’ll have amaranth and war To see the women so upset And torque to knowledge kept us new Upon our own new prism bow To forts and Union Jack to here benew And in the silence applause Our very spirits good To tame with verse Upon this space- and queried vine For Christ ahead- and very question to this rose That if I, the only one, did sleep- away the Winter that is unknown to receive- the precious star upon this Heaven- that I saw- where you were there And time repent that I was born To treasured land as where you were A many hands to just belie- our heads together many through Your light is here to join in they- our other brethren in esteem And we will walk away in light At Ventnor Crossing- and her stars The time remains until our distance- from the forest leg and curve And with these stars we will collapse Forever change- upon our knees.

Let My Heart Follow London

Let in tiding And as in time To fall away the log Let bright spirits- sit in her And do no disarray Times to street the war In paying men to suffer- Peace upon our goal And Victory then For each path A swollen set of cinders Each in her the grey eyes With hope in wise all hold Days in nights to Japanese To bitter off because- we tasted sweet An arm for our medallions To pay and see And policy, the grind Sad but true of government And what we waited for The document in sea Lines persist, to hold and pier For us to Royal protect The days and nights of her In this rose we keep No mansion but to vector And I, as Ross became To prowess and abide Seeking all cure in all Even if at swerved The dust of holded oath And what our just besure A difficult deterrent- But trust the day Without this random arch And to our Cross Nights ahead are freedom To sit with fire- upon a deck Boards shall to and from Resistance keep Our rally in the boots and just as mad- the ground, our merchant cellar To this be our call To know and when such profit Because of you, this choice I offer And I have hanged a man To blight and hist’ry came And nights to keep our hold Let us tow upon him then And Vict’ry then The Earth shall stop all war And come as not to rise Repugnant rain in umber Collectants rest And was it war in time To see unkept What prison then asunder Tried to band the deep Beknighted field Of Hist’ry War Keeping shallow cards And seven men to Rothesay Within this cowl and Arthur Be depleted then, of deathful globe Places then afar The solemn wind will gale A fragrant, nightly war At three of deep remain To shallow all the tongue And distance as they flew Parts of morgue to flame Our luck to Grande Bretange Owing scarce to be This mind and then No thought of war Our keep was interhere Do no abelusk to rust The verdant men at three- Calling czar to London For in this noise at full Arrest our peer This bruise of peer Away to keep us water And strangling Cross The duty wept And misery unfloor But to mission blow As frighted kin- and madrigal We bed to those afield To gift our neighbour Children new At Essex Knights in favour And indecision fell The ride of shores within To mercy pale And friends may meet At calm, forbidded star And in this flow An errant voice Watch to them below The Justice war and Judge Amaiming histr’l wonder And all of face- allusion then Derided never peace But frail wind Because of her The Washerwoman rise For places taught Alight in me The Earth shall know her star Of Acadie at shore And won a London man.

I had a joke i was ready to write last night. i was lying in bed, staring at the ceiling, and for a moment it felt perfect. Now it’s gone, so i guess we’ll stick with me not having jokes, just my dry, grumpy writing with occasional sprinkles of sarcasm. I think im getting sick. And no not mentally i meant physically, my housemate was sick a few days ago, And i spent most of the time making fun of how he has been acting like the world was collapsing. I guess now its my turn, consequences. it follows us all like a shadow, no matter how we may try to run away from it or how fast we walk, we’re eventually caught up. Anyways. I fell asleep as soon as i got back from work and woke up around eight or nine in the evening as usual. Something about sleeping for a ridiculous amount of time should probably concern me more than it does. Then again, I’ve always slept more than i should.

At this point, i assume my body just sees free time and mistakes it for a medical emergency.

Sincerely, still losing arguments to my mattress. Sleep’s most loyal customer.

When the world appears to be collapsing; when the well runs dry; when only one grain of rice remains in your cupboard; when soil transforms into dust; when your scaffolds creak and crack beneath the weight of ending—

You are at the beginning.

Grieve. Then step forward.

There are greater days ahead.

I am one week away from going out of office for an unknown amount of time.

This time it is not by choice. For most people, this may be an opportunity to reset, reconnect, and slow down. For me, I have already done that once and I am unconvinced this time will be the same. This is a forced pause, undefined in length and not entirely of my choosing. It does not spark joy or inspire creativity, however I refuse to sit idly by. I will deliberately be productive and will show up even when there is nowhere to be.

I am being given time, a resource most people never have enough of. I have big plans and a large to-do list to complete. Most importantly, I want to challenge and surprise myself. I know I am more capable than I allow myself to believe, yet that inner voice lingers loudly. I suppose that is why I am starting this.

I refuse to be defined by my reality and limited by my circumstances.

Tier 2 Skulk A large, spined amphibian with translucent skin that matches its surroundings and eyes that look like stone spheres. Motives & Tactics: Live to fight another day, petrify predators, protect nest

DC: 14 | TH: 9/16 | HP: 4 | SP: 3 ATK: +4 | Bite: Melee | 2d4+6 phy Experiences: Camouflage +3

FEATURES Electroreceptors – Passive: The Axolotlisk has advantage on detecting Hidden creatures when in the same body of water. Ambush – Action: While Hidden, make an attack against a target within Close range. On a success, deal 2d6+8 physical damage. Submerge – Action: While in water, become Hidden until after the Axolotlisk’s next attack. Attacks made while Hidden from this feature have advantage. Live to Fight Another Day – Reaction: If the Axolotlisk takes Severe damage or drops below half its HP, you can mark a Stress to have it flee to the nearest body of water and take its Submerge action. Petrifying Gaze – Reaction: When the Axolotlisk takes damage from an attack within Close range, you can spend a Fear to force the attacker to make an Instinct Reaction Roll. On a failure, they begin to turn to stone, marking an HP and starting a Petrification Countdown (4). This countdown ticks down when the Axolotlisk is attacked. When it triggers, the target must make a death move. If the Axolotlisk is defeated, all petrification countdowns end.

---

This product includes materials from the Daggerheart System Reference Document 1.0, © Critical Role, LLC. under the terms of the Darrington Press Community Gaming (DPCGL) License. More information can be found at https://www.daggerheart.com. There are no previous modifications by others.

La ville, pourtant toute en collines, en vagues rocheuses, immobiles – on loge dans ses moindres plissures – se figure [à soi (disant)] telle un trou par lequel passer sans cesse. De la fenêtre, trop petite, mesquine, on peut voir une partie du trou, et deviner, à la rumeur, aux sonorités, aux lueurs, ce qui demeure caché. [Quand la porte est ouverte, il est préférable de regarder en se tenant dans le fond du couloir, où règne une odeur de bonde pas lavée, de vaisselle et d’égout] : on voit dans l’embrasure rectangulaire le carré du trou de la ville, de nuit surtout, tant la ville est nuit.

#Fenêtresurville #Didascalies

Fenêtre sur ville

八階まで吹き抜ける光は妙に白く、棚という棚が過剰に照らされていた。雑貨も食品も衣類も玩具も、分類されているようでいて実際には混ざり合い、何でも売っている巨大な小売店の中で、私は灰色の股下が長いジャージを履いていた。普段なら選ばない格好なのに、ここではそれが妙にしっくりきた。たまにしか来ない場所なのに、なぜか落ち着く。

何を買いに来たのかは分からなかった。ただ、クレーンゲームの列が放つ色とりどりの光を眺めながら、流れに身を任せて上から下へ降りていく。それだけで十分だった。

景色の切れ端は、エスカレーターの移動とともに勝手に浮かんでは沈んでいく。昔、この店の入口近くで見かけた人のことを思い出す。恐竜の形をした妙な履き物を引きずりながら歩いていたその人は、顔の輪郭さえ曖昧だった。それでも全体の空気だけで心が傾いた記憶がある。人を好きになる理由など、本当は顔でも言葉でもなく、遠くから見た重心の置き方のようなものなのかもしれなかった。

七階の衣料品売り場を抜けると、地下通路の途中にあるような古いアーケード街を思い出した。昭和の婦人服店が横一列に並び、地上から差し込む光だけが時代から取り残されていた場所だ。連れが体調を崩して建物の奥へ消えたあの日、私はただ長い通路の端まで歩き、折り返して戻った。何かを待つ時間というのは、歩くこととよく似ている。目的地は最初から存在せず、引き返すことまで含めて一つの移動なのだ。

六階まで降りると、学校の記憶が混ざり始めた。机を端へ寄せ、椅子だけを輪にして並べた教室。順番に理由を述べていく時間。遊びたいから、誰かと話したいから、そんな種類の答えはすぐに出尽くした。最後のほうで、普段ほとんど目立たない生徒が予想外の角度から言葉を落とした瞬間、空気は奇妙な方向へねじれていった。自分の番が来た頃には残された表現がなくなっていて、口を開く前から全員の視線だけが集まっていた。あの感覚は今も覚えている。選択肢が多いように見えて、実際には何も選べない瞬間の重さを。

五階では家具が並んでいた。そこを歩いていると、自分の家を思い出した。安心した途端に身体が冷えていくような感覚。感情の起伏が少なく、表情もほとんど変わらない生活。その単調さが部屋の形と不思議に調和していた。落ち着く場所というのは、必ずしも幸福な場所ではない。ただ変化が少ないだけの場合もある。

四階に降りるころには、昔の職場の景色が現れた。シーツを伸ばし、皺を消しながら働いていた頃、年上の誰かが忘れた夢について問いかけてきたことがある。また別の日には、他人の視線がどこへ向いていたのかを指摘する感覚を初めて理解した。人は相手を見ているつもりで、自分の見たいものしか見ていない。思い出の中の顔もまた、いくつかの決まった表情に整理されて保存されている。

三階では、自立だけを唯一の正解として語る大人たちの声が聞こえる気がした。一人で生き、一人で決め、一人で立つこと。それだけが成熟の形として提示される世界。しかし本当は、多くの人が流れに乗せられながら移動しているだけなのではないか。今の私がエスカレーターに運ばれているように。

二階の食品売り場では、魚や惣菜の匂いが漂っていた。その匂いは旅館の夕食を連想させた。卓上を埋め尽くすほどの料理の豊かさに身を委ねていると、それまで気にしていた小さな失敗など自然と遠ざかっていく。熱い油の中で具材が静かに煮える小皿料理の香りまで思い出し、空腹とも懐かしさともつかない感覚が胸の奥を通り過ぎた。

そして一階。

自動ドアの向こうに夜の街が広がっている。出会った頃は電車で向かった場所へ、いつからか車で行くようになったことも思い出す。公園の入口に咲く桜は美しかったが、本当に心を動かしたのは、その先の橋を渡ったあとに広がる風景だった。入口はいつも入口に過ぎない。

店を出ても何かを買った記憶は残っていなかった。ただ、上から下まで降りてきた時間だけが身体の中に残っている。次の日もまた、その続きを生きる気がした。夏休みが終わらずにどこかへ伸びていくような感覚。そして、実際には持っていないはずなのに、乗ったことのないオープンカーの鍵だけをポケットに入れているような、不思議な余裕があった。

Today was the first day of my business trip that I get to spend with someone and I hung out with A like I normally do. It was a wonderful time as always, and you’re the end of the night I told him about how he is my gold standard of humor in a person, and how I don’t think that is a reasonable goal. He also mentioned that he felt the same which I thought was sweet. I’m just so incredibly grateful that I got to know him, and that I get to have him as a lifelong friend.

Some days when I am sleep deprived and lonely, I just want to see the world burn, and on those days, my mind goes into dark, but also weird places.

Some symptoms are

1. When i see despair in someone’s eyes i feel extreme happiness

2. I visualize a violent death for myself and feel the same extreme happiness

I would say though that typically the dark thoughts I have are directed inward instead of outward. I usually have no desire to harm others. But I do sometimes visualize myself on a strange operating table, bound by thick metal wires, and the flesh on my limbs spread apart in half with a straight cut down the middle to expose bone. The happiness I feel is even stranger, a frenetic happiness that causes deranged laughs to escape from my lips. It’s a combination of feeling stimmed and despairing at my life and hating everything that I am. And because I feel pain and feel isolated from others, I wish the others could be just as unhappy as me and know me through that. So this culminates in a wish for the world to burn, along with an exciting, violent end to my existence.

On 4 February 2026, the Ahrefs content team published a single chart that should have been treated like a public health alert. It showed that when an AI Overview appears at the top of a Google search results page, the top-ranked organic link beneath it now receives 58 per cent fewer clicks than the same page would have received before AI Overviews existed. In April 2025, the same analysis had measured the decline at 34.5 per cent. In nine months, a feature initially described as an enhancement to search has roughly doubled the amount of traffic it diverts from the websites whose content it summarises. The 300,000 keywords Ryan Law and Xibeijia Guan analysed were not edge cases. They were the queries the open web has historically depended on for its survival.

That chart did not make front pages. AI Overviews did.

The numbers it represents arrive at a peculiar moment. In late January 2026, individual publishers logging into their AdSense dashboards discovered overnight earnings had collapsed by anything between fifty and ninety per cent. Daily revenue of five hundred dollars fell to thirty-five. Country-level coverage dropped to figures that read like a postscript: Germany down sixty-four per cent, France sixty-three, Italy seventy-six, Spain ninety. Google later attributed the failure to third-party tag recognition issues in Ad Manager that cascaded through Ad Exchange. Whatever its proximate cause, the incident gave thousands of small publishers a glimpse of what life on the other side of the search economy now looks like. The trapdoor had not opened. It had merely groaned.

Press Gazette's May 2026 audit of the fifty most popular US news sites confirmed the broader pattern. Almost half had seen their year-on-year traffic in April fall by 20 per cent or more, despite a steady stream of breaking news. Forbes recorded the worst decline among the top fifty, down by nearly half. AP News had shed 46 per cent of its visits in a year. Athlon Sports lost 48 per cent. Some publishers grew, most notably Substack's newsletter network and Men's Journal, the latter quadrupling its traffic, but the direction of travel for the legacy news web was unmistakable: down, and accelerating.

This article is about that descent. It is also about who, in the end, gets to decide whether it continues.

The shape of the disappearance

Begin with the mechanics, because the mechanics matter. AI Overviews are the AI-generated summaries that now appear above traditional search results on Google for a growing share of queries. They are produced by Gemini, Google's family of large language models, drawing on a vast index of web content that includes, of course, the journalism that publishers have spent decades producing. The Overview answers the user's question directly. The links to the underlying sources remain, but they sit underneath an answer that has, by design, made clicking them unnecessary.

For two decades, this would have been considered a hostile redesign of the search results page. Search engines extracted value from the open web by indexing it, but they returned value through referrals: a query went in, a link came out, the publisher received a visitor who could be shown advertisements, persuaded to subscribe, or counted in the metrics that justified the salary of the reporter who wrote the piece. It was an implicit bargain rather than a contract, but it was durable. The bargain is now visibly breaking.

Cloudflare's data on the asymmetry between what AI companies take and what they give back is unsparing. In June 2025, Google's crawler scraped websites fourteen times for every referral it sent. OpenAI's crawler scraped 1,700 times for every referral. Anthropic's, the most extractive of the major systems, scraped 73,000 times for every visitor it sent on. By July 2025, Anthropic's ratio had risen to 38,000 pages crawled for each referred page visit, an imbalance that, as Cloudflare's Matthew Prince has argued repeatedly, is incompatible with the survival of the businesses that supply the underlying content. The Security Boulevard analysis published in April 2026 framed it crisply: large language models scrape publisher content thousands of times for every single referral they send back, destroying the advertising and subscription revenue that pays for the reporting being consumed.

There is something almost geological about the slowness with which this realisation has settled. Search engines have always taken more than they gave back in any narrow accounting; the value they created accrued elsewhere, in the form of an open web worth searching. AI search dispenses with that justification. The user gets the answer. The model gets the training data. The platform gets the advertising slot at the top of the page. The publisher gets a citation, sometimes, in a panel that the user is empirically unlikely to expand.

The case studies are not anecdotes

The temptation, when writing about systemic decline, is to reach for individual stories that humanise the abstraction. The trouble with the AI Overviews story is that the abstraction has already eaten the stories whole, and the bodies are not metaphorical.

The Planet D, a travel blog founded in 2008 by Dave Bouskill and Debra Corbeil, lost half its traffic in the months after Google launched AI Overviews in May 2024. Staff were laid off. Traffic then fell another 90 per cent. The site stopped publishing earlier in 2025. Charleston Crafted, a home improvement blog, lost 70 per cent of its traffic between March and May 2024 and saw a 65 per cent decrease in advertising revenue. Stereogum, one of the longest-running independent music publications on the open web, reported a 70 per cent collapse in ad revenue in 2025. Its founder, Scott Lapatine, attributed most of the damage to AI Overviews, though Facebook's and X's deprioritisation of links played a supporting role, and announced a transition to paid subscriptions in the hope of replacing what the platform had taken.

These are not boutique websites that misjudged the market. They are precisely the kind of mid-sized specialist publishers the early web was supposed to make possible: small enough to be intimate, large enough to be professional, dependent on advertising revenue that scaled with audience attention. AdExchanger, in its January 2026 reckoning, documented that publishers across the spectrum had lost between 20 and 90 per cent of their traffic and revenue as AI Overviews became the default mode of search. Business Insider's organic search traffic fell by 55 per cent between April 2022 and April 2025. HuffPost's desktop and mobile sites lost half their search referrals over the same period. The Reuters Institute's Digital News Project, in its 2026 predictions report led by Nic Newman, found global Google search traffic to news publishers had fallen by 33 per cent in 2025, with Google Discover down 21 per cent. The newsrooms surveyed expected a further drop of 43 per cent over the next three years. Only 38 per cent of news executives reported feeling confident about the year ahead, down from 60 per cent four years earlier.

The Reuters Institute's framing is worth quoting in spirit if not in length: 2026 is not the year that AI is coming for journalism. It is the year journalism's existing distribution layer has begun to dismantle itself in real time.

What is being defunded, exactly

Now consider what that distribution layer used to fund. The advertising and subscription revenue that has flowed through publisher websites paid for many things. It paid for celebrity gossip, listicles, sponsored content, and considerable quantities of search-optimised filler the open web will not, in itself, miss. But the same revenue stream also paid for the journalism no one else funds.

In the United States, the Medill State of Local News Report for 2025, led by Tim Franklin and informed by the foundational research of Penny Abernathy, found the number of news desert counties, those with no local news organisation at all, had risen to 213. Another 1,524 counties had a single remaining news source. Roughly 50 million Americans now live with limited or no access to local news. Newspaper closures continued at more than two a week, with the steepest losses concentrated in small, independently owned publications. Over two decades, the United States has lost nearly 3,500 newspapers and more than 270,000 newspaper jobs.

The numbers can be read in two ways. One is to note the local news crisis predates AI Overviews by a decade; print advertising's collapse and the dominance of social media did most of the damage first. The other reading, which is closer to the truth, is that the digital advertising economy that succeeded print was the lifeline allowing surviving local outlets and digital startups to make a partial recovery. Two-thirds of the more than 300 local news startups launched over the past five years are digital-only, and most depend on a combination of organic search traffic, advertising, and newsletter subscriptions. The decline in search referrals the Reuters Institute is tracking is not abstract for those outlets. It is the difference between an additional reporter and a wound-down operation.

Court reporting is a particularly clean example because the structure of the work makes the dependency visible. Covering a magistrates' court or a county court is labour-intensive, often unglamorous, and largely unprofitable except as part of a larger publishing operation whose other pages subsidise the public-interest reporting. When the operation's economic base erodes, court reporting is among the first beats to be cut, because no commercial entity is willing to pay directly for it. The Arizona Supreme Court's recent introduction of AI-generated summaries of rulings is a striking symbolic moment: a court system has begun automating the explanation of its own decisions because the human stenographers and beat reporters who once did the work are no longer reliably present in the room. The summaries will draw, inevitably, on the journalism that used to be written by those reporters, until that journalism, too, becomes scarce enough that the summaries begin to fail.

Health and science coverage is similarly load-bearing. During the pandemic, the role of science reporters in translating epidemiological evidence into public understanding was visible to anyone watching. Investigative reporting is even more concentrated. ProPublica, the Bureau of Investigative Journalism, regional non-profits and a handful of legacy newsrooms produce the bulk of accountability work in the English-speaking world. The economics of investigation are brutal: a single piece can take months and produce no traffic until it does. The cross-subsidy from high-volume, lower-effort content that finances the slow work is precisely what AI Overviews are dismantling. When the page about who the richest person in the world is no longer drives traffic to Forbes, the part of Forbes that does actual reporting becomes that much harder to sustain.

This is the load-bearing element the regulatory debate keeps gesturing at without quite saying. The damage from AI Overviews is not evenly distributed across content types. It is concentrated, by the logic of summarisation, on the pieces that can be summarised: definitional content, explainer journalism, listicles, evergreen reference material. The investigative scoop, the eyewitness reportage, the court transcript, the science explainer that took three weeks to get right: those are harder to extract, but they sit in publishing operations whose business model depends on the extractable pieces continuing to earn. The summary eats the canapés. The kitchen closes anyway.

The platforms' defence

Google's case for AI Overviews has been made most consistently by Sundar Pichai, who has argued in several settings that AI Overviews send users to a wider variety of websites than traditional search, and that publishers are misreading early data. At Google Cloud Next 2026 he sketched a future in which search becomes an agent management layer, with AI models interpreting queries, synthesising answers, and executing tasks across services. Asked about journalism in a June 2025 podcast with Lex Fridman, he said news and journalism would play an important role in the future, and that Google was committed to it.

The trouble with this defence is that it requires accepting the platform's metrics about its own behaviour. The Ahrefs methodology was deliberately constructed to control for the kind of measurement noise Google has previously invoked to explain away earlier declines. It compared 150,000 keywords that triggered AI Overviews against 150,000 informational-intent keywords that did not, using aggregated Google Search Console data covering the period before and after AI Overviews' widespread rollout. The 58 per cent decline is not a vibe. It is the result of one of the better-instrumented experiments the open web is capable of running on itself. And in February 2026, Penske Media Corporation, the publisher of Rolling Stone, Variety, Deadline, and The Hollywood Reporter, submitted that same Ahrefs analysis as part of its federal court memorandum opposing Google's motion to dismiss its antitrust lawsuit. The lawsuit, filed in September 2025, alleges Google has abused its search monopoly to compel publishers to accept AI summarisation of their content as the price of continued visibility in search. Penske's central argument is that the historic bargain, content for traffic, has been unilaterally rewritten and that publishers were given a choice that is no choice: leave Google search altogether, or accept the cannibalisation.

Google has moved to dismiss. Its position is that AI Overviews are summaries of information responsive to a user's query, not a separate product, and that displaying an Overview does not deprive users of alternatives. The same argument, more or less, is being made in Europe, where the European Publishers Council filed a formal antitrust complaint with the European Commission on 10 February 2026. The complaint, brought under Article 102 of the Treaty on the Functioning of the European Union, alleges Google's AI Overviews and AI Mode constitute an abuse of dominance: the dominant gatekeeper, in EPC chairman Christian Van Thillo's framing, is using its market power to take publishers' content without consent, without fair compensation, and without giving publishers a realistic way to protect their journalism. The EPC's membership reads like a roster of the European newsroom: DMG Media, The Guardian, News UK, Le Monde, El País, The New York Times. The European Commission had already announced an antitrust investigation into Google's use of publisher content for AI training in December 2025.

The platform's most consistent rhetorical move in response has been to insist the alternative to AI Overviews would be worse: a search experience that fails to keep pace with user expectations set by ChatGPT, Claude, Perplexity and other answer engines, all of which are themselves drawing on the same publisher content with even more extreme crawl-to-referral ratios. There is a real argument here, but it is also self-serving. The choice between AI Overviews and a competitor's worse extraction is a choice the platforms have set up for themselves. The choice the publishers are asking to make, which is whether their content should be used in AI summarisation at all without consent or remuneration, is the one the platforms have so far refused to offer in any meaningful form.

What the regulators have proposed

In January 2026 the UK Competition and Markets Authority, having already designated Google as having strategic market status in general search and search advertising in October 2025, proposed a set of conduct requirements that would force the platform to offer publishers a genuine opt-out from AI Overviews. The proposal is unusual in its directness: publishers would be able to withhold their content from AI Overviews and from the training of Google's broader generative AI services, including Gemini and Vertex, without losing visibility in traditional organic search. Google would also be required to ensure publisher content is properly attributed in AI results. The consultation closed on 25 February 2026. As of mid-May 2026, the CMA is reviewing responses and is expected to issue final conduct requirements in the coming months.

The opt-out, if implemented, would be the first time a major regulator has unbundled the historic implicit bargain at the level of explicit policy. Until now, the choice for publishers has been all-or-nothing: be in Google, accept whatever Google does with your content; or leave Google, lose most of your audience. The CMA's proposal would create a third option: stay in Google's index, but refuse the AI summarisation. The PPA, representing UK consumer magazine and B2B publishers, responded with cautious support. The News Media Alliance in the United States, led by Danielle Coffey, has called for similar interventions and described Google's late-January 2026 opt-out announcement as a welcome sign the company is starting to listen to publishers, while noting the gesture came only in response to sustained regulatory pressure.

There are good reasons to be cautious about what an opt-out actually achieves. A publisher that withdraws from AI Overviews and AI Mode loses presence in the surface Google is increasingly making the default. The competing AI search products, from OpenAI's SearchGPT to Perplexity to Anthropic's web-aware models, would not necessarily be covered by a Google-specific remedy. And the negotiating asymmetry between an individual publisher and a multi-billion-dollar platform remains stark, even with a regulator's hand on the scale. The European Publishers Council's complaint anticipates this and asks the Commission to go further: not just opt-outs but compulsory licensing, statutory remuneration, and structural separation of AI summarisation from the search interface.

The most interesting technical proposal has come, unexpectedly, from infrastructure. Cloudflare's Matthew Prince launched pay-per-crawl in private beta in July 2025, allowing website owners to charge AI crawlers a micropayment for each scrape. The platform sits at a useful chokepoint: roughly a fifth of the open web routes through Cloudflare in some form, which means a meaningful share of crawlers can be metered or blocked at the network layer rather than at the level of individual publisher policy. Pay-per-crawl assumes what regulators have been slow to acknowledge: the consent regime for AI training and AI summarisation is not, in any meaningful sense, opt-out. It is opt-in by silence, enforced by the absence of an enforcement mechanism.

A scenario worth taking seriously

Imagine, for a moment, the trajectory of a single mid-sized regional title under current conditions. The paper, a hypothetical composite of the kind described in the Medill report, employs twenty-two journalists across news, courts, council coverage, sport, and a small lifestyle desk. Its digital advertising revenue, the bulk of its income since print declined a decade ago, is roughly evenly split between Google AdSense and a direct-sold programmatic stack. Half of its traffic comes from Google search. By the end of 2024, AI Overviews had begun appearing on the kinds of queries that drove most of its evergreen traffic: how to register to vote, what time the local library opens, when the new school term starts, the names of councillors. By April 2025, the Ahrefs measurement at 34.5 per cent decline already meant a perceptible drop. By the time the February 2026 update lands and the figure climbs to 58 per cent, the paper has lost roughly a third of its overall digital traffic and close to forty per cent of its programmatic ad inventory.

Then, on 14 January 2026, AdSense earnings collapse for forty-eight hours. The technical fault is rectified, but the publisher's senior leadership, looking at the chart, understands they have just glimpsed the underlying volatility of their revenue base. The board commissions a review. By April 2026, when Press Gazette publishes its audit of the top 50 US sites, the paper has cut six positions: two court reporters, the local government beat reporter, a science writer who had been part-funded by a foundation grant, and two subeditors. Coverage of the magistrates' court reverts to police press releases. The council's licensing committee, previously covered by a reporter who knew the regulars, is now reported on, when at all, from agenda papers downloaded the morning after meetings.

This is not a thought experiment offered as melodrama. It is the rough operational shape of the choices being made, right now, in dozens of newsrooms. The Medill report's underlying finding, that closures and contractions are accelerating among small and mid-sized publishers, is not a function of AI Overviews alone. It is the product of compounded pressures: declining print circulation, social media de-prioritisation of links, programmatic advertising's collapsing yields, and now the redirection of search traffic to summarisation. The question is not whether journalism would have struggled without AI Overviews. It is whether AI Overviews are the policy choice that turns a difficult adjustment into an irreversible one.

Who decides

The question of who should decide how the value is distributed is the hardest one, and the one most likely to be answered by default rather than by design. Several candidates present themselves.

The first is the platforms. Google's stated position is that the existing bargain remains intact, that traffic patterns are simply shifting, and the company is adjusting its product to keep publishers visible. The series of updates announced in early 2026, including Further Exploration links and subscription labels, are real, but they are platform-administered concessions. Their existence depends on the platform's continued belief that they are necessary. As soon as the regulatory pressure abates, the architecture of the search results page is once again at the platform's discretion. The implicit governance is that whoever owns the surface decides the terms.

The second is governments and regulators. The UK CMA's strategic market status designation and proposed conduct requirements represent the most ambitious attempt yet to translate the implicit bargain into explicit policy. The European Commission, with the EPC's complaint now in its tray and a December 2025 investigation already running, has both the legal tools, in the form of the Digital Markets Act, and the political will. The US position is more fragmented: the Department of Justice has Google in court on separate antitrust grounds, and Penske's lawsuit is making its way through the federal courts, but congressional action on AI-specific competition policy remains largely aspirational. Regulators have the legitimacy to draw the line. The question is whether they can move quickly enough to matter, and whether opt-outs are a sufficient remedy or merely a way of formalising the existing power asymmetry.

The third is publishers themselves, acting collectively. The history here is not encouraging. Publishers have repeatedly failed to coordinate effectively against platform pricing power, partly because they compete with one another and partly because individual deals, of the kind Google and OpenAI have signed with selected outlets, fragment the bargaining unit. The European Publishers Council's complaint is a notable exception: a coalition action that names a structural problem rather than negotiating individual remunerations. The challenge is whether collective action can be organised at a global scale, given that the platforms operate globally and the publishers are dispersed across legal jurisdictions with different competition regimes.

The fourth is citizens. This is the candidate the policy debate has, so far, almost entirely avoided. The decision to redirect the economic value of journalism from the institutions that produce it to the platforms that summarise it has not been put to anyone. There has been no white paper, no green paper, no parliamentary debate framed around the question of what local accountability journalism is worth to a democracy and how its provision should be secured. The CMA's consultation is the closest thing to a public process and its remit is properly narrow, scoped to competition law rather than to the wider question of whether the architecture of information distribution should be a matter of private commercial discretion at all. The asymmetry between the scale of the decision and the smallness of the public forum in which it is being taken is, on any measure, striking.

A line that should be drawn

The position this article takes, after working through the data, is that the redirection of journalism's revenue base to AI summarisation is happening too quickly, on too large a scale, and with too little public deliberation for any reasonable observer to treat it as a market adjustment. It is a transfer of value. It is being effected by parties that did not produce the underlying content. The mechanisms by which it is occurring are, if not formally illegal, then certainly inconsistent with the bargains under which the content was produced. The regulatory and legal responses, in the UK, the EU, and through the Penske litigation in the United States, are appropriate and overdue. They should be supported, sharpened, and extended.

But the deeper point is that the question is not, ultimately, a competition law question alone. It is a democratic infrastructure question. The journalism being defunded is the journalism that makes local government legible, that holds courts and police accountable, that translates scientific findings into civic understanding, and that surfaces wrongdoing in time for it to be addressed. None of that is produced by the AI systems now distributing it. Some of it, the explainers and definitional content, can be reproduced after the fact by models trained on what previous journalism produced. The investigations, the eyewitness reportage, the long-cultivated source relationships, the appearance in court each week to take a note: those cannot be summarised because they have to be done first.

The communities that depend on that reporting, which is to say all communities, do not currently have a meaningful seat at the table where the value transfer is being decided. The first task of any serious policy response is to give them one. That means treating publisher opt-outs as a floor, not a ceiling; mandating compensation regimes for content used in AI summarisation; investing in public-interest journalism funds drawn from a levy on platform revenues, on the model some European jurisdictions have begun to consider; and, perhaps most importantly, naming the situation honestly. The web has not broken. It has been broken open, and someone with a basket is collecting what falls out.

What the journalists who produced this material would have wanted, had they been asked, is not the right framing, because most of them were not asked. What the readers who valued the reporting would have chosen is not the framing either, because they were not consulted. The decision is being made by the platforms that own the surfaces, the publishers who lack the leverage to refuse, and the regulators who are catching up. The 58 per cent figure Ahrefs published in February is a measurement of how much of the old settlement has already been cleared away. The questions that remain, about who gets to build what replaces it, and on whose terms, are still, just barely, open.

If they are to be answered in a way that preserves anything of the journalism the open web sustained, the conversation needs to happen now, in public, with the people who depend on the reporting in the room. The alternative, which is the trajectory already underway, is that the answer will be supplied by default, by the entity with the surface and the model and the advertising slot at the top of the page. That entity has already made its preferences clear. It will summarise what is left.

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References

1. Law, R. and Guan, X., “Update: AI Overviews Reduce Clicks by 58%“, Ahrefs Blog, 4 February 2026. https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/
2. “AI Overviews Reduce Clicks by 34.5%”, Ahrefs Blog, April 2025. https://ahrefs.com/blog/ai-overviews-reduce-clicks/
3. Press Gazette, “Top 50 US news websites: Half of sites see traffic fall 20% or more in a year”, May 2026. https://pressgazette.co.uk/media-audience-and-business-data/media_metrics/most-popular-websites-news-us-monthly-3/
4. AdExchanger, “The AI Search Reckoning Is Dismantling Open Web Traffic, And Publishers May Never Recover”, January 2026. https://www.adexchanger.com/publishers/the-ai-search-reckoning-is-dismantling-open-web-traffic-and-publishers-may-never-recover/
5. Security Boulevard, “The AI Content Crisis: How LLMs Are Draining Media Revenue, and the Technologies Fighting Back”, April 2026. https://securityboulevard.com/2026/04/the-ai-content-crisis-how-llms-are-draining-media-revenue-and-the-technologies-fighting-back/
6. ALM Corp, “AdSense Revenue Plunge January 2026: 90% Earnings Drop Hits Publishers Globally”, January 2026. https://almcorp.com/blog/adsense-revenue-plunge-january-2026-causes-solutions-recovery/
7. PPC Land, “Google ad platform failures slash publisher revenue up to 90% overnight”, January 2026. https://ppc.land/google-ad-platform-failures-slash-publisher-revenue-up-to-90-overnight/
8. European Publishers Council, “European Publishers Council files formal antitrust complaint against Google over AI Overviews and AI Mode”, 10 February 2026. https://www.epceurope.eu/post/european-publishers-council-files-formal-antitrust-complaint-against-google-over-ai-overviews-and-ai
9. UK Competition and Markets Authority, “CMA proposes package of measures to improve Google search services in UK”, 28 January 2026. https://www.gov.uk/government/news/cma-proposes-package-of-measures-to-improve-google-search-services-in-uk
10. UK Competition and Markets Authority, “CMA confirms Google has strategic market status in search services”, 10 October 2025. https://www.gov.uk/government/news/cma-confirms-google-has-strategic-market-status-in-search-services
11. Press Gazette, “Google exploring updates to let publishers opt out of AI Overviews”, January 2026. https://pressgazette.co.uk/news/google-ai-overviews-search-cma-proposals/
12. Newman, N., “Journalism, Media, and Technology Trends and Predictions 2026”, Reuters Institute for the Study of Journalism, University of Oxford, January 2026. https://reutersinstitute.politics.ox.ac.uk/journalism-media-and-technology-trends-and-predictions-2026
13. Cloudflare, “The crawl before the fall, of referrals: understanding AI's impact on content providers”, Cloudflare Blog, 2025. https://blog.cloudflare.com/ai-search-crawl-refer-ratio-on-radar/
14. Axios, “Penske Media sues Google over AI search overviews”, 14 September 2025. https://www.axios.com/2025/09/14/penske-media-sues-google-ai
15. Columbia Journalism Review, “The Creative Approach Behind Penske's AI Lawsuit”, 2026. https://www.cjr.org/analysis/penske-ai-lawsuit-google-tactics.php
16. Lapatine, S., “Getting Killed By AI”, Stereogum, November 2025. https://stereogum.com/2478838/stereogum-relaunch/tcb
17. Medill School of Journalism, Northwestern University, “News deserts hit new high and 50 million have limited access to local news, study finds”, State of Local News Report 2025, October 2025. https://www.medill.northwestern.edu/news/2025/news-deserts-hit-new-high-and-50-million-have-limited-access-to-local-news-study-finds.html
18. News/Media Alliance, “Statement on Google AI Mode Opt-Out Announcement: Cautiously Optimistic”, January 2026. https://www.newsmediaalliance.org/nma-statement-on-google-opt-out-announcement/
19. TechCrunch, “Cloudflare launches a marketplace that lets websites charge AI bots for scraping”, 1 July 2025. https://techcrunch.com/2025/07/01/cloudflare-launches-a-marketplace-that-lets-websites-charge-ai-bots-for-scraping/
20. Press Gazette, “Global publisher Google traffic dropped by a third in 2025”, 2026. https://pressgazette.co.uk/media-audience-and-business-data/google-traffic-down-2025-trends-report-2026/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

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

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

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

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